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A straightforward and efficient spectrum technique was created using Ortho-chloranil as the electron acceptor (-acceptor) in a charge transfer (CT) complex formation reaction to determine the concentration of famotidine (FMD) in solutions. Compared to the double-distilled blank solution, the reaction result detected a definite violet colour at a maximum absorption wavelength of 546 nm, For concentrations range 2—28 µg/ml, the technique demonstrated excellent compliance with Beer-Law and Lambert's, as evidenced by its molar absorptivity of 2159.648 L mol−1 cm–1. Lower detection limits of 0.3024 µg/ml and 1.471 µg/ml, respectively, were discovered. The complexes of famotidine and Ortho-chloranil were found to have a 2:1 stoichiometry. Additionally, the suggested approach effectively estimated famotidine concentrations in pharmaceutical formulations, particularly in tablet form., Validerad;2024;Nivå 2;2024-03-07 (hanlid);Full text license: CC BY

A straightforward and efficient spectrum technique was created using Ortho-chloranil as the electron acceptor (-acceptor) in a charge transfer (CT) complex formation reaction to determine the concentration of famotidine (FMD) in solutions. Compared to the double-distilled blank solution, the reaction result detected a definite violet colour at a maximum absorption wavelength of 546 nm, For concentrations range 2—28 µg/ml, the technique demonstrated excellent compliance with Beer-Law and Lambert's, as evidenced by its molar absorptivity of 2159.648 L mol−1 cm–1. Lower detection limits of 0.3024 µg/ml and 1.471 µg/ml, respectively, were discovered. The complexes of famotidine and Ortho-chloranil were found to have a 2:1 stoichiometry. Additionally, the suggested approach effectively estimated famotidine concentrations in pharmaceutical formulations, particularly in tablet form., Validerad;2024;Nivå 2;2024-03-07 (hanlid);Full text license: CC BY

BACKGROUND: The degree of atrophy and fatty infiltration of rotator cuff muscle belly is a key predictor for cuff repairability. Traditionally, Goutallier grading of fatty infiltration is assessed at sagittal scapular Y-view. Massive rotator cuff tears are associated with tendon retraction and medial retraction of cuff musculature, resulting in medialization of the muscle bulk. Thus, standard Y-view can misrepresent the region of interest and may misguide clinicians when assessing repairability. It is hypothesized that by assessing the muscle belly with multiple medial sagittal magnetic resonance imaging (MRI) sections at the medial scapular body, the Medial Scapular Body-Goutallier Classification (MSB-GC) will improve reliability and repeatability, giving a more representative approximation to the degree of fatty infiltration, as compared with the original Y-view. METHODS: Fatty infiltration of the rotator cuff muscles were classified based on the Goutallier grade (0-4) at 3 defined sections: section 1, original Y-view; section 2, level of suprascapular notch; and section 3, 3 cm medial to the suprascapular notch on MRI scans. Six subspecialist fellowship-trained shoulder surgeons and 3 musculoskeletal radiologists independently evaluated deidentified MRI scans of included patients. RESULTS: Of 80 scans, 78% (n = 62) were massive cuff tears involving the supraspinatus, infraspinatus, and subscapularis tendons. Interobserver reliability (consistency between observers) for Goutallier grade was excellent for all 3 predefined sections (range: 0.87-0.95). Intraobserver reliability (repeatability) for Goutallier grade was excellent for all 3 sections and 4 rotator cuff muscles (range: 0.83-0.97). There was a moderate to strong positive correlation of Goutallier grades between sections 1 and 3 and between sections 2 and 3 and these were statistically significant (P < .001). There was a reduction in the severity of fatty infiltration on the Goutallier classification from s

Contains fulltext : 305057.pdf (Publisher’s version ) (Closed access), People with diabetes often encounter stigma (ie, negative social judgments, stereotypes, prejudice), which can adversely affect emotional, mental, and physical health; self-care, access to optimal health care; and social and professional opportunities. To accelerate an end to diabetes stigma and discrimination, an international multidisciplinary expert panel (n=51 members, from 18 countries) conducted rapid reviews and participated in a three-round Delphi survey process. We achieved consensus on 25 statements of evidence and 24 statements of recommendations. The consensus is that diabetes stigma is driven primarily by blame, perceptions of burden or sickness, invisibility, and fear or disgust. On average, four in five adults with diabetes experience diabetes stigma and one in five experience discrimination (ie, unfair and prejudicial treatment) due to diabetes, such as in health care, education, and employment. Diabetes stigma and discrimination are harmful, unacceptable, unethical, and counterproductive. Collective leadership is needed to proactively challenge, and bring an end to, diabetes stigma and discrimination. Consequently, we achieved unanimous consensus on a pledge to end diabetes stigma and discrimination.

In 2010, the International Diabetes Federation put out a call to action to “stop discrimination against people with diabetes”.6 The International Diabetes Federation recommended enabling people with diabetes to claim their rights and responsibilities (as later detailed in their Charter4); increasing public awareness of diabetes and reducing diabetes stigma; and supporting people with diabetes to be at the centre of this response. This call to action was a novel, ambitious, and crucial step forward. In 2013, the first systematic review of diabetes stigma showed wide-ranging potential harms but a scarcity of research focused on this important issue.1 Subsequently, both advocacy and research on diabetes stigma have increased substantially, including raising awareness, sharing experiences, and understanding the nature, extent, and repercussions of diabetes stigma. Arguably, there has been greater and earlier recognition in research of the stigma associated with other health conditions (eg, cancer and mental illness in the 1960s, epilepsy in the 1970s, HIV in the 1980s–1990s, and obesity in the 2000s) than with diabetes.7, 8, 9 Insights from these areas suggest that research and advocacy, People with diabetes often encounter stigma (ie, negative social judgments, stereotypes, prejudice), which can adversely affect emotional, mental, and physical health; self-care, access to optimal health care; and social and professional opportunities. To accelerate an end to diabetes stigma and discrimination, an international multidisciplinary expert panel (n=51 members, from 18 countries) conducted rapid reviews and participated in a three-round Delphi survey process. We achieved consensus on 25 statements of evidence and 24 statements of recommendations. The consensus is that diabetes stigma is driven primarily by blame, perceptions of burden or sickness, invisibility, and fear or disgust. On average, four in five adults with diabetes experience diabetes stigma and one in five experience discrimination (ie, unfair and prejudicial treatment) due to diabetes, such as in health care, education, and employment. Diabetes stigma and discrimination are harmful, unacceptable, unethical, and counterproductive. Collective leadership is needed to proactively challenge, and bring an end to, diabetes stigma and discrimination. Consequently, we achieved unanimous consensus on a pledge to end diabetes stigma and discrimination.

Samera F Al Basri,1 Jawaher A Al Abdali,2 Hala M Alzubaidi,2 Abeer A Almarhabi,2 Maryam A Alzubaidi,2 Gadi Al Qarni,2 Nada Y Alzubaidi,2 Aliyah Aldabli,2 Amna AlMagaadi,2 Laila A Alamri,2 Ghadeer S AlQarni,2 Areej H AlAbdli,2 Bashaier H AlGhamdi,2 Zahra A AlNashri2 1Obstetrics and Gynecology, King Abdulaziz University Hospital, Jeddah, Saudi Arabia Obstetrics and Gynecology, Faculty of Medicine, Jeddah, Saudi Arabia; 2Faculty of Medicine, Umm Al-Qura University, Al-Qunfudah, Saudi ArabiaCorrespondence: Samera F Al Basri, Obstetrics and Gynecology, King Abdulaziz University Hospital, Jeddah, Saudi Arabia, Tel +966505653412, Email salbasri@kau.edu.saObjective: To study the knowledge of Saudi women regarding contraceptive use.Methodology: In Al-Qunfudah, Saudi Arabia, 1107 Saudi women aged 18 to 45 years old participated in a cross-sectional descriptive study utilizing a closed-question survey. Data were compiled using SPSS v 21 software.Results: The participants in our study were 1107 females between the ages of 18 and 45. The majority of them were married, educated, and had used or were using oral contraceptives (OC) (50.2%), mostly for family planning (81.1%). Fear of OC side effects was stated by 39.9% of women who had never used it as a reason for not using it, and some women were unsure about consequences, such as mood changes (20.2%) and headaches (28%); 44.7% of women did not believe that they needed OC. Many women were ignorant of OC’s health and menstrual cycle benefits, with only 7.9% and 5.8% agreeing that it can help with premenstrual syndrome and acne problems, respectively. Majority of women said that they learned about contraception through doctors and friends/relatives, but 73.0% felt that the information available from health-care service was inadequate.Conclusion: The findings revealed that women in this region were lack awareness regarding contraceptive techniques, including OC, as well as its side effects and potential health hazards. It

Al Basri SF, Al Abdali JA, Alzubaidi HM, et al. Open Access J Contracept. 2022;13:61–71. The authors have advised the affiliation list on page 61 is incorrect. The correct author list and affiliations are as follows. Samera F Al Basri,1,2 Jawaher A Al Abdali,3 Hala M Alzubaidi,3 Abeer A Almarhabi,3 Maryam A Alzubaidi,3 Gadi Al Qarni,3 Nada Y Alzubaidi,3 Aliyah Aldabli,3 Amna AlMagaadi,3 Laila A Alamri,3 Ghadeer S AlQarni,3 Areej H AlAbdli,3 Bashaier H AlGhamdi,3 Zahra A AlNashri3 1Obstetrics and Gynecology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia; 2Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; 3Faculty of Medicine, Umm Al-Qura University, Al-Qunfudah, Saudi Arabia Read the original article

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

Krishna Vakil,1 Tigestu Alemu Desse,2 Elizabeth Manias,2,3 Hamzah Alzubaidi,1,4 Bodil Rasmussen,5– 8 Sara Holton,5,6 Kevin P Mc Namara1 1Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria, Australia; 2School of Nursing and Midwifery, Centre for Quality and Patient Safety Research, Institute for Health Transformation, Faculty of Health, Deakin University, Melbourne, Victoria, Australia; 3School of Nursing and Midwifery, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia; 4College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates; 5School of Nursing and Midwifery, Centre for Quality and Patient Safety Research – Western Health Partnership, Institute for Health Transformation Deakin University, Geelong, Victoria, Australia; 6The Centre for Quality and Patient Safety Research in the Institute of Health Transformation -Western Health Partnership, Western Health, Melbourne, Victoria, Australia; 7Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 8Faculty of Health Sciences, University of Southern Denmark and Steno Diabetes Center, Odense M, DenmarkCorrespondence: Krishna Vakil, Deakin Rural Health, Building D Level 2, Deakin University, Princes Hwy (PO Box 423), Warrnambool, Victoria, 3280, Australia, Tel +61 422 365 879, Email kvakil@deakin.edu.auBackground: First-generation migrants from South Asia account for a considerable proportion of the immigrant populations in high-income, western countries and are at a high risk of developing complex, chronic diseases such as cardiovascular disease and diabetes. Yet, previous systematic reviews have not synthesized information about the healthcare needs and preferences of such migrants and the best ways for health services to provide them with appropriate, culturally sensitive, patient-centered care. The aim of this study is to systematically review the international

Background: First-generation migrants from South Asia account for a considerable proportion of the immigrant populations in high-income, western countries and are at a high risk of developing complex, chronic diseases such as cardiovascular disease and diabetes. Yet, previous systematic reviews have not synthesized information about the healthcare needs and preferences of such migrants and the best ways for health services to provide them with appropriate, culturally sensitive, patient-centered care. The aim of this study is to systematically review the international evidence about first-generation, South Asian migrants’ healthcare experiences from the patients’ perspectives. Methods: Five databases were searched for qualitative, quantitative, and mixed methods studies published between January 1990 and April 2020. Fourteen thousand, six hundred and forty-four papers were retrieved and screened using pre-determined eligibility criteria. Sixty-one papers were included in this narrative synthesis. Relevant qualitative findings from the included papers were thematically analyzed, and quantitative findings were summarized. Results: Five themes emerged from findings: 1) Healthcare services engaged; 2) the language barrier; 3) experiences and perceptions of healthcare advice; 4) the doctor–patient relationship; and 5) the role of patients’ families in supporting access and delivery of healthcare. Conclusion: The findings indicate that communication barriers reduce the cultural and linguistic appropriateness of healthcare. Potential solutions include employing healthcare staff from the same cultural background, training healthcare professionals in cultural competence, and proactively including first-generation, South Asian migrants in decision-making about their healthcare. Future research should explore South Asian migrants’ experiences of multimorbidity management, continuity of care, interdisciplinary collaboration, the formation of treatment plans and goals, Background: First-generation migrants from South Asia account for a considerable proportion of the immigrant populations in high-income, western countries and are at a high risk of developing complex, chronic diseases such as cardiovascular disease and diabetes. Yet, previous systematic reviews have not synthesized information about the healthcare needs and preferences of such migrants and the best ways for health services to provide them with appropriate, culturally sensitive, patient-centered care. The aim of this study is to systematically review the international evidence about first-generation, South Asian migrants’ healthcare experiences from the patients’ perspectives. Methods: Five databases were searched for qualitative, quantitative, and mixed methods studies published between January 1990 and April 2020. Fourteen thousand, six hundred and forty-four papers were retrieved and screened using pre-determined eligibility criteria. Sixty-one papers were included in this narrative synthesis. Relevant qualitative findings from the included papers were thematically analyzed, and quantitative findings were summarized. Results: Five themes emerged from findings: 1) Healthcare services engaged; 2) the language barrier; 3) experiences and perceptions of healthcare advice; 4) the doctor–patient relationship; and 5) the role of patients’ families in supporting access and delivery of healthcare. Conclusion: The findings indicate that communication barriers reduce the cultural and linguistic appropriateness of healthcare. Potential solutions include employing healthcare staff from the same cultural background, training healthcare professionals in cultural competence, and proactively including first-generation, South Asian migrants in decision-making about their healthcare. Future research should explore South Asian migrants’ experiences of multimorbidity management, continuity of care, interdisciplinary collaboration, the formation of treatment plans and goals as littl

Recently, the convergence between Blockchain and IoT has been appealing in many domains including, but not limited to, healthcare, supply chain, agriculture, and telecommunication. Both Blockchain and IoT are sophisticated technologies whose feasibility and performance in large-scale environments are difficult to evaluate. Consequently, a trustworthy Blockchain-based IoT simulator presents an alternative to costly and complicated actual implementation. Our primary analysis finds that there has not been so far a satisfactory simulator for the creation and assessment of blockchain-based IoT applications, which is the principal impetus for our effort. Therefore, this study gathers the thoughts of experts about the development of a simulation environment for blockchain-based IoT applications. To do this, we conducted two different investigations. First, a questionnaire is created to determine whether the development of such a simulator would be of substantial use. Second, interviews are conducted to obtain participants’ opinions on the most pressing challenges they encounter with blockchain-based IoT applications. The outcome is a conceptual architecture for simulating blockchain-based IoT applications that we evaluate using two research methods; a questionnaire and a focus group with experts. All in all, we find that the proposed architecture is generally well-received due to its comprehensive range of key features and capabilities for blockchain-based IoT purposes., Validerad;2023;Nivå 2;2023-08-09 (hanlid);Funder: EPSRC (EP/V042017/1)

A Service Level Agreement (SLA) establishes the trustworthiness of service providers and consumers in several domains; including the Internet of Things (IoT). Given the proliferation of Blockchain technology, we find it compelling to reconsider the assumption of trust and centralised governance typically practised in SLA management including monitoring, compliance assessment, and penalty enforcement. Therefore, we argue that, such critical tasks should be operated by blockchain-based smart contracts in a non-repudiable manner beyond the influence of any SLA party. This paper envisions an IoT scenario wherein a firefighting station outsources end-to-end IoT operations to a specialised service provider. The contractual relationship between them is governed by an SLA which stipulates a set of quality requirements and violation consequences. The main contribution of this paper lies in designing, deploying and empirically experimenting a novel blockchain-based SLA monitoring and compliance assessment framework in the context of IoT. This is done by utilising Hyperledger Fabric (HLF), an enterprise-grade blockchain technology. Our work highlights a set of considerations and best practice at two sides, the IoT application monitoring-side and the blockchain-side. Moreover, it experimentally validates the reliability of the proposed monitoring approach, which collects relevant metrics from each IoT component and examines them against the quality requirements stated in the SLA. Finally, we propose a novel design for smart contracts at the blockchain-side, analyse and benchmark the performance, and demonstrate that the new design proves to successfully handle Multiversion Concurrency Control (MVCC) conflicts typically encountered in blockchain applications, while maintaining sound throughput and latency., Validerad;2023;Nivå 2;2023-04-06 (hanlid);Funder: The Engineering and Physical Sciences Research Council, EPSRC (EP/V042017/1)

A Service Level Agreement (SLA) establishes the trustworthiness of service providers and consumers in several domains; including the Internet of Things (IoT). Given the proliferation of Blockchain technology, we find it compelling to reconsider the assumption of trust and centralised governance typically practised in SLA management including monitoring, compliance assessment, and penalty enforcement. Therefore, we argue that, such critical tasks should be operated by blockchain-based smart contracts in a non-repudiable manner beyond the influence of any SLA party. This paper envisions an IoT scenario wherein a firefighting station outsources end-to-end IoT operations to a specialised service provider. The contractual relationship between them is governed by an SLA which stipulates a set of quality requirements and violation consequences. The main contribution of this paper lies in designing, deploying and empirically experimenting a novel blockchain-based SLA monitoring and compliance assessment framework in the context of IoT. This is done by utilising Hyperledger Fabric (HLF), an enterprise-grade blockchain technology. Our work highlights a set of considerations and best practice at two sides, the IoT application monitoring-side and the blockchain-side. Moreover, it experimentally validates the reliability of the proposed monitoring approach, which collects relevant metrics from each IoT component and examines them against the quality requirements stated in the SLA. Finally, we propose a novel design for smart contracts at the blockchain-side, analyse and benchmark the performance, and demonstrate that the new design proves to successfully handle Multiversion Concurrency Control (MVCC) conflicts typically encountered in blockchain applications, while maintaining sound throughput and latency., Validerad;2023;Nivå 2;2023-04-06 (hanlid);Funder: The Engineering and Physical Sciences Research Council, EPSRC (EP/V042017/1)

Recently, the convergence between Blockchain and IoT has been appealing in many domains including, but not limited to, healthcare, supply chain, agriculture, and telecommunication. Both Blockchain and IoT are sophisticated technologies whose feasibility and performance in large-scale environments are difficult to evaluate. Consequently, a trustworthy Blockchain-based IoT simulator presents an alternative to costly and complicated actual implementation. Our primary analysis finds that there has not been so far a satisfactory simulator for the creation and assessment of blockchain-based IoT applications, which is the principal impetus for our effort. Therefore, this study gathers the thoughts of experts about the development of a simulation environment for blockchain-based IoT applications. To do this, we conducted two different investigations. First, a questionnaire is created to determine whether the development of such a simulator would be of substantial use. Second, interviews are conducted to obtain participants’ opinions on the most pressing challenges they encounter with blockchain-based IoT applications. The outcome is a conceptual architecture for simulating blockchain-based IoT applications that we evaluate using two research methods; a questionnaire and a focus group with experts. All in all, we find that the proposed architecture is generally well-received due to its comprehensive range of key features and capabilities for blockchain-based IoT purposes., Validerad;2023;Nivå 2;2023-08-09 (hanlid);Funder: EPSRC (EP/V042017/1)

Background: First-generation migrants from South Asia account for a considerable proportion of the immigrant populations in high-income, western countries and are at a high risk of developing complex, chronic diseases such as cardiovascular disease and diabetes. Yet, previous systematic reviews have not synthesized information about the healthcare needs and preferences of such migrants and the best ways for health services to provide them with appropriate, culturally sensitive, patient-centered care. The aim of this study is to systematically review the international evidence about first-generation, South Asian migrants’ healthcare experiences from the patients’ perspectives. Methods: Five databases were searched for qualitative, quantitative, and mixed methods studies published between January 1990 and April 2020. Fourteen thousand, six hundred and forty-four papers were retrieved and screened using pre-determined eligibility criteria. Sixty-one papers were included in this narrative synthesis. Relevant qualitative findings from the included papers were thematically analyzed, and quantitative findings were summarized. Results: Five themes emerged from findings: 1) Healthcare services engaged; 2) the language barrier; 3) experiences and perceptions of healthcare advice; 4) the doctor–patient relationship; and 5) the role of patients’ families in supporting access and delivery of healthcare. Conclusion: The findings indicate that communication barriers reduce the cultural and linguistic appropriateness of healthcare. Potential solutions include employing healthcare staff from the same cultural background, training healthcare professionals in cultural competence, and proactively including first-generation, South Asian migrants in decision-making about their healthcare. Future research should explore South Asian migrants’ experiences of multimorbidity management, continuity of care, interdisciplinary collaboration, the formation of treatment plans and goals, Background: First-generation migrants from South Asia account for a considerable proportion of the immigrant populations in high-income, western countries and are at a high risk of developing complex, chronic diseases such as cardiovascular disease and diabetes. Yet, previous systematic reviews have not synthesized information about the healthcare needs and preferences of such migrants and the best ways for health services to provide them with appropriate, culturally sensitive, patient-centered care. The aim of this study is to systematically review the international evidence about first-generation, South Asian migrants’ healthcare experiences from the patients’ perspectives. Methods: Five databases were searched for qualitative, quantitative, and mixed methods studies published between January 1990 and April 2020. Fourteen thousand, six hundred and forty-four papers were retrieved and screened using pre-determined eligibility criteria. Sixty-one papers were included in this narrative synthesis. Relevant qualitative findings from the included papers were thematically analyzed, and quantitative findings were summarized. Results: Five themes emerged from findings: 1) Healthcare services engaged; 2) the language barrier; 3) experiences and perceptions of healthcare advice; 4) the doctor–patient relationship; and 5) the role of patients’ families in supporting access and delivery of healthcare. Conclusion: The findings indicate that communication barriers reduce the cultural and linguistic appropriateness of healthcare. Potential solutions include employing healthcare staff from the same cultural background, training healthcare professionals in cultural competence, and proactively including first-generation, South Asian migrants in decision-making about their healthcare. Future research should explore South Asian migrants’ experiences of multimorbidity management, continuity of care, interdisciplinary collaboration, the formation of treatment plans and goals as littl

Our recent intensive study has found that physics-informed neural networks (PINN) tend to be local approximators after training. This observation leads to this novel physics-informed radial basis network (PIRBN), which can maintain the local property throughout the entire training process. Compared to deep neural networks, a PIRBN comprises of only one hidden layer and a radial basis "activation" function. Under appropriate conditions, we demonstrated that the training of PIRBNs using gradient descendent methods can converge to Gaussian processes. Besides, we studied the training dynamics of PIRBN via the neural tangent kernel (NTK) theory. In addition, comprehensive investigations regarding the initialisation strategies of PIRBN were conducted. Based on numerical examples, PIRBN has been demonstrated to be more effective and efficient than PINN in solving PDEs with high-frequency features and ill-posed computational domains. Moreover, the existing PINN numerical techniques, such as adaptive learning, decomposition and different types of loss functions, are applicable to PIRBN. The programs that can regenerate all numerical results can be found at https://github.com/JinshuaiBai/PIRBN., Comment: 48 pages, 26 figures

Exact travelling wave solutions to the two-dimensional stochastic Allen-Cahn equation with multiplicative noise are obtained through the hyperbolic tangent (tanh) method. This technique limits the solutions to travelling wave profiles by representing them with a finite tanh power series. This study focuses on how multiplicative noise affects the dynamics of these travelling waves, in particular, occurring of wave propagation failure due to high levels of noise., Comment: Need to be revised in section of the derivation of exact travelling wave solutions

The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. Therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a $k$ nearest neighbour ($k$NN) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations; despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. The $k$NN model outperforms SVM by 5\% and the ISO also demonstrates a reduction of 4\% inaccuracy deviation compared to regular SO.

This thesis investigated novel deep learning techniques for advanced medical imaging applications. It addressed three major research issues of employing deep learning for medical imaging applications including network architecture, lack of training data, and generalisation. It proposed three new frameworks for CNN network architecture and three novel transfer learning methods. The proposed solutions have been tested on four different medical imaging applications demonstrating their effectiveness and generalisation. These solutions have already been employed by the scientific community showing excellent performance in medical imaging applications and other domains.

In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources.

Identification of plant disease is affected by many factors. The scarcity of rare or mild symptoms, the sensitivity of segmentation is influenced by light and shadow of images capturing conditions, and symptoms characteristics are represented by multiple lesions of varied colours on the same leaf at different stages of infection. Traditional approaches face several problems: contrast handling leads to mild symptoms being undetected and deals with edges results in curved surfaces and veins being considered new regions of interest. Thresholding of segmentation restricts it to a specific range of values, which prevents it from dealing with an entire area (healthy, injured, or noise). Deep learning approaches also face problems of dealing with imbalanced datasets. The existence of overlapped symptoms on the same leaf sample is rare. Most deep models detect a single type of lesion at a single time. Masks with a single type of infection are used for training these models that lead to misclassification. Manual annotation of symptoms is considered time-consuming. Therefore, the proposed framework in this study is an attempt to overcome certain drawbacks of traditional segmentation approaches to generate masks for deep disease classification models. The main objective is to label datasets based on a semi-automated segmentation of leaves and disordered regions. There is no need to manage contrast or apply filters that keep lesion characteristics unchanged. As a result, every pixel in the predetermined lesions is selected accurately. The approach is applied to three different datasets with single and multiple infections. The obtained overall precision is 90%. The average intersection over the union of the injured regions is 0.83. The brown and the dark brown lesions are more accurately segmented than the yellow lesions.

Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patients across the healthcare system. Robust and accurate analysis of medical data is crucial to achieving a successful diagnosis from physicians. Traditional diagnostic methods are highly time-consuming and prone to handmade errors. Cost is reduced and performance is improved by adopting computer-aided diagnosis methods. Usually, the performance of traditional machine learning (ML) classification methods much depends on both feature extraction and selection methods that are sensitive to colors, shapes, and sizes, which conveys a complex solution when facing classification tasks in medical imaging. Currently, deep learning (DL) tools have become an alternative solution to overcome the drawbacks of traditional methods that make use of handmade features. In this paper, a new DL approach based on a hybrid deep convolutional neural network model is proposed for the automatic classification of several different types of medical images. Specifically, gradient vanishing and over-fitting issues have been properly addressed in the proposed model in order to improve its robustness by means of different tested techniques involving residual links, global average pooling layers, dropout layers, and data augmentation. Additionally, we employed the idea of parallel convolutional layers with the aim of achieving better feature representation by adopting different filter sizes on the same input and then concatenated as a result. The proposed model is trained and tested on the ICIAR 2018 dataset to classify hematoxylin and eosin-stained breast biopsy images into four categories: invasive carcinoma, in situ carcinoma, benign tumors, and normal tissue. As the experimental results show, our proposed method outperforms several of the state-of-the-art methods by achieving rate values of 93.2% and 89.8% for both image- and patch-wise image classification tasks, respectively. Moreover, we fine

Substantially reduced life expectancy for people with serious mental illness compared with the general population is primarily driven by physical health issues, of which cardiovascular disease is the leading cause. In this narrative review, we examine the evidence base for use of metformin and other antidiabetic agents as a means for reducing this excess cardiometabolic disease burden. Evidence from randomised controlled trials (RCTs) suggests substantial potential for metformin to prevent or manage weight gain and glycaemic impairment induced by atypical antipsychotic medications, whereas the impact of metformin on other cardiometabolic risk factors is less consistent. Evidence from RCTs also suggests potential benefits from glucagon-like peptide-1 receptor agonists (GLP-1RAs), particularly for addressing cardiometabolic risk factors in people using atypical antipsychotic medications, but this is based on a small number of trials and remains an emerging area of research. Trials of both metformin and GLP-1RAs suggest that these medications are associated with a high prevalence of mild-moderate gastrointestinal side effects. The heterogeneous nature of participant eligibility criteria and of antipsychotic and antidiabetic drug regimens, alongside short trial durations, small numbers of participants and paucity of clinical endpoints as trial outcomes, warrants investment in definitive trials to determine clinical benefits for both metformin and GLP-1RAs. Such trials would also help to confirm the safety profile of antidiabetic agents with respect to less common but serious adverse effects. The weight of RCT evidence suggests that an indication for metformin to address antipsychotic-induced weight gain is worth considering in Australia. This would bring us into line with other countries.

Background: Mental disease burden is increasing globally, and a substantial shortage of mental health professionals remains. Community pharmacists can improve population mental health outcomes to bridge the mental health care gap. However, there is a paucity of data on community pharmacists’ provision of mental health care. Objective: To assess community pharmacist-delivered care to people with mental illness in the United Arab Emirates (UAE), focusing on dispensing and counseling practices, pharmacists’ confidence and comfort in providing care, and attitudes and beliefs towards mental illness. Methods: This was a mixed-methods study with an exploratory, sequential design. Semi-structured interviews explored community pharmacists’ practices, challenges and strategies to improve care. Data were analyzed thematically, and the results guided questionnaire development. The questionnaire was also informed by the Framework of Core Mental Health Competencies for All Pharmacy Professionals and other relevant literature and administered to community pharmacists in four out of seven emirates. Logistic regression was used to identify the predictors of pharmacist practices. Results: In the interviews, community pharmacists described adopting a precautionary attitude and perceived their role as a dispensing one. They reported challenges such as emotional discomfort and lack of training but highlighted the need for a different approach to patients with mental illness. In total, 252 pharmacists completed the questionnaire, and 74% reported performing at least five counseling practices. Logistic regression showed that pharmacists who performed a higher number of practices (≥5) were younger, received continuing education on mental health care in the last two years, and had higher confidence levels. Conclusion: UAE community pharmacists performed basic dispensing practices and reported discomfort during encounters with patients with mental illness. Training on patient-centered commun

Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challenging or even impossible in many real-world applications, such as structural risk estimation and medical diagnosis. This presents a significant barrier to the practical implementation of DL in these fields. Physics-guided deep learning (PGDL) is a novel type of DL that can integrate physics laws to train neural networks. This can be applied to any systems that are controlled or governed by physics laws, such as mechanics, finance and medical applications. It has been demonstrated that, with the additional information provided by physics laws, PGDL achieves great accuracy and generalisation in the presence of data scarcity. This review provides a detailed examination of PGDL and offers a structured overview of its use in addressing data scarcity across various fields, including physics, engineering and medical applications. Moreover, the review identifies the current limitations and opportunities for PGDL in relation to data scarcity and offers a thorough discussion on the future prospects of PGDL., Comment: 26 Pages, 4 figures, 2 tables, 116 references. This work is going to submit to Nature Machine Intelligence

Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-based computational solid mechanics. Besides, two prevailingly used physics-informed loss functions for PINN-based computational solid mechanics are summarised. Moreover, numerical examples ranging from 1D to 3D solid problems are presented to show the performance of PINN-based computational solid mechanics. The programs are built via Python coding language and TensorFlow library with step-by-step explanations. It is worth highlighting that PINN-based computational mechanics is easy to implement and can be extended for more challenging applications. This work aims to help the researchers who are interested in the PINN-based solid mechanics solver to have a clear insight into this emerging area. The programs for all the numerical examples presented in this work are available on https://github.com/JinshuaiBai/PINN_Comp_Mech., Comment: 32 pages, 20 figures are include in this manuscript

Recently, distributed ledger technologies like blockchain have been proliferating and have attracted interest from the academic community, government, and industry. A wide range of blockchain solutions has been introduced, such as Bitcoin, Ethereum, and Hyperledger technologies in the literature. However, tools for evaluating these solutions and their applications are still lacking, limiting the exploration of their potentiality and associated challenges/limitations. That is, experimenting with real blockchain networks usually requires a solid budget; and thus, sophisticated blockchain simulators can facilitate designing and evaluating solutions before the actual implementation stage. The quality of such simulators depends on several factors such as usability, reliability, provided capabilities, and supported features. This paper aims to provide a systemic mapping review of blockchain simulators focusing on these quality factors. This paper also sheds light on the configuration parameters (inputs) and produced metrics (outputs) supported by each simulator. Furthermore, it investigates which metrics supported by each simulator are scientifically validated/evaluated. Moreover, code quality comparison is carried out to assess the source code of the covered simulators. The results reveal that no simulator fully covers the wide operational range of features and capabilities of existing blockchain technologies. However, several promising efforts exist in the domain of blockchain simulation with interesting and useful features. Finally, we discuss the subject of blockchain simulation and provide our insight into the matter.

Hend M Al-Atif,1 Hasan S AL-Ghamdi,2 Waad Abdulaziz Alzubaidi,3 Nejad Mubasher Alnaem,3 Saad Hussain Qahtani3 1Department of Internal Medicine, College of Medicine, King Khalid University, Abha City, Kingdom of Saudi Arabia; 2Internal Medicine Department, Division of Dermatology, Faculty of medicine, Albaha University, Albaha City, Kingdom of Saudi Arabia; 3College of Medicine, King Khalid University, Abha City, Kingdom of Saudi ArabiaCorrespondence: Hend M Al-Atif, Tel +966556464612, Email hotaif@kku.edu.saPurpose: The utilization rate of complementary and alternative medication (CAM) is on the rise, especially for the general population. Despite the lack of scientific support, CAM has been used for decades and is more often used among chronic patients. This research examines the prevalence of CAM use in dermatology patients in the area of Aseer, Saudi Arabia.Materials and Methods: This research was done using a descriptive hospital-based cross-sectional survey among adults who attended the dermatological clinics at the major hospitals in the Aseer area of Southern Saudi Arabia. The dermatology outpatients were approached individually and were asked to fill out a survey about their usage of CAM therapies in the preceding year. The questionnaire included questions on patients’ socio-demographic data and clinical data on their dermatological condition, including duration and the effect on their daily life. The occurrence and pattern of CAM use and the type of CAM utilized were also examined.Results: 353 dermatological patients filled out the study questionnaire. Patients varied in age from 18 to above 60 years, with an average of 26.1 ± 12.9 years. 203 (57.5%) patients were females. 120 (34%) of the study patients reported using folk medicine with doctor medication for their dermatological disorder, 84 (23.8%) used folk medicine and stopped using medications prescribed by a doctor, while 149 (42.2%) never used folk medicine. The most used CAM was olive oil (22%)

Developing innovative informatics approaches aimed to enhance fetal monitoring is a burgeoning field of study in reproductive medicine. Several reviews have been conducted regarding Artificial intelligence (AI) techniques to improve pregnancy outcomes. They are limited by focusing on specific data such as mother's care during pregnancy. This systematic survey aims to explore how artificial intelligence (AI) can assist with fetal growth monitoring via Ultrasound (US) image. We used eight medical and computer science bibliographic databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM Library, Google Scholar, and the Web of Science. We retrieved studies published between 2010 to 2021. Data extracted from studies were synthesized using a narrative approach. Out of 1269 retrieved studies, we included 107 distinct studies from queries that were relevant to the topic in the survey. We found that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound images (n=19). Classification is the most used method (n=42), followed by segmentation (n=31), classification integrated with segmentation (n=16) and other miscellaneous such as object-detection, regression and reinforcement learning (n=18). The most common areas within the pregnancy domain were the fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen (n=10), and lastly the fetus face (n=10). In the most recent studies, deep learning techniques were primarily used (n=81), followed by machine learning (n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI techniques played a crucial role in predicting fetal diseases and identifying fetus anatomy structures during pregnancy. More research is required to validate this technology from a physician's perspective, such as pilot studies and randomized controlled trials on AI and its applications in a hospital setting., Comment: 25 pages, 4 figures, submitted to Artificial Intelligence in Medicine

In spite of, several mathematical approaches of the Lorenz solver system have been declared, fast and effective approaches has always been the direction in which scientists are trying to achieve. Based on this challenge, this paper initiates to boost the processing of Lorenz ordinary differential equations (ODE) system by applied hardware accelerator to take advantage of parallel architecture. A chaotic solution is found for various parameters and primary conditions. Specifically, Lorenz attractor was a group of chaotic outputs of the Lorenz equation. The 3D plotted the shape of Lorenz attractor was like “‘butterfly wings” which depend on initial conditions of the nonlinear system. Lorenz has more application in the real world as in biomechanical, physical, Control systems. As noted earlier, the Lorenz system is a scheme of ordinary differential equations exhibiting chaotic behavior for certain parameter values and initial conditions. The result presents the different Lorenz waveform of various parameter values to calculate the time of execution for each case study on a different platform.

With increasing popularity holographic method, 3D scene and augmented reality, needless to say, that 3D holography would be playing the most important role of real-time recording display. This paper demonstrates a setup that shows and records the scene in a real-time 3D appearance. We speed up the holographic processing by using a hardware accelerator to take advantage of its parallel specificities (architecture). The results clarify the system’s ability for viewing the holographic objects by applying four cameras running at the same time with a difference in partial of millisecond attributed to reason the clock of cameras and the VGA update.

Fruits classification is a challenging task due to the several types of fruits. To classify fruits more effectively, we propose a new deep convolutional neural network model to classify 118 fruits classes. The proposed model combines two aspects of convolutional neural networks, which are traditional and parallel convolutional layers. The parallel convolutional layers have been employed with different filter sizes to have better feature extraction. It also helps with backpropagation since the error can backpropagate from multiple paths. To avoid gradient vanishing problem and to have better feature representation, we have used residual connections. We have trained and tested our model on Fruits-360 dataset. Our model achieved an accuracy of 100% on a divided image set from the training set and achieved 99.6% on the test set, which outperformed previous methods.

Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pretrained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets. Subjects Bioinformatics, Artificial Intelligence, Computer Vision

Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose level in the blood and alert user to the type-1 Diabetes class once a certain critical level is surpassed. This can lead the body of the patient to work at critical levels until the medicine is taken in order to reduce the glucose level, consequently increasing the risk of causing considerable health damages in case of the intake is delayed. To overcome the latter, a new approach based on cutting-edge software and hardware technologies is proposed in this paper. Specifically, an artificial intelligence deep learning (DL) model is proposed to predict glucose levels in 30 min horizons. Moreover, Cloud computing and IoT technologies are considered to implement the prediction model and combine it with the existing wearable CGM model to provide the patients with the prediction of future glucose levels. Among the many DL methods in the state-of-the-art (SoTA) have been considered a cascaded RNN-RBM DL model based on both recurrent neural networks (RNNs) and restricted Boltzmann machines (RBM) due to their superior properties regarding improved prediction accuracy. From the conducted experimental results, it has been shown that the proposed Cloud&DL-based wearable approach achieves an average accuracy value of 15.589 in terms of RMSE, then outperforms similar existing blood glucose prediction methods in the SoTA.

The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a descri

In this paper, we present a comparison of four proposed hybrid deep convolutional neural network models for diabetic foot ulcer (DFU) classification to discriminate between abnormal (DFU) and normal (healthy skin) classes. Increasing the depth in single branch deep convolutional neural networks does not always significantly contribute to their overall performance. It may actually lead to a drop in performance due to gradient vanishing issue. Therefore, our proposed models were designed based on the concept of multiple branches network. Traditional convolutional layers and multi-branch parallel convolutional layers were combined to design four deep aggregated models. All four models have six blocks of parallel convolutional layers, but the number of branches of parallel convolutional layers ranges from two to five. Parallel convolutional layers have been employed using different filter sizes on the same input and then concatenated for better feature extraction. To overcome the issues of overfitting and a small amount of training data, we applied several data augmentation techniques. The proposed models were trained with original images first, then with original images plus augmented images, which improved the performance. We empirically prove that a model with four branches outperforms models with two, three, or five branches of parallel convolutions in the task of DFU classification. This model also outperformed the latest DFU classification methods by achieving an F1 score of 95.8% on the DUF dataset.

We consider a biochemical model that consists of a system of partial differential equations based on reaction terms and subject to non--homogeneous Dirichlet boundary conditions. The model is discretised using the gradient discretisation method (GDM) which is a framework covering a large class of conforming and non conforming schemes. Under classical regularity assumptions on the exact solutions, the GDM enables us to establish the existence of the model solutions in a weak sense, and strong convergence for the approximate solution and its approximate gradient. Numerical test employing a finite volume method is presented to demonstrate the behaviour of the solutions to the model.

Deep Learning (DL) requires a large amount of training data to provide quality outcomes. However, the field of medical imaging suffers from the lack of sufficient data for properly training DL models because medical images require manual labelling carried out by clinical experts thus the process is time-consuming, expensive, and error-prone. Recently, transfer learning (TL) was introduced to reduce the need for the annotation procedure by means of transferring the knowledge performed by a previous task and then fine-tuning the result using a relatively small dataset. Nowadays, multiple classification methods from medical imaging make use of TL from general-purpose pre-trained models, e.g., ImageNet, which has been proven to be ineffective due to the mismatch between the features learned from natural images (ImageNet) and those more specific from medical images especially medical gray images such as X-rays. ImageNet does not have grayscale images such as MRI, CT, and X-ray. In this paper, we propose a novel DL model to be used for addressing classification tasks of medical imaging, called MedNet. To do so, we aim to issue two versions of MedNet. The first one is Gray-MedNet which will be trained on 3M publicly available gray-scale medical images including MRI, CT, X-ray, ultrasound, and PET. The second version is Color-MedNet which will be trained on 3M publicly available color medical images including histopathology, taken images, and many others. To validate the effectiveness MedNet, both versions will be fine-tuned to train on the target tasks of a more reduced set of medical images. MedNet performs as the pre-trained model to tackle any real-world application from medical imaging and achieve the level of generalization needed for dealing with medical imaging tasks, e.g. classification. MedNet would serve the research community as a baseline for future research., Comment: 6 Pages

Background: Ischemia–reperfusion injury (IRI) is one of the major risk factors implicated in morbidity and mortality associated with cardiovascular disease. During cardiac ischemia, the buildup of acidic metabolites results in decreased intracellular and extracellular pH, which can reach as low as 6.0 to 6.5. The resulting tissue acidosis exacerbates ischemic injury and significantly affects cardiac function. Methods: We used genetic and pharmacologic methods to investigate the role of acid-sensing ion channel 1a (ASIC1a) in cardiac IRI at the cellular and whole-organ level. Human induced pluripotent stem cell–derived cardiomyocytes as well as ex vivo and in vivo models of IRI were used to test the efficacy of ASIC1a inhibitors as pre- and postconditioning therapeutic agents. Results: Analysis of human complex trait genetics indicates that variants in the ASIC1 genetic locus are significantly associated with cardiac and cerebrovascular ischemic injuries. Using human induced pluripotent stem cell–derived cardiomyocytes in vitro and murine ex vivo heart models, we demonstrate that genetic ablation of ASIC1a improves cardiomyocyte viability after acute IRI. Therapeutic blockade of ASIC1a using specific and potent pharmacologic inhibitors recapitulates this cardioprotective effect. We used an in vivo model of myocardial infarction and 2 models of ex vivo donor heart procurement and storage as clinical models to show that ASIC1a inhibition improves post-IRI cardiac viability. Use of ASIC1a inhibitors as preconditioning or postconditioning agents provided equivalent cardioprotection to benchmark drugs, including the sodium-hydrogen exchange inhibitor zoniporide. At the cellular and whole organ level, we show that acute exposure to ASIC1a inhibitors has no effect on cardiac ion channels regulating baseline electromechanical coupling and physiologic performance. Conclusions: Our data provide compelling evidence for a novel pharmacologic strategy involving ASIC1a blockade as

Background: Ischemia–reperfusion injury (IRI) is one of the major risk factors implicated in morbidity and mortality associated with cardiovascular disease. During cardiac ischemia, the buildup of acidic metabolites results in decreased intracellular and extracellular pH, which can reach as low as 6.0 to 6.5. The resulting tissue acidosis exacerbates ischemic injury and significantly affects cardiac function. Methods: We used genetic and pharmacologic methods to investigate the role of acid-sensing ion channel 1a (ASIC1a) in cardiac IRI at the cellular and whole-organ level. Human induced pluripotent stem cell–derived cardiomyocytes as well as ex vivo and in vivo models of IRI were used to test the efficacy of ASIC1a inhibitors as pre- and postconditioning therapeutic agents. Results: Analysis of human complex trait genetics indicates that variants in the ASIC1 genetic locus are significantly associated with cardiac and cerebrovascular ischemic injuries. Using human induced pluripotent stem cell–derived cardiomyocytes in vitro and murine ex vivo heart models, we demonstrate that genetic ablation of ASIC1a improves cardiomyocyte viability after acute IRI. Therapeutic blockade of ASIC1a using specific and potent pharmacologic inhibitors recapitulates this cardioprotective effect. We used an in vivo model of myocardial infarction and 2 models of ex vivo donor heart procurement and storage as clinical models to show that ASIC1a inhibition improves post-IRI cardiac viability. Use of ASIC1a inhibitors as preconditioning or postconditioning agents provided equivalent cardioprotection to benchmark drugs, including the sodium-hydrogen exchange inhibitor zoniporide. At the cellular and whole organ level, we show that acute exposure to ASIC1a inhibitors has no effect on cardiac ion channels regulating baseline electromechanical coupling and physiologic performance. Conclusions: Our data provide compelling evidence for a novel pharmacologic strategy involving ASIC1a blockade as

Phono-Cardio-Gram (PCG) is an effective technique of detecting various heart abnormalities and malfunctions. Several PCG segmentation algorithms, each with its advantages and disadvantages, have been developed and tested. Unfortunately, most of these algorithms fail to diagnose heart conditions in real time. This paper introduces a real-time methodology by utilizing the field programmable gate array (FPGA) hardware to speed up the processing time very successfully. The type of FPGA hardware is Altera DE2 Cyclone II board with NIOS II soft processor. The algorithm of discrete wavelet transforms (DWT) with Shannon energy and spectra signal are used to extract the PCG features. The algorithm is also applied to identify the normal heart condition and nine abnormal cases. The results show that the methodology worked extremely efficient and suitable for real-time heart diagnosis purposes.

Sickle cell anemia is an abnormal red blood cell which leads to blood vessel obstruction joined by painful episodes and even death. It is also called abnormal hemoglobin. Hemoglobin is responsible for passing oxygen through the blood vessel for all over the body. Normal red blood cells are in a circular shape and they are compact and flexible, enabling them to move freely through small capillaries. On the other hand, abnormal red blood cells are in sickle shape and they are stiff and angular causing them to become stuck in small capillaries. Due to that, it will be a reason for pain to patients and lead to low oxygen and dehydration. The manual assessment, classification, and counting of biological cells require for an immense spending of time and it may lead to wrong classification and counting since red blood cells are millions in one smear. Also, cells classification is challenging due to heterogeneous and complex shapes, overlapped cells and a variety of colors. We overcome these drawbacks by introducing a new robust and effective deep Convolutional Neural Network to classify Red Blood Cells (RBCs) in three classes namely: normal (‘N’) abnormal (sickle cells anemia type (‘S’)) and miscellaneous (‘M’). In order to improve the results further, we have used our model as features extractor then we applied an error-correcting output codes (ECOC) classifier for the classification task. Our model with ECOC showed outstanding performance and high accuracy of 92.06%.

Convolutional Neural Network (CNN) has been extensively used for image recognition due to its great accuracy. This accuracy is achieved through emulating the optic nerves behavior in living human beings. The speedy progress of the current applications derived from deep learning algorithms has extra enhanced research and developments. More specifically, several deep CNN accelerators have been planned on FPGA-based platform, due to its fast development round, reconfigure-ability, and high performance. The FPGA is extremely faster than the CPU because it based on parallel mechanism, as well as, consumes very low energy. This paper employs the FPGA in establishing the CNN architecture of type VGG16 model. The FPGA solves the convolutional computations for accelerating the computation time by 11%, without losing much fidelity when using 16-bit fixed-point data format rather than 32-bit floating-point data format.

Red Blood Corpuscles (RBCs) form the main cellular component of human blood. RBCs in common physiological status has a circular form in the front view and bi-concave form in side view. In case of a person is infected with anemia RBCs form as sickle-shaped cells which drive to blood vessel obstruction joined by painful episodes and even death. Precise and robust cells classification and counting are essential in evaluating the level of anemia disease danger. The classification and counting of red blood cells (normal and abnormal RBC cells) are challenging due to the complex and heterogeneous shapes and overlapped cells. In this paper, we propose a new robust approach to classify red blood cells to two groups: Normal and Abnormal RBC cells based on area and Eccentricity of each cell, then count the total number of normal and abnormal RBC cells individually. For the sake of comparison, we also implement the latest Sickle cell research, which uses circular Hough transform. We compare our approach to circular Hough transform in the same execution environment. Our new approach sets the state-of-the-art performance in term of effectiveness (cell counting) and efficiency (execution time).

With the rapid development of the Internet, many problems regarding the processing of electronic documents have emerged, such as content filtering, information retrieval, security and searching. The security problem of electronic documents mainly focuses on copyright protection, content integrity authentication, monitoring, and other. The digital watermarking as an effective technique for copyright protection, content integrity, and originality authentication has been widely paid attention. The digital documents are different from the printed document where the carrier of it is computer disk, hard disk, or CD magnetic materials, so it is easy to tamper, damaged, deleted and copied. Therefore, there is an urgent need for an effective tool which can verify whether the content of the document has tampered or not. This is because the secure authentication of the document is very important. In this paper, the color document authentication based on QR code and digital watermarking has been implemented with a novel technique. Experimental results show that the designed structure has a good invisibility and ideal capacity. Furthermore, the tampered regions can be precisely detected.

Sickle cell anemia (SCA) is a blood disease, which causes distortion in the shape of Red Blood Cells (RBCs) and becomes like a crescent. Traditional methodologies of classifying and counting RBCs that have been used by medical analysts are time-consuming, as well as, cost-effective. In addition, it is possible to make errors throughout the classifying and counting stages. We overcome these limitations by proposing a novel convolutional neural network model that classifies the RBCs into three categories: Normal, Abnormal (sickle shape) and other blood content. We enhanced the model’s efficiency using a hardware accelerator called FPGA (Altera DE2 Cyclone II) to take advantage of its parallelism features. We have evaluated the model’s efficiency with different platforms to show the differentiation in terms of time execution and power computation. Based on our knowledge, the results show that our proposed model has achieved the best accuracy (87.15%) and has high efficiency for real-time diagnosis.