Abstract— Deep learning is a general term for dealing with a complicated neural network with multiple layers. Convolutional Neural Networks (CNN) is a type of deep neural network, and there are many other types. CNNs are popular because they have very useful applications to image recognition and have demonstrated a great achievement in image classification. The idea behind this paper is to propose a framework that is able to classify liver lesions of Metastases and Hemangiomas by using Convolutional neural networks. We used dataset from Jiangsu University hospital with 154 liver lesions. We evaluated the performance of our proposed model and we compared AlexNet with our model to determine the performance and the classification strength. The classification accuracy for our model was 87.75%, an F1 score of 0.90 and an MCC 0.51. We have found our model much better than AlexNet. Index Terms— Deep learning, Convolutional Neural Networks, Liver lesions, Classification. INTRODUCTION THE liver is a large, meaty organ that sits on the right side of the belly. Weighing about 3 pounds, the liver is reddish-brown in color and feels rubbery to the touch. Normally you can’t feel the liver, because it’s protected by the rib cage. Metastasis means that cancer spreads to a different body part from where it started. When this happens, doctors say the cancer has “metastasized.” Your doctor may also call it “metastatic cancer,” “advanced cancer,” or “stage 4 cancer.” But these terms can have different meanings. For example, a cancer that is large but has not spread to another body part can also be called advanced cancer or locally advanced cancer. Metastases are the plural form of metastasis. Metastases most commonly develop when cancer cells break away from the main tumor and enter the bloodstream or lymphatic system. These systems carry fluids around the body. This means that the cancer cells can travel far from the original tumor and form new tumors when they settle and grow in a different part of the body. Hemangioma is a noncancerous (benign) mass in the liver. A liver hemangioma is made up of a tangle of blood vessels. Other terms for a liver hemangioma are hepatic hemangioma and cavernous hemangioma. Most cases of liver hemangiomas are discovered during a test or procedure for some other condition. People who have a liver hemangioma rarely experience signs and symptoms and don’t need treatment. In this paper, we propose a framework to use deep features extracted from Convolutional Neural Networks (CNNs) along with a Softmax as a classifier to build our model for liver lesions classification. In medical imaging tasks, annotations are made by radiologists with expert knowledge on the data and task. Also, these small lesions cannot be detected by X-rays and are only detectable by a CT scan. Even after the detection, it takes a considerable amount of effort and experience on the part of radiologists to detect and label the nodules as benign or as a probable case of malignancy. Considering the large number of cases encountered by radiologists every day there is a constant pressure on them to analyse a huge amount of data and make a decision as quickly as possible based on the analysis. Figure1. a. Dataset examples of metastases and hemangiomas liver lesions Figure1. b. Input lesion ROI examples, ROI extraction process from a 2-D CT slice of the liver. AlexNet is the name of a convolutional neural network which has had a large impact on the field of machine learning, specifically in the application of deep learning to machine vision. It famously won the 2012 ImageNet LSVRC-2012 competition by a large margin (15.3% VS 26.2% (second place) error rates). The network had a very similar architecture as LeNet by Yann LeCun et al but was deeper, with more filters per layer, and with stacked convolutional layers. It consisted of 11×11, 5×5, 3×3, convolutions, max pooling, dropout, data augmentation, ReLU activations, SGD with momentum. It attached ReLU activations after every convolutional and fully-connected layer. AlexNet was trained for 6 days simultaneously on two Nvidia Geforce GTX 580 GPUs which is the reason for why their network is split into two pipelines (..). RELATED WORK Convolutional Neural Networks (CNNs) have been demonstrated as very powerful techniques in broad range of tasks and in various fields of studies such as computer vision, language processing, image processing, and medical image analysis. Recently, several methods have been proposed to classify liver lesions. In…the authors presented methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation, the results increased to 85.7% sensitivity and 92.4% specificity. In… the authors developed a method to distinguish cyst from metastases and they tested different models by using Convolutional Neural Networks (CNNs) yielded accuracy of 84%. They used deep learning to classify liver lesions in CT images. In …. Tajbakhsh et al, they sought to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, they considered 4 distinct medical imaging applications in 3 specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from 3 different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pretrained CNNs fine-tuned in a layer-wise manner. In …they described a novel method for automated diagnosis of liver lesions in portalphase computed tomography (CT) images that improves over single-dictionary BoVW methods by using an image patch representation of the interior and boundary regions of the lesions. The classification accuracy for the images from the two institutions was 99% and 88%, respectively, and 93% for a combined dataset. In this study, we evaluated the performance of a customized, sequential CNN in the task of classifying liver lesions Metastases and Hemangiomas. The contributions of this work are the following: a) We compare our model, with pre-trained model, multi-layered and highly flexible architecture. b) We have used the batch normalization to normalize the input layer by adjusting the activations and to reduce overfitting; we have put the batch normalization before the input after every layer and dropouts between the fully-connected layers. c) The performance of classifiers is evaluated by two widely-used measures: Matthews Correlation Coefficient (MCC), F1 – Score (F1 Score is used to measure a test’s accuracy) MATERIALS AND METHODS 3.1. Dataset In this work, the dataset contains 154 liver lesions collected from Jiangsu hospital by searching medical records for cases of Metastases and Hemangiomas. The largest metastasis lesion was …mL, while the hemangiomas largest was …mL. The data is available in DICOM formats. In this work, we preprocessed our images by converting them from their DICOM formats to a NumPy arrays. We have chosen the NumPy arrays because it provides a high-performance multidimensional array object, and tools for working with these arrays and NumPy array has advantages as less memory, fast, convenient. 3.2.Proposed model In this section, we explain each part of our model, is shown in Figure 2. in detail. Image preprocessing: in this stage, normalization is performed. The images were resizing to 224 x 224-pixel resolutions to suit the input requirements of customized and pre-trained CNNs and normalized to assist in faster convergence. Figure 2. The proposed model. Feature extraction: Convolutional Neural Network model is used for feature extraction process. Convolutional Neural Networks (CNN) belong to a specific category of Neural networks methods. CNN has not only been able to learn image feature representations automatically, but they have also outperformed many conventional hand-crafted feature techniques (…) The detailed Explanation of our model (CNN). Figure 3. Architecture of the CNN for liver lesion classification. The first convolutional layer performs 5×5 convolutions with a 4×4 stride and with valid padding; followed by a ReLU layer, pooling layer of size 2×2. Batch Normalisation before passing it to the next layer to reduce the over-fitting. The second convolutional layer perform 5X5 convolutions with a 1×1 stride and with valid padding; followed by a ReLU layer, pooling layer of size 2×2. Batch Normalisation before passing it to the next layer to reduce the over-fitting. The next convolutional layer performs 3×3 convolutions with a 1×1 stride and with valid padding; followed by a ReLU layer. Batch Normalisation before passing it to the next layer to reduce the over-fitting. The next convolutional layer performs 3×3 convolutions with a 1×1 stride and with valid padding; followed by a ReLU layer. Batch Normalisation before passing it to the next layer to reduce the over-fitting. The next convolutional layer performs 3×3 convolutions with a 1×1 stride and with valid padding; followed by a ReLU layer, pooling layer of size 2×2. Batch Normalisation before passing it to the next layer to reduce the over-fitting. The next layer is a 0.4% dropout layer in this work dropout is used to regularize dense layers. The final layer of our model is the prediction layer that outputs the predicted value for each image. The classification: The classifier is needed after feature extraction to find the corresponding label for every test image. The Softmax activation is normally applied to the very last layer in a neural net, instead of using ReLU, sigmoid, tanh, or another activation function. The reason why Softmax is useful is because it converts the output of the last layer in your neural network into what is essentially a probability distribution. In this work we use Softmax classifier; the Softmax layer calculates the normalized exponential function to calculate the output activation function. Pcr|x,θ=Px,θ|crpcr∑j=1kPx,θ|cjpcj=expar x,θ∑j=1kexpajx, θ 3.3.Training Procedure In this stage, we present the training for the models. Normalization: Mean centering and standard deviation normalization was applied to each image patch. Train/Validation/Test split: our dataset contains 154 liver lesions. The training set of 84 lesions images, 35 lesions images as a validation set, and the test set contained 35 lesions images. Batch size: A batch size of 32 images was used for training. Optimization and Learning Rate Scheduling: The Adam optimization algorithm  was used with an initial learning rate of 10–3. This learning rate was reduced by a factor of 2 after 10 epochs without improvements in the validation set accuracy, repeatedly. The allowed minimum learning rate was 10–10. Iterations: The initial number of epochs was 10–3. Training time on an NVIDIA Titan Xp GPU was 2.45h for our model and 4.25 for AlexNet model. 3.4.AlexNet AlexNet is a convolutional neural network that is trained on more than a million images from the ImageNet database (). AlexNet is one of the most important and famous of the CNN architectures (.) The AlexNet architecture consists of five convolutional layers, with three max pooling layers then three fully-connected layers and finally a 1000-way Softmax classifier. Figure 4. Architecture of AlexNet. 3.5.Evaluation To evaluate how good is our model, we use the following metrics: The accuracy, precision, and recall(..). The accuracy of the models is calculated following equation: Accuracy= tp tntp tn fp fn Accuracy is the fraction of predictions our model got right. Accuracy = Number of correct predictions Total number of predictions. Precision=tptp fp Precision refers to the percentage of your results which are relevant. The precision is the ratio of true positives and the total positive predictions. Recall=tptp fn Recall refers to the percentage of total relevant results correctly classified by your algorithm. The recall is the ratio of true positives and the total, actual positives. F1 Score is the combination between precision and recall. The range for F1 Score is [0, 1]. F1 = 2* Recall * PrecisionRecall Precision The Matthews correlation Coefficient (MCC) has a range of -1 to 1 where -1 indicates a completely wrong binary classifier while 1 indicates a completely correct binary classifier. MCC to measure and maximize the overall accuracy of the classification model. MCC=tp/n–spps1–s1–p=tptn–fpfntp fptp fntn fptn fn RESULTS AND DISCUSSION In this work, we propose a framework that is able to classify liver lesions of Metastases and Hemangiomas by using Convolutional neural networks.AlexNet is one of the best and a popular CNN that is composed of both stacked and connected layers. Table 1 presents the accuracy and quality metrics for our framework CNN using the liver lesion dataset from Jiangsu hospital and the training process for our model took 2.45 hours. We have an average accuracy of 87.75%. Table 2 presents the accuracy and quality metrics for the AlexNet model using the liver lesion dataset from Jiangsu hospital and training process took 4.52hours. we have an average accuracy of 86.67%. Table 3 presents the comparison between AlexNet model and our Model performance. We have found our model higher than that of AlexNet. Table 4 presents Comparison of AlexNet and the custom CNN classification strength. We can see our model is much better and it is a strong classifier for this dataset. We also see that our model has a significantly lower false positive rate (FPR) than AlexNet. For this case we can see that AlexNet is not a strong classifier for this dataset but it does better with more generalized image identification. Table1: Accuracy and quality metrics for the CNN classifier. Table2: Accuracy and quality metrics for the AlexNet. Table3: Comparison of AlexNet and Our model performance Accuracy = (tp tn)/ (tp tn f p fn), Precision = tp/ (tp f p), Recall = tp/ (tp fn), FPR = fp/ (tn f p), and FPR = fn/ (tp fn). Table4: Comparison of AlexNet and our Model classification strength CONCLUSION In this work, a convolutional neural network model was implemented to classify liver lesions of Metastases and Hemangiomas. The experimental results show that our model is a much stronger classiﬁer and can achieve an accuracy of 87.82%. AlexNet is not best suited to the problem of liver lesion classification. We have found our model achieve better performance than AlexNet. We have used the python for coding and we used Keras to build our CNN. For future work we need more data for getting higher accuracy result. REFERENCES ALEXNET ….G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012 Related work 1 Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, and Hayit Greenspan, Member, IEEE”GAN-based Synthetic Medical Image Augmentationfor increased CNN Performancein Liver Lesion Classification” 2 K. Yasaka, H. Akai, O. Abe, and S. Kiryu, “Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced ct: a preliminary study,” Radiology, vol. 286, no. 3, pp. 887–896, 2017. 3 N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B.Gotway, and J. Liang, “Convolutional neural networks for medical imageanalysis: Full training or fine tuning?” IEEE Transactions on MedicalImaging, vol. 35, no. 5, pp. 1299–1312, May 2016. 4 I. Diamant, A. Hoogi, C. F. Beaulieu, M. Safdari, E. Klang, M. Amitai, H. Greenspan, and D. L. Rubin, “Improved patch-based automated liver lesion classification by separate analysis of the interior and boundary regions,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 6, pp. 1585–1594, Nov 2016. CNN K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, “Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective,” IEEE Access, 2017. classificationS. Minaee and Y. Wang, “Palmprint Recognition Using Deep Scattering Convolutional Network,” arXiv preprint arXiv:1603.09027, 2016 AlexNET ImageNet. http://www.image-net.org accuracy https://developers.google.com/machine-learning/crash-course/classification/accuracy adam optimization P.D. Kingma and J. Ba, “Adam: A method for stochastic opti?mization,” arXiv preprint arXiv:1412.6980, 2014.
MU Effect Of The 2007 2008 Financial Crisis On Enterprise Risk Management Discussion.
I Need help with an assignment. BEST WORK NEEDEDEach question answered for a quarter page cited in APA format.The main concept is on “The bank of America”Questions:1. Discuss what caused the financial crisis of 2007-2009?2. How did the government and central bank of the host country respond to the financial crisis of 2007-2009?3. Compare their responses with the Government of Canada and the Central Bank of Canada4. How did these responses differ from those taken during the major developing country debt crisis in the 1980s?5. What impact did the crisis of 2007-2009 had on the host country’s equity and bond market and other financial markets?6. What similarities (2007-2009 crisis) exist between experiences in Canada, USA and the host country?7. Where there any changes to the Basel Accord after the financial crisis of 2007-2009? Discuss8. What lessons were learnt from the financial crisis of 2007-2009 and what steps have been taken to date regarding the improvement of the banking regulatory systems?
Leg ulcers are distinct as a “trouncing of skin under the knee on the leg or foot which takes further than six weeks to cure” (Dale et al., 1983). They stand for a frequently happening (as well as chronic) constant predicament, causing substantial suffering plus uneasiness to those who endure as of them. It is predictable that amid 1.5 -3.0 per 1,000 inhabitants have lively leg ulcers, this number growing to about 20 per 1,000 in persons in excess of 80 years of time. By means of the mounting figures of adult populace in civilization, these numbers are put to increase supplementary, rising the requirement for leg ulcer handling in the prospect. Variation in practice Broad differences in medical perform plus the medical administration of leg ulcers are obvious as of preceding investigate studies plus assessments assumed on this subject. This is chiefly in relation to the correctness of appraisal as well as the variety of involvements used to pleasure leg ulcers. A learn concluded in the Mersey area by Roe et al (1993). Designated substantial differences in the treatment appraisal of leg ulcers – for instance in relation to the captivating of foot pulses, the utilizing of Doppler ultrasound, and the appraisal of soreness plus the early capacity of leg ulcers. There is extensive disparity in perform, as well as facts of needless pain as well as expenses suitable to insufficient supervision of venous leg ulcers in the society. Rulings such as these exemplify a important require to recover the supervision of leg ulcers, not slightest to advance the superiority of existence for those who experience as of them. Understanding as of proposals arrangement to get improved the society based treatment supervision of leg ulcers – for instance the latent for additional medical as well as price efficient perform during extensive espousal of confirmation based interferences. By means of additional efficient organization of leg ulcers, it has been optional that it would be practical to anticipate 70 per cent of venous leg ulcers to cure in a twelve week time, even though hardly any studies have attain this curative pace. Recognition of the key elements of concern This phase concerned recognizing the entire core subject pertinent to the appraisal as well as supervision of patients through venous leg ulcers. The chief constituents of concern are recognized as of pre-existing methodically urbanized instructions plus methodical appraisals. Intended for the cause of the expansion the medical do lessons as well as the organization of patients by means of venous leg ulcers as well as the effectual Health Care statement, firmness treatment for venous leg ulcers were used to recognize the core subjects pertinent to effectual mind. The medical perform principle was shaped by means of a healthy loom -proposals were relying on methodical evaluations of the writing as well as the conclusion of a multidisciplinary agreement cluster. Exercise of bacterial wash The proposal in the medical perform teaching is that practice bacteriological washing is needless if not there is confirmation of medical disease. Where washes had been in use, the grounds agreed on the statistics compilation pieces were scrutinized. These incorporated odour, exudates, escalating soreness, ruddiness, and cellulites plus worsening of the ulcer. The outstanding not appropriate answer in the initial inspection comprised two suitable motives eczema needing twice a day steroid ointment, plus heaviness painful on the heel; 11 unsuitable motives counting leg oedema, exudate as well as preceding DVT; as well as in six cases no grounds were given. Considerably better statistics of patients established compression bandaging on the second review compared to the first (Chi-square=22.56, DF=1, p=0.000). So as to determine whether compression bandaging was being applied properly, its use was then cross-tabulated with ankle brachial stress indices over and below the disconnect value of 0.8. An indicator of more than or up to 0.8 signifies that there is no verification of major arterial disease. Consequently, if the patient has a venous leg ulcer (as strong-minded by clinical assessment) then the patient is appropriate for treatment with compression bandaging. Compression bandaging was applied to patients who had ABPI of <0.8 in 25 cases in the first audit (5.2%) and 30 cases in the second audit (5.3%). This is a total of 55 people whose venous leg ulcers were not treated according to the instruction. The amount did not progress among the first and second audit. Of those patients with ankle brachial pressure index of less than 0.8, who would not be measured appropriate for compression, a large proportion had received compression bandaging Compression stockings The guideline suggests the use of compression stockings for patients with healed ulcers, to decrease venous ulcer recurrence rates. The work shows whether compression stockings were given to patients, whether patients received education about compression stockings. In support of both these criteria, about half the forms were returned with these data missing. It would consequently be unsuitable to draw conclusions from these results, but the results are comparable in both audits. Missing data for whether compression stockings were given were 417 (49.8%) omissions in the first audit and 320 (42.2%) in the second and for whether education was given, 423 (50.5%) in the first review and 308 (40.6%) in the second. The moment taken for the major leg ulcer to cure decreased considerably among the two audit periods. In the second audit 69.4% of venous leg ulcers had healed in twelve weeks or under, as opposed to 54.7% in the first audit. There is a marked transfer, with far fewer ulcers taking 25 weeks or longer to heal. The missing data for this variable were 13.1% in the first audit and 5.2% in the second. Nursing time spent in the treatment of venous leg ulcers The median time spent treating leg ulcers was eight hours in the first review and six hours in the second audit. This represents a 25% decline in median time spent treating ulcers. The range of times spent decreased dramatically between the two audits. This may have been because the long-standing venous leg ulcers healed during or after the first data collection period, with fewer long-standing ulcers represented during the second audit. Test of significance for difference in median times between first and second audits. Mann-Whitney test showed the decrease in time spent treating ulcers between audits one and two to be significant (Z=-3.64, p=0.00). Further analysis of audit data Further analysis of the data was performed using the categorical variable, healed or improved versus other. The proportion of missing data was very low, 0.4% in the first audit and 1.1% in the second audit. The proportion of ulcers healed or improved increased from 82.6% to 90.0%. Chi squared testing shows this result to be significant (Chisquare= 22.33, DF=1, p=0.000). In order to ascertain the reasons associated with sites that did not show improvement across the two audits, a binary variable was created reflecting sites that did not improve versus those that did. A logistic regression analysis was performed with the binary variable, sites not improving versus those improving as the dependent variable. The independent variables were entered in three blocks. The first block contained the possible confounding patient variables of age and gender. The second block contained the possible confounding ulcer variables of ulcer size, ulcer duration and average ABPI. The third block contained the care variables of interest: whether the nurse at assessment was F grade or above _ whether or not the patient was seen at a leg ulcer clinic _ whether or not compression bandaging was applied _ all possible interactions of the above three variables. This third block of variables was entered using a stepwise forward likelihood ratio method. Table 3.19 shows the significant variables from block 3 of the analysis that explain sites failing to improve versus those that did between the first and second audits, having controlled for differences in patient and ulcer attributes: This analysis demonstrates that sites improving between the first and second audits were significantly more likely to be applying compression bandaging, and significantly more likely to be treating patients in leg ulcer clinics. In addition, compression bandaging was significantly more likely to be applied if a nurse of F grade or above performed the initial assessment, as indicated by the significant interaction term. The total cost of venous leg ulcers has been estimated to lie in the range £294 to £650 million each year in the UK. However such estimates give little indication of the degree to which costs can be affected by different management strategies. It is important to carry out an analysis of the economic implications of different management strategies to ensure that quality gains are made while contributing to the goal of efficient use of scarce NHS resources. The RCN and partners in 1998 developed clinical guidelines for the management of patients with venous leg ulcers. It was recognised that the guidelines should be subject to economic evaluation to provide information on the comparative costs and outcomes of leg ulcer management during their introduction. This evaluation was carried out as part of the National Sentinel Audit of the Management of Venous Leg Ulcers. The audit used criteria based on the guideline and was conducted at two time periods, giving comparative information on the impact of the guidelines. It should be noted that the guidelines were not disseminated in isolation from other initiatives in this area – for example the Effective Health Care Bulletin on compression therapy for venous leg ulcers had been widely circulated in 1997. Thus, although the economic evaluation seeks to address the cost effectiveness of the introduction of the guideline, there may be other influences on practice which cannot be accounted for and which may limit the generalisability of the findings. Economic evaluation allows the issue of cost effectiveness to be addressed. For a change in practice to be cost effective, either resource use is lower and the benefits are the same or greater than before, or resource use is the same but outcomes are improved. If resource use is greater and outcomes are improved, further research is required. The results suggest that the introduction of clinical guidelines for the nursing management of venous leg ulcers in the community is cost effective. The results show statistically significantly lower mean total costs and improved outcomes after the introduction of the guidelines. These results were robust to variations in the assumptions used to calculate the costs. A possible concern about the conclusions drawn is that they are based on a subset of patients. Bias may be introduced if these patients are different to the larger group. However it is suggested this is not the case. It details resource use for all patients managed for up to and including 26 weeks and shows statistically significant differences in resource use before and after the introduction of the guidelines. These differences are reflected in the differences in costs shown in table 4.3. However a lot of data were missing on level of input and there is no way of knowing if the group with full data recorded had more or less input than the larger group. This is a concern as the amount of contact with patients is the key factor influencing total costs. Furthermore, there are other elements of resource use that may be affected by a change in practice, which were not picked up by the information collected in the audit. These include recurrence rates and the costs of specialist referral beyond the primary referral. Even given these concerns, however, the data collected and presented above suggest that the leg ulcer management guideline has led to a cost-effective change in practice. 4.5 Conclusions The results of the economic evaluation show lower costs and improved outcomes after the introduction of clinical guidelines for the management of venous leg ulcers in the community. This is a cost effective change in practice, giving more benefit for less cost.
MU Effect Of The 2007 2008 Financial Crisis On Enterprise Risk Management Discussion
LAW: Kazakhstan-Torture-Persecution, Extradition, writing homework help
LAW: Kazakhstan-Torture-Persecution, Extradition, writing homework help.
LAW: Kazakhstan-Torture-Persecution, Extradition 15 pagesThis is open book assignment: you must all the
available materials related to this “case”,Please clarify governments all negative action- against to mus lims, torture, persecution against this king of
citizens;- about extradition, what’s is going to happens to the people
who been or/and will be return to Kazakhstan,- bring clear examples about the people were return and what
happened to them.- also need to bring example from “Dublin convention”.
What it is, how it works, positive and effectiveness of this conventionIf you have
any questions please do not hesitate to contact me and askRecommendation for sources (websites): Human Rights
Watch, forum18.com, us government country reports, and many more….Please use any source you find and make sure add citations (very
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LAW: Kazakhstan-Torture-Persecution, Extradition, writing homework help
Complete Human Resource Management Task (JEFF)
online assignment help Complete Human Resource Management Task (JEFF). Need help with my Business question – I’m studying for my class.
Recall your chosen firm and industry you have been using throughout the course. For this assignment, you will identify the top three major safety and health issues in your firm, and write a policy on each, consistent with Occupational Safety & Health Administration (OSHA) standards. There is a minimum requirement of 300 words for each of the three job policies.
Each of the five domains of OSHA must be considered when writing these three policies:
Hazard communication: How will you notify people of potentially dangerous or unhealthy work conditions?
Blood-borne pathogens: How will you protect employees from blood-borne pathogens such as AIDS?
Personal protective equipment (PPE): What equipment or tools will your employees in this job require to work safely?
Cumulative trauma disorders (CTDs): How will you prevent CTDs that come from repetitive movement (e.g., carpel tunnel syndrome)?
Work assignments: How will you handle potentially dangerous work assignments, especially to protect unborn babies?
NOTE: This is a three-part assignment. All three of the policies (300 words each) you write should go on one document. This one document is what will be uploaded to Blackboard.
Any sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations in APA format.
Complete Human Resource Management Task (JEFF)
Write a 500-word descriptive statement that details the focus and explains the portfolio. This will be an umbrella-like description of the portfolio including the justification for the selections and issues/topics covered in the portfolio. Be sure to include this statement with the portfolio.Here is brief of me , so you have idea what to write about The previous difficulties that were in the life of Saudi women in the extreme era and how I tried to bring out and highlight the Arab beauty and intelligence and that the Saudi woman is a modern beautiful care of art and beauty and fashion through my focus on beauty and fashion and liberation and art in general. The role of art in the life of women, and I often imagine that art for women is her world, she is an artist in her dealings, and an artist in her motherhood. Artist women sow success in everything that revolves around them in a beautiful and optimistic manner and women are free regardless of their environment and whatever their status, nothing stands in front of her freedom and my work shows and reflects the beauty and sense of women
NUR 445 Denver School of Nursing Week 5 Advocating for Patients Discussion
NUR 445 Denver School of Nursing Week 5 Advocating for Patients Discussion.
Step 1 In The Neighborhood, read:Carol Ramsey:Season 1, Episode 12Season 2, Episodes 11 and 12Season 3, Episodes 2 and 3Karen Williams:Season 1, Episodes 3, and 10-12Season 2, Episodes 8 and 14Season 3, Episodes 7 and 11Step 2 In an initial post of approximately 150 words, respond to the prompt.After reading The Neighborhood cases and viewing the interactive lesson presentation, describe an example of a time when you properly advocated for your patient or patient population. Include any barriers encountered and how the issue was resolved.Step 3 Read other students’ posts and respond to at least two of them by Friday at 11:59pm MT.In your response posts, cite any alternative strategies that could have addressed or resolved the problem The Neighborhood characters encountered.
NUR 445 Denver School of Nursing Week 5 Advocating for Patients Discussion