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Coronavirus is the irresistible sickness brought about through the Covid, SARS-CoV-2, which is a respiratory microbe. WHO first learned of this new virus from cases in Wuhan, People's Republic of China on 31 December 2019. People of all ages who experience fever and/or cough associated with trouble breathing or windedness, chest agony or weight, or loss of discourse or development should look for clinical consideration right away. If possible, call your health care provider, hotline or health facility first, so you can be directed to the right clinic. A great many people (about 80%) recuperate from the illness without requiring clinic treatment. About 20% of the individuals who get COVID-19 become truly sick and require oxygen, with 5% turning out to be basically sick and requiring concentrated consideration. Complications leading to death may include respiratory letdown, severerespiratory distress syndrome (SRDS), sepsis and infectedupset, thromboembolism, and/or multiorgan malfunction, including injury of the heart, liver or kidneys. In rare situations, children can develop a severe inflammatory syndrome a few weeks after infection. Anyone with symptoms should be tested, wherever possible. People who do not have symptoms but have had close contact with someone who is, or may be, infected may also consider testing - check with your local health guidelines. While a person is waiting for test results, they should remain isolated from others. Where testing capacity is limited, tests should first be done for those at higher risk of infection, such as health workers, and those at higher risk of severe illness such as older people, especially those living in seniors' residences or long-term care facilities. In this review, we give a concise diagram of the effect that COVID-19 has in disease development and therapy, and feature the arising need to consider the function of COVID-19 contamination in malignant growth movement and treatment. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks. To overcome these limitations, we present an intelligent two-phase dual authentication framework that improves the security of sensor-to-server communication in IoMT environments. During the registration phase, our framework is based on Elliptic Curve Diffie-Hellman (ECDH) for rapid key exchange, and during real-time communication, our framework uses the Advanced Encryption Standard Galois Counter Mode (AES-GCM) to encrypt data securely. The efficiency of the proposed framework was rigorously tested through simulations that evaluated encryption-decryption time, computational cost, latency, and packet delivery ratio. The security resilience was also evaluated against man-in-the-middle, replay, and brute force attacks. The results show that encryption/decryption time is reduced by over 45%, overall computational cost by 45.38%, and latency by 28.42% over existing approaches. Furthermore, the framework achieved a high packet delivery ratio and strong defense against cyber threats for maintaining the confidentiality and integrity of the medical data across IoMT networks. However, the dual authentication approach doesn't affect the functionality of medical IoT devices while enhancing IoMT security, which makes it an ideal integration option for existing healthcare systems., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO's resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection. The dataset includes 315 raw images, along with 630 labeled and 630 masked images for railroad semantic segmentation. The dataset consists of 315 original images captured by the UAV for object detection and obstacle detection. To increase dataset diversity for training purposes, we applied data augmentation techniques, which expanded the dataset to 2002 augmented and annotated images for obstacle detection cover six different classes of obstacles on railroad lines. Additionally, we provide the original 315 images along with a script for augmentation, allowing users to generate their own augmented data as needed, offering a more sustainable and customizable option. Each image in the dataset is accurately annotated with bounding boxes and labeled under six categories, including person, boulder, barrel, branch, jerry can, and iron rod. This comprehensive classification and detailed annotation make the dataset an essential tool for researchers and developers working on computer vision applications in the railroad domain., Competing Interests: Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024. The Author(s).)

This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Predicting court rulings has gained attention over the past years. The court rulings are among the most important documents in all legal systems, profoundly impacting the lives of the children in case of divorce or separation. It is evident from literature that Natural language processing (NLP) and machine learning (ML) are widely used in the prediction of court rulings. In general, the court decisions comprise several pages and require a lot of space. In addition, extracting valuable information and predicting legal decisions task is difficult. Moreover, the legal system's complexity and massive litigation make this problem more serious. Thus to solve this issue, we propose a new neural network-based model for predicting court decisions on child custody. Our proposed model efficiently performs an efficient search from a massive court decisions database and accurately identifies specific ones that especially deal with copyright claims. More specially, our proposed model performs a careful analysis of court decisions, especially on child custody, and pinpoints the plaintiff's custody request, the court's ruling, and the pivotal arguments. The working mechanism of our proposed model is performed in two phases. In the first phase, the isolation of pertinent sentences within the court ruling encapsulates the essence of the proceedings performed. In the second phase, these documents were annotated independently by using two legal professionals. In this phase, NLP and transformer-based models were employed and thus processed 3,000 annotated court rulings. We have used a massive dataset for the training and refining of our proposed model. The novelty of the proposed model is the integration of bidirectional encoder representations from transformers (BERT) and bidirectional long short-term memory (Bi_LSTM). The traditional methods are primarily based on support vector machines (SVM), and logistic regression. We have performed a comparison with the state-of-the-art model. The efficient results indicate that our proposed model efficiently navigates the complex terrain of legal language and court decision structures. The efficiency of the proposed model is measured in terms of the F1 score. The achieved results show that scores range from 0.66 to 0.93 and Kappa indices from 0.57 to 0.80 across the board. The performance is achieved at times surpassing the inter-annotator agreement, underscoring the model's adeptness at extracting and understanding nuanced legal concepts. The efficient results proved the potential of the proposed neural network model, particularly those based on transformers, to effectively discern and categorize key elements within legal texts, even amidst the intricacies of judicial language and the layered complexity of appellate rulings., Competing Interests: The authors declare there are no competing interests., (©2024 Abrar et al.)

Insider threats pose a significant challenge in cybersecurity, demanding advanced detection methods for effective risk mitigation. This paper presents a comparative evaluation of data imbalance addressing techniques for CNN-based insider threat detection. Specifically, we integrate Convolutional Neural Networks (CNN) with three popular data imbalance addressing techniques: Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE, and Adaptive Synthetic Sampling (ADASYN). The objective is to enhance insider threat detection accuracy and robustness in imbalanced datasets common to cybersecurity domains. Our study addresses the lack of consensus in the literature regarding the superiority of data imbalance addressing techniques in this field. We analyze a human behavior-based dataset (i.e., CERT) that reports users' Information Technology (IT) activities with a substantial number of samples to provide a clear conclusion on the effectiveness of these balancing techniques when coupled with CNN. Experimental results demonstrate that ADASYN, in conjunction with CNN, achieves a ROC curve of 96%, surpassing SMOTE and Borderline-SMOTE in enhancing detection accuracy in imbalanced datasets. We compare the results of these three hybrid models (CNN + imbalance addressing techniques) with state-of-the-art selective studies focusing on ROC, recall, and accuracy measures. Our findings contribute to the advancement of insider threat detection methodologies., (© 2024. The Author(s).)

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model's effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs., (© 2024. The Author(s).)

Spontaneous stress fracture of the scapular spine associated with rotator cuff tendinopathy is a rare complication in an elderly patient. Rotator cuff tendinopathy is a common condition characterized by degeneration and inflammation of the rotator cuff tendons. On the other hand, stress fractures are small cracks or breaks in bones that occur in response to repetitive or excessive mechanical loading. We present a rare case of a 79-year-old female patient complaining of persistent right shoulder pain that was unresponsive to medication which worsened in the last week and associated with a limited range of motion and inability to raise her arms above her head. Imaging studies revealed a nondisplaced fracture involving the mid-aspect of the scapular spine with surrounding callus formation and adjacent reactive changes. Stress fractures are often observed in weight-bearing bones, but they can also occur in non-weight bearing bones like the scapular spine. The association between rotator cuff tendinopathy and stress fractures of the scapular spine is uncommon and not well-documented in the literature. The biomechanics of the shoulder joint and altered force transmission due to rotator cuff pathology may contribute to the development of stress fractures. This case highlights the need to consider uncommon complications, such as stress fractures, in patients with shoulder pain. A comprehensive approach and early recognition of rare complications can guide appropriate diagnostic and treatment strategies to prevent potential complications and optimize patient outcomes., (© 2024 The Authors. Published by Elsevier Inc. on behalf of University of Washington.)

The rise of Electric Vehicles (EVs) has introduced significant advancement and evolution in the electricity market. In smart transportation, the EVs have earned more popularity because of its numerous benefits including lower carbon footprints, higher performance, and sophisticated energy trading mechanisms. These potential benefits have resulted in widespread EV adoption across the world. Despite its benefits, energy management remains the biggest challenge in EVs and it is mainly because of the lack of Charging Stations (CSs) near EVs. This creates a demand for an effective, secure and reliable energy management framework for EVs. This study presents a secure data and energy trade paradigm based on Blockchain (BC) in the Internet of EVs (IoEV). BC technology prepares for the high volume of EV integration that serves as the foundation for the next generation, and to assist in developing unique privacy-protected BC-based D-Trading and storage Models. Entities evaluated for the proposed model include Trusted Authority (TA), Vehicles, Smart Meters, Roadside Units (RSU), BC, and Inter-Planetary File System (IPFS). In addition, E-trading involves several phases, including the acquiring E-trading demand requests, E-trading response requests, request matching and token assignment. Moreover, account mapping is performed using a Mayfly Pelican Optimization Algorithm (MPOA), which is created by merging the Mayfly Algorithm (MA) and Pelican Optimization Algorithm (POA). Various security features are used to protect data and energy trade in IoEV, including encryption, hashing, polynomials, and others. The testing results revealed that the MPOA outperformed the state-of-the-art results regarding memory consumption, trading rate, transaction cost, and trading energy volume with values of 4.605 MB, 91%, 0.654, and 90 kW, respectively., (© 2024. The Author(s).)

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC., (© 2024. The Author(s).)

Learning Analytics Tools (LATs) can be used for informed decision-making regarding teaching strategies and their continuous enhancement. Therefore, LATs must be adopted in higher learning institutions, but several factors hinder its implementation, primarily due to the lack of an implementation model. Therefore, in this study, the focus is directed towards examining LATs adoption in Higher Learning Institutions (HLIs), with emphasis on the determinants of the adoption process. The study mainly aims to design a model of LAT adoption and use it in the above context to improve the institutions' decision-making and accordingly, the study adopted an extended version of Technology Acceptance Model (TAM) as the underpinning theory. Five experts validated the employed survey instrument, and 500 questionnaire copies were distributed through e-mails, from which 275 copies were retrieved from Saudi employees working at public HLIs. Data gathered was exposed to Partial Least Square-Structural Equation Modeling (PLS-SEM) for analysis and to test the proposed conceptual model. Based on the findings, the perceived usefulness of LAT plays a significant role as a determinant of its adoption. Other variables include top management support, financial support, and the government's role in LATs acceptance and adoption among HLIs. The findings also supported the contribution of LAT adoption and acceptance towards making informed decisions and highlighted the need for big data facility and cloud computing ability towards LATs usefulness. The findings have significant implications towards LATs implementation success among HLIs, providing clear insights into the factors that can enhance its adoption and acceptance. They also lay the basis for future studies in the area to validate further the effect of LATs on decision-making among HLIs institutions. Furthermore, the obtained findings are expected to serve as practical implications for policy makers and educational leaders in their objective to implement LAT using a multi-layered method that considers other aspects in addition to the perceptions of the individual user., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Background The maximum amount of knee flexion after total knee replacement is largely determined by the knee's posterior condylar offset (PCO). Using magnetic resonance imaging (MRI), this study examined the relationship between PCO and the thickness of the femoral posterior condylar cartilage (PCC) in healthy people. Methodology We reviewed the medical records of 300 skeletally mature patients who did not exhibit symptoms of knee arthritis and had undergone MRI for traumatic soft tissue knee injuries that did not affect the femoral PCC. Results The study cohort consisted of 300 participants, of whom 68.3% (205) were male, and 31.7% (95) were female aged between 18 and 59 years, with a mean age of 31.13 ± 8.83 years. Most participants were under 30 years of age (45.7%), and the mean body mass index was 27.52 ± 5.64 kg/m 2 . The total medial distance was 28.50 ± 3.11 mm, ranging from 21.20 to 39.80 mm. The medial PCC was 1.71 ± 0.63 mm, ranging from 0.60 to 4.00 mm. The medial bony PCO was 38.40 mm, ranging from 18.80 to 38.40 mm. The total lateral distance was 25.24 ± 3.16 mm, ranging from 13.50 to 34.90 mm. The lateral PCC was 1.48 ± 0.75 mm, ranging from 0.30 to 10.70 mm. Finally, the lateral bony PCO was 23.76 ± 3.19 mm, ranging from 11.99 to 32.8 mm. There was a statistically significant weak positive relationship between the bony lateral PCO and the patients' age in females only (p = 0.016; r = 0.00-0.39). There was a statistically significant mean difference in the total medial distance, medial PCC, and lateral PCC between the two knees (p < 0.05). Conclusions These findings shed light on the factors influencing these parameters, offer insightful information about the associations between particular patient characteristics and knee measurements, and may help guide clinical evaluations and treatment decisions., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Aljuhani et al.)

Dendritic fibromyxolipoma (DFML) is a benign, very rare, and slow-growing soft tissue tumor commonly involving the muscular fascia of the foot, calf, shoulders, back, or head and neck muscles. Many authors consider dendritic fibromyxolipoma a variant of spindle cell lipoma composed of a plexiform vascular pattern, dendritic cytoplasmic processes, and keloidal collagen. Only a few cases have been reported in the shoulder region, and the presented case represents the second case in English literature whose histopathology showed intramuscular involvement. Recognition of such an entity is essential because it is considered a scarce type of benign tumor that can be mistaken for other aggressive neoplasms of myxoid pathology. We present a case of a dendritic fibromyxolipoma around the right shoulder with intramuscular involvement to the superficial fibers of the right trapezius muscle., (© 2023 The Authors. Published by Elsevier Inc. on behalf of University of Washington.)

Ceramic matrix composites (CMCs) reinforced with nanocarbon have attracted significant interest due to their potential to enhance mechanical, thermal, and electrical properties. Although the investigation of carbon-based materials such as graphene and carbon nanotubes as additives for advanced ceramics has been widespread, the utilization of metal-organic framework (MOF)-derived nanocarbons in CMCs remains largely unexplored. We extended our previous proof-of-concept investigations by demonstrating the effectiveness of a different type of MOF-derived carbon as a reinforcing phase in an alternative ceramic matrix. We employed spark plasma sintering (SPS) to consolidate yttria-stabilized zirconia (YSZ) and zeolitic imidazolate framework (ZIF-67) powder blends at 1300 °C and a uniaxial pressure of 50 MPa. YSZ serves as the ceramic matrix, whereas ZIF-67 serves as the nanocarbon source. The composite exhibits a highly significant improvement in fracture toughness with an increase of up to 13% compared to that of the YSZ monolith. The formation of ZIF-derived nanocarbon interlayers is responsible for the observed enhancement in ductility, which can be attributed to their ability to facilitate energy dissipation during crack propagation and inhibit grain growth. Furthermore, the room-temperature electrical conductivity of the sintered samples demonstrates a substantial improvement, primarily due to the in situ formation of nanocarbon-based fillers, reaching an impressive 27 S/m with 10 wt % ZIF-67 content. Based on the results, it can be inferred that the incorporation of in situ MOF-derived nanocarbons into CMCs leads to a substantial improvement in both the mechanical and electrical properties.

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.

Background: Infantile hemangioma is the most frequent benign vascular tumor of infancy, which has a specific clinical history of early growth, followed by spontaneous remission. Since the fortuitous discovery of propranolol's efficacy in 2008, the management of infantile hemangioma has been quickly developing., Methods: This study is a retrospective cohort study. Electronic search in the patient's registry of King Khalid University Hospital, Riyadh, Saudi Arabia, was performed using the keywords hemangioma, haemangioma, infantile hemangioma, and vascular tumors. The search revealed a total of 101 subjects for which 56 were included and 45 were excluded., Results: A total of 56 patients with infantile hemangioma were evaluated in this study. The majority were females. The F: M ratio is 3.4:1. The highest percentage of the patients was delivered by the elective cesarian section, that is, 23 (41.1%), followed by spontaneous vaginal delivery, that is, 19 (33.9%). Full-term patients were 27 (48%), whereas the pre-term patients were 21 (37%). The total number of patients who developed hyperkalemia while on propranolol was 12 (31%). There was no statistically significant difference (P > 0.05) between patients who developed hyperkalemia and patients who did not develop hyperkalemia in terms of gender, gestational age, mode of delivery, size and location of hemangioma, or concomitant topical timolol use., Conclusion: Hyperkalemia is benign and transient, although solid conclusive opinions cannot be drawn because of the small sample size and the retrospective nature of the study., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Journal of Family Medicine and Primary Care.)

Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively., Competing Interests: No conflicts of interest., (Copyright © 2022 Hadaate Ullah et al.)

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods., Competing Interests: There are no conflicts of interest associated with publishing this paper., (Copyright © 2022 Tahir Hussain et al.)

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients' activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric., Competing Interests: There are no conflicts of interest associated with publishing this paper., (Copyright © 2022 Muhammad Junaid Ibrahim et al.)

The intuitionistic fuzzy set (IFS) and bipolar fuzzy set (BFS) are all effective models to describe ambiguous and incomplete cognitive knowledge with membership, non-membership, negative membership, and hesitancy sections. But in daily life problems, there are some situations where we cannot apply the ordinary models of IFS and BFS, separately. Hence, there is a need to combine both the models of IFS and BFS into a single one. A tripolar fuzzy set (TFS) is a generalization of IFS and BFS. In circumstances where BFS and IFS models cannot be used individually, a tripolar fuzzy model is more dependable and efficient. Further, the IFS and BFS models are reduced to corollaries due to the proposed model of TFS. For this purpose in this article, we first consider some novel operations on tripolar fuzzy information. These operations are formulated on the basis of well-known Dombi T-norm and T-conorm, and the desirable properties are discussed. By applying the Dombi operations, arithmetic and geometric aggregation operators of TFS are proposed, and we introduce the concepts of a TF-Dombi weighted average (TFDWA) operator, a TF-Dombi ordered weighted average (TFDOWA) operator, and a TF-Dombi hybrid weighted (TFDHW) operator and explore their fundamental features including idempotency, boundedness, monotonicity, and others. In the second part, we propose TF-Dombi weighted geometric (TFDWG) operator, TF-Dombi ordered weighted geometric (TFDOWG) operator, and TF-Dombi hybrid geometric (TFDHG) operator. The features and specific cases of the mentioned operators are examined. Enterprise resource planning (ERP) is a management and integration approach that organizations employ to manage and develop many aspects of their operations. The study's primary contribution is to employ TFS to create certain decision-making strategies for the selection of optimal ERP systems. The proposed operators are then used to build several techniques for solving multiattribute decision-making (MADM) issues with TF information. Finally, an example of ERP system selection is investigated to demonstrate that the techniques suggested are trustworthy and realistic., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Minhaj Afridi et al.)

Drosophila melanogaster is an important genetic model organism used extensively in medical and biological studies. About 61% of known human genes have a recognizable match with the genetic code of Drosophila flies, and 50% of fly protein sequences have mammalian analogues. Recently, several investigations have been conducted in Drosophila to study the functions of specific genes exist in the central nervous system, heart, liver, and kidney. The outcomes of the research in Drosophila are also used as a unique tool to study human-related diseases. This article presents a novel automated system to classify the gender of Drosophila flies obtained through microscopic images (ventral view). The proposed system takes an image as input and converts it into grayscale illustration to extract the texture features from the image. Then, machine learning (ML) classifiers such as support vector machines (SVM), Naive Bayes (NB), and K -nearest neighbour (KNN) are used to classify the Drosophila as male or female. The proposed model is evaluated using the real microscopic image dataset, and the results show that the accuracy of the KNN is 90%, which is higher than the accuracy of the SVM classifier., Competing Interests: There is no conflict of interest between the authors., (Copyright © 2022 Channabasava Chola et al.)

The most important influence of this assessment is to analyze some new operational laws based on confidential levels (CLs) for complex Pythagorean fuzzy (CPF) settings. Moreover, to demonstrate the closeness between finite numbers of alternatives, the conception of confidence CPF weighted averaging (CCPFWA), confidence CPF ordered weighted averaging (CCPFOWA), confidence CPF weighted geometric (CCPFWG), and confidence CPF ordered weighted geometric (CCPFOWG) operators are invented. Several significant features of the invented works are also diagnosed. Moreover, to investigate the beneficial optimal from a large number of alternatives, a multi-attribute decision-making (MADM) analysis is analyzed based on CPF data. A lot of examples are demonstrated based on invented works to evaluate the supremacy and ability of the initiated works. For massive convenience, the sensitivity analysis and merits of the identified works are also explored with the help of comparative analysis and they're graphical shown.

One of the most dominant and feasible technique is called the PHF setting is exist in the circumstances of fuzzy set theory for handling intricate and vague data in genuine life scenario. The perception of PHF setting is massive universal is compared to these assumptions, who must cope with two or three sorts of data in the shape of singleton element. Under the consideration of the PHF setting, we utilized some SM in the region of the PHF setting are to diagnose the PHFDSM, PHFWDSM, PHFJSM, PHFWJSM, PHFCSM, PHFWCSM, PHFHVSM, PHFWHVSM and demonstrated their flexible parts. Likewise, a lot of examples are exposed under the invented measures based on PHF data in the environment of medical diagnosis to demonstrate the stability and elasticity of the explored works. Finally, the sensitive analysis of the presented works is also implemented and illuminated their graphical structures.

Two-dimensional half-metallicity without a transition metal is an attractive attribute for spintronics applications. On the basis of first-principles calculation, we revealed that a two-dimensional gallium nitride (2D-GaN), which was recently synthesized between graphene and SiC or wurtzite GaN substrate, exhibits half-metallicity due to its half-filled quasi-flat band. We found that graphene plays a crucial role in stabilizing a local octahedral structure, whose unusually high density of states due to a flat band leads to a spontaneous phase transition to its half-metallic phase from normal metal. It was also found that its half-metallicity is strongly correlated to the in-plane lattice constants and thus subjected to substrate modification. To investigate the magnetic property, we simplified its magnetic structure with a two-dimensional Heisenberg model and performed Monte Carlo simulation. Our simulation estimated its Curie temperature ( T C ) to be ∼165 K under a weak external magnetic field, suggesting that transition metal-free 2D-GaN exhibiting p orbital-based half-metallicity can be utilized in future spintronics.

Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional, and highly redundant. The classification of gene expression profiles is considered to be a (NP)-Hard problem. Feature (gene) selection is one of the most effective methods to handle this problem. A hybrid cancer classification approach is presented in this paper, and several machine learning techniques were used in the hybrid model: Pearson's correlation coefficient as a correlation-based feature selector and reducer, a Decision Tree classifier that is easy to interpret and does not require a parameter, and Grid Search CV (cross-validation) to optimize the maximum depth hyperparameter. Seven standard microarray cancer datasets are used to evaluate our model. To identify which features are the most informative and relative using the proposed model, various performance measurements are employed, including classification accuracy, specificity, sensitivity, F 1-score, and AUC. The suggested strategy greatly decreases the number of genes required for classification, selects the most informative features, and increases classification accuracy, according to the results., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Hanaa Fathi et al.)

The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing "Within Blocks" and "Before Classifier" methods. Various metrics such as the accuracy, loss, precision, recall, F 1-score, confusion matrix, and training time are recorded to evaluate the models' efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet "Before Classifier" models are more efficient than "Within Blocks" CBAM-ResNet models. Thus, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the "Before Classifier" of CBAMResNet models is more efficient in recognising MSL and it is worth for future research., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (Copyright © 2021 Rehman Ullah Khan et al.)

The main purpose of this manuscript is to present a novel idea on the q -rung orthopair fuzzy rough set ( q -ROFRS) by the hybridized notion of q -ROFRSs and rough sets (RSs) and discuss its basic operations. Furthermore, by utilizing the developed concept, a list of q -ROFR Einstein weighted averaging and geometric aggregation operators are presented which are based on algebraic and Einstein norms. Similarly, some interesting characteristics of these operators are initiated. Moreover, the concept of the entropy and distance measures is presented to utilize the decision makers' unknown weights as well as attributes' weight information. The EDAS (evaluation based on distance from average solution) methodology plays a crucial role in decision-making challenges, especially when the problems of multicriteria group decision-making (MCGDM) include more competing criteria. The core of this study is to develop a decision-making algorithm based on the entropy measure, aggregation information, and EDAS methodology to handle the uncertainty in real-word decision-making problems (DMPs) under q -rung orthopair fuzzy rough information. To show the superiority and applicability of the developed technique, a numerical case study of a real-life DMP in agriculture farming is considered. Findings indicate that the suggested decision-making model is much more efficient and reliable to tackle uncertain information based on q -ROFR information., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Shahzaib Ashraf et al.)

Background: The multifactorial nature of rosacea and chronic spontaneous urticaria (CSU) pathogenesis complicates the achievement of satisfactory treatment outcomes. 13C urea breath test (UBT) has been identified as an accurate, non-invasive, and quick procedure to detect the presence of Helicobacter pylori ( H. pylori ) with high sensitivity and specificity., Objective: In this study, we aim to assess the correlation between H. pylori infection and rosacea and CSU patients., Methods: A cross-sectional, observational study was conducted on patients with rosacea and CSU in the dermatology clinic at King Khalid University Hospital in Riyadh, Saudi Arabia. History and physical examination were performed by a dermatologist. H. pylori 13C-UBT detection was performed in all subjects., Results: In total, 114 patients were included in this current study, with 41 rosacea and 73 urticaria patients. The vast majority of our subjects were females (96.5%). The mean (±SD) age was 42.3 (±12.7). More than half (58.8%) of the examined samples were positive for 13C-UBT; however, positive results were significantly higher in the rosacea patients (73.2%) compared to the urticaria group (50.7%), with a p-value of 0.019., Conclusion: Our findings underline the significant association of H. pylori with rosacea and CSU regardless of the presence or absence of gastrointestinal symptoms. We thus recommend the inclusion of H. pylori testing in the routine workup of CSU and rosacea patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, AlBalbeesi et al.)

Ocular involvement in pemphigus vulgaris (PV) is relatively rare. The conjunctiva and eyelids are considered the most common affected sites in ocular pemphigus. Scleritis is rarely reported as a manifestation of PV. We present a case report of anterior scleritis as a manifestation of PV and its response to rituximab therapy., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Middle East African Journal of Ophthalmology.)

Background: Prediabetes has been considered to be a reversible condition; a modification of lifestyle and other intervention can be successfully applied during the prediabetes period to prevent the development of type 2 diabetes. The purpose of the present study was to assess knowledge of prediabetes and its risk factors for the community in the Al-Ahsa region. Design and method: A cross-sectional community-based study was conducted in the Al-Ahsa region from mid-to-late December 2018. A sample size of 812 was determined using a single-proportion formula. Results: Of the 812 respondents who gave consent to participate in the interview; the male to female ratio was 1.1:1. 13.2% of the respondents reported that they had diabetes. Among the respondents, 87.1% had a high level of knowledge of prediabetes, while 12.9% had low-to-moderate knowledge. 84% of males 40 years of age or older, 88.7% (384) of people with university or higher education, and 95.1% (78) of people who worked as health practitioners had high knowledge of prediabetes. Overall, there was a statistically significant association between age and prediabetes knowledge (χ 2 =5.006, p=0.025). Occupation also showed a significant statistical association with prediabetes knowledge (χ 2 =9.85, p=0.02). Conclusion: Knowledge is considered an important factor in the prevention of prediabetes and diabetes. People in Al-Ahsa demonstrated a high level of knowledge regarding some risk factors for prediabetes. However, there were a number of deficiencies in the knowledge of prediabetes risk factors and preventive measures as well as in general knowledge of prediabetes, which may lead to a high prevalence of prediabetes and diabetes., (©Copyright: the Author(s).)

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.

Background: Educational institutes around the globe are facing challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Online learning is being carried out to avoid face to face contact in emergency scenarios such as coronavirus infectious disease 2019 (COVID-19) pandemic. Students need to adapt to new roles of learning through information technology to succeed in academics amid COVID-19., Objective: However, access and use of online learning resources and its link with satisfaction of students amid COVID-19 are critical to explore. Therefore, in this paper, we aimed to assess and compare the access & use of online learning of Bruneians and Pakistanis amid enforced lockdown using a five-items satisfaction scale underlying existing literature., Method: For this, a cross-sectional study was done in the first half of June 2020 after the pandemic situation among 320 students' across Pakistan and Brunei with a pre-defined questionnaire. Data were analyzed with statistical software package for social sciences (SPSS) 2.0., Results: The finding showed that there is a relationship between students' satisfaction and access & use of online learning. Outcomes of the survey suggest that Bruneian are more satisfied (50%) with the use of online learning amid lockdown as compared to Pakistanis (35.9%). Living in the Urban area as compared to a rural area is also a major factor contributing to satisfaction with the access and use of online learning for both Bruneian and Pakistanis. Moreover, previous experience with the use of online learning is observed prevalent among Bruneians ( P = . 000), while among friends and family is using online learning ( P = .000) were encouraging factors contributed to satisfaction with the use of online learning among Pakistanis amid COVID-19. Correlation results suggest that access and use factors of online learning amid COVID-19 were positively associated with satisfaction among both populations amid COVID-19 pandemic. However, Bruneian is more satisfied with internet access (r = 0.437, P < . 000) and affordability of gadgets (r = 0.577, P <  . 000) as compare to Pakistanis (r = 0.176, P < .050) and (r = 0.152, P < .050 )., Conclusion: The study suggested that it is crucial for the government and other policymakers worldwide to address access and use of online learning resources of their populace amid pandemic., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Elsevier Ltd. All rights reserved.)

Objective  The aim of this study is to present our experience in dealing with middle ear adenomas (MEAs), very rare tumors of the middle ear. Methods  The medical notes of individuals with MEAs treated in tertiary referral; academic settings were retrospectively reviewed. We recorded the presenting symptoms, imaging findings, and pathology results. We additionally examined our surgical outcomes, follow-up period, recurrence, and morbidity. Results  We identified four patients with MEAs: two males and two females with an average age of 36.25 years (range = 27-51 years). Despite the detailed imaging studies, including computed tomography and magnetic resonance imaging with intravenous contrast administration, a biopsy was essential in setting the diagnosis. Total surgical resection was achieved in all patients without any recurrence over an average of 6 years (range = 3-10 years). Complete ipsilateral deafness was the commonest surgical morbidity due to footplate infiltration by the tumor. Conclusion  Total surgical resection is the treatment of choice in MEAs to minimize the risk for recurrence; this can come with morbidity, mostly sensorineural deafness. Given the very limited literature, long-term follow-up is recommended., Competing Interests: Conflict of Interest None declared., (Thieme. All rights reserved.)

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.

High-dose tigecycline therapy is gaining wide acceptance in treating infections caused by multidrug-resistant bacteria. There are no reports of cutaneous hyperpigmentation with the use of high-dose tigecycline. Here we report a case of a woman who developed reversible cutaneous hyperpigmentation within 48 h of receiving high-dose tigecycline., Competing Interests: Conflict of interest statement: The authors declare that there is no conflict of interest., (© The Author(s), 2020.)

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.

Correction for 'Controlled p-type substitutional doping in large-area monolayer WSe2 crystals grown by chemical vapor deposition' by Stephen A. Campbell et al., Nanoscale, 2018, 10, 21374-21385.

Tungsten diselenide (WSe 2 ) is a particularly interesting 2D material due to its p-type conductivity. Here we report a systematic single-step process to optimize crystal size by variation of multiple growth parameters resulting in hexagonal single crystals up to 165 μm wide. We then show that these large single crystals can be controllably in situ doped with the acceptor Niobium (Nb). First principles calculations suggest that substitutional Nb doping of W would yield p-doping with no gap trap states. When used as the active layer of a field effect transistor (FET), doped crystals exhibit conventional p-type behavior, rather than the ambipolar behaviour seen in undoped WSe 2 FETs. Nb-doped WSe 2 FETs yield a maximum field effect mobility of 116 cm 2 V -1 s -1 , slightly higher than its undoped counterpart, with an on/off ratio of 10 6 . Doping reduces the contact resistance of WSe 2 , reaching a minimum value of 0.55 kΩμm in WSe 2 FETs. The areal density of holes in Nb-doped WSe 2 is approximately double that of undoped WSe 2 , indicating that Nb doping is working as an effective acceptor. Doping concentration can be controlled over several orders of magnitudes, allowing it to be used to control: FET threshold voltage, FET off-state leakage, and contact resistance.

Text classification is one domain in which the naive Bayesian (NB) learning algorithm performs remarkably well. However, making further improvement in performance using ensemble-building techniques proved to be a challenge because NB is a stable algorithm. This work shows that, while an ensemble of NB classifiers achieves little or no improvement in terms of classification accuracy, an ensemble of fine-tuned NB classifiers can achieve a remarkable improvement in accuracy. We propose a fine-tuning algorithm for text classification that is both more accurate and less stable than the NB algorithm and the fine-tuning NB (FTNB) algorithm. This improvement makes it more suitable than the FTNB algorithm for building ensembles of classifiers using bagging. Our empirical experiments, using 16-benchmark text-classification data sets, show significant improvement for most data sets.

Endochondral ossification is the process by which long bones are formed; the process of long bone formation is regulated by numerous factors such as transcription factors, cytokines, and extracellular matrix molecules. Human hormone Nuclear receptors (hHNR) are a family of ligand-regulated transcription factors that are activated by steroid hormones, such as estrogen and progesterone, and various lipid-soluble signals, including retinoic acid, oxysterols, and thyroid hormone. Whole genome microarray data from our previous study revealed that most hHNR's are up-regulated during osteoblast differentiation in hMSCS. NR2F1 was among the highest expressed hHNR during osteogenesis, NR2F1 belongs to the steroid/thyroid hormone nuclear receptor superfamily. NR2F1 is designated as an orphan nuclear receptor because its ligands are unknown. NR2F1 plays a wide range of roles, including cell differentiation, cancer progression, and central and peripheral neurogenesis. Identifying signaling networks involved in osteoblast differentiation is important in orchestrating new therapeutic and clinical applications in bone biology. This study aimed to identify alterations in signaling networks mediated by NR2F1 in osteoblast differentiation. siRNA-mediated down-regulation of NR2F1 leads to impairment in the differentiation of hBMSC-TERT to osteoblast; gene-expression results confirmed the down-regulation of osteoblast markers such as RUNX2, ALPL, OSC, and BSP. Global whole gene expression analysis revealed that most down-regulated genes were associated with osteoblast differentiation (DDIT3, BMP2). Pathway analysis revealed prominent signaling pathways that were down-regulated, including the TGFβ pathway and MAPK pathway. Functional studies on NR2F1 transfected cells, during osteoblast differentiation in combination with TGFβ1 and BMP-2, showed that TGFβ1 does not recover osteoblast differentiation, whereas BMP-2 rescues osteoblast differentiation in NR2F1 siRNA transfected cells. Thus, our results showed that BMP-2 could intervene in NR2F1 down-regulated signaling pathways to recover osteoblast differentiation., (Copyright © 2018 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.)

Episiotomy should be cut at certain internationally set criteria to minimize risk of obstetric anal sphincter injuries (OASIS) and anal incontinence. The aim of this study was to assess the accuracy of cutting right mediolateral episiotomy (RMLE). An institution-based prospective cohort study was undertaken in a Palestinian maternity unit from February 1, to December 31, 2016. Women having vaginal birth at gestational weeks ≥24 or birthweight ≥1000 g and with intended RMLE were eligible ( n =240). Transparent plastic films were used to trace sutured episiotomy in relation to the midline within 24-hour postpartum. These were used to measure incisions' distance from midline, and suture angles were used to classify the incisions into RMLE, lateral, and midline episiotomy groups. Clinical characteristics and association with OASIS were compared between episiotomy groups. A subanalysis by profession (midwife or trainee doctor) was done. Less than 30% were RMLE of which 59% had a suture angle of <40° (equivalent to an incision angle of <60°). There was a trend of higher OASIS rate, but not statistically significant, in the midline (16%, OR: 1.7, CI: 0.61-4.5) and unclassified groups (16.5%, OR: 1.8, CI: 0.8-4.3) than RMLE and lateral groups (10%). No significant differences were observed between episiotomies cut by doctors and midwives. Most of the assessed episiotomies lacked the agreed criteria for RMLE and had less than optimal incision angle which increases risk of severe complications. A well-structured training program on how to cut episiotomy is recommended.

The electrical properties of multilayer MoS2/graphene heterojunction transistors are investigated. Temperature-dependent I-V measurements indicate the concentration of unintentional donors in exfoliated MoS2 to be 3.57 × 10(11) cm(-2), while the ionized donor concentration is determined as 3.61 × 10(10) cm(-2). The temperature-dependent measurements also reveal two dominant donor levels, one at 0.27 eV below the conduction band and another located at 0.05 eV below the conduction band. The I-V characteristics are asymmetric with drain bias voltage and dependent on the junction used for the source or drain contact. I-V characteristics of the device are consistent with a long channel one-dimensional field-effect transistor model with Schottky contact. Utilizing devices, which have both graphene/MoS2 and Ti/MoS2 contacts, the Schottky barrier heights of both interfaces are measured. The charge transport mechanism in both junctions was determined to be either thermionic-field emission or field emission depending on bias voltage and temperature. On the basis of a thermionic field emission model, the barrier height at the graphene/MoS2 interface was determined to be 0.23 eV, while the barrier height at the Ti/MoS2 interface was 0.40 eV. The value of Ti/MoS2 barrier is higher than previously reported values, which did not include the effects of thermionic field emission.