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The network system is defined as the combination of streets and alleys. Although the potential of alleys in complementing street networks is acknowledged, the topic has not received a considerable attention in urban planning practice and scholarship. This paper evaluates alleys’ role in the combined street-alley networks by calculating Information Centrality, a metric in Multiple Centrality Assessment (MCA). The combined network of streets and alleys in thirteen neighborhoods of Abu Dhabi and Dubai have been studied. Findings indicate that alleys may or may not contribute to increment of network efficiency. Generally, the contribution of alleys to efficiency is more significant if the corresponding street network has low efficiency.

Abstract Objectives To investigate the clinical and epidemiological factors associated with severe COVID-19 cases in hospitalized patients across two emirates within the United Arab Emirates (UAE). Methods A retrospective observational analytical study analysed data from 738 medical records and conducted 573 in-depth interviews with patients hospitalized across multiple healthcare centers in the UAE, between 29 January 2020 and 14 October 2021. Regression analysis predicted risk factors for COVID-19 severity. Results Main risk factors identified were crowding (aOR 1.919; 95%CI 1.144, 3.221), obesity (aOR 2.383; 95%CI 1.332, 4.263), diabetes (aOR 11.14; 95%CI 2.653–46.797), severe dehydration (aOR 3.219; 95%CI 2.161, 4.795), cough or sore throat (aOR 1.607; 95%CI 1.032, 2.502), shortness of breath (aOR 1.921; 95%CI 1.294, 2.853), increased days from symptom onset to admission (aOR 1.055; 95%CI 1.006, 1.105), elevated ANC (aOR 1.263, 95%CI 1.121, 1.424), and AST/SGOT (aOR 1.055, 95% CI 1.016, 1.095). Protective factors included smoking (aOR 0.367; 95%CI 0.182, 0.740), first dose of COVID-19 vaccination (aOR 0.595; 95%CI 0.377, 0.93), higher oxygen saturation (aOR 0.853; 95%CI: 0.801, 0.907) and elevated ALC (aOR 0.540; 95%CI 0.323, 0.905). Conclusion Identifying risk factors is crucial for high-risk individuals who may require closer monitoring to improve their outcomes. This can provide guidance for surveillance systems and early detection strategies to mitigate the impact of future outbreaks.

Abstract Background Non-suicidal self-injury (NSSI) is an emerging critical medical condition among adolescents and young adults worldwide. They use different methods for expressing their emotional needs. The aim of this study was to identify the methods of NSSI used by adolescents and young adults who presented with different psychiatric disorders, and their utilized functions. Subjects/materials and methods A case–control study was done on 100 late adolescent and young adult patients who attended Suez Canal University with psychiatric disorders. Sociodemographic data was obtained then they were assessed by the Mini-International Neuropsychiatric Interview and the Brief Non-suicidal Self-Injury Assessment Tool. Results Among the case group, 54% used scratching for self-harm while 36% used cutting. The most common body areas were hands (44%) and arms (42%). Eighty percent of the case group endorsed non-suicidal self-harm for dealing with anger, while 78% to cope with uncomfortable feelings and 72% to relieve stress with no statistically significant difference between males or females, nor among psychiatric comorbidities. Conclusion The most common method of NSSI was scratching and it was followed by cutting and banging. The most common body areas were hands and arms. Patients endorsed NSSI for various motives and several functions. However, Items on the automatic negative reinforcement scales were the most common functions, especially when dealing with anger.

Abstract Unicellular eukaryotes represent tremendous evolutionary diversity. However, the molecular mechanisms underlying this diversity remain largely unexplored, partly due to a limitation of genetic tools to only a few model species. Paramecium caudatum is a well-known unicellular eukaryote with an unexpectedly large germline genome, of which only two percent is retained in the somatic genome following sexual processes, revealing extensive DNA elimination. However, further progress in understanding the molecular mechanisms governing this process is hampered by a lack of suitable genetic tools. Here, we report the successful application of gene knockdown and protein localization methods to interrogate the function of both housekeeping and developmentally regulated genes in P. caudatum. Using these methods, we achieved the expected phenotypes upon RNAi by feeding, and determined the localization of these proteins by microinjection of fusion constructs containing fluorescent protein or antibody tags. Lastly, we used these methods to reveal that P. caudatum PiggyMac, a domesticated piggyBac transposase, is essential for sexual development, and is likely to be an active transposase directly involved in DNA cleavage. The application of these methods lays the groundwork for future studies of gene function in P. caudatum and can be used to answer important biological questions in the future.

Abstract Objective The simultaneous presence of celiac disease and type 1 diabetes (T1DM) is coupled with more hazards of comorbidities and complications. This current study aimed to screen for celiac disease in Egyptian children with type 1 diabetes and evaluate its impact on glycemic control. Methods A cross-sectional study was verified with 200 Egyptian children diagnosed with T1DM and having a diabetic duration of less than five years. Testing for anti-tissue transglutaminase IgA (tTG-IgA), anti-tissue transglutaminase IgG (tTG-IgG), anti-Endomysial IgA (EMA), and Hb A1c levels were done. Results The serological screening revealed that 11 cases (5.5%) tested positive; 8 children with T1DM (4.0%) showed tTG-IgA antibodies ≥ 10 times the upper limit of normal (ULN) with at least one symptom; and 3 cases (1.5%) had levels between 20 and 50 IU/ml (considering a cut-off point of 10 U/ML for positive results). Intestinal biopsy was performed for these three cases, with one case detected to have subtotal villous atrophy, resulting in an overall prevalence of celiac disease in T1DM as 4.5%. Children with positive screening exhibited a higher insulin dose, a higher HbA1c, an increased frequency of hypoglycemic attacks, and recurrent DKA compared to negative cases. A negative correlation was detected between tTG-IgA antibodies with height Z score and hemoglobin level, while a positive correlation was found between tTG-IgA antibodies and HbA1c level. Conclusion Undiagnosed celiac disease in children with T1DM negatively impacted metabolic control and affected their general health.

Abstract Cancer chemoresistance is a problematic dilemma that significantly restrains numerous cancer management protocols. It can promote cancer recurrence, spreading of cancer, and finally, mortality. Accordingly, enhancing the responsiveness of cancer cells towards chemotherapies could be a vital approach to overcoming cancer chemoresistance. Tumour cells express a high level of sphingosine kinase-1 (SphK1), which acts as a protooncogenic factor and is responsible for the synthesis of sphingosine-1 phosphate (S1P). S1P is released through a Human ATP-binding cassette (ABC) transporter to interact with other phosphosphingolipids components in the interstitial fluid in the tumor microenvironment (TME), provoking communication, progression, invasion, and tumor metastasis. Also, S1P is associated with several impacts, including anti-apoptotic behavior, metastasis, mesenchymal transition (EMT), angiogenesis, and chemotherapy resistance. Recent reports addressed high levels of S1P in several carcinomas, including ovarian, prostate, colorectal, breast, and HCC. Therefore, targeting the S1P/SphK signaling pathway is an emerging therapeutic approach to efficiently attenuate chemoresistance. In this review, we comprehensively discussed S1P functions, metabolism, transport, and signaling. Also, through a bioinformatic framework, we pointed out the alterations of SphK1 gene expression within different cancers with their impact on patient survival, and we demonstrated the protein–protein network of SphK1, elaborating its sparse roles. Furthermore, we made emphasis on different machineries of cancer resistance and the tight link with S1P. We evaluated all publicly available SphK1 inhibitors and their inhibition activity using molecular docking and how SphK1 inhibitors reduce the production of S1P and might reduce chemoresistance, an approach that might be vital in the course of cancer treatment and prognosis. Graphical Abstract

Abstract Recently, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, caused > 6 million deaths. Symptoms included respiratory strain and complications, leading to severe pneumonia. SARS-CoV-2 attaches to the ACE-2 receptor of the host cell membrane to enter. Targeting the SARS-CoV-2 entry may effectively inhibit infection. Acid sphingomyelinase (ASMase) is a lysosomal protein that catalyzes the conversion of sphingolipid (sphingomyelin) to ceramide. Ceramide molecules aggregate/assemble on the plasma membrane to form “platforms” that facilitate the viral intake into the cell. Impairing the ASMase activity will eventually disrupt viral entry into the cell. In this review, we identified the metabolism of sphingolipids, sphingolipids' role in cell signal transduction cascades, and viral infection mechanisms. Also, we outlined ASMase structure and underlying mechanisms inhibiting viral entry 40 with the aid of inhibitors of acid sphingomyelinase (FIASMAs). In silico molecular docking analyses of FIASMAs with inhibitors revealed that dilazep (S = − 12.58 kcal/mol), emetine (S = − 11.65 kcal/mol), pimozide (S = − 11.29 kcal/mol), carvedilol (S = − 11.28 kcal/mol), mebeverine (S = − 11.14 kcal/mol), cepharanthine (S = − 11.06 kcal/mol), hydroxyzin (S = − 10.96 kcal/mol), astemizole (S = − 10.81 kcal/mol), sertindole (S = − 10.55 kcal/mol), and bepridil (S = − 10.47 kcal/mol) have higher inhibition activity than the candidate drug amiodarone (S = − 10.43 kcal/mol), making them better options for inhibition.

Abstract Background A considerable body of research has demonstrated that reducing sitting time benefits health. Therefore, the current study aimed to explore the prevalence of sedentary behavior (SB) and its patterns. Methods A total of 6975 university students (49.1% female) were chosen randomly to participate in a face-to-face interview. The original English version of the sedentary behavior questionnaire (SBQ) was previously translated into Arabic. Then, the validated Arabic version of the SBQ was used to assess SB. The Arabic SBQ included 9 types of SB (watching television, playing computer/video games, sitting while listening to music, sitting and talking on the phone, doing paperwork or office work, sitting and reading, playing a musical instrument, doing arts and crafts, and sitting and driving/riding in a car, bus or train) on weekdays and weekends. Results SBQ indicated that the total time of SB was considerably high (478.75 ± 256.60 and 535.86 ± 316.53 (min/day) during weekdays and weekends, respectively). On average, participants spent the most time during the day doing office/paperwork (item number 4) during weekdays (112.47 ± 111.11 min/day) and weekends (122.05 ± 113.49 min/day), followed by sitting time in transportation (item number 9) during weekdays (78.95 ± 83.25 min/day) and weekends (92.84 ± 100.19 min/day). The average total sitting time of the SBQ was 495.09 ± 247.38 (min/day) and 58.4% of the participants reported a high amount of sitting time (≥ 7 hours/day). Independent t-test showed significant differences (P ≤ 0.05) between males and females in all types of SB except with doing office/paperwork (item number 4). The results also showed that male students have a longer daily sitting time (521.73 ± 236.53 min/day) than females (467.38 ± 255.28 min/day). Finally, 64.1% of the males reported a high amount of sitting time (≥ 7 hours/day) compared to females (52.3%). Conclusion In conclusion, the total mean length of SB in minutes per day for male and female university students was considerably high. About 58% of the population appeared to spend ≥7 h/day sedentary. Male university students are likelier to sit longer than female students. Our findings also indicated that SB and physical activity interventions are needed to raise awareness of the importance of adopting an active lifestyle and reducing sitting time.

Background and Study Aim. This study compares the effects of high-intensity interval training (HIIT) on unstable and stable surfaces on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics. Material and Methods. A nonrandomized trial study was conducted on twenty-seven well-trained male vault players who were assigned into 3 equal groups, a HIIT unstable group (UG), a HIIT stable group (SG), and the control group (CG). The training period for the subjects lasted eight weeks, with 3 sessions each week. All of the aforementioned variables were assessed both before and after the training period. Results. The main findings indicate that both UG and SG showed greater improvement than the CG on explosive strength, balance, agility, and Tsukahara vault performance in gymnastics (ES= 0.52 to 0.68, P 0.05). Conclusions. Accordingly, HIIT with unstable surfaces can be used as an alternative method to improve explosive strength and balance. Also, it can be used alongside stable surface exercises when developing agility.

Developing a sustainable competitive advantage has emerged as a pivotal objective for organizations due to the dynamic and constantly evolving business environment, challenges modern organizations encounter, rapid market fluctuations, and intense competition. This study aims to examine the impact of strategic agility on sustainable competitive advantage and the mediating role of strategic renewal within an emerging economy such as Jordan. The study collected data from 217 executives holding senior and intermediate positions in telecommunications companies in Jordan. This paper utilized partial least squares structural equation modeling (PLS-SEM) with SmartPLS4 software to test hypotheses and assess the measurement and structural models. According to the findings, strategic agility has a significant positive impact on sustainable competitive advantage (β = 0.590, t = 8.042, p ≤ 0.000) and high explanation power (R2 = 0.828), which means that 82.8% of the variance in sustainable competitive advantage has been explained by strategic agility and strategic renewal. Moreover, strategic renewal partially mediates the relationship between strategic agility and sustainable competitive advantage. In addition, the study revealed that the model’s predictive power was medium. This paper contributes to the body of knowledge and existing literature about the impact of strategy renewal and agility on sustainable competitive advantage in Jordanian telecommunications companies. Organizations incorporating strategic agility and renewal into their strategy can manage uncertainties, swiftly adjust to changes, and attain sustainable competitive advantage.

This paper presents a single-element linearly polarized (LP) patch antenna characterized by a wide impedance bandwidth and low profile, optimized for 5G V2X applications. By inserting air gaps into the LP patch antenna structure, a circularly polarized (CP) antenna is generated. Moreover, a cross-stubs technique is employed to generate CP from LP, thereby enhancing the axial ratio (AR) bandwidth. In this work, we consider both linear and circular polarization patch antennas designed to operate suitable for V2X applications. The resonating structure for both antennas is designed on a 0.787 mm Rogers RT-duroid 5880 substrate with a relative permittivity of 2.2. The proposed design demonstrates resonance within the frequency range of 24 to 34 GHz and an AR bandwidth from 26.4 to 32.1 GHz. Additionally, the proposed design achieves a realized gain of 5.5 dBi along with a radiation efficiency of 94%. To extend the antenna’s gain capabilities, the single-element CP antenna is transformed into a series-fed 9-element array, achieving a maximum realized gain reaching 15.2 dBi and sidelobe levels (SLL) exceeding -17 dB. All proposed antennas are simulated, followed by fabrication and measurements, with the results demonstrating a good level of agreement between simulation and measurement. The proposed antennas’ low profile, wideband performance, and fabrication simplicity position them as ideal candidates for enhancing connectivity in next-generation V2X communications within the millimeter wave (mmWave) band.

Object Classification in Remote Sensing Imagery holds paramount importance for extracting meaningful insights from complex aerial scenes. Conventional methods encounter challenges in achieving precision amidst diverse landscape features. This paper introduces an innovative hybrid model to enhance the accuracy of remote sensing multi-object classification. Incorporating a feature-level fusion approach inspired by successful methods, we leverage Adoptive Fuzzy C-means segmentation for precise object classification and Conditional Random Field labeling. Our model excels in capturing diverse features within remote sensing images using multiple feature extraction methods. The distinctive feature of our methodology lies in the thoughtful incorporation of a Deep Belief Network. Through rigorous experimental evaluations on two standard datasets, our proposed system demonstrates exceptional performance, emphasizing its significant potential for advancing methodologies in remote sensing multi-object classification. This tactful integration results in substantial improvements, yielding high accuracies of 97.24% (UCM) and 96.84% (RESISC45). The proposed model is methodologically novel and effective solution for advancing remote sensing image classification.

This work evaluates the performance of four machine learning models (MLMs): support vector machine (SVM), K-nearest neighbor (KNN), discriminant analysis (DA), and logistic regression (LR) in predicting the biodegradability of chemicals, a critical factor for assessing environmental risks. For this purpose, the RDKit library was initially employed to extract nine fingerprints from a dataset consisting of 1717 chemical compounds. Subsequently, the Information Gain (IG) feature ranking algorithm was used to identify the top 100 predictive features for each fingerprint. The MLM Hyperparameters were optimized, and informative features were selected using a multi-objective genetic algorithm (MOGA), which identified dominant pairs (MLM/fingerprint) maximizing F1-score while minimizing number of features. MACCS, Layered, and Avalon molecular fingerprints associated with the different MLM showed superior performance, with KNN/MACCS achieving a cross-validated F1-score of 87.63% using only 13 features. Afterwards, the final classification models were constructed using the solution with the highest cross-validated F1-score complement for each combination of MLM/fingerprint. These models were then evaluated on both the training and test datasets. In the training subset, the MACCS fingerprint consistently outperformed others, achieving cross-validated AUC scores above 90% for all models (SVM: 91%, KNN: 90.4%, DA: 90.9%, LR: 91.32%). The SVM/MACCS pair demonstrated the highest accuracy (94.17%), specificity (95.84%), and F1-score (93.14%). In the test subset, the SVM/Layered pair exhibited the highest accuracy (84.01%) and specificity (88.09%). The DA/Avalon combination achieved the highest sensitivity (84.40%), while the SVM/Avalon pair reached the highest F1-score (82.58%). These results underscore the effectiveness of MACCS fingerprints for biodegradation classification across various models. Additionally, Permutation Feature Importance (PFI) and Shapley Additive Explanations (SHAP) have identified the key MACCS features influencing biodegradation classifications in the SVM model. These methods highlighted MACCS bit numbers 154, 145, 142, 144, and 156 as the most crucial contributors to the SVM model’s predictions.

Compiler is a tool that converts the high-level language into assembly code after enabling relevant optimizations. The automatic selection of suitable optimizations from an ample optimization space is a non-trivial task mainly accomplished through hardware profiling and application-level features. These features are then passed through an intelligent algorithm to predict the desired optimizations. However, collecting these features requires executing the application beforehand, which involves high overheads. With the evolution of Natural Language Processing (NLP), the performance of an application can be solely predicted at compile time via source code analysis. There has been substantial work in source code analysis using NLP, but most of it is focused on offloading the computation to suitable devices or detecting code vulnerabilities. Therefore, it has yet to be used to identify the best optimization sequence for an application. Similarly, most works have focused on finding the best machine learning or deep learning algorithms, hence ignoring the other important phases of the NLP pipeline. This paper pioneers the use of NLP to predict the best set of optimizations for a given application at compile time. Furthermore, this paper uniquely studies the impact of four vectorization and seven regression techniques in predicting the application performance. For most applications, we show that tfidf vectorization and huber regression result in the best outcomes. On average, the proposed technique predicts the optimal optimization sequence with a performance drop of 18%, achieving a minimum drop of merely 0.5% compared to the actual best combination.

Preventive maintenance of rotating machinery, such as motors, is pivotal for cost reduction in manufacturing facilities. Condition-based maintenance is considered the driving force in the health management of industrial machinery, which necessitates the development of efficient, able-to-generalize, and reusable approaches to help manufacturing facilities identify operational conditions. This study proposes an approach for classifying the operational settings of motors based on vibration data. This was accomplished by first creating a merged class of weight distribution and speed percentage ranges to be classified. These two operational settings can be classified to address sustainable manufacturing concerns as they relate to the development of systematic maintenance plans. Ensemble learning was then used as the machine learning algorithm of choice for the classification task. It was found that ensemble learning is capable of producing reasonable classification accuracy results that are superior to those of single-model approaches. In addition, the testing size and window size of the segmented vibration signals have been identified to have a clear effect on classification accuracy that is considered consistent with the logical trend. The proposed approach addresses the need for manufacturing facilities to utilize efficient and accurate algorithms for cost-effective, and sustainable maintenance practices.

Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In this survey, we provided a comprehensive overview of the state-of-the-art research on Trustworthy FL, exploring existing solutions and key foundations relevant to Trustworthiness in FL. There has been significant growth in the literature on trustworthy centralized Machine Learning (ML) and Deep Learning (DL). However, there is still a need for more focused efforts toward identifying trustworthiness pillars and evaluation metrics in FL. In this paper, we proposed a taxonomy encompassing five main classifications for Trustworthy FL, including Interpretability and Explainability, Transparency, Privacy and Robustness, Fairness, and Accountability. Each category represents a dimension of trust and is further broken down into different sub-categories. Moreover, we addressed trustworthiness in a Decentralized FL (DFL) setting. Communication efficiency is essential for ensuring Trustworthy FL. This paper also highlights the significance of communication efficiency within various Trustworthy FL pillars and investigates existing research on communication-efficient techniques across these pillars. Our survey comprehensively addresses trustworthiness challenges across all aspects within the Trustworthy FL settings. We also proposed a comprehensive architecture for Trustworthy FL, detailing the fundamental principles underlying the concept, and provided an in-depth analysis of trust assessment mechanisms. In conclusion, we identified key research challenges related to every aspect of Trustworthy FL and suggested future research directions. This comprehensive survey served as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.

This paper presents a novel design of a compact, low-profile, Substrate Integrated Waveguide (SIW) based Multiple-Input Multiple-Output (MIMO) antenna operating at the 2.45 GHz Industrial, Scientific, Medical (ISM) band. The antenna consists of two quarter-mode (QM) SIW cavity resonators and one diamond-shaped complementary split ring resonator (CSRR) slot etched on each cavity. The unique feature of this geometry is the ability to tune the operating frequency of the dominant mode to a lower frequency range by rotating the slot in the range of 0°-180°. The excitation of both cavities is achieved using microstrip feedlines. By placing both cavities in an orthogonal configuration, a significant isolation level of around -28 dB between the two ports is achieved. MIMO metrics parameters, including the envelope correlation coefficient (ECC) and diversity gain (DG), mean effective gain (MEG), and channel capacity loss (CCL) have been investigated, validating the MIMO capabilities of the proposed design. Due to its compact dimensions, minimal profile, and alignment with the ISM band, the antenna lends itself seamlessly to integration with healthcare devices, facilitating its deployment within Wireless Body Area Network (WBAN) applications. The robust performance of the antenna in the vicinity of the human body has been verified by investigating the S11 against frequency on different body parts such as the arm, head, and chest of the voxel phantom. The proposed design has been experimentally tested, and the measured responses closely agree with the simulations. The antenna exhibits a front-to-back ratio better than 10 dB and peak measured gain values of 5.0 dBi.

Background: Sodium nitrite (NaNO2) is a chemical substance used to enhance taste, add color, and keep food products fit for consumption for a longer time. NaNO2 gives rise to a negative adverse effect on male reproductive function. Odontonema cuspidatum (OC) is a natural plant that possesses antioxidant capacity. Aim: Our research evaluates the potential beneficial effect of Odontonema cuspidatum extract on the harmful effects caused by NaNO2 on the testicular tissue and sperm characteristics of male rats. Methods: Four groups with a total of forty rats: the control, the NaNO2-received group, the OC-administered group, and the fourth group received both NaNO2 and OC. All groups were administered daily for two months. Sperm characteristics, testicular antioxidant status and histopathological changes were evaluated. Results: Coadministration of NaNO2 and OC, in comparison with NaNO2 alone, contributed to a notable enhancement in acrosomal integrity, decreasing sperm abnormalities and restoring serum testosterone levels. Moreover, such coadministration reduced the oxidative stress marker, MDA, and increased SOD in testicular tissue, lowering TNF-α gene expression, and increasing the expression of P450scc and StAR genes. Additionally, the NaNO2 and OC combination decreased the testicular histopathological changes and the Caspase-3 and PCNA immunoexpression in seminiferous tubules compared with the NaNO2 group. Conclusion: The extract of Odontonema cuspidatum exhibited the ability to decrease oxidative stress and ameliorate the detrimental effects caused by NaNO2. [Open Vet J 2024; 14(1.000): 304-315]

Advancements in smartphone sensor technologies have significantly enriched the field of human activity recognition, facilitating a wide array of applications from health monitoring to personal navigation. This study utilized such advancements to explore human locomotion and localization recognition using data from accelerometers, microphones, gyroscopes, magnetometers, and GPS, applying Deep Polynomial Neural Networks (DPNN) and Multilayer Perceptron (MLP) across three datasets: the Continuous In-The-Wild Smart Watch Activity Dataset, the Huawei Locomotion Dataset, and the Extra Sensory Dataset. We employ two distinct approaches for activity recognition: Deep Polynomial Neural Networks (DPNN) for deep learning-based feature extraction and Multilayer Perceptron (MLP) with manual feature extraction techniques, including Linear Predictive Coding Cepstral Coefficients (LPCC), step length, signal magnitude area, spectral, and sound features. Through rigorous experimentation, we achieved remarkable accuracy in recognizing both locomotion and localization activities, with DPNN consistently outperforming MLP in terms of accuracy. Specifically, for the Continuous In-The-Wild Dataset, DPNN achieved a 93% accuracy rate for localization activities and 95% for locomotion activities, while MLP recorded 86% and 91% in the respective categories. Similarly, on the Huawei Locomotion Dataset, DPNN attained 95% accuracy for localization and 97% for locomotion, with MLP achieving 88% and 91%, respectively. Furthermore, the application of these models to the Extra Sensory Dataset yielded 92% accuracy for both localization and locomotion activities with DPNN, and 90% and 89% with MLP. In our study, we observed that in terms of accuracy, DPNN emerges as the clear winner; however, it is computationally expensive. Conversely, MLP, while being less accurate, stands out for its computational efficiency. This study not only highlights the effectiveness of incorporating advanced machine learning techniques in interpreting sensor data but also emphasizes the trade-offs between computational demands and accuracy in the domain of human activity recognition. Through our comprehensive analysis, we contribute valuable insights into the potential of smartphone sensors in enhancing activity recognition systems, paving the way for future innovations in mobile sensing technology.

Abstract Background Camel filariasis induced variable clinical syndromes characterized by fever, lethargy, localized dermal lesions, loss of condition, and testicular and scrotal swelling. The objective of the present work focused on clarifying the diagnostic importance of clinical findings, serum testosterone, and semen analysis as well as blood smear and testicular histopathology as a differential tool between only balanoposthitis without filariasis male camels group (OnlyBpgr) and balanoposthitis-filariasis infected male camels group (BpFlgr). The study also monitored the associations between the severity of ticks’ infestations in investigated male camels and the occurrence of balanoposthitis only or balanoposthitis with filariasis. Results and conclusions The study reported significant correlation between serum testosterone, serum cortisol, and sperm vitality and abnormalities percentages. The study included male camels (n = 250) classified into three groups: healthy control group (Contgr; n = 30), OnlyBpgr (n = 210), and BpFlgr (n = 10). These male camels were clinically and laboratory examined, and skin scraping tests and testicular histopathology were conducted. The study confirmed the association of the changes in clinical findings, whole blood picture, serum testosterone, serum cortisol, and semen analysis, with OnlyBpgr and BpFlgr. These changes were more prominent in BpFlgr than in OnlyBpgr. Skin scraping test results revealed a higher severity of live ticks’ infestation in BpFlgr than in OnlyBpgr because, unlike OnlyBpgr, all camels in BpFlgr (n = 10) were suffering from live ticks’ infestation. It also concluded the higher efficacy of histopathology of testicular tissues in male camels as a diagnostic tool for adult filaria in balanoposthitis-affected male camels than blood smear because all cases of camel filariasis in the current work were negative for microfilaria on microscopic examination of diurnal blood smear as well as testicular histopathology revealed detection of adult filaria in all camel filariasis associated with balanoposthitis. Strong correlation relationships were demonstrated between serum testosterone, serum cortisol, and semen analysis results. Positive correlations were reported between serum testosterone levels and sperm vitality percentages. However, negative correlations were stated between serum testosterone and each of serum cortisol and sperm abnormalities either in Contgr, OnlyBpgr, or BpFlgr.

Ensuring the security and safety of passengers and cargo through effective baggage screening presents a critical challenge in high-traffic environments like airports, where traditional manual processes are affected by high error rates, fatigue among security personnel, and privacy concerns. These issues underscore the urgent need for sophisticated automated systems capable of accurately detecting concealed threats. Advancements in deep learning have introduced more refined approaches to baggage screening, yet they struggle with capturing the global context of threats and challenges presented by clutter, concealment, and occlusion. Addressing these issues, this paper presents the Multi-Scale Hierarchical Transformer for X-ray Detection (MHT-X), a novel framework that surpasses conventional limitations by integrating a multi-scale contour mapping technique with a hierarchical feature extraction process using visual Transformers. MHT-X is distinctive in its ability to generate multi-level contour maps that unveil complex features of threat objects, facilitating their accurate identification. It employs a top-down approach for hierarchical feature processing, capturing vital spatial characteristics essential for precise threat localization through an efficient encoder-decoder mechanism. MHT-X represents a significant advancement in the detection of concealed and obstructed threat objects in X-ray baggage scans. By combining multi-scale contour maps with a hierarchical feature extraction process, it significantly enhances detection accuracy and outperforms traditional Convolutional Neural Network (CNN) models by effectively capturing the global context of illegal items with advanced feature representation. The proposed framework is rigorously validated on two public datasets, dubbed CLCXray, and PIDray, where it outperforms the state-of-the-art (SOTA) methods by achieving the mean average precision score of 65.26%, and 78.19%, respectively.

To address the critical need for enhancing Quality of Service (QoS) monitoring in the logistics service delivery domain, this paper introduces a blockchain-based QoS monitoring framework that aims to automate service delivery and dispute resolution processes. Traditional QoS monitoring solutions rely heavily on human judgment and intervention, which may increase operational costs and reduce service reliability. Such behaviour highlights the demand for more efficient and transparent logistics services, which underlines the importance of utilizing blockchain technology’s immutable and decentralized nature. The proposed framework in this paper employs a graph-based approach to transform QoS requirements into Deterministic Finite Automata (DFA) format. This strategy simplifies delivery monitoring and the identification of service violations through efficient DFA traversal. By using the Ethereum network as the deployment environment, we demonstrate that traversing a DFA is computationally efficient and reduces operational costs. Extensive experiments were conducted to evaluate the cost-effectiveness of the framework, showing that monitoring a delivery using this framework costs approximately ${\$}$ 2.59. This finding underscores the framework’s advantages in operational cost optimization compared to traditional human-based methods. Moreover, the decentralized nature of our proposed framework allows customers and businesses to define and monitor QoS parameters jointly. Therefore, the business partners can establish a transparent and trust-based relationship.

Background: Increased blood glucose levels (hyperglycemia) are a hallmark of type 1 diabetes mellitus (T1DM), a chronic autoimmune illness marked by insulin insufficiency as a result of the destruction of the pancreatic islet cells. T1DMs a widespread metabolic disorder in children. The loss of cells is due to autoimmunity in mostndividuals (70-90%) with T1DM(concomitant with the formation of T1DM-associated autoantibodies). Autoantibodies are often detected months or even years before the start of symptoms in people with T1DM. These autoantibodies are indicators of the onset of autoimmunity rather than pathogens. Many autoimmune diseases have symptoms of T1DM. Such factors may profoundly impact clinical care of the illness, particularly in children. The four most common autoimmune disorders are vitiligo, Addison's disease, celiac disease, and autoimmune thyroid disease. Objectives: To outline the association of T1DM as an autoimmune disease with other comorbid autoimmune illnesses Conclusion: Several endocrine and non-endocrine autoimmune diseases are strongly linked to type 1 diabetes mellitus.

Background: The human body contains trace levels of minerals and other micronutrients. Particularly advantageous for physiologic processes, a wide range of biochemical processes, stabilizing proteins and enzymes, and serving as cofactors for different enzymes. These essential micronutrients have a major physiological effect and are significantly associated with diabetes. Cobalt, boron, chromium, copper, Sulphur, iodine, fluoride, selenium, manganese, zinc, and molybdenum are examples of trace elements. Sodium, potassium, calcium, phosphorus, magnesium, and iron related to macro elements. The main focus of this review is the effect of particular minerals and trace elements on childhood insulin-dependent diabetes. Objective: Our understanding of how minerals and trace elements affect insulin-dependent diabetes in children will be improved as a result of this review, which is its main goal. Conclusion: The interaction, development, and outcomes of insulin-dependent diabetes in children are significantly influenced by minerals and trace elements.

Background: The most common kind of birth abnormality is a heart problem. When the heart doesn't form properly during pregnancy or birth, it's called a congenital heart defect. CHDs are associated with high rates of mortality and disability. Multiple processes are controlled by the cytokine growth differentiation factor-15 (GDF-15). Because of its continuous overexpression in both CVD and diabetes, GDF-15 is recommended as a biomarker for the existence and severity of both illnesses. In contrast, a large body of research indicates that GDF-15 has a protective role in the regulation of inflammation, endothelial cell function, insulin sensitivity, and weight gain, and is cardioprotective in myocardial infarction (MI). Objectives: Aim of the work is to give highlight on (GDF15) in pediatric cardiac diseases of varies aetiologies. Conclusion: It has been discovered that the cytokine growth differentiation factor-15 (GDF-15) is elevated in a number of clinical states, including hypertension, cardiovascular illness, and oxygen deprivation. GDF-15 is also found to be elevated in HF and PH (pulmonary hypertension). An increased level of GDF-15 is seen in newborns and children with PH-CHD, suggesting its use as a diagnostic biomarker.

This extensive review article provides a comprehensive analysis of control strategies for power sharing in Multi-Terminal High Voltage Direct Current (MT-HVDC) systems utilizing voltage source converters (VSC) and multi-level modular converters (MMC). MT-HVDC systems have become essential components of modern power infrastructures due to their ability to efficiently transmit energy over long distances, connect renewable energy sources, and enhance grid stability. Effective power sharing control is of uttermost importance in these systems for ensuring equitable energy distribution between terminals and grid reliability. The paper begins with a thorough explanation of the fundamental concepts underlying MT-HVDC systems, emphasizing their significance, and examining the various configurations of such systems. The discussion then examines the functions of VSCs and MMCs in these systems, highlighting their distinct advantages and applications. The review analyses in depth the complex power sharing control strategies within VSC-based and MMC-based multi-terminal HVDC systems, as well as a number of control methods, algorithms, and optimization techniques. In addition, the article discusses difficulties and solutions associated with power sharing control in MT-HVDC systems, such as communication and synchronization issues.

The aim of this study is to evaluate the potential changes in land cover and their impacts on rainwater harvesting systems, with a focus on Egypt's northwestern coastal region. To accomplish this objective, we utilized the kinematic runoff and erosion model (KINEROS2) to estimate the effects of projected land use changes on runoff and sediment load. The results demonstrated satisfactory performance of the model, with average Nash-Sutcliffe Efficiency values of 0.75 for calibration and 0.77 for validation, indicating successful simulations. The analysis revealed that land use changes had minimal impact in the base scenario (S1), resulting in moderate fluctuations in estimated sediment load. Conversely, the supplementary scenarios (S2 and S3) exhibited more pronounced alterations in land use, resulting in significant deviations in model estimations, notably indicating a substantial reduction in soil erosion. The integration of land use change analysis with KINEROS2 modeling emphasizes the potential for implementing effective management strategies.

The study aims to determine whether exposing EFL Saudi undergraduate students to TED talks could effectively contribute to the improvement of their oral language proficiency. To achieve this goal, 32 English as a Foreign Language Saudi undergraduate students were recruited to participate in the study. These students were selected based on their willingness to engage in the study and their availability for participation. The study employed a pre-test and post-test design, indicating that the participants' oral skills were assessed both before and after they were exposed to the TED talks. This design enables the researchers to measure the potential improvement in oral skills by comparing the scores obtained from the two tests. The test scores were evaluated across multiple dimensions, including fluency, content, vocabulary, grammar, and pronunciation. The results indicated that the overall scores of the EFL students improved after they watched the TED talks. This improvement suggests that the exposure to TED talks had a positive impact on the participants' oral skills, particularly in terms of fluency, which was the primary focus of the study.

Background: Zinc's role in enzymatic activities against oxidative stress may influence microvascular complications in young T1DM patients. Conversely, elevated AGE levels in pediatric T1DM correlate with oxidative stress, contributing to complications like retinopathy and nephropathy. Understanding the intricate interplay between zinc and AGEs holds promise for targeted interventions in disease management. Objectives: To assess the relationship between zinc, advanced glycation end products (AGEs), and microvascular complications in children with type 1 diabetes mellitus. Patients and methods: The study included 60 pediatric diabetic patients categorized into two groups based on microvascular complications into group A whom are diabetic patients with albuminuria either macro or micro albuminuria, group B whom are diabetic without albuminuria. A control group comprised 30 healthy children. Clinical examinations, BMI calculations, and biochemical analyses, including HbA1c, RBS, urine albumin/creatinine ratios, and serum zinc levels, were conducted. ELISA assays and colorimetric methods were employed for pentosidine and zinc assessments. Results: The study found significantly elevated Serum Pentosidine (4.94 ± 6.35 pg/ml) and decreased serum Zinc levels (23.25 ± 8.4 µg/dl) in cases compared to controls, with p-values of 0.0005 and

The method examines the challenging issues in tracking multiple autonomous ground vehicles (AGVs) using a motion capture system (MoCap) accessible by a wireless network such as standard IEEE 802.11a WIFI protocol. A state-of-art technology such as global positioning system may have limitations in their accuracy and restricted lines of sight. Tracking in these environments entail various complexities such as target (AGV) occlusion, clutter, and the electromagnetic interference that can interrupt communication between AGV and ground-based control station. We present a novel idea that exploits IR signals emitted by the MoCap to fetch position data reflected from AGVs. MoCap uses the Motive software to manipulate the position data necessary for the tracking filter. This method adopted the fixed-interval smoothing based on the joint integrated track splitting filter (FIsJITS) for detecting and tracking the vehicles. FIsJITS obtains track state estimation in both forward and backward directions within fixed time measurement intervals. The multi-track backward predictions are fused in the forward-path track to obtain a-priori smoothing predictions, followed by a smoothing state estimation. This approach also calculates the smoothing target existence probability (TEP) to reinforce target detection, allowing a tracking system to simultaneously track multiple vehicles efficiently within cluttered environments. For a seamless data processing, we utilize the WIFI through a wireless access point (WAP) to transfer the data from MoCap to the computer system where Motive and tracking system softwares are running simultaneously. We conducted the real-time experiments to demonstrate the tracking performance of the proposed AGV system outperforming existing algorithms.

Abstract A 16-port massive Multiple-Input-Multiple-Output (mMIMO) antenna system featuring a high gain and efficiency is proposed for millimeter-wave applications. The antenna system consists of 64 elements with a total size of 17 λo × 2.5λo, concerning the lowest frequency. Each 2 × 2 (radiating patch) subarray is designed to operate within the 25.5–29 GHz frequency range. The antenna's performance in terms of isolation, gain, and efficiency has been significantly improved by utilizing the proposed unique double and epsilon negative (DNG/ENG) metamaterials. The array elements are positioned on top of a Rogers RT5880 substrate, with ENG metamaterial unit cells interposed in between to mitigate coupling effects. Additionally, the DNG metamaterial reflector is positioned at the rear of the antenna to boost the gain. As a result, the metamaterial-based mMIMO antenna offers lower measured isolation reaching 25 dB, a maximum gain of 20 dBi and an efficiency of up to 99%. To further analyze the performance of the MIMO antenna, the diversity gain and enveloped correlation coefficient are discussed in relation to the MIMO parameters.

We investigate the use of high-dimensional quantum key distribution (HD-QKD) for wireless access to hybrid quantum-classical networks. We use $d$-dimensional time-phase encoded states to exchange a secret key between an indoor wireless user and the central office on the other end of the access network. We evaluate the performance in the case of transmitting quantum and classical signals over the same passive optical network by accounting for the impact of background noise induced by the Raman-scattered light on the QKD receiver. We also take into account the loss and background noise that occur in indoor environments as well as finite key effects in our analysis. By studying the system under nominal simulation conditions, we identify regimes of operation in which an HD-QKD system with $d=4$ can outperform its qubit-based counterpart.

The occupancy datasets are useful for planning important buildings’ related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants’ count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants’ count in each room. The models’ performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively.

Smart livestock farming utilizes technology to enhance production and meet food demand sustainably. This study employs surveys and case studies to gather data and information, subsequently analyzing it to identify opportunities and challenges. The proposed solutions encompass remote sensing, technology integration, farmer education, and stakeholder engagement. The research delves into smart technologies in animal production, addressing opportunities, challenges, and potential solutions. Smart agriculture employs modern technology to improve efficiency, sustainability, and animal welfare in livestock farming. This includes remote monitoring, GPS-based animal care, robotic milking, smart health collars, predictive disease control, and other innovations. Despite the great promise of smart animal production, there are existing challenges such as cost, data management, and connectivity. To overcome these challenges, potential solutions involve remote sensing, technology integration, and farmer education. Smart agriculture provides opportunities for increased efficiency, improved animal welfare, and enhanced environmental conservation. A well-planned approach is crucial to maximize the benefits of smart livestock production while ensuring its long-term sustainability. This study confirms the growing adoption of smart agriculture in livestock production, with the potential to support the sustainable development goals and deliver benefits such as increased productivity and resource efficiency. To fully realize these benefits and ensure the sustainability of livestock farming, addressing cost and education challenges is essential. Therefore, this study recommends promoting a positive outlook among livestock stakeholders and embracing smart agriculture to enhance farm performance.

Machines need to be able to recognize and understand complex visual surroundings to function at their best in a variety of contexts. Here, we address the difficult problem of multi-object recognition to obtain a sophisticated knowledge of complex visual environments, tackling issues such as size, occlusion, fluctuations in object traits, and complicated backdrops. Our contribution is to provide novel methods (Gaussian mixture model and mean-shift algorithms) for inferring multiple object segmentation in complicated visuals, introducing a unique multiclass object classification strategy utilizing benchmark datasets. Notably, by utilizing local appearance, texture, and geometry characteristics, our technique considerably improves classification accuracy by integrating a Multi-Layer Perceptron (MLP) with area signatures and local descriptors. By facilitating accurate object matching and identification based on local appearance, texture, and geometric features, local descriptors are essential for collecting particular information and regions of interest in images. When compared to state-of-the-art methods, empirical validation on MSRC and Corel 10k datasets shows better performance, especially when managing object occlusion problems. With an accuracy of 90.6% and 89.69%, respectively, our suggested system performs better than industry standards for multi-object classification on both datasets, highlighting the significant progress our method makes to the area of multiclass object classification in challenging visual contexts.

An analytical model for depletion-mode MOSHEMTs for high-temperature applications is compared against the experimental GaN HEMT data of the AlGaN/GaN MOSHEMT for temperature dependence of 2DEG simulated at 75 °C and 125 °C. Both temperatures reduce the 2DEG density by 4 % in the GaN HEMT and 3 % in the AlGaN/GaN MOSHEMT. The cause of this diminishing effect is determined to be the decrease of the conduction band offset at high temperatures. Additionally, the device performance degrades at high temperatures due to the immature behaviour of GaN material when it operates at high-power dissipation with poor thermal conductivity. The simulated AlGaN/GaN MOSHEMT performance is comparatively improved compared to the experimental AlGaN/GaN HEMT devices. This improvement could be used to understand the nature of the 2DEG density vs the temperature, hence could enhance the experimental performance of the AlGaN/GaN MOSHEMT.

Polymer based composite high voltage insulators (HVIs) due to their hydrophobic surfaces have consistently outperformed the traditional ceramic insulators in highly contaminating coastal-desert environments. However, the harsh environment causes polymer surfaces of these HVIs undergo rapid ageing leading to permanent loss of hydrophobicity. In this paper, we have presented three samples of 380 kV composite HVIs with a service life of over ten years near west coast of the Kingdom of Saudi Arabia. Material investigation using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy revealed substantial surface deterioration and change in the composition. Hydrophobicity investigations revealed that surface of the HVIs have become hydrophilic leading to accumulation of continuous films of water. Electrical withstand strength was evaluated using steam fog test showing significant decrease from the rated values. The distribution of electric field around an insulator is a key design criterion for HVIs. The consequences of loss of hydrophobicity on the electric field distribution across insulator surface is explained analytically and through numerical simulations based on Finite Element Method (FEM). The pristine and field aged HVIs are modelled and were covered with realistic water distributions observed during hydrophobicity tests. It has been observed that the intensity of electric field on field aged insulator surpasses the corona inception threshold at certain areas of the insulator, resulting in the initiation of corona discharge on the insulator’s surface. This corona inception further causes surface deterioration and accelerated ageing in composite HVI.

Background: Autism spectrum disease (ASD) represented as a collection of neurodevelopmental disorders marked by reduced reciprocal socializations and communications. Mood conditions are frequent in autism spectrum disease (ASD). Objectives: The present work aimed to evaluate prevalence, symptoms and treatment of different types of mood disorders in kids with autism spectrum disorder (ASD), with ages between 2 to 16 yrs. Methods: We have searched literature in Pub Med and Google scholar. Conclusion: Early age, male gender, low socioeconomic states, residence, consanguinity, old parent’s age, represented conditions that are accompanying with a raised hazard for ASD in population. Mood disorder including anxiety, bipolar disorders, depressions, and obsessive-compulsive disease (OCD) beside attention deficit hyper-activity disorders (ADHD) are the utmost occurring co morbidities. Selective serotonin reuptake inhibitor (SSRI) and anti-psychotics were defined for mood disorders management.

Background: Pneumonia and other lower respiratory tract infections are the most leading causes of death worldwide. Pneumonia is common and is associated with significant morbidity and mortality between children. Good and early diagnosis is a very important factor to avoid severe complications and drawbacks, and proper management of patients is vital in the prognosis of pneumonia. Although in the developed world the diagnosis depends mainly on the radiographic findings, but the World Health Organization has classified and defined pneumonia subjected to the clinical findings found by visual inspection and on the timing of the respiratory rate. Objectives: The aim of this review: is to evaluate the indicators of pneumonia severity in children under-five years of age. Conclusion: Pneumonia in children is related to significant morbidity and mortality; however, data are limited in predicting which children will have negative outcomes, including clinical deterioration, severe disease, or development of complications

Abstract Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.

Abstract Background Soluble guanylate cyclase (sGC) stimulators have been investigated for heart failure (HF) in several randomized controlled trials (RCTs). However, its place in the management guidelines of either HFrEF or HfpEF is still inconclusive. Methods We conducted a network meta-analysis synthesizing RCTs investigating sGC for HF management, which were retrieved by systematically searching five databases until January 24th, 2023. Dichotomous outcomes were pooled using risk ratio (RR) along with confidence interval (CI). Results Eight RCTs with a total of 7307 patients were included. Vericiguat 10 mg significantly reduced the composite cardiovascular (CVS) mortality and HF hospitalization in HF (RR: 0.88, 95% CI [0.79; 0.98]) and in HFrEF (RR: 0.87, 95% CI [0.78; 0.97]); however, it was not effective in HFpEF (RR: 0.69, 95% CI [0.15; 3.05]). Also, vericiguat 10 mg showed no difference compared to placebo regarding the incidence of all-cause mortality (RR: 0.96, 95% CI [0.84; 1.10]), any adverse events (AEs) (RR: 0.94, 95% CI [0.83; 1.07]), any serious AEs (RR: 0.91, 95% CI [0.81; 1.01]), and any AEs leading to drug discontinuation (RR: 1.14, 95% CI [0.92; 1.40]). Conclusion Vericiguat 10 mg was effective in reducing the composite CVS mortality and HF hospitalization, with an acceptable safety profile. This was only observed in HFrEF patients, but not in HFpEF patients. However, our data regarding other agents (riociguat and praliciguat) and HFpEF can be underpowered, warranting further RCTs to clarify vericiguat 10 mg place in HFrEF management guidelines and to investigate sGC stimulators for HFpEF in large-scale trials.

Abstract Background Bacterial infections are considered a leading cause of hospitalization and death globally. There is still a need for a rapid and feasible biomarker for bacterial infections. Heparin-binding protein (HBP) was shown to be related to bacterial infections. The objective of the study is to investigate the diagnostic accuracy of HBP in bacterial infections. Methods Articles were screened in PubMed, SCOPUS, Web of Science, and Cochrane to recognize eligible studies. We included studies investigating the diagnostic accuracy of HBP and reported the necessary data to construct 2 × 2 tables. A univariate analysis was conducted to determine the pooled sensitivity and specificity, and a bivariate diagnostic random-effects model was used to calculate the optimal cut-off point. Results The analysis comprised sixteen studies in total. Plasma HBP showed a sensitivity of 0.90 (95% CI: [0.79, 0.96]) and a specificity of 0.87 (95% CI: [0.66, 0.96]) in diagnosing bacterial infections using blood samples. Pooling data from seven studies revealed that HBP in cerebrospinal fluid (CSF) has sensitivity and specificity of 96% (95% CI: [0.85, 0.99]), and 95% (95% CI: [0.89, 0.97]), respectively, for the diagnosis of bacterial meningitis. In urinary tract infections (UTI), urine-HBP was revealed to have a high diagnostic value in discriminating bacterial from non-bacterial UTI infection at a cut-off value of 32.868 ng/ml with sensitivity and specificity of 87%. Conclusion HBP has shown a high diagnostic accuracy of bacterial infections, including UTI and meningitis. Further studies are needed to determine its prognostic value and whether it could guide antibiotic therapy.

Abstract In the current research, the impacts of portfolio assessment (PA) on the grit tendencies, motivation, and willingness to communicate (WTC) of Saudi Arabian EFL learners were examined. The population of this study was fifty-eight EFL learners who were assigned to two groups in order to accomplish this purpose. Following that, both groups were given the WTC, grit tendencies, and motivation pre-tests to gauge the participants' proficiency in the dependent variables. After that, the students in the control group received the instruction without the use of PA, while the experimental group (EG) received treatment utilizing PA. Following the treatment, the two groups completed the WTC, grit tendencies, and motivation post-tests. The results revealed differences between the two groups' post-test scores, with the EG performing better. These findings imply that teachers can exploit portfolios to provide their students with relevant content that will help them improve their language proficiency.

BACKGROUND: Acute coronary syndrome (ACS) comprises a spectrum of diseases ranging from unstable angina (UA), non-ST elevation myocardial infarction (non-STEMI) and ST elevation myocardial infarction (STEMI). Treatment of ACS without STEMI (NSTEMI-ACS) can vary, depending on the severity of presentation and multiple other factors. OBJECTIVE: Analyze the NSTEMI-ACS patients in our institution. DESIGN: Retrospective observational SETTING: A tertiary care institution with accredited chest pain center PATIENTS AND METHODS: The travel time from ED booking to the final disposition for patients presenting with chest pain was retrieved over a period of 6 months. The duration of each phase of management was measured with a view to identify the factors that influence their management and time from the ED to their final destination. The data was analyzed using descriptive statistics. MAIN OUTCOME MEASURES: Travel time from ED to final destination SAMPLE SIZE: 300 patients RESULTS: The majority of patients were males (64%) between 61 and 80 years of age (45%). The median disposition time (from ED booking to admission order by the cardiology team) was 5 hours and 19 minutes. Cardiology admissions took 10 hours and 20 minutes from ED booking to the inpatient bed. UA was diagnosed in 153 (51%) patients and non-STEMI in 52 (17%). Coronary catheterization was required in 79 (26%) patients, 24 (8%) had coronary artery bypass grafting (CABG) and 8 (3%) had both catheterization and CABG. CONCLUSION: The time from ED booking to final destination for NSTEMI-ACS patients is delayed due to multiple factors, which caused significant delays in overall management. Additional interventional steps can help improve the travel times, diagnosis, management and disposition of these patients. LIMITATIONS: Single center study done in a tertiary care center so the results from this study may not be extrapolated to other centers.

Background The overexpression of estrogen receptor beta (ERβ) and its correlation with prognosis of malignant mesothelioma remains controversial and unexplored. This study aimed at studying ERβ expression in tumour tissue in patients with pleural mesothelioma and its association with clinical and pathological factors and survival analysis. Material and methods A prospective cohort study was conducted on patients who were diagnosed with malignant pleural mesothelioma (MPM) in Ain Shams university hospitals, the primary end point was to correlate ERβ overexpression that was analysed by immunohistochemistry (IHC) on paraffin tissue blocks with 1-year overall survival (OS) among MPM patients, the secondary endpoint was to correlate ERβ overexpression with 1-year progression free survival (PFS) and response to platinum-based chemotherapy among MPM patients. Results A total 42 MPM patients were included, High ERβ expression (modified Allred score 7-9) was reported in 9.5% of the patients (4 cases), moderate expression (modified Allred score 4-6) was reported in 28.6% of the patients(12 cases), weak and negative expression ( modified Allred score 0-3) was reported in 61% of the patients(26 cases), percent of ERβ positive cases was 69% of the total patients, there was no statistical difference as regard age (p value:0.8), sex(p value:0.4), stage (p value: 0.71), ECOG (p value :0.84) , histological subtype(p value:0.17) and response to platinum based treatment (p value:0.5) across the 3 groups of patients (high, moderate , weak and negative ER beta on tumour tissues) there was no statistically significant difference in OS and PFS for ERβ overexpression. Conclusion The overexpression of ERβ was not associated with improved OS or PFS in mesothelioma patients, despite high rate of ERβ positive cases in our analysis, larger sample size would be recommended for more solid conclusions

In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power (i.e., the average of the transmission power of all satellites on the shortest path) and network latency (i.e., the end-to-end latency of the shortest path). The results show that the mean network latency (i.e., the mean of network latency over all time slots) decreases and mean average satellite transmission power (i.e., the mean of average satellite transmission power over all time slots) increases with an increase in LISL range. For the Toronto–Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto–Istanbul and Toronto–London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence.

In recent years, the incidence of cancers, inflammatory diseases, Alzheimer’s disease, glucose metabolism disorder and diabetes has increased alarmingly which demands more research into the discovery of new drug candidates to treat these human diseases. Main phytochemicals from Humulus lupulus L. (hops) have been demonstrated to have positive impacts on human health, and prenylated flavonoids are one of the major groups of bioactive phytochemicals found in this plant. Thus, this review summarizes the role of major prenylated components in hops in human diseases including cancer, inflammation and viral infections. In silico studies of prenylated bioactive compounds against various drug targets such as histone deactylases (HDACs), sirtuins (SIRTs), and acetylcholinesterase (AChE), and the molecular molecular interactions between protein and ligand have also been included. Furthermore, the structure-activity relationships (SAR) studies on these compounds are highlighted. This review concludes that the prenylated phytochemicals from H. lupulus L., including xanthohumol (XN), isoxanthohumol (IXN), 8-prenylnaringenin (8-PN) and 6-prenylnaringenin (6-PN), have promising roles in human health and may contribute to new drug discovery and development.

Objective: Diabetes ranks as the most prevalent ailment in developing nations. Vital steps to mitigate the consequences of diabetes include early detection and expert medical intervention. A highly effective approach for identifying diabetes involves assessing the specific indicators associated with this condition. When it comes to automated diabetes detection, frequently encountered datasets frequently exhibit gaps in data, which can markedly impact the effectiveness of machine learning models. Methods: The aim of this study is to propose an automated method for predicting diabetes, with a focus on appropriately dealing with missing data and improving accuracy. The proposed framework makes use of K-Nearest Neighbour (KNN) imputed features along with a Tri-ensemble voting classifier model. Results: By incorporating the KNN imputer, the presented model demonstrates impressive performance metrics, including an accuracy of 97.49%, precision of 98.16%, recall of 99.35%, and an F1 score of 98.84%. The study conducted a thorough comparison of this proposed model against seven alternative machine learning algorithms, assessing them under two conditions: one with omitted missing values and another with the KNN imputer applied. These findings support the proposed model’s efficacy, highlighting its superiority over currently established state-of-the-art techniques. Conclusion: This research explores the problem of missing data in diabetes diagnosis and highlights the efficacy of the KNN-imputed technique. The results are promising for healthcare practitioners as they could facilitate early detection and improve the quality of diabetic patient care.

A compact, circularly-polarized (CP), end-fire, $4\times 1$ continuous beamsteering antenna array implemented using substrate-integrated waveguides for 5G mobile terminals is proposed in this paper. The purpose of this system is to complement the radiation patterns of planar phased arrays on the faces of a typical mobile terminal. The proposed system seamlessly integrates previously-presented antennas and polarizers, with adjustable phase shifters and a feeding network in a multi-layered, single stack, printed circuit board (PCB) for continuous beamsteering in a compact package. Through the use of forward and reverse couplers, and the stacking of the phase shifters on top of the feeding network, a compact form factor of $7.6\times 4.1\times 0.38\lambda _{o}^{3}$ is achieved. The system was fabricated and assembled through standard PCB processes. The measurements illustrated that the system can achieve a continuous beamsteering range of ±35° with an axial ratio better than 3 dB and a gain variation less than 3 dB. A measured impedance bandwidth (BW) of 8.9% at 28 GHz is also demonstrated. Given its performance, size, ease of manufacture, integrated control lines and single RF feed, the proposed system is a good candidate for 5G millimeter-wave continuous beamsteering in mobile terminals.