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Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as 'at risk.' Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data analysis, and in particular the design and use of deep neural networks (DNNs) for detecting heart disease based on routine clinical data. Our main contribution is the design, evaluation, and optimization of DNN architectures of increasing depth for heart disease diagnosis. This work led to the discovery of a novel five layer DNN architecture-named Heart Evaluation for Algorithmic Risk-reduction and Optimization Five (HEARO-5)-that yields best prediction accuracy. HEARO-5's design employs regularization optimization and automatically deals with missing data and/or data outliers. To evaluate and tune the architectures we use k-way cross-validation as well as Matthews correlation coefficient (MCC) to measure the quality of our classifications. The study is performed on the publicly available Cleveland dataset of medical information, and we are making our developments open source, to further facilitate openness and research on the use of DNNs in medicine. The HEARO-5 architecture, yielding 99% accuracy and 0.98 MCC, significantly outperforms currently published research in the area.
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A Novel Deep Learning Approach to Improving Heart Disease Diagnosis
Nathalie-Soﬁa Tomov
heart attacks and strokes occur in people who have not been ﬂagged as ’at risk.’ Thus, there
is an urgent need to improve the accuracy of heart disease diagnosis. To this end, I investigate
diagnosis. This work led to the discovery of a novel ﬁve layer DNN architecture – named Heart
Evaluation for Algorithmic Risk-reduction and Optimization Five (HEARO-5) – that yields best
prediction accuracy. HEARO-5’s design employs regularization optimization and automatically
deals with missing data and/or data outliers. To evaluate and tune the architectures I use k-
way cross-validation as well as Matthews correlation coeﬃcient (MCC) to measure the quality of
our classiﬁcations. The study is performed on two datasets: 1) the publicly available Cleveland
dataset of medical information, and 2) a dataset of echocardiogram reports from the University of
Tennessee Sports Medicine practice. The HEARO-5 architecture, yielding 99% accuracy and 0.98
MCC on both datasets, signiﬁcantly outperforms currently published research in the area.
Keywords: machine learning, DNN, cardiology, translational medicine, cardiovascular disease,
hyperparameter optimization
Corresponding author
Preprint submitted to Elsevier June 23, 2021
1. Introduction
Heart disease is the leading cause of death worldwide, killing twenty million people per year
(WHO)[26]. An accurate and early diagnosis could be the diﬀerence between life and death for
people with heart disease. However, doctors misdiagnose nearly 1/3 of patients as not having
heart disease (British Medical Bulletin) [6], causing these patients to miss out on potentially life-5
saving treatment. This is a problem of increasing concern, as the number of Americans with heart
failure is expected to increase by 46 percent by 2030 (American Heart Association)[11]. What
makes diagnosing heart disease a challenging endeavor for any physician is that while chest pain
and fatigue are common symptoms of atherosclerosis, as many as 50 percent of people lack any
symptoms of heart disease until their ﬁrst heart attack (Center for Disease Control) [4]. Discovering10
biomarkers (for heart disease) – measurable indicators of the severity or presence of some disease
– is preferred [28], but in many cases there are no clear biomarkers, and multiple tests may be
required and analyzed together. Most doctors use the guidelines recommended by the American
Heart Association (AHA) [9], which test eight widely recognized risk factors such as hypertension,
cholesterol, smoking, and diabetes [8, 10]. However, this risk assessment model is ﬂawed since it15
rests on an assumed linear relationship between each risk factor and heart disease outcome, while
the relationships are complex and with non-linear interactions [25]. Oversimpliﬁcation may cause
doctors to make errors in their predictions, or overlook important factors that could determine
whether or not a patient receives treatment. Doctors must also know how to interpret the diagnostic
implications of medical tests which vary between patients and require tremendous expertise.20
The use of Machine Learning (ML) data analysis techniques can alleviate the need for hu-
man expertise and the possibility of human error while increasing prediction accuracy[12]. ML
algorithms apply ﬂexible prediction models based on learned relationships between variables in
the input dataset. This can prevent the oversimpliﬁcation of ﬁxed diagnosis models such as the
AHA guidelines. In fact, a neural network algorithm with 76 percent accuracy has been proven to25
correctly predict 7.6% more events than the AHA method [21]. Here, we signiﬁcantly improve on
these already very promising ML results by designing and tuning deep neural network (DNN) ar-
chitectures of increasing depth for detecting heart disease based on routine clinical data. We show
that a ﬂexible design and the subsequent tuning of the (many) hyperparameters of a DNN can
yield up to 99% accuracy. The results were evaluated and validated using k-way cross-validation as30
well as Matthews correlation coeﬃcient (MCC) to measure the quality of the classiﬁcations. The
best results were obtained on a novel ﬁve-layer DNN – named Heart Evaluation for Algorithmic
Risk-reduction and Optimization Five (HEARO-5) – that employs regularization optimization and
automatically deals with missing data and/or data outliers. The HEARO-5’s accuracy is 99%
with an MCC of 0.98, which signiﬁcantly outperforms currently published research in the area, to35
further establish the appeal of using ML data analysis in diagnostic medicine.
2. Literature Review
There are a number of research papers that use artiﬁcial neural networks to improve heart
disease diagnosis. Yu et al. concluded that a neural network topology with two hidden layers
was an accurate model with 94% test data accuracy [27]. They focus on the multiplicity of risk40
factors in constructing their model to classify features before determining a possible diagnosis.
This study concluded that neural networks are an eﬀective method of analyzing cases when it is
impossible to create a strict mathematical model but where there is a suﬃciently representative
set of samples. Vinodhini et al. build on this research by performing feature classiﬁcation with
statistical models such as the chi square and then using a neural network as a predictive model [24].45
This method proved successful overall, but exhibited weaker performance when given redundant
attributes. Loh et al. demonstrate the accuracy of deep neural networks by proving their ability to
learn from nonlinear relationships in data [16]. However, they faced the issue of overﬁtting, when
an algorithm learns too much from training data and becomes less capable of applying itself to
unfamiliar data. Kim et al. help address the problem of overﬁtting by ranking features, training50
the neural network with each feature ranking, and then training the neural network to output a
potential diagnosis [14]. This helps ensure the network learns from numerically weighted important
relationships in training data which can also be applied to unfamiliar data.
3. Contribution to the Field
While the current algorithms are eﬀective, there is always a compelling need for improved55
algorithms that diagnose heart disease more accurately using accessible tests. To this end, the
work described here makes the following main contributions:
The design of a unique and ﬂexible heart disease diagnosis tool based on variable-layer DNN
with regularization optimization that solely uses routine clinical data;
Development of HEARO-5 – a specialized 5-layer DNN architecture for detecting heart dis-60
ease, based on the evaluation and tuning of hyperparameters – that is of very high accuracy
(99% and 0.98 MCC), signiﬁcantly outperforming currently published research in the ﬁeld;
The HEARO framework as a DNN data analytics research tool in diagnostic medicine and
HEARO-5 as a benchmark, making them available for comparison and further studies, facil-
itating openness and research on the use of DNN techniques in medicine.65
4. DNN Background, Design, and Implementation
4.1. Overview
The heart disease diagnostic tool that we designed uses standard fully-connected NNs. To con-
struct the DNN architectures for hearth disease diagnosis, one can use one out of the many currently
available frameworks, including TensorFlow [29], Keras, PaddlePaddle, Caﬀe, MagmaDNN [31],70
etc. The most compute intensive building block for the DNN is the matrix-matrix multiplication
(GEMM), which is available to use through highly optimized math libraries like CUBLAS, CUDNN,
MKL, MAGMA [34], and others. As the data that we have and need for training is not that large
(see Section 5.1), we developed a parametrized DNN in Python, using NumPy as a backend for
the linear algebra routines needed. The code is vectorized for performance, expressed in terms of75
matrix-matrix multiplications, and therefore can be easily ported to C/C++ code, calling highly
optimized BLAS/GEMM implementations, which provides functional and performance portability
across various computing architectures [31]. Our design and implementation is inspired by Andrew
Ng’s DNN designs and courses on deep learning, available on Coursera [30].
. . .
input layer
hidden layer 1 hidden layer L-1
layer L
size 13xN
(size 1xN)
Z1 = W1 A0 + b1
A1 = σ1 ( Z1 )
A0 = X
ZL-1 = WL-1 AL-2 + bL-1
AL-1 = σL-1 ( ZL-1 )
ZL = WL AL-1+ bL
AL = σL ( ZL )
dZL = AL - Y
dWL = dZL ATL-1 / N
dbL = np.sum(dZL, axis=1, keepdims =True)/N
dZ1 = WT2 dZ2 .* σ1( Z1 )
dW1 = dZ1 AT0 / N
db1 = np.sum(dZL, axis=1, keepdims =True)/N
. . .
. . .
. . .
. . .
0) 1) L-1) L)
Figure 1: Parametrized DNN architecture and the main computational steps for its training. Training data Xconsists
of 13 features (routine clinical data per patient; can also be parametrized) and Ntraining examples. Weights W, b
are trained using batch stochastic gradient descent method to make predictions AL”match” the given outcomes Y.
4.2. Flexible DNN design80
The framework design, notations, and main computational steps that we investigate are il-
lustrated on Figure 1. As shown, the neural network is organized into Lfully-connected ’layers’
(i= 1, ..., L) with ninodes (or artiﬁcial neurons) per layer that function together to make a pre-
diction. The connections between layers i1 and iare represented by numerical weights, stored
in matrix Wiof size ni×ni1, and vector biof length ni. Thus, if the input values for layer i,85
given by the values at the ni1nodes of layer i1, are represented as a vector ai1of size ni1,
the output of layer iwill be a vector of size ni, given by the matrix-vector product Wiai1+bi.
As training will be done in parallel for a batch of nb vectors, the inputs ai1will be matrices Ai1
of size ni1×nb and the outputs will be given by the matrix-matrix products Zi=WiAi1+bi,
where ”+” adds bito each of the nb columns of the resulting matrix.90
4.3. Main DNN building blocks
The Forward propagation process, given by steps 0, ..., L, represents a non-linear hypothe-
sis/prediction function HW,b(X)ALfor given inputs Xand ﬁxed weights W, b. The weights
must be modiﬁed so that the predictions HW,b(X) become close to given/known outcomes stored
in Y. This is known as a classiﬁcation problem and is a case of so called supervised learning. The95
modiﬁcation of the weights is deﬁned as a minimization problem on a convex cost function J, e.g.,
W,b J(W, b),where J(W, b) = 1
yilog HW,b(xi) + (1 yi) log(1 HW,b(xi)).
This is solved by a batch stochastic gradient descent method – an iterative algorithm using a batch
of nb training examples at a time. The derivatives of Jwith respect to the weights (Wand b) are
derived over the layers using the chain rule for diﬀerentiating compositions of functions. They are
computed then by the backward propagation steps L+1, ..., 2L, and used to modify their respective100
weights Wi, biduring the iterative training process for each layer ias:
Wi=WiλdWi, bi=biλdbi,
where λis a hyperparameter referred to as learning rate. The σ1, ..., σLfunctions are the activation
functions (possibly diﬀerent) for the diﬀerent layers of the network, and σ0are their derivatives.
We have coded activation function choices for ReLU, sigmoid, tanh, and leaky ReLU. The ”.*”
notation is for point-wise multiplication.105
4.4. Algorithmic Optimization: Regularization
Regularization is a standard technique that prevents overﬁtting by penalizing large weight val-
ues. DNNs tend to assign higher weight values for certain training data points, which corresponds
to a high variance. Regularization helps address the problem of high variance on training data,
which can improve accuracy on test data. Regularization is typically done by adding a penalty110
term of the form α
2N||W, b||2to the cost function J, where ||W, b|| is some norm of the weights, e.g.,
L1 or L2. The regularization parameter αimposes a penalty on large weights, thereby ensuring
that we do not overﬁt training data. Another advantage of regularization is that it can prevent
an algorithm from learning from data outliers, which is essential for a smaller dataset such as
the heart disease patient set used in this research. Regularization causes the outliers to remain115
in the dataset, but reduces the algorithm’s likelihood of learning from these values. Therefore,
we add regularization to our model to investigate possible improvements in accuracy by reducing
overﬁtting and automatically decreasing the impact of any outliers.
4.5. Hyperparameter Optimization
Part of the challenge of coding a neural network is structuring it so it is both accurate and120
eﬃcient. One needs to determine how many layers to use, how many nodes per layer to use, etc.
This was critical for example in deep convolutional networks for image recognition, where signiﬁcant
improvement on the prior-art conﬁgurations was achieved by pushing the depth to 16-19 weight
layers, e.g., as in the popular VGG network [32]. Here also, we have determined that tuning for
the depth and number of nodes per layer is critical for the accuracy. Moreover, we parametrized125
our framework, referred to as HEARO further on, as given above and in Figure 1.
The parameters control the network conﬁguration and accuracy, and therefore, the network
must be highly optimized/tuned for them. These conﬁguration parameters are also called hyper-
parameters. A HEARO conﬁguration is determined by the following list of hyperparameters:
HEARO hparams = [L, n1, ..., nL, σ1, ..., σL, λ, α, nb, epochs],(1)
where epochs is the number of training iterations throughout the entire training set X,σi is the130
activation function for layer i(1, 2, 3, or 4 for ReLU, sigmoid, tanh, or leaky ReLU, respectively),
and the rest are as given above.
Thus, given a list of hyperparameters HEARO hparams, the HEARO framework trains itself
(determining weights W, b) on a given input training data set Xand speciﬁed outcomes Y, and
the challenge now becomes how to select the ”best” hyperparameters.135
5. Optimization Methodology and the HEARO-5 Architecture
5.1. Information About Dataset 1
HEARO uses training and test data from the University of California Irvine machine learning
repository. Data have been preprocessed, where missing values are replaced with the value -1 to
prevent them from signiﬁcantly impacting the algorithm’s model. There were approximately 12140
missing values total. We applied also feature scaling to unit length. This dataset, provided by
the Cleveland Clinic Foundation, contains 75 total attributes of patient medical information for
303 patients[7]. The following 11 attributes are used: 1) age, 2) sex, 3) chest pain type, 4) resting
blood pressure, 5) cholesterol, 6) fasting blood sugar, 7) resting electrocardiographic results, 8)
maximum heart rate achieved, 9) exercise-induced angina, 10) ST depression, and 11) slope of145
the peak exercise ST segment. These attributes have been selected as optimal features by other
researchers using this dataset [1] because they are considered most closely linked to heart disease.
Chest pain type is categorized by number, where 1 represents typical angina provoked by exer-
cise or stress, and 2 represents atypical angina which is persistent chest discomfort [13]. Common
metrics such as resting blood pressure, cholesterol, and fasting blood sugar can be indicative of a150
patients general health and the state of their blood vessels, which is often shaped by the accumu-
lation of plaque as an indicator of developing heart disease. Electrocardiogram results are visual
representations of the hearts activity, and can help doctors or algorithms determine if it is pump-
ing at a normal rate or if circulation is impeded. An ST-T wave abnormality can be measured by
wave height, and often has several implications: a ventricular aneurysm, coronary artery spasm, or155
artery tightness [5]. These are all indicators of heart failure, and are therefore important features
to an algorithm diagnosing heart disease. Slope of the peak exercise ST segment is a similar way of
visually assessing the hearts function when it must circulate more blood, in the case of exercise [15].
In the dataset, this is characterized by the values 1, representing upward slope, 2, representing ﬂat
slope, and 3, representing downward slope.160
This dataset is used because it is publicly accessible and therefore improves the reproducibility
of results. HEARO uses these eleven features to diagnose heart disease because of their diversity,
availability, and ability to identify heart disease at diﬀerent stages of development. The combination
of these features can create a model that accurately evaluates relationships between diverse patient
conditions and heart disease diagnosis [19].165
5.2. Information About Dataset 2
The second dataset was used for real-world clinical validation of the HEARO-5 framework as
a platform that can be applied to a variety of data. This dataset, compiled as part of a pilot test
partnership with the University of Tennessee, consists of 350 echocardiogram reports taken from
current student athletes at the University of Tennessee (UT). The variables that were studied are170
as follows: age, race, sex, aortic root diameter, end-diastolic left ventricular posterior wall thickness
(LVPWd), interventricular septal thickness (IVS), left ventricular outﬂow tract diameter (LVOT),
left anterior descending coronary artery diameter (LAD), right ventricular inner diameter (RVID),
ejection fraction (EF), and body surface area (BSA), in addition to whether each patient had high
enough risk to require additional testing.175
Outcomes are recorded as whether or not an abnormality was detected, what type of abnormal-
ity if any was observed, and whether the patient was ﬂagged for further cardiac testing. The types
of abnormalities include the following: right ventricular dilation, hypertrophic cardiomyopathy,
atrial septal defect (speciﬁcally patent foramen ovale), and aortic regurgitation. This is accompa-
nied by physicians’ notes regarding type of follow-up required - i.e routine yearly check-up, stress180
test, or bubble test. Within the processed training dataset, abnormal outcomes with any follow-up
recommendation are coded as ’1’ and normal outcomes are coded as ’0’.
Most college athletic medical screenings involve echocardiograms. Therefore, this dataset is
used as validation for the software’s potential application within sports medicine programs as an
automated decision support tool. Through partnership with UT Sports Medicine, the presented185
software framework is being developed into an Application Programming Interface that integrates
with the UT patient management platform to be used in a clinical setting.
5.3. Accuracy Evaluation
In addition to measuring the percent accuracy, we also use K-fold cross validation to evaluate
the accuracy. This is a standard technique for evaluating more accurately predictions, especially190
when the size of the training data set is small, like in our case. We use it also to ﬂag possible cases
of overﬁtting, which is often a threat when extensively tuning and the data set used is small. In
order to maintain about a 2:1 training to test data ratio, we mostly use 3-fold cross validation,
where two parts are assigned for training and one for testing.
Furthermore, we use the Matthews correlation coeﬃcient (MCC) to analyze the algorithm’s195
generalization abilities given a dataset with unbalanced class outcomes. In the Cleveland machine
learning repository dataset, the class distribution of the two possible cases (0/1) is as follows: 164
’0’ instances and 139 ’1’ instances. The MCC evaluates how well the algorithm performs on all
possible data outcomes regardless of their ratio within the dataset. This adds further analysis to
the potentially biased measure of percent accuracy. MCC is deﬁned through the following formula:200
p(T P +F P )(T P +F N)(T N +F P )(T N +F N ),
where TP represents true positives, TN represents true negatives, FP represents false positives,
and FN represents false negatives.
5.4. Optimization Methodology
The HEARO framework gives a user the ﬂexibility to easily run and compare diﬀerent con-
ﬁgurations based on their accuracy tests described in Section 5.3, which makes it a very good205
candidate for so called empirical optimization/tuning [33]. This is a process where a large number
of possible conﬁgurations are generated (1) and run on a given platform to discover the one that
gives the best results.
The eﬀectiveness of empirical optimization depends on the chosen parameters to optimize, and
the search heuristic used. A disadvantage is the time cost of searching for the best conﬁguration210
variant, but in the case of this research, this is not a problem as the size of the data set is not
that large. Furthermore, the search space is restricted by taking L= 2..10, nL = 1, ni = 1..13,
λ∈ {0.001,0.01,0.1}, L2 regularization with α∈ {0,0.7,1},nb =N, and epochs = 6000.
Exhaustively testing the search space described above, using automated Python scripts running
in parallel to randomly generate 10,000 diﬀerent conﬁgurations and run them, revealed that the215
following conﬁguration:
HEARO-5 = [5,9,7,5,3,1,1,1,1,1,2,0.01,0.7,200,6000]
gives best accuracy, as further illustrated next in the results section.
6. Results
The empirical optimization within the search space described in Section 5.4 revealed that the
HEARO-5 architecture yields best accuracy. The percent accuracy of HEARO-5 with α= 0 (no220
regularization) is compared to the accuracy of conﬁgurations with 2 and 7 layers in Figure 2, Left.
All graphs indicate an algorithm’s performance on test data unless speciﬁed otherwise. Figure 2,
Right and Figure 5, Left show the eﬀect of the learning rate λon HEARO-5 and a 7-layer HEARO
architecture, respectively.
Figure 2: Left: Accuracy comparison of HEARO-5 vs. 2 and 5 layer networks. Right: Eﬀect of λin HEARO-5.
Figure 3: Left: Eﬀect of λon 7-layer HEARO architecture. Right: Eﬀect on regularization on HEARO-5 variants.
HEARO-5 with α= 0.7 exhibited 99% accuracy on test data, and a Matthews correlation225
coeﬃcient of 0.98. This is shown in Figure 5, Right, and further discussed in Section 6.3. On the
clinical echocardiogram data gathered through pilot testing, this previously validated framework
achieved 99% accuracy on test data, and a Matthews correlation coeﬃcient of 0.96.
6.1. Feature Ranking
Once the accuracy was obtained, each feature was successively eliminated from the training230
dataset to gain a sense of which variables are most important to the algorithm’s learning proce-
Figure 4: Left: Accuracy of HEARO-5 Framework on Echocardiogram Data. Right: Distribution of Classiﬁcation
Outcomes on Echocardiogram Data (TP = true positives; TN = true negatives; FP = false positives; FN = false
Figure 5: Left: Distribution of classiﬁcation outcomes (TP = true positives; TN = true negatives; FP = false
positives; FN = false negatives
dure. A script was coded to remove one feature from the original dataset and run the HEARO-5
framework with this revised dataset containing all but one feature. By this method, the algorithm
was run eleven times, each time excluding a diﬀerent feature.
The results of this procedure show that most important feature, resulting in the lowest accuracy235
when eliminated, was blood sugar (17 missed diagnoses after elimination). In order from most
signiﬁcant to least signiﬁcant, the features are as follows with the number of inaccurate results
in parentheses: fasting blood sugar (17), chest pain type (13), ST depression induced by exercise
(13), slope of peak ST segment (12), exercise induced angina (11), age (11), sex (11), cholesterol
(9), maximum heart rate achieved (8), resting blood pressure (5), resting ECG results (4).240
6.2. Comparison with Previously Published Results
Stanford researchers used a convolutional neural network and obtained precision, recall and F1
scores of 0.80, 0.82, and 0.80 respectively [22]. HEARO-5 outperforms these results, as it achieves
precision, recall, and F1 of 0.98, 1, and 0.99 respectively. In 2016, Aravinthan et al. applied a
Naive Bayes classiﬁer and artiﬁcial neural network to this dataset with accuracy of 81.3% and245
82.5%, respectively [1]. A study published in the International Journal of Computer Applications
(Marikani) obtained results of 95.4% and 96.3% accuracy for classiﬁcation tree and random forest
algorithms [17].
Of interest is also to mention logistic regression and the fact that it is the least accurate
algorithm, likely due to its approach to ﬁtting a ﬂuctuating features data with non-linear correlation250
to heart disease.
Furthermore on accuracy, the K-fold cross validation tests conﬁrmed that HEARO-5 eﬀectively
reduces overﬁtting, as the cross validated accuracy was approximately the same as accuracy on
test data with the set ratio.
The 0.98 MCC of HEARO-5 illustrates the HEARO-5 accurate evaluation of all class outcomes.255
Matthews correlation coeﬃcient ranges from -1 to 1, where 1 represents perfectly balanced accuracy.
Therefore, results of 0.98 MCC and 99% accuracy are indicative of the algorithm’s comprehensive
data analysis model that is not skewed towards any particular outcome.
The results of clinical validation on echocardiogram data provides further evidence of the al-
gorithm’s accuracy and versatility. Because the algorithm continues to achieve 99% accuracy on260
a dataset containing diﬀerent metrics, it has the potential to be ﬂexibly applied as a platform for
clinical decision support. The number of false negatives could be reduced with access to more
data, as the learning procedure would beneﬁt from a greater variety of patients with abnormali-
ties. Because this dataset consists of young (18-24) athletes, heart abnormalities are less common.
The dataset is being expanded continuously by UT Sports Medicine, thereby helping address this265
potential issue.
6.3. Eﬀect of Regularization
While the unregularized DNN exhibits a discrepancy between training accuracy and test accu-
racy (99% on training, 93% on test), regularization increased the accuracy on test data to 99%.
The regularization improved the accuracy on test data by reducing the impact of outliers (and/or270
missing data) on training data. On a relatively small dataset, outliers can inhibit the algorithm’s
ability to learn from consistent relationships in training data, and do not add scientiﬁc value.
Therefore, by regulating the eﬀect of outliers on learning, regularization improves the algorithms
ability to generalize while maintaining the same scientiﬁc standard. Because regularization reduces
overﬁtting on training data, the algorithms accuracy is expected to decrease on training data. In275
the case of HEARO-5 with α= 0 (unregularized), a training accuracy of 99% with somewhat
lower test accuracy is indicative of overﬁtting (see Figure 5, Right). The algorithm is learning
from the noise outlier values in the training data, detracting from its ability to generalize broader
relationships in data. Learning these relationships is vital to accuracy on an unfamiliar dataset.
7. Conclusions and Future Directions280
This work investigated and showed the potential of using DNN-based data analysis for detecting
heart disease based on routine clinical data. The results show that, enhanced with ﬂexible designs
and tuning, DNN data analysis techniques can yield very high accuracy (99% accuracy and 0.98
MCC), which signiﬁcantly outperforms currently published research in the area, to further establish
the appeal of using ML DNN data analysis in diagnostic medicine. Pending reviews and publication,285
we are preparing to release the HEARO software framework as an open source, and HEARO-5 as
a benchmark, making the software available for comparison and further facilitating openness and
research on the use of DNN techniques in medicine.
While the current developments are mostly research with excellent proof-of-concept results,
further research and development is necessary in order to turn it into a robust diagnostic tool, e.g.,290
that doctors consult and use routinely to make a more informed diagnosis. One potential drawback
of the model is that it uses a fairly limited dataset, and therefore can only output a general heart
disease risk evaluation. Future directions involve expanding the dataset so the algorithm can output
more speciﬁc information on the exact type of heart abnormality. Thus, research is needed in the
data analytics area and its intersection with data-based medical diagnosis – including automatic295
search for best features, as well as possible features expansion or features reduction, e.g., due
to lack of certain clinical data. Future directions include extending this analysis to construct a
more thorough model that includes heart visualizations and CT image data. More features can
provide more data for the algorithm to learn from, creating a more complex model and ensuring
a more accurate and detailed prediction. Another area of future research would involve using300
speed optimization tools and accelerated linear algebra backends such as MagmaDNN for GPUs
to improve the algorithm’s ability to process large amounts of data and ﬁnd best conﬁgurations in
parallel. HEARO is currently being developed into a production quality software package with a
friendly user interface, e.g., to facilitate use by doctors or even patients directly.
8. Conﬂict of Interest Statement305
Declarations of interest: none No funding was received for this work.
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Full-text available
Data mining have great potential for healthcare industry to enable health systems to systematically use data and identify the efficiency and improve care with reduce cost. Due to complexity of healthcare and slower improvement of technology adoption needs to implement effective data mining. In data mining there are variety of techniques and methods to suggest or decision making knowledge in database and extracting or support prediction of data. With growing research on heart disease prediction system, this research work has developed a Heart disease prediction with comparative study of several data mining algorithms. Many data mining prediction tools are there in worldwide to make prediction for medical data. In this study it is observed that compare several algorithms to analysis the performance, accuracy of the heart disease prediction system. The commonly used techniques of heart disease prediction and their analytics, summaries are summarized in this research work.
Full-text available
2017 Summer Research Experiences for Undergraduate (REU), Research Experiences in Computational Science, Engineering, and Mathematics (RECSEM), Knoxville, TN
Full-text available
Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.
Full-text available
A major type of heart disease identified all over the world is Myocardial Infarction (MI), commonly called as heart attack. Sudden blockage in a coronary artery by a blood clot due to damage or death of heart muscle is called Myocardial Infarction (MI). Many researches have attempted to detect MI from identified risk factors using intelligent and data mining algorithms. The commonly used data set for MI detection is Cleveland data set. The purpose of the present study is to investigate the risk factors of Cleveland data set and their significant role in MI detection.
Full-text available
Background Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.
Full-text available
Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).


 Serratus Posterior Superior Trigger Point

Chiropractor Chandler AZ

Serratus Posterior Superior Trigger Point

serratus posterior superior trigger point

The origin of the serratus posterior muscle is the nuchal ligament (or ligamentum nuchae) and the spinous processes of the vertebrae (C7 through T3). The muscle’s insertion is in the upper borders of the 2nd through 5th ribs, and the nerves are the 2nd through 5th intercostal nerves.

The trigger points in the serratus posterior superior will often cause pain near or under the shoulder blades, or in other areas around the upper extremities. Pain from the serratus posterior superior can cause a "deep ache" under the upper portion of the scapula. The trigger points create a pain that can extend down the posterior aspect of the shoulder and arm to the ulnar side of the forearm, hand and little finger. In addition the pain sometimes manifests as numbness into the C8-T1 distribution of the hand. The images below identify trigger-point patterns in the serratus posterior superior.

People often feel the burning behind the shoulder blade when slouching at a computer or sleeping on their shoulder. Reaching in front of them and lifting a gallon of milk out of the top shelf causes sharp radiating pain.

Conservative Treatments

Therapeutic treatments for addressing soft tissue injuries involve massage therapy, manual therapy, trigger point therapy, Graston Technique, ASTYM, or Active Release Technique. These treatments increase blood flow, decrease muscle spasms, enhance flexibility, speed healing, and promote proper tissue repair.

Treatment for shoulder injuries often requires a variety of exercises, stretches, conservative treatments, medical treatments, and home therapies. Shoulder injuries can become chronic if the appropriate steps are not taken.

Elbow injuries often occur in people with severe or chronic shoulder injuries. People begin trying to alter their shoulder motions to protect it. Unfortunately these altered body mechanics tend to overwhelm the muscles and tendons around the elbow. People often develop a secondary cubital tunnel syndrome, tricep tendonitis, medial epicondylitis, pronator teres syndrome, double crush, carpal sprains, wrist tendinitis, de quervain's tendonitis, lateral epicondylitis, finger extensor strains, or carpal tunnel syndrome.

Treatment for elbow injuries can be extensive if the tendinosis is severe. Mild strains can be treated at home with PRICE, home stretches, and exercises. Don’t wait for damage to both the shoulder and elbow to seek treatment and therapy.

Medical Treatments

Cervical spinal disc bulges and herniations onto the spinal cord or nerve root produce different symptoms and location of symptoms. Pain radiating in the hand is one symptom; along with numbness, weakness, fatigue, loss of sensation, or reduced reflexes. Your chiropractor, physical therapist, occupational therapist, or physician will evaluate your condition and make a proper diagnosis and treatment recommendations. Ask them any questions you might have about your injury.

Many people do have arthritis or degenerative changes in their shoulder, elbow, wrist, finger, or thumb joints. Arthritis does not mean you will always have pain in the joints. Degenerative arthritis means the structural Integrity of the bones have changed which alters its gliding, sliding, and hinging motions. The more severe the arthritic changes the easier it becomes to aggravate the joint and produce pain.

Osteoarthritis and rheumatoid arthritis will also slow healing and recovery time. Fortunately arthritic changes does not mean you will always have pain. Future pain will not necessarily be from the arthritis. People with severe shoulder arthritis can have no pain in their day to day activities. In fact many times people blame their arthritis pain on tendinosis or tendonitis of the shoulder stabilizing tendons that attach around the shoulder joint. Conservative treatment can improve shoulder pain; and people will have dramatically less pain with their work and home activities.

Many therapeutic exercises can help restore proper strength and endurance to the elbow flexor muscles. Isometric exercises are often the initial treatment exercises. Followed by single plane rubber band exercises for elbow flexion, extension, pronation, and supination movements. Dynamic exercises involving stability ball push-ups can be performed on the wall or floor. The more unstable of the surface the more effort and stabilization is required of all the upper extremity muscles.

Push-ups on a stability ball enhances neuromuscular learning throughout the neck, scapula, shoulder, upper arm, and lower arm muscles. Additional strength exercises can be found on the arm and shoulder strengthening pages.


Why Governments Lie

Recently, I posted a short article explaining that the essence of the so-called “fake news” phenomenon lies not so much in the relaying of outright falsehoods, but in the selection, ordering and presentation of information in such a way as to lend a wrong impression. In passing, we noted also that the majority of “disinformation” is fostered not by private individuals posting on their Twitter or Facebook accounts, but by the government itself. In this present article, we will attempt to get to the bottom of why it is that the state is committed to disseminating so many officially believed falsehoods. As we shall see, the basic reasons are, at bottom, very simple, and are only tangentially related to the fact that governments are staffed by shameless amoralists.
First is the fact that the existence of truth on the one hand, and the discovery of truth on the other, are not the same thing. The former may be fixed and immovable, but the latter requires productive effort. Its costs real resources to discover, verify, authenticate and disseminate facts, resources which must be diverted from other uses. In short, truth costs money, and is subject to a trade-off, just like anything else. Each and every one of us must decide for ourselves the point at which spending more on obtaining information ceases to be worthwhile. In fact, it’s worth noting in this regard that no person can exist as a permanent sceptic. Even the most doubting of individuals must reach a point at which he stops investigating, testing and verifying, and accepts at least some propositions as true. The alternative is to starve to death, because the eternally doubtful would never even accept that a meal on his dinner table was able to satiate his hunger, for instance.
Given this, it follows that the discovery of truth cannot be divorced from the values of those who have control over resources necessary to engage in that discovery. In a generally free market, the vast majority of resources are owned privately. Thus, the discovery of truth would be a part of the market process, just the same as any other. The precise information that is discovered, the way in which it is discovered, the standard against which it is verified, together with the methods by which it should be disseminated would all be governed by the purchasing preferences of consumers. Those firms which estimate these matters correctly would make profits, those which do not would make losses.
To illustrate, let us take an example of a clearly ridiculous piece of information: the length of time it takes to fry an egg on the bonnet of a car in the scorching sun. Car manufacturers and vendors could spend a portion of their time and money breaking eggs onto the hoods of their cars, arming their employees with stopwatches to estimate how long it takes to produce a breakfast. After a certain number of tests, such information could then be published in the car’s brochure. Clearly, however, this knowledge is unlikely to be of much interest to prospective purchases of automobiles. Having done nothing whatsoever to increase sales, these vendors have inflicted onto their bottom-line a dead-weight cost. Thus, they are likely to go out of business sooner than those vendors which avoided wasting their resources in this manner.
The same is true of the yardstick by which information should be verified. Information can come in a variety of different standards, from mere gossip and chatter all the way up to detailed, rigorous studies that may cost hundreds or thousands of pounds to undertake. The number of resources that we are willing to commit to a process of verification also has to be determined. Generally speaking, for relatively trivial or less costly matters, we will be content with lower standards of veracity; the greater the cost (or the risk of loss) the more likely it is that we will seek an enhanced level of accuracy. For example, we are likely to be more thorough when purchasing a house or making a major investment than when we are buying a pack of ball point pens.
In determining what is valuable to its customers, a firm may find that the latter are willing to pay a premium if the firm undertakes more detailed studies to determine what a product can do. A manufacturer of washing detergent may find that sales increase even at a higher price if he tests the product on 10,000 washes, publishing the results to show just how efficient it is. Alternatively, the same firm may find that customers prefer a cheaper product that they are willing take a chance with and, as such, the study won’t be undertaken. Once again, we can see that it is individual people who are the ultimate arbiters of the standards of veracity, in just the same way as they are the ultimate determiners of standards of quality and safety. Discovery of truth and information is part and parcel of the trade-offs that have to be made when dealing with scarce resources.
However, if it is the state, rather than private individuals, that has power of disposal over a vast quantity of resources, then it is the state that is the primary determinant of how those resources should be deployed. It is, therefore, the state that has greater sway in deciding which information is worth investigating, the standard to which it should be held, and whether or not it should be acted upon.
It would be bad enough if the state was merely incompetent in this regard, for in just the same way as a nationalised shoe industry would produce substandard shoes, so too will a nationalised information provider furnish us with information of a lower quality. But the game is changed entirely when the state has an active agenda, with goals to fulfil at the expense of your own. In that instance, everything that comes to be known, believed and disseminated would be tailored to fulfilling the state’s priorities, not yours. It is no small wonder, therefore, that the vast majority of such information would turn out to be false, at least from the perspective of what is good for you and for your life; for its primary purpose will be not to inform, but to persuade, cajole and intimidate you into following the state’s line.
This provides a segue into the second reason, which concerns the nature of the relationship between the state and the people. Most people assume that they and their government – especially a “democratically elected” one – co-exist in some kind of symbiosis. The people elect the government while the government ensures we are furnished with such things as institutions of law and order, and at least some form of social welfare. In other words, the interests of the state and the people are supposed to align – we choose them to do a job for us. Belief in this notion is why the state usually achieves, at least, the minimum level of tolerance from the populace necessary to ensure its survival; we may not like it, we may whinge about it, we may think it does things wrong, but at least we can’t say that it is categorically inimical to our flourishing.
Every aspect of this neat little description is wrong. Because the state always operates through the use of force (or the threat thereof), every state action necessarily involves benefiting one person at the expense of another. Taxes must be taken from Peter to pay Paul; regulations must hamper company A (usually a small business) to the benefit of company B (most likely a large, politically connected multinational). Even services which are of an apparently necessary and universal benefit – such as the provision of law and order – can be a net expense to the vast majority when you consider a) the inefficiency with which the state runs them, and b) there is little guarantee that the precise way in which the state chooses to provide these services is beneficial for each particular taxpayer. This is before we even consider the existence of those who use the state as a source of pillage and plunder, extracting unearned privileges at the expense of the mulcted masses.
States can only survive, however, if they maintain the illusion that they are universally beneficial – that they are for us, with us and by us. As such, they must continue to tell us that everything they do for us is for our own good. This is the original lie that is the source of all others. The more that the state grows, the more nooks and crannies of our lives into which it weaves its tentacles, then the more this lie must spread like a cancer to infect an ever greater portion of our societal tissue. In fact, ironically, the illusion of symbiosis with the state can increase the more paternal the latter becomes; the more that the state fosters our dependence upon it, the more likely it is that we will reach for its teat at the first sign of trouble.
All of this, moreover, explains why states always end up engaging in the censorship and persecution of dissenters. For whenever the state’s activities are subjected to rational scrutiny, the only conclusion must always be the state is a detriment, not a benefit. We soon discover that there are better ways to provide certain services other than through the state’s monopolistic provision; we realise that its management of problems and crises tends to make things worse for ordinary people rather than better; it is easy enough to discover social engineers, rent seekers and plunderers masquerading as saints as saviours behind the state’s façade of legitimacy. The state cannot allow these facts to be realised, and so there will always be an assault on truth-tellers. Should the state becomes absolute, then the discovery and dissemination of all information throughout the entirety of our economic, social and cultural life will necessarily be strangled by the state’s choke-hold. The state would simply collapse if this was not the case. The flourishing of the state is inherently incompatible with the free dissemination of ideas.
This is why your government must always lie.


Air Diffusion Agencies

Air Diffusion Agencies is a renowned Australian manufacturer of state of the art accessories and technologies to provide you with a comprehensive range of components for your heating, ventilating, and/or air-conditioning (HVAC) system.

We are a proudly 100% Australian family owned and managed company that is market driven by our valuable customers to design and provide innovative products that will meet your needs today, and in the future.


Oral argument was held on October 31, 2000 in this important land use case. On July 25, 2000, WLF had filed a brief with the U.S. Supreme Court on behalf of itself and the Allied Educational Foundation, urging it to reverse a lower court decision upholding federal jurisdiction over isolated wetlands under the Commerce Clause of the Constitution merely because migratory birds were observed on the land. In its brief, WLF argued that the Corps’ so-called “migratory bird rule” exceeds the authority conferred on the federal government under the Commerce Clause. The actual or potential presence of migratory birds on private property does not involve commercial transactions or economic activity, WLF argued.


India stun England in a thriller to qualify for CWG 2022 Final

BIRMINGHAM: Left-handed opener Smriti Mandhana shone with a blistering half-century before the bowlers held their nerves calm to hand India a thrilling victory over the hosts England in the first semi-final of Commonwealth Games 2022.

Following their victory over the tournament’s favourites England in the semi-final, India have confirmed at least a silver medal in the ongoing Games.

The Asian side will compete in the gold medal bout tomorrow and await either New Zealand or Australia in the final.

In the match, left-handed opener Mandhana led the way with a quickfire half-century while Jemimah Rodrigues chipped in with an unbeaten 44 to aid India to a competitive total of 164/5 in the allotted 20 overs.

Mandhana top-scored with 61 off 32 balls, laced with eight boundaries and three sixes, while Rodrigues scored 44 off 31 deliveries including seven boundaries.

For England, Freya Kemp was the pick amongst the bowlers as she claimed two wickets, while Katherine Brunt and Nat Sciver made one scalp each.

Set to chase 165, the hosts of the Games England, fell just four runs short of the victory as they could score 160/6 in the allotted overs.

The hosts batters displayed a dismal start right from the start as the openers failed to provide a solid foundation, while the middle-order remained a touch too slow in the proceedings, costing England to keep their gold expedition alive.

English skipper Sciver top-scored for the side with 41, however, faced 43 deliveries in course of her defending knock. Wicket-keeper batter Amy Jones, on the other hand, followed back with 31 off 24 deliveries but remained unlucky as she lost her wicket in a run-out.

Sneh Rana shone with the ball for India, taking two wickets and bowling a brilliant final over, defending 14 runs, while Deepti Sharma struck out a batter.

READ: Sharif downs New Zealand’s Hawkins, inches closer to gold in CWG


Writing An Informative Essay

However, we are keen on details, and that’s the reason we wish to cultivate the same mindset in you. During the preliminary introduction of the studying, we are supplied with a fast illustration of the mother character, Mrs. Bryson, who is the mom of the narrator, Bill. We are given an outline that leads to the premise that she is a bumbling, disorganized buffoon floundering about each day to… Essay samples, particularly these on a topic associated to your task, are an excellent way to find inspiration on a topic to put in writing about. Additionally, your matter must be attention-grabbing enough to draw a readerâs attention.

The physique might be a number of paragraphs long, comprising the bulk of the essay. This is where you’ll provide additional particulars about the main idea. Be certain you delve deeply into every space beforehand outlined in the introduction. The expository essay explains, illustrates, or interprets one thing in a way that becomes easy for the readers. This essay provides a fair analysis of the subject based mostly on facts without the writerâs opinions. It is one other common kind of informative essay in which you describe a problem and then suggest its solution.

This leaves room for editing to incase the consumer needs modifications accomplished to the work delivered by our writers. As such, we never compromise on the standard of our assignment services and try to maintain the highest requirements of work for all our clients. Our writers craft each paper rigorously to match the necessities on the directions kind while on the same time being surgical and objective while handling client work.

Now, this is the purpose where a draft proves important. It helps you focus solely on the principle elements, and keep away from being wordy. A agency ending summarizes the whole discussion of the paper. Also, a author reinforces the central message that she or he was conveying to the reader.

Research your chosen topic and draft your essay define first to save you time. Once you get a clear and logical paper structure, you’ll write your essay step-by-step without a downside. For any type of essay, you have to do a great amount of analysis. This is particularly https://boxfordhistoricalsociety.com/contact-form/ true for informative essays the place you’ll be incorporating details and data obtained by thorough analysis. When writing the physique of an informative paper, it is best to break the paragraph down into four distinct steps. The finest practice is to position it on the end of the introductory paragraph to summarize your argument.

With us, you can get quick write my essay for me help on any topic. Whether itâs an informative or persuasive essay â count on us to handle any task. Just giving details about a topic can seem actually broad, which is why there are several forms of informative essays. It feels great to finally write an informative essay to the end.

Writing a evaluate and distinction informative essay would allow you to focus on Smiley’s efficiency at the awards show, rather than simply simply defining her as a pop star. You may evaluate and contrast her controversial efficiency with others from the past that were additionally seen as scandalous at the time. In addition to the three strategies, there are several ways you’ll find a way to arrange an informative essay that gives a definition. The most important thing is to current them in a logical order that is smart, and there is not one methodology that’s best in each case. Some group schemes you might contemplate include presenting examples from most essential to least or presenting them chronologically.

It could be narrow or broad depending on the level and depth of the essay. For example, the claim for a high-school informative paper can be broader than a claim written at the college degree. It supplies a logical and simple method to comply with a discussion. In both essays, your objective is to explain and educate.

From textbooks to reports to tutorials like this one, informative writing imparts necessary and helpful details about a subject. Writing a conclusion to an informative paper can be onerous as there isn’t a argument to conclude â there’s solely info to summarize. A good conclusion for these varieties of essays should help the knowledge provided on the subject. It also needs to clarify why these matters are essential. Having the right idea is amongst the first tips to discover inspiration for analysis. Once you’ve a compelling idea, youâll have the ability to make an informative essay define with ease.


Do you develop a nuclear power plant off of Lake Michigan?

How many nuclear power plants are in Lake Michigan?

Currently Operating Nuclear Reactors

Of those, four nuclear reactors are at three generating stations on two sites along the south shore of Lake Ontario, three reactors are at three sites on the shores of Lake Erie, and five reactors are at three sites on the shores of Lake Michigan.

Can you build a nuclear power plant anywhere?

Nuclear power plants can be built essentially anywhere they can access the large amounts of water needed for cooling, anywhere from 95,000 to 227,000 litres per MWh of power generated. … Many nuclear reactors are built in earthquake-prone areas, especially in Japan, and many are built at the water’s edge.

Is Lake Michigan radioactive?

Radioactive water was released into Lake Michigan before Palisades nuclear plant shutdown Sunday. … But by the time the water reached the lake, the level of radioactivity had been diluted to the point where it did not represent a health or safety risk, a spokeswoman for the NRC said. “There was no danger to the public.

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Where are the nuclear power plants in Michigan located?

At the southwest corner of the state on the shore of Lake Michigan sits the state’s largest nuclear power plant. The Donald C. Cook Nuclear Plant, located in Bridgman, Michigan, consists of two reactors that each have a capacity rating of roughly 1,100 megawatts.

Does Michigan have nuclear power plants?

Michigan’s three nuclear power plants, D.C. Cook Units 1 and 2, Fermi 2 and Palisades, supply 22 percent the State’s electricity generation; natural gas fired generation provides about 8 percent, and 4 percent is provided by renewable energy.

Does Michigan have nuclear power?

Nuclear power is a significant source of electrical power in Michigan, producing roughly one-quarter of the state’s supply. The three active nuclear power plants supply Michigan with about 30% of its electricity.

What would happen if all the nuclear power plants explode?

If all the reactors in the USA melted down and say 1/3 exploded, the country would collapse completely into chaos. The economy would be destroyed. Martial law could be declared. People would flee the areas around the reactors, but there would be no where to go, and no way to get there for most people.

Is it safe to live next to a nuclear power plant?

Yes, is safe to live near Nuclear Power Plant.. The fact is, cancer rates and risks in general are lower around NPP. That has nothing to do with the plant itself, but instead with the higher standard of living of the people who live and work there.

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Where would a nuclear power plant be most likely to be constructed?

Nuclear plant construction

Most reactors currently planned are in Asia, with fast-growing economies and rapidly-rising electricity demand. Many countries with existing nuclear power programmes either have plans to, or are building, new power reactors.

What is leaking into Lake Michigan?

An unknown orange substance from the U.S. Steel plant in Portage was found leaking into a Lake Michigan tributary on Sunday evening. State and federal environmental agencies are working with U.S. Steel to investigate the cause of the leak and find out if it’s harmful.

How many nuclear power plants are in the US?

Is there nuclear waste in the Great Lakes?

It’s worth noting that the US has dozens of high-level nuclear waste storage sites along the Great Lakes and has had them for many years.


Which of the following is best electric insulator?

Which of the following is an insulator of electricity?

Some common insulator materials are glass, plastic, rubber, air, and wood. Insulators are used to protect us from the dangerous effects of electricity flowing through conductors. Sometimes the voltage in an electrical circuit can be quite high and dangerous.

What elements are the best electrical insulators?

The elements that belong to group 18 of the modern periodic table can be considered as some of the best electric insulators since they all contain completely filled valence shells and are, therefore, stable. Other examples of elements that are good insulators are sulfur and silicon.

Which of the following is a good insulator?

Which of the following are insulators?

Which one is the good conductor of electricity?

Copper. One of the most commonly used metals to conduct electricity is copper. As a material, copper is pliable, easy to wrap or solder, which makes it the best choice when large amounts of wiring are needed.

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Which metal is the best insulator of electricity?

Two of these substances are electrical conductors and two are insulators. Gold and lead, being metals, make poor insulators. Diamonds and concrete are nonmetallic and have good insulating properties, but diamond would make the better insulator, due to its strong resistivity.

Which of the following are good electrical insulators quizlet?

wood and distilled water are examples of good electrical insulators.

Which is the best heat insulator?

The Hydrogen is having the least thermal conductivity among water, alcohol, zinc and hydrogen but hydrogen has blasting nature, Alcohol is used as fuel and zinc is the good conductor heat being a metal. So water is best insulator of heat among water, alcohol, zinc, hydrogen.

Which of the following is not a good electrical insulator?

The correct answer is Gold. Gold is not an electric Insulator. Gold is a good conductor of electricity. An electrical conductor is a substance in which electrical charge carriers, usually electrons, move easily from atom to atom with the application of voltage.


Peaces | 9780571366590 | Helen Oyeyemi

In stock

Format:Paperback / softback

Published:04/08/2022 by Faber & Faber

Peaces is the story of Otto and Xavier Shin, a couple who embark on a mysterious train journey that takes them far beyond any destination they could have anticipated. As the carriages roll along they discover each is more curious and fascinating than the last, becoming embroiled in this strange train and its intrigue. Who is Ava Kapoor, the sole full-time inhabitant of the train, and what is her relationship to a man named Prem? Are they passengers or prisoners? We discover who orchestrated the journey, hurtling them all into their past for clues.

This is a brilliant, wise, strange and, above all, beautiful novel.

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Chinese Festivals

China is known for the varieties of its traditional festivals. According to the Tong sheng, an ancient Chinese almanac, there are at least nine major festivals, twenty-four solar periods, and one hundred forty celebrations in a year. Festivals commemorate seasonal, temporal, or phenological changes. They are social events rich with local customs that celebrate a particular theme or significant event. Festivals are inseparable from Chinese life and culture, and reflect the relationship between people and nature. Respect for harmonious relationships and caring for both the elderly and the young are on display in various festivals and customs.

Agriculture was the foundation of ancient China and the early rulers considered it vital. Two important celebrations developed from agricultural activities: the spring and winter festivals. For farmers who have always “relied on heaven to eat,” the first solar period (the beginning of spring) was considered a time of celebration. The Spring Festival is also known as the New Year. In Chinese the word nian (year) was synonymous with “harvest.” Since, for the most part, grain ripened just once a year, “harvest” was then gradually extended from being the name of an agricultural cycle to the name of the festival. The Spring Festival originated from the “hunting sacrifice” of early times. These celebrations brought people into the streets in vast numbers beginning on the twenty-third of the twelfth lunar month to the Lantern Festival on the fifteenth of the first month.

The concept of nian is closely related to the periodicity of astronomical and agricultural patterns in the past. In the repetition of spring planting, summer hoeing and weeding, autumn harvesting, and winter storing, people always have had reason to celebrate their harvest; in the course of time, it became the most important celebration of the year. The graph nian in oracle bone and bronze inscriptions, indeed, bears the meaning of fruitful harvests and ripened grain.

The winter solstice is also known as the Winter Festival. It has a few other names such as, changzhi (arrival of the longest night), duanzhi (arrival of the shortest day), and zhiri (advent day). These three words all share a common sound: zhi which means “to reach towards,” or “to stretch” towards the ultimate. Ancient society attached such importance to the winter solstice that there was a saying that “the winter solstice is as important as the year.” After the Han dynasty (206 BCE–220 CE), it was known as the Winter Festival. On this day, families gather to prepare delicacies, make sacrifices to their ancestors, and congratulate each other. In many ways, it is just like the big celebration for the New Year. The winter solstice is one of the twenty-four solar periods. Astronomers in ancient times divided the celestial sphere into 360 degrees as a means to equally divide the year. The spring equinox, summer solstice, autumnal equinox, and winter solstice were each mapped onto the sphere at ninety-degree intervals. In the solar calendar, the Winter Solstice occurs on December 22nd or 23rd of each year.

The twenty-four solar periods were established by the ancient Chinese through long-term agricultural experiments that the combined knowledge of astronomy and phenology necessary for growing crops in the best manner. Foreign calendars include only four solar periods (spring equinox, summer solstice, autumnal equinox, and winter solstice), but the Chinese calendar has “four beginnings” (for example, the Beginning of Spring, the Beginning of Autumn) which mark the start of the four seasons.

In addition to the Spring and Winter festivals, China also has other important celebrations that are related to religious sacrifice, filial piety, or the warding off of evil spirits, as well as several other cultural practices. For example, the Lantern Festival developed from the sacrifice offered to the most respected deity of all—Tai yi; it reached its current form under Emperor Wu’s reign (141–87 BCE) in the Han. The Mid-Autumn Festival, on the other hand, originated from the archaic rituals of the autumn and lunar sacrifices (the latter was made to the moon during the night of the autumnal equinox). The Double Seven festival, which celebrates the one night of the year when the stars Cowherd (Altair) and Weaving Maid (Vega) are allowed to meet, also confirms the existence of the Queen Mother of the West who punished them with separation. When Cowherd and the Weaving Maid married, they violated the Queen Mother’s command. As punishment, she placed the Milky Way between them, forever separating the pair.

The careful attention to funeral rites and subsequent commemorating sacrifices are part of the tradition of ancestor worship and have had a deep influence on the development of the Qingming Festival. Compared to modern society, when unexpected and inexplicable disasters occur, people prayed for heaven’s blessing and sought ways to protect themselves. The Dragon Boat Festival and the Chongyang Festival came about because of these practices.

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What to look for when choosing a software testing services company

software testing company

Are you evaluating different options for entrusting testing and quality activities to the hands of specialists? It will take some effort to choose a software testing company that will help you achieve the best business results. In addition, there are certain things you need to keep in mind.

In this article, we will show you some key factors to guide your search for a good software testing services company. Let us see:

  1. Why is it important to be clear about the relationship you are looking for?
  2. What do you need to know about a Software Testing and Quality Company?
  3. What do we know about the company, and what is its reputation with its customers?
  4. Why is it important to carefully consider the testers’ skills?
  5. How to decide what needs to be completed before testing?
  6. Why is it important for you to understand the testing platform and have good communication?
  7. Why consider the budget?
  8. How to make sure you have good security protocols in place?
  9. Why is choosing a testing company so important?

You may want to hire a software service testing or a team of testers for a short, one-time project for your software testing. Or you may want to strengthen your team or outsource testing. All of these choices influence the length of any potential business relationship.

Some software testing companies are used to doing small projects for one-off clients and others will be more selective, generally preferring not to take on short-term projects. What role do you want the testing company to take – do you expect them to advise and consult on testing strategy, or are you simply looking for more partners to join your team and carry out your testing plan?

Whatever the case may be, make sure you have a clear idea about: The duration of the relationship (Short vs. long term) what engagement model you are looking for (Team augmentation vs. managed testing). What type of services do you need (Software test execution, consulting.)

Size. You can find a software testing company with only a couple of employees up to thousands. Do you want to work with a pure-play boutique company or with one that offers several services including testing? Can they be in the same country and continent, or on the other side of the world? Each of these options has a specific rate and quality level.

Price Range. As mentioned above, location and skill level will greatly influence rates. For example, a tester in San Francisco will have a much higher rate than a tester with the same skills in South America, Europe, or Asia.

So, if you want an expert in a specific area of testing, of course, you will have a higher rate in contrast to a junior tester. Keep in mind that when it comes down to it, you can only choose two characteristics: high quality, fast and cheap.

Do your testing solutions offerings align with the needs of your business? It is also important to know if they care about the same aspects as your company. Some software testing companies focus on providing low rates and faster responses, while others highlight their ability to work in agile contexts. Likewise, some companies emphasize that they are better at certain types of testing, such as performance testing, than others.

If we are so careful when it comes to who prepares our food, we should be careful about who is testing our software! It is very important to gather as much information as possible from each testing and quality company.

First, you could look for customer testimonials on the company’s website; also, visit other specialized review sites such as Clutch, or look for articles on relevant technology portals. Clutch is a highly regarded review site for software testing service providers. Clutch thoroughly verifies each review before publishing it.

3.1 Why is it important to have experienced testing specialists in the field?

Not all software is the same. The risks, technologies, and timelines that teams face vary greatly by business and industry. For example, software used for healthcare requires an understanding of industry standards and thorough security testing.

Check to see if the software testing company has experience working for companies similar to yours or in the same industry, so you have more confidence in their capabilities. There are many areas in testing, so most companies will have several points to highlight in those areas.

Also, there are companies with expertise in testing for all aspects of software quality. It is well known that each quality attribute has an associated software test that verifies whether or not the quality meets customer and user expectations.

3.2 What technology and toolset does the company use for its services?

One aspect that should not be overlooked is to check or ask the company directly if they have experience with the testing tools your company uses. If you are not sure which one to use, ask if they can make a diagnosis and give you a good recommendation.

Remember that it is unrealistic for a testing company to have knowledge of each testing tool on the market, but keep in mind that: The tester is more important than the instrument because after all, the instrument is just that, a tool. The real work is done by the human behind it.

Fortunately, if a tester has the experience, for example, with one type of testing tool in several browsers, it will not be too difficult for him to learn to use another tool that is essential to carry out software testing.

Examine the qualifications of the people who will be tested. Are they specialized in software testing or are they more of an IT generalist? Either can serve, depending on your software testing needs.

If you are specifically looking to tackle quality assurance, a specialized software tester is a good choice. Their experience and expertise will allow them to approach the project from angles that your IT generalist might not. On the other hand, if your problems may have more than a QA angle, the broader knowledge base of an IT generalist may represent a better choice.

Beware if you cannot get a clear answer about the capabilities of your software testers. If the testing team cannot describe the specific qualifications of your testers (or if they will not let you talk to them directly), that may mean they are outsourcing or contracting out their software testing services.

While both are beneficial depending on needs and budget, both offshore testing and crowdsourcing testing can mean variability in the skills of those doing the actual software testing. You will not be able to tell if your software testers have the standard testing skill, high skills, or no testing experience at all.

Your software testing firm should feel familiar with your existing QA tools and practices. They do not need to know your company’s exact processes (although it helps). However, they should be familiar with your team’s tracking software. They should also test their software on as many platforms as possible.

While there is no guarantee that a company offering software testing in Dubai is familiar with all platforms, development tools, and project management systems, knowing what they know (and what you already have) will give you an idea of what to expect during the collaboration.

Not all software testing companies are equally prepared to start a project without a little client preparation. Some companies expect you to define the test conditions, manage the testing process, and interpret the test results. In other words, some companies are only prepared to do the heavy lifting and not the preparation or analysis.

It can get difficult to choose the best company to handle this task. You must determine your ideal level of quality, cost, experience, and how far you want to go when it comes to handling the software testing process. Fortunately, choosing a software testing company is much easier when you know as much as possible about the company beforehand.

You must understand which testing platform QA contracting agencies employ. Some agencies vary their testing platforms depending on the requirements of your application. There exist also many services that make use of the cloud environment to ensure a secure and easily accessible testing environment.

In certain cases, testers become so accustomed to the software they test that they no longer see several defects. In such cases, beta testers or other early release testing options on the web would guarantee that your customers get the best possible software. Employing beta users to report bugs is also a low-cost testing method for testing service companies

Whatever business you are in, communication becomes significantly important. Clear and timely communication means they have a clear idea of your testing objectives. An easy way to identify these companies is to look at their clientele. If they have numerous long-term clients, they are good communicators. In addition, there goes without saying that they provide a good testing service.

7. Why consider the budget?

Once you have determined the quality of your work, it is time to examine its cost. You probably have an idea of what most companies charge for software testing services. With time you should spend studying the reputation of their testing company prospects.

Compare that price range with the prices of your potential prospects. Are their costs more than you can anticipate for your level of excellence or higher than you can realistically budget for? If this is the case, eliminate those firms from your list.

Of course, if the firm undercharges, it may indicate that the firm is not a good fit for you. If a firm has suspiciously cheap rates, it could imply that it outsources its software testing or other services. Therefore, understand the market rates and make your decision wisely.

Operational security remains one of the necessary reasons companies cannot ignore when choosing a software testing vendor. Security is always a top consideration for any organization, and there are multiple ways to implement it.

You must consider any necessary protocols to defend against breaches or breaches of any kind. Make sure you fully understand and are familiar with the data and security standards that your chosen vendor can provide. If not, there is an option to sign a non-disclosure agreement (NDA).

Your program and its source code are important intellectual property rights. Make sure to investigate thoroughly how to protect this property. Some factors to consider when deciding to outsource to an outside company are a strong privacy and intellectual property policy and educate people on how to handle data.

9. Why is choosing a testing company so important?

Not all software testing companies are ready to start a project without client preparation. Some companies expect you to define test conditions, manage the test process, and interpret test results. On Connect Tech, we recommend caution when choosing a testing company.

Our agency ofers a variety of solutions adapted to the highest standards. We offer services that include development, cybersecurity, online marketing, and so much more. You can find out more by visiting our main website, or you can reach out to learn more ways in which we can help you.

Do you need more information to find the perfect testing company? Contact our agents at Connect Tech for more information. To contact us, simply call us at +971 43 316 688 or email us at CONTACT@CONNECTECH.DEV. We are ready to serve you.

The Talent Point is a platform in which you will be able to explore diverse job opportunities in the Middle East. If you are looking to start a new chapter in your professional life, get started on You can also send your resume to

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All About Daily Atlanta News

How do you eliminate white mold?

Jul 31

You've likely heard of Neem oil or baking soda as methods to get rid of white mold. What about toxic mold? Toxic molds can lead to serious illness and may be inhaled, inhaled, or directly handled. The signs of exposure may range from irritation to life-threatening illnesses such as nerve system disorders, and cancer.

While the majority of white mold are harmless, toxic molds can cause many symptoms, including skin irritation, eye irritation and respiratory distress. This is why hiring a professional to get rid of toxic mold is essential.

Neem oil

Neem oil, which is a natural insecticide that plants can use, is effective against various types of mildew and mold that could damage your plants. It can be applied to stop powdery mildew growing on blossom stems and leaves. Powdery mildew can be challenging in damp or stagnant conditions. Since it's a systemic insecticide, neem oil can also be employed to treat plants as a soil drench. It is able to hinder the growth of the insect and may even interfere with its reproduction cycle. These pests can also be killed by neem oil as a foliar treatment.

To avoid fungal infections, it is possible to spray diluted neem oils on the soil around your houseplants. You should be conscious that the neem oils can be highly infectious and should not be applied to plants at night. After about an hour, it is suitable for pollinators. In addition, fungal and pest problems are highly contagious, so it is advised to treat plants within the vicinity of the affected zones.

Neem oil is safe but it does have some drawbacks. While it can eliminate mildew, it doesn't hurt beneficial insects like bees. The plant's leaves will be shining and healthy. It is safe for beneficial insects and plants, even though it isn't 100 100% efficient. Neem oil isn't harmful to butterflies, bees, or ladybugs. Therefore, it's safe to make use of.

Since neem oil is a substance it is necessary to dilute it prior to using it for your plants. Mix two teaspoons of the oil with a gallon water and spray on the leaves and undersides of leaves. Repeat this procedure every other day. You should not burn more than a few leaves. Before applying neem oil, make sure you only test one leaf.

Baking soda

One of the most commonly used remedies at home for white mold is baking soda. Mixing a quarter teaspoon of baking soda with a cup of water will remove the mold and kill the spores. This mix can also decrease the toxic effects of mold and remove odors associated with the presence of mold. While it may not be the only option to get rid of mold baking soda is a fantastic substance to keep in your home.

Vinegar is a different natural cure. The acidity of vinegar is enough to kill 82 % of mold spores. As a protection measure white vinegar can be applied to a surface that is soiled. It is also possible to use baking soda as well as distilled wine for treating the stubborn white mold. The solution should be left on the mold for about an hour before being removed using a moist cloth.

Vinegar is yet another effective and cost-effective method to get rid of mold. This solution contains antibacterial qualities and is able to be applied on mold with a disposable towel or a scrub brush. Let the mixture dry and then clean the mold with a damp towel. It is then possible to apply the mixture onto the surface after the white mold is gone. And remember to avoid contact with the mold if it is possible. A good way to avoid mold in the near future is to control moisture in your home.

If the mildew is not gone after a few days, you can use a paste of baking soda and water. If it remains, you can repeat the process several times. You can also use white vinegar and hydrogen peroxide. It is most effective when you are able to eliminate the mildew and prevent it from growing. Baking soda is not an effective option. Hydrogen peroxide is a powerful chemical that kills the mildew and also mold naturally.

White vinegar can kill 82% of all kinds of mold, including black mould. White vinegar is milder than the majority of cleaning products and is safe to use on any surface. The odor of the vinegar isn't unpleasant however it kills the mold spores that reside beneath the surface. You must wait at least 60 minutes following applying the vinegar on the affected region. Then, clean the vinegar off with a damp cloth. It will disappear within a few hours.


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Protesters urge power to the people

Ljubljana, 11 September - The 21st consecutive Friday protests in the centre of Ljubljana placed the people's power in the focus as the organisers evoked the provision in the Slovenian Constitution that says power is vested in the people. They urged both individuals and the civil society to stand up to the government.

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