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Abstract The major challenge for the construction industry is to design and produce sustainable construction materials that are efficient in their performance and can be affordable for construction projects. The objective of this study is to determine the viability of incorporating waste products such as sugarcane bagasse ash (SCBA) to produce concrete with polypropylene (PP) fibers. SCBA is an industry waste that has certain pozzolanic properties, however, it shows limited mechanical properties when used as a cement replacement. To improve the mechanical and durability performance, metakaolin (15%) and PP fiber (0.5%, 1%, and 1.5%) were incorporated in the SCBA blend as a ternary additive. Some of the important characteristics explored include compressive strength, tensile strength, density, water absorption, acid resistance, and sorptivity. The study reveals that the incorporation of 15% metakaolin in the composite enhanced the compressive strength by 7%, 8.2%, and 9.1% for PP fiber additions of 0.5%, 1%, 1.5%, while the acid resistance enhanced by 4%, 6% and 8% relative to the control mix for the same value of pp fiber. Furthermore, cost evaluation confirms that the overall costs of concrete with 15% metakaolin and 5% SCBA were 12.2% less than the control concrete, thus making this option economically feasible. This research proves that the inclusion of SCBA, metakaolin, and PP fibers into the concrete mixture brings a sustainable approach that enhances mechanical properties and durability while assisting sugar manufacturing plants in the proper disposal of wastes.

In today's world, enhancing the performance of recycled aggregate concrete is an essential need, as it ensures the effective utilization of demolished construction waste. The advantages of fly ash and polypropylene fiber in conventional strength concrete are well known. However Further research is required to evaluate the impact of using fly ash, silica fume and polypropylene fiber in case of RA and its effect on the properties of recycled aggregate concrete (RAC). This study evaluates the effects of using 50%, 75%, and 100% recycled aggregate (RA) and 1%, 2%, and 3% polypropylene fiber with a fixed amount of fly ash and silica fume 87 kg/m3 and 21.75 kg/m3. The objective is to determine how these combinations affect concrete strength and durability properties (compression, tensile strength, acid resistance and water absorption). This study also investigates the reliability of non-destructive tests, ultrasonic pulse velocity and rebound number, in the presence of polypropylene (PP) fibers and fly ash with recycled aggregate. The experimental investigation showed that the combination of RA based concrete, fly ash (FA), and polypropylene fiber (PPF) improved concrete durability and strength. The concrete compressive strength made with 50% RA was 13% lesser than that of control concrete, but with the incorporation 3% PP fiber the same mix achieved 5% higher strength than control mix. On the other hand, the concrete compressive strength made with 75% RA and 100% RA was experimentally found 19% and 22% lower, respectively than control mix. The combinations containing constant fly ash and PPF, in addition to 50% recycled concrete aggregate (RCA), demonstrate greater resistance when exposed to sulfuric acid (H2SO4) environments, in comparison to the control mixture. However, the worst performance is shown by mix (100% RA).

Background: Acute myocardial infarction (AMI) remains a common reason for admission to the intensive care unit (ICU). However, there is limited data comparing outcomes for patients with AMI admitted to specific ICUs. Objectives: The purpose of this study was to assess clinical outcomes between patients with AMI requiring invasive mechanical ventilation admitted to the medical ICU (MICU) compared to cardiac (CICU). Methods: We utilized the Vizient Clinical Data Base to identify patients with a primary diagnosis of AMI between October 2015 and December 2019 and requiring invasive mechanical ventilation. Using multivariable logistic regression, we compared clinical outcomes for patients admitted to the MICU vs CICU. Results: We identified 12,639 patients, 25.2% (n = 3,185) of which were admitted to a MICU and 74.8% (n = 9,454) to a CICU. Patients admitted to a CICU were more likely to present with STEMI (57.0% vs 42.8%), cardiogenic shock (46.0% vs 37.4%), and require mechanical circulatory support and vasoactive medications (all, P 750 beds), and teaching hospitals (all, P

The climate change crisis is primarily associated with carbon dioxide emissions from fossil fuel-based electricity generation, which significantly contribute to the greenhouse gas effect. The implementation of renewable energy technologies can mitigate environmental impacts and promote a sustainable future. This study aimed to conduct a techno-economic feasibility analysis and optimize performance parameters for a 50 MWe capacity Linear Fresnel Reflector (LFR) concentrated solar power plant for nine stations under various climatic conditions. The analysis was conducted based on Levelized Cost of Electricity (LCOE) and Capacity Factor (CF) to select a suitable Heat Transfer Fluid (HTF) for each station, by varying Solar Multiple (SM) from 1.0 to 6.0 and adjusting Thermal Energy Storage (TES) hours from four to sixteen. The HTFs selected for economically feasible projects at different stations were Hitec Solar Salt, Hitec, and Therminol VP-1. The combination of SM and TES hours was optimized for economic feasibility, targeting a minimum CF of 36 % and a maximum LCOE of 13.0 ¢/kWh. The optimum SM and TES hours were found to range from 2.0 to 5.8 and from six to thirteen, respectively. An LCOE of less than 7.5 ¢/kWh and a CF of more than 80 % was achieved for two stations with SM of 6.0 and TES of 16 h using Therminol VP-1. Stations with annual mean direct normal irradiance values higher than 1500 kWh/m2 and clearness index values over 0.58 are suitable for commercial power plants. This study contributes to achieving the United Nation's sustainable development goals by promoting the use of renewable energy technologies.

Background IDH1 mutations are common in many cancers, however, their role in promoting the Warburg effect remains elusive. This study elucidates the putative involvement of mutant-IDH1 in regulating hypoxia-inducible factor (HIF1-α) and Sine-Oculis Homeobox-1 (SIX-1) expression. Methodology Genetic screening was performed using the ARMS-PCR in acute myeloid leukemia (AML), brain, and breast cancer (BC) cohorts, while transcript expression was determined using qPCR. Further, a meta-analysis of risk factors associated with the R132 mutation was performed. Results Approximately 32% of AML and ∼60% of glioma cases were mutants, while no mutation was found in the BC cohort. ‘AA’ and TT’ were associated with higher disease risk (OR = 12.18 & 4.68) in AML and had significantly upregulated IDH1 expression. Moreover, downregulated HIF1-α and upregulated SIX-1 expression was also observed in these patients, suggesting that mutant-IDH1 may alter glucose metabolism. Perturbed IDH1 and HIF-α levels exhibited poor prognosis in univariate and multivariate analysis, while age and gender were found to be contributory factors as well. Based on the ROC model, these had a good potential to be used as prognostic markers. A significant variation in frequencies of R132 mutations in AML among different populations was observed. Cytogenesis ( R 2 = 12.2%), NMP1 mutation status ( R 2 = 18.5%), and ethnic contributions ( R 2 = 73.21%) were critical moderators underlying these mutations. Women had a higher risk of R132 mutation (HR = 1.3, P < 0.04). The pooled prevalence was calculated to be 0.29 (95% CI 0.26-0.33, P < 0.01), indicating that IDH1 mutations are a significant prognostic factor in AML. Conclusion IDH1 and HIF1-α profiles are linked to poor survival and prognosis, while high SIX-1 expression in IDH1 mutants suggests a role in leukemic transformation and therapy response in AML.

The emergence and widespread of tigecycline resistance undoubtedly poses a serious threat to public health globally. The exploration of combination therapies has become preferred antibacterial strategies to alleviate this global burden. In this study, tigecycline-resistant tet(X4)-positive Escherichia coli were selected for adjuvant screening. Interestingly, 9-aminominocycline (9-AMC), one of the tigecycline metabolites, exhibits synergistic antibacterial activity with tigecycline using checkerboard assay. The efficacy in vitro and in vivo was evaluated, and the synergistic mechanism was further explored. The results suggested that 9-AMC combined with tigecycline could inhibit the growth of antibiotic resistant bacteria, efficiently retard the evolution of tet(X4) gene and narrow the drug mutant selection window. In addition, the combination of tigecycline and 9-AMC could destroy the normal membrane structure of bacteria, inhibit the formation of biofilm, remarkably reduce the level of intracellular ATP level, and accelerate the oxidative damage of bacteria. Furthermore, 9-AMC is more stable in the bind of Tet(X4) inactivating enzyme. The transcriptomics analysis revealed that the genes related to the 9-AMC and tigecycline were mainly enriched in ABC transporters. Collectively, the results reveal the potentiation effects on tigecycline and the probability of 9-AMC as a novel tigecycline adjuvant against tet(X4)-positive Escherichia coli, which provides new insights for adjuvant screening.

Understanding the mechanisms involved in the movement and clearance of mucus within the human airways is important from a physiological perspective. A mathematical model of muco-ciliary two-layered fluid flow in a finite two-dimensional channel is proposed. Due to the presence of both the airway ciliary layer (ACL) and the periciliary liquid layer (PCL) in airways epithelial cells, the two-layered model is adopted. The third grade fluid models is used to characterize the viscoelastic nature of mucus secreted in the airways. The mathematical modeling of two-layer flow problem is simplified by using long wavelength and small Reynolds number approximation. A formulated set of partial differential equations (PDEs) is solved for series-form solutions of velocity, temperature, concentration, and pressure gradient by using the Adomian decomposition method (ADM). The analysis delineated that with an increase in pressure gradient at airways entrance ξ the velocity and temperature increases while concentration decreases. Deborah numbers impact ACL and PCL velocities differently, with De(A) influencing flow direction and De(P) causing fluid to flow faster in the forward direction. It also emphasizes the significance of the rate of thermal diffusion, viscous dissipation, and relaxation time. The results showed that the suggested two-layered muco-ciliary model provides a better description of mucus transport in the human airways. The pressure gradient at the airways entrance has a significant impact on mucus transport through the airways.

Edge computing is increasingly gaining acceptance in both industrial applications and academic research due to its potential to reduce delays and provide clients with enhanced service quality. However, mobile edge computing presents several significant challenges, particularly given its diverse applications, high user mobility, and the dynamic nature of numerous Internet of Things (IoT) devices. Managing multiple tasks efficiently becomes difficult, especially with the limited resources of these devices. This study introduces a task-offloading model that utilizes an Artificial Neural Fuzzy Inference System (ANFIS) within a layered architecture to address these challenges. The proposed model is designed to optimize task offloading in such dynamic environments. The ANFIS-based model is simulated using MATLAB’s Neuro Fuzzy Logic Toolbox. The layered architecture of the ANFIS model underwent extensive testing, achieving 70% training and 30% testing, and demonstrating an overall low error rate of 0.29505, thereby validating the model’s effectiveness in managing task offloading in mobile edge computing.

In the recent era, the practical implementation of Autonomous Vehicular Networks (AVNs) with the vulnerable Vehicle-to-Vehicle (V2V) communication of autonomous vehicles and inadequate intelligent decision-making systems has become a primary concern in smart city mobility. This has led to the traffic congestion concerns such as time wastage, compromised safety, decreased durability and reliability of transportation infrastructure and V2V communication short delay and Roadside Units (RSUs), and reduced traffic flow. To address these issues, secure AVN communication and smart decision-making for autonomous vehicles in smart cities are of utmost importance. It ensures safety on roads, durability of the infrastructure, transparency, reliability, traffic congestion reduction and transportation efficiency. MapReduce is a reliable distributed computing paradigm which is able to analyze and process enormous AVN data in parallel. It contributes to smoother traffic flow by identifying the patterns and providing actionable insights for real-time decision making to decrease congestion. A private blockchain AVN can efficiently solve the problems of data security and reliability by providing tamper-proof record of all the transactions, hence enhancing reliability, and also offering a trusted solution of unauthorized access in real-time V2V communication. Explainable Artificial Intelligence (XAI) which is an efficient way to analyze fairness in traffic data over time providing transparency and availability of intricate traffic patterns, improving real-time traffic management with V2V communication and RSUs and reducing short delays that may occur as well as enabling traffic flow and the development of predictive traffic models that assist in decision making. This research proposed an XAI-based transparent model integrating MapReduce for processing large amounts of data and private blockchain technology for secured and tamper-proof vehicular communication. This proposed model is a promising solution for addressing the AVN data security issues and reliability of the system, mitigating negative effects of traffic congestion, and improving the transparency of decision making on the transport efficiency in smart cities. The proposed model provides a better performance than the previous approaches and gets 96% of the accuracy and 4% of miss rate.

Cervical cancer, the second most prevalent cancer among women worldwide, is primarily attributed to the human papillomavirus (HPV). Despite advances in healthcare, it remains a significant cause of mortality among women across diverse regions, surpassing other hereditary cancers. Early detection is pivotal, as survival rates exceed 90% when the disease is identified in its early stages. In response to this critical need, we introduce WFC2DS (Web Framework for Cervical Cancer Detection System), a novel expert web system specifically designed to revolutionize cervical cancer diagnosis. WFC2DS integrates a sophisticated ensemble of machine learning classification algorithms, including Artificial Neural Network (ANN), AdaBoost, K-Nearest Neighbor (KNN), Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree (DT). This ensemble approach enables a comprehensive analysis of a large dataset comprising information from 858 patients with 36 attributes, with the primary objective being the early detection of cervical cancer, using the last attribute, Biopsy, as the target variable. Our evaluation criteria encompass accuracy, specificity, sensitivity, and the F1 score. Among the algorithms, RFC and DT emerge as the most promising, demonstrating exceptional performance with an accuracy of 98.1% and an F1 score of 0.98. AdaBoost shows an accuracy of 97.4% and an F1 score of 0.98, ANN attains an accuracy of 97.7% and an F1 score of 0.96, SVM achieves an accuracy of 96.2% and an F1 score of 0.96, and KNN reaches an accuracy of 90.6% with an F1 score of 0.91. This research significantly contributes to reducing the global burden of cervical cancer, emphasizing transformative advancements in women’s healthcare. WFC2DS, with its cutting-edge machine learning techniques, not only improves the accuracy of cervical cancer diagnosis but also enhances the overall healthcare landscape for women worldwide.

This paper presents two Elliptical Trajectories for Efficient Data-gathering and Delay-tolerant (TETD2) routing schemes for Underwater Wireless Sensor Networks (UWSNs). The TETD2 protocol consists of two phases. The first phase involves creating an atomic-shaped trajectory for an Autonomous Underwater Vehicle. In second phase, to improve the delay tolerance of the protocol, communication between random nodes (RNs), destination nodes (DNs), autonomous underwater vehicles (AUVs) and the sink node have been proposed. The latency between RNs in the network reveals the necessary time for choosing the prospective forwarders for making association linkages. The shortest path from a multipath environment sends a packet from its origin to the destination node using the RNs-to-DNs model in the shortest time possible. To gather data as quickly as possible, the novel trajectory model maximizes the number of DNs for RNs associations. In addition, a cooperation process has been introduced to allow for data sharing from parent AUV to child and onward to the sink node. The performance of TETD2 was tested using the network simulator (NS-2) and compared to the currently used method for data-gathering, the Effective Data Gathering (AEDG) protocol. The simulation results showed that TETD2 outperformed the ASEDG in terms of throughput and end-to-end delay. The TETD2 protocol offers a promising solution for efficient data-gathering in UWSNs and has the potential to significantly improve the performance of such networks.

The rapid advancement of grid modernization and the proliferation of smart grids have engendered a critical need for cyber-physical security. Recent cyber-attacks targeting grid infrastructure, notably leading to substantial blackouts in Ukraine, underscore the vulnerabilities and potentially catastrophic consequences of such incursions. These attacks, whether stemming from cyber threats such as Denial of Service (DOS), False Data Injection Attacks (FDIA), or complex cyber-physical manipulations, emphasize the imperative of robust cybersecurity protocols in smart grid operations. This research investigates a pivotal approach to fortify and safeguard smart grid systems by integrating blockchain technology with wireless sensor nodes. By leveraging a Proof of Authority (PoA) Ethereum Blockchain framework, the study delves into the transformative capabilities of Blockchain within Supervisory Control and Data Acquisition (SCADA) networks. Specifically, it examines configurations across IEEE 14-bus, 30-bus, and 118-bus topologies. In addition to elucidating the inherent vulnerabilities in traditional SCADA systems, this study meticulously evaluates an array of performance matrices. Statistical analyses encompassing mean, standard deviation, skewness, kurtosis, and confidence levels provide nuanced insights into the efficacy of blockchain mechanisms in enhancing SCADA resilience against contemporary cyber threats. This research endeavors to bridge the gap in modern cybersecurity paradigms by fusing blockchain technology with wireless sensor nodes. By fortifying data integrity, elevating the reliability of data transmission, and augmenting trustworthiness within SCADA infrastructures, this study aims to present robust solutions to the escalating cybersecurity challenges faced by smart grid systems.

This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and addressing the challenge of unscheduled nodes within the communication network. The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. To implement these optimization techniques, the Scheduled Routing Algorithm for Localization (SRAL) is introduced, aiming to enhance node scheduling and localization. This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. The challenges of node localization, RREQ reconstruction at the beacon level, and increased RO, along with End-to-End delays and unreliable data forwarding, have a significant impact on overall communication in underwater environments. The proposed framework, along with the hybridized metaheuristic algorithms, show great potential in improving node localization, optimizing scheduling, reducing energy costs, and enhancing reliable data delivery in the Internet of Underwater Things (IoUT)-based network.

Hospital-acquired infections, particularly Central Venous Line-Associated Bloodstream Infections (CLABSIs), are a noteworthy concern in intensive care units (ICUs). CLABSIs not only extend hospital stays but also increase healthcare costs and pose serious threats to patients' well-being. This narrative literature review explores the evidence-based strategies for the prevention of CLABSIs in critical care settings. It emphasizes the importance of proper infection control measures, such as hand hygiene, maximal barrier precautions, and chlorhexidine skin preparation, to reduce the risk of infection. The choice of catheter insertion site, duration of catheter use, and nurse-patient ratios are also discussed as crucial factors in CLABSI prevention. Additionally, post-insertion care, including daily bathing with chlorhexidine, catheter patency maintenance, and dressing changes, is highlighted. The review underscores the need for healthcare workers to adhere to best practices and guidelines to minimize CLABSI rates, ultimately enhancing patient safety and reducing healthcare burdens. The findings emphasize the significance of informed practices in preventing CLABSIs and the potential for improved patient outcomes with their implementation.

Background: Diabetes mellitus (DM) constitutes a spectrum of persistent metabolic disorders originating from irregular glucose metabolism due to insufficient insulin production, compromised insulin function, or a combination of the two, leading to elevated blood sugar levels. This study assessed the influence of non-surgical periodontal therapy on glycated hemoglobin (HbA1c) levels in individuals with Type 2 Diabetes. Methods: This quasi-experimental study was conducted at Khyber College of Dentistry, Peshawar, with 40 participants diagnosed with Type 2 diabetes for more than 3 years and suffering from chronic periodontitis. Patients with Type 1 diabetes, pregnant women, and those with other medical complications were excluded. HbA1c levels were assessed at baseline, and non-surgical periodontal therapy was administered. Patients were educated and scheduled for a follow-up after 3 months when HbA1c levels were reassessed. A paired t-test was employed to compare HbA1c levels pre-and post-intervention. Results: In this study, males were 29 (58%) whereas 21 (42%) females. The mean value of HbA1c was less post-treatment with non-surgical periodontal therapy (6.928±0.866%) than pretreatment (9.048±0.73%) and results were statistically significant (p

The present global environment is facing growing issues linked to production of concrete, mostly due to high consumption of concrete as the dominating construction material globally. In today's climate of environmental sustainability, recycled concrete production using RA (recycled aggregates) requires a more holistic approach. This study examined how recycled aggregate (RA), metakaolin, silica fume and fly ash affect mechanical characteristics (compressive strength, split tensile strength), and durability characteristics (acid resistance, absorption, sorptivity) of concrete. The objective of this investigation is to figure out how the potential of RA based concrete can be improved so that they can be used to their maximum extent. To achieve the desired outcome, the study involved testing three distinct groups of concrete samples, each containing different percentages (25%, 50%, and 75%) of recycled aggregate (RA) with a constant amount of silica fume, (25 kg/m3). The first group was used as the control mix, while the second group incorporated 10% fly ash, and the third group included 15% metakaolin. The findings of this research show that the RA concrete mechanical properties as well as durability can be significantly improved by incorporating 15% metakaolin and 10% fly ash. The investigation involves the examination of all ternary blends within two distinct acidic environments, specifically a 5% hydrochloric acid (HCl) solution and sulfuric acid (H2SO4). Both the ternary mixes (metakaolin and fly ash) with the combination of silica improve all characteristics. The quality assurance, cost analysis and the reduction of CO2 emissions are carried out, utilizing RA (recycled aggregates) as a substitute for NA (natural aggregate). In case of producing superior structural concrete, it is recommended based on results to limit the replacement percentage of recycled aggregate (RA) to a maximum of 50% in presence of 10% fly ash and 15% metakaolin, otherwise the RA replacement percentage should not be more than 25%.

Cystinosis is a rare autosomal recessive disorder in which cystine crystals accumulate within the cellular lysosomes, causing damage to multiple organs. Due to challenges with the stringent cysteamine treatment regimen and side effects, adherence is often sub-optimal. This study aimed to assess the level of adherence to cysteamine therapy among cystinosis patients in Saudi Arabia and its impact on their quality of life. Electronic medical record data of 39 cystinosis patients from the Department of Nephrology at King Faisal Specialist Hospital and Research Center in Saudi Arabia were reviewed, and 25 patients were included in this study. Out of the 25 patients included in the final analysis, 64% (n = 16) were female. The mean age was 19.04 years. Almost all patients (23/25, 92%) were on oral IR cysteamine therapy, and 52% (13/25) were on topical cysteamine eye drop treatment. Of the 15 patients who responded to the Morisky Medication Adherence Scale-8 (MMAS-8) questionnaire, only 4 (26.7%) were highly adherent to cysteamine therapy. Most of the respondents (7/15, 46.7%) showed a medium level of treatment adherence. Based on the medication possession ratio for oral cysteamine, only 6 out of 23 patients (26.1%) were found to be 96–100% adherent. For the cysteamine eye drops, only 5/13 patients (38.4%) were 76–95% adherent. The 36-Item Short Form Health Survey (SF-36) used to assess patients’ health-related outcomes showed that their quality of life was affected in the domains of ‘social functioning’ and ‘energy/fatigue.’ Despite a small sample size, this study shows sub-optimal adherence to cysteamine treatment in patients from Saudi Arabia. The possible reasons for low treatment adherence could be a high frequency of administration and treatment-related side effects.

This case study explores a complex clinical presentation of an 85-year-old male diagnosed with immune-mediated thrombocytopenia (ITP) with coexistent positive autoimmune markers. A multidisciplinary approach was crucial in managing the patient, involving hematology, rheumatology, urology, and respiratory specialists. Despite tailored treatments, including platelet transfusions, immunosuppressive therapies, and antibiotics, the patient developed complications including hospital-acquired pneumonia, pulmonary infection, and acute kidney injury. Extensive discussions with the family led to the decision to transition the patient to comfort care, emphasizing the challenges in managing complex cases and the importance of a comprehensive care plan.

Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia.

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as “yes” and “no.” Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model’s behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology’s robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

This study investigated the virulence potential and antibiotic susceptibility analysis of non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups, which are significant cause of food borne diseases. A study collected 800 samples of dairy bovine raw milk through various sources, 500 from milk shops, 200 from dairy farms, 26 from milk collection centers, and 74 from street vendors. Using a standard method, E. coli was detected in 321 out of the 800 samples collected. Out of the 321 E. coli-positive samples isolated, 148 were identified as STEC using selective media, specifically Cefixime Tellurite Sorbitol MacConkey's Agar (CT-SMA). Out of the 148 positive samples, 40 were confirmed as STEC non-O157 strains using multiplex PCR, indicating a prevalence of 5% (40 out of 800 samples). STEC isolates were subjected to antimicrobial susceptibility testing, and all isolates were resistant to at least one or more antimicrobials tested through the disk diffusion method, revealed high resistance to Amoxicillin 100%, Ceftriaxone 50%, and Penicillin 44.5%, and notably 44% of the strains exhibited Streptomycin resistance, while Enrofloxacin 55%, Florfenicol 50% and Norfloxacin 44%, demonstrated the highest susceptibility. Out of 40 STEC non-O157, twelve were subjected to Multi Locus Sequence Typing (MLST) sequencing through Illumina Inc. MiSeq platform's next-generation sequencing technology, United States. The genome investigation evidenced the persistence of twelve serotypes H4:O82, H30:O9a, H4:O82, H16:O187, H9:O9, H16:O113, H30:O9, H32:O, H32:O, H32, H32, and H38:O187, linked to the potential infections in humans. Conclusion: STEC isolates showed resistance to multiple antimicrobials, raising concerns for both animal and public health due to widespread use of these drugs in treatment and prevention. The study contributes new insights into monitoring STEC in raw milk, emphasizing the critical role of whole genome sequencing (WGS) for genotyping and sequencing diverse isolates. Still a deficiency in understanding STEC pathogenesis mechanisms, ongoing surveillance is crucial for safeguarding human health and enhancing understanding of STEC genetic characteristics.

This study examines the effects of the Regional Comprehensive Economic Partnership (RCEP) on the value-added trade of food and non-food sectors. This study uses a global computable general equilibrium (CGE) model coupled with an extension module for the origin decomposition of value-added flows embodied in gross trade. The results suggest that by cutting down tariff and non-tariff barriers, the RCEP would significantly stimulate the economies of and gross trade among Asia–Pacific countries involved in the agreement. The potential benefits of the RCEP will be overestimated if we ignore the origin of value added and measure the benefits by gross exports. The domestic components of bilateral value-added flows between RCEP members would increase greatly, indicating an increasingly integrated value chain between RCEP members. Import taxes and non-tariff barriers for processed food, textiles and clothes, and heavy manufacturing are relatively significant in the region, so the RCEP would significantly improve their value-added exports. The domestic component of value-added exports in agricultural products and processed food from RCEP members would be increased significantly, indicating that the closely integrated food value chain boosts the food economies of RCEP members.

Nurses are recognized for their vigorous role in healthcare, own untapped potential in politics. Their advocacy skills, honed through patient care, could be harnessed to make policies for improved healthcare access and health equity. The representation of nurses in politics is unreasonably low, in U.S., only 10 out of 535 Congress members are nurses, despite being the largest group of healthcare professionals. Similarly, there is absence of nursing representation in Pakistan's parliament. To tie this gap, nurses should be inspired to enter politics, provided with mentorship, policy training, and financial support.

Focusing on sustainable development, the demand for alternative materials in concrete, especially for Self-Compacting Concrete (SCC), has risen due to excessive cement usage and resulting CO2 emissions. As Compressive Strength (CS) is dominant among concrete properties, this research concentrates on developing SCC by incorporating Rice Husk Ash (RHA) and Marble Powder (MP) as cement and filler replacements, respectively, while applying Machine Learning (ML) and Deep Learning (DL) techniques to forecast the CS of RHA/MP-based SCC. The research further evaluates material characteristics, with a strong emphasis on ML and DL for CS prediction. Concrete samples with various mixed ratios were cast and examined after 91 days to collect data for model application. In the experimental technique, 133 samples were gathered, and CS was predicted using seven input factors (cement, RHA, MP, superplasticizer, coarse aggregate, fine aggregate, and water) in an 80:20 ratio. Various ML algorithms, including linear regression, ridge regression, lasso regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and boosting methods such as gradient boost (GB), XG boost (XGB), and adaptive boosting (ADB) are employed, along with the DL technique of backpropagation neural network (BPNN) with different optimizer algorithms (Adam, SGD, RMSprop) to predict CS in RHA/MP-based SCC. The predicted data is further validated using evaluation parameters such as R-squared (R2), mean squared error (MSE), normalized root mean squared error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Comparatively, ML ensemble algorithms and BPNN using Adam and RMSprop optimizers demonstrate high accuracy in predicting CS outcomes, indicated by their high coefficient correlation R2 values and low error values.

Cement mortar is the primary choice for construction due to its widespread usage. However, certain applications demand high-performance cement mortar. In this pioneering study, we explore using agriculture waste products, industrial by-products, and naturally occurring kaolinite clay as potential additives in composite cement. Our research aims to assess these materials' physical and mechanical attributes to ascertain their efficacy in elevating cement performance. To achieve this, we replaced 20% of the cement in the composite with silica-fume and metakaolin, following a precise mix design fraction of 1:1 (binding materials: sand) for the mortar composites. We successfully identified the optimum combination of metakaolin and silica fume through systematic experimentation that maximizes compression strength. Subsequently, we introduced various proportions of coir fibers (ranging from 3% to 15% by weight) into the cement composite to enhance its structural support capabilities. We carefully calibrated the ratio of the water-to-binding material (ranging from 0.35 to 0.6) to maintain the workability of the cement mixtures. Next, we subjected the cement composites to meticulous curing in water for 7 days, 14 days, and 28 days, respectively, to observe their performance over time. Our comprehensive study encompassed the investigation of critical parameters, including moisture content, density, and water absorption, as well as essential mechanical properties such as compressive strength and modulus of rupture. A total of 270 specimens underwent rigorous testing, yielding compelling results. The composite featuring a combination of 10% silica fume, 10% metakaolin, and 6% coconut fibers showcased superior mechanical and physical properties. SEM and EDX studies also showed dense and packed microstructure of mortar samples. These findings showcase the remarkable potential of this specific composite composition as an environmentally conscious and sustainable alternative to enhance cement performance in construction applications. This research advances eco-friendly construction practices by harnessing the strength of waste-derived materials and naturally occurring resources, supporting a more sustainable and resilient approach to infrastructure development.

This study investigates the potential of metakaolin as a mineral admixture to enhance the performance of concrete mixtures with varying proportions of recycled aggregates (RA). Sixteen distinct concrete combinations were formulated by substituting natural aggregates (NA) with RA at 0 %, 50 %, 75 %, and 100 % and incorporating metakaolin at 0 %, 5 %, 10 %, and 15 % as a replacement for cement. Experimental investigations were conducted to evaluate the effects of metakaolin (as a cement replacement) and RA (as a replacement for NA) on key concrete characteristics, including density, water absorption, compressive and tensile strength, and acid resistance. The results revealed that an increase in RA reduced mechanical properties, such as compressive strength, tensile strength, and dry density. Additionally, RA-based concrete mixtures exhibited higher water absorption and void volume than those with natural aggregates. However, including metakaolin in RA-based concrete significantly reduced the void volume and water absorption. The optimal proportion of metakaolin substitution was 15 %, resulting in the best overall performance for RA-based concrete mixtures. Notably, the highest compressive and splitting tensile strength (41.6 MPa and 4.3 MPa, respectively) were observed at 90 days in mixtures with 0 % RA and 15 % metakaolin. Similarly, the highest hardened density and lowest water absorption (2494.7 kg/m3 and 4.7 %, respectively) were recorded in samples with 0 % RA and 15 % metakaolin. Furthermore, concrete mixes containing 15 % metakaolin and 50 % RA demonstrated comparable qualities to conventional concrete, indicating that sustainable concrete can be produced by utilizing a significant quantity of waste concrete (15 % metakaolin and 50 % RA) without compromising strength criteria. This study sheds light on the potential of incorporating metakaolin and recycled aggregates as environmentally friendly alternatives in the construction industry, contributing to sustainable and eco-conscious practices.

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

] Accurate classification of brain tumors is essential for effective medical diagnosis and treatment planning. Traditional approaches rely on convolutional neural networks (CNNs) for tumor detection, but they often suffer from high computational demands due to the large number of parameters. In this paper, we propose a novel approach for brain tumor classification using involutional neural networks (InvNets), which are designed to mitigate the parameter-intensive nature of CNNs. Unlike the spatial-agnostic and channel-specific convolution kernel, the involution kernel is location-specific and channel-agnostic. This location-specific operation allows the network to adapt to various visual patterns with respect to different spatial locations, enhancing its ability to capture intricate features within the medical images. Our study focuses on a four-class brain tumor classification problem, aiming to differentiate between different tumor types based on medical imaging data. In a comparative analysis, we demonstrate that conventional CNNs require over 4 million parameters, whereas our proposed InvNets require less than 0.2 million parameters, making them more efficient and resource-friendly. The evaluation of both CNNs and InvNets is carried out using standard performance matrices: accuracy, precision, recall, F1 score, and AUC-ROC values. Our findings reveal that the InvNets consistently outperform traditional CNNs. The InvNet architecture achieves an impressive 92% accuracy rate, showcasing its potential for accurate brain tumor classification. This improved accuracy, combined with the reduced parameter count, highlights the effectiveness of InvNets for medical image analysis tasks, especially in scenarios with limited computational resources.

Scientific document classification is an important field of machine learning. Currently, scientific document category identification is done manually. There are already defined taxonomies available for categorizing scientific documents, such as the Association for Computing Machinery Computing Classification System (ACM CCS) and Bibsonomy. These taxonomies facilitate authors in the categories of their manuscripts. The incorporation of research work from a variety of domains in the assignment takes on the form of a Multi-Label Classification (MLC). Using MLC, it is possible to assign more than one class to a single document. To address the problem of MLC in its entirety, two distinct methods are used (Problem Transformation and Algorithm Adaptation). The MLC dataset is transformed into one or more single-label datasets through the application of the problem transformation technique. Whereas, a single classifier is modified during the algorithm adaptation process so that it can predict multiple labels. Currently, document classification is done using various techniques in the literature, but none of them paid much attention to the problem of imbalance in Multi-Label Datasets (MLD). However, many effective techniques for dealing with imbalance are available in the literature. The goal of this study is to find an effective technique for balancing datasets before multi-label classification to get better predictions for the classes with fewer instances. Six MLDs, nine transformation techniques and seven classifiers are evaluated in this research work. The proposed research will result in a more accurate recommendation of a research topic for a document. For imbalanced MLDs, LPROS is the best resampling technique using statistical tests. When compared to the other classifiers, the BRkNN classifier is better for MLC. This research will facilitate the classification of documents into their respective classes which can be used by various citation indexes.

Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Neural Networks (RNN), Alex Net, and ResNet can be considered as a broad research direction in the identification and classification of critical diseases. CNN and its particular variant, usually named U-Net Segmentation, has made a revolutionary advancement in the classification of medical diseases, specifically retinal diseases. However, because of the feature extraction complexity, U-Net has a significant flaw in high memory and CPU consumption while moving the whole feature map to the corresponding decoder. Furthermore, it can be concatenated to the unsampled decoder feature map avoids reusing pooling indices. In this research work, a convolutional neural network (CNN) model is proposed for multi-class classification problems with the efficient use of memory consumption. The proposed model has been evaluated on a standard benchmark dataset of Eye Net, having 32 classes of retinal diseases. From experimental evaluation, it has been concluded that the proposed model performs better regarding memory management and accuracy. The overall comparison has been performed based on precision, recall, and accuracy with different numbers of epochs and time consumption by each step. The proposed technique achieved an accuracy of 95% on the Eye-net dataset.

Abstract Climate change is becoming more and more remarkable which has an obvious impact on crop yields all over the world. Future climate scenario data was simulated by 5 climate models recommended by ISI-MIP under 4 RCP scenarios, in which the approximate scenarios with global warming by 1.5 °C and 2 °C were selected. Applying DSSAT and GTAP models, the per unit yield changes of maize in the world under global warming by 1.5 °C and 2.0 °C were analyzed and the market prices of maize at national and global levels were simulated. The results showed that, the risk of maize yield reduction under 2.0 °C scenario was much more serious than 1.5 °C scenario; the ratios of yield changes were separately 0.18% and − 10.8% under 1.5 °C and 2.0 °C scenarios. The reduction trend of total maize production is obvious in the top five countries and the main producing regions of the world, especially under the 2.0 °C scenario. The market price of maize would increase by around 0.7% and 3.4% under 1.5 °C and 2.0 °C scenarios. With the quickly increasing population in the world, it is urgent for all countries to pay enough attention to the risk of maize yield and take actions of mitigation and adaptation to climate change.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and its resulting disease, coronavirus disease 2019 (COVID-19), has spread to millions of people worldwide. Preliminary data from organ transplant recipients have shown reduced seroconversion rates after the administration of different SARS-CoV-2 vaccination platforms. However, it is unknown whether different vaccination platforms provide different levels of protection against SARS-CoV-2. To answer this question, we prospectively studied 431 kidney and liver transplant recipients (kidney: n = 230; liver: n = 201) who received either the ChAdOx1 vaccine (n = 148) or the BNT-162b2 vaccine (n = 283) and underwent an assessment of immunoglobulin M/immunoglobulin G spike antibody levels. The primary objective of the study is to directly compare the efficacy of two different vaccine platforms in solid organ transplant recipients by measuring of immunoglobulin G (IgG) antibodies against the RBD of the spike protein (anti-RBD) two weeks after first and second doses. Our secondary endpoints were solicited specific local or systemic adverse events within 7 days after the receipt of each dose of the vaccine. There was no difference in the primary outcome between the two vaccine platforms in patients who received two vaccine doses. Unresponsiveness was mainly linked to diabetes. The rate of response after the first dose among younger older patients was significantly larger; however, after the second dose this difference did not persist (p = 0.079). Side effects were similar to those that were observed during the pivotal trials.

With growing demands for portable electronics, flexible aqueous energy storage devices such as supercapacitors and Zn‐based batteries have drawn tremendous interest from both academic and industrial fields. Organo‐hydrogels, crosslinked amphiphilic polymers filled with organic solvents and water, are regarded as an ideal electrolyte candidate for portable electronic applications, especially due to their superior environmental adaptation. Organo‐hydrogels can reserve the advantages of the corresponding hydrogels and achieve some unique features such as broad temperature tolerance, high mechanical stability, strong interfacial interaction, and decent ionic conductivity. This review outlines the composition fundamentals, preparation methods, and comprehensive properties of reported organo‐hydrogel electrolytes. In particular, supercapacitors and Zn‐based batteries that operate under harsh temperature conditions and unusual mechanical deformations endowed by organo‐hydrogel electrolytes are further highlighted. Moreover, a unique perspective on the current challenges and future development directions of the organo‐hydrogel electrolytes for flexible energy storage is also provided.

BackgroundOf around 2 billion Muslims worldwide, approximately 1.5 billion observe Ramadan fasting (RF) month. Those that observe RF have diverse cultural, ethnic, social, and economic backgrounds and are distributed over a wide geographical area. Sleep is known to be significantly altered during the month of Ramadan, which has a profound impact on human health. Moreover, sleep is closely connected to dietary and lifestyle behaviors.MethodsThis cross-sectional study collected data using a structured, self-administered electronic questionnaire that was translated into 13 languages and disseminated to Muslim populations across 27 countries. The questionnaire assessed dietary and lifestyle factors as independent variables, and three sleep parameters (quality, duration, and disturbance) as dependent variables. We performed structural equation modeling (SEM) to examine how dietary and lifestyle factors affected these sleep parameters.ResultsIn total, 24,541 adults were enrolled in this study. SEM analysis revealed that during RF, optimum sleep duration (7–9 h) was significantly associated with sufficient physical activity (PA) and consuming plant-based proteins. In addition, smoking was significantly associated with greater sleep disturbance and lower sleep quality. Participants that consumed vegetables, fruits, dates, and plant-based proteins reported better sleep quality. Infrequent consumption of delivered food and infrequent screen time were also associated with better sleep quality. Conflicting results were found regarding the impact of dining at home versus dining out on the three sleep parameters.ConclusionIncreasing the intake of fruits, vegetables, and plant-based proteins are important factors that could help improve healthy sleep for those observing RF. In addition, regular PA and avoiding smoking may contribute to improving sleep during RF.

Electricity theft is one of the main nontechnical losses (NTLs) in distributed networks which cause significant harm to the power grids. As power grids provide the centralized power to all the connected consumers, therefore, any fraudulent consumption can cause harm to the power grids which can damage the whole electric power supply and can influence its quality. The detection of such fraudulent consumers becomes difficult when there is a large amount of data. Smart grids can be used to solve this problem as it provides a two-way electricity flow which allows someone to detect, reenact and apply new changes to the electric data flow. The existing systems for electricity theft detection, works on the principle of one dimensional (1-D) electric data, which provides poor accuracy in theft detection. Therefore, an ensemble model based on convolutional neural network and extreme gradient boosting (CNN-XGB) model is presented in this paper. In this model both one dimensional (1-D) and two-dimensional (2-D) electricity consumption data are used to pass to the CNN model. Proposed model achieved the accuracy of 92% for electricity theft detection, which is better than existing models.

The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study's objective is to evaluate COVID-19's psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers.

The use of short message service (SMS) and e-mail have increased too much over the last decades. 80% of people do not read e-mails while 98% of cell phone users daily read their SMS. However, these communication media are unsafe and can produce malicious attacks called spam. The e-mails that pretend to be from a trusted company to provide “financial or personal information” are phishing e-mails. These e-mails contain some links; users might download malicious software on their computers when they click on them. Most techniques and models are developed to automatically detect these “SMS and e-mails” but none of them achieved 100% accuracy. In previous studies using machine learning (ML), spam detection using a small dataset has resulted in lower accuracy. To counter this problem, in this paper, multiple classifiers of ML and a classifier of deep learning (DL) were applied to the SMS and e-mail dataset for spam detection with higher accuracy. After conducting experiments on the real dataset, the researchers concluded that the proposed system performed better and more accurately than previously existing models. Specifically, the support vector machine (SVM) classifier outperformed all others. These results suggest that SVM is the optimal choice for classification purposes.

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.

Abstract Electro‐organic synthesis has attracted a lot of attention in pharmaceutical science, medicinal chemistry, and future industrial applications in energy storage and conversion. To date, there has not been a detailed review on electro‐organic synthesis with the strategy of heterogeneous catalysis. In this review, the most recent advances in synthesizing value‐added chemicals by heterogeneous catalysis are summarized. An overview of electrocatalytic oxidation and reduction processes as well as paired electrocatalysis is provided, and the anodic oxidation of alcohols (monohydric and polyhydric), aldehydes, and amines are discussed. This review also provides in‐depth insight into the cathodic reduction of carboxylates, carbon dioxide, CC, C≡C, and reductive coupling reactions. Moreover, the electrocatalytic paired electro‐synthesis methods, including parallel paired, sequential divergent paired, and convergent paired electrolysis, are summarized. Additionally, the strategies developed to achieve high electrosynthesis efficiency and the associated challenges are also addressed. It is believed that electro‐organic synthesis is a promising direction of organic electrochemistry, offering numerous opportunities to develop new organic reaction methods.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Agriculture is a critical domain, where technology can have a significant impact on increasing yields, improving crop quality, and reducing environmental impact. The use of renewable energy sources such as solar power in agriculture has gained momentum in recent years due to the potential to reduce the carbon footprint of farming operations. In addition to providing a source of clean energy, solar tracking systems can also be used for remote weather monitoring in the agricultural field. The ability to collect real-time data on weather parameters such as temperature, humidity, and rainfall can help farmers make informed decisions on irrigation, pest control, and other crop management practices. The main idea of this study is to present a system that can improve the efficiency of solar panels to provide constant power to the sensor in the agricultural field and transfer real-time data to the app. This research presents a mechanism to improve the arrangement of a photovoltaic (PV) array with solar power and to produce maximum energy. The proposed system changes its direction in two axes (azimuth and elevation) by detecting the difference between the position of the sun and the panel to track the sun using a light-dependent resistor. A testbed with a hardware experimental setup is designed to test the system’s capability to track according to the position of the sun effectively. In the end, real-time data are displayed using the Android app, and the weather data are transferred to the app using a GSM/WiFi module. This research improves the existing system, and results showed that the relative increase in power generation was up to 52%. Using intelligent artificial intelligence techniques with the QoS algorithm, the quality of service produced by the existing system is improved.

In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I‐UWSANs and cause high end‐to‐end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z‐Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well‐known algorithms like energy‐aware scalable reliable and void‐hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

Although napping is commonly used as a strategy to improve numerous physical and cognitive performances, the efficacy of this strategy for improving postural balance has not yet been elucidated. Thus, the aim of this study was to conduct a comprehensive examination of the effect of a 60 min nap opportunity (N60) on different components of postural control. Ten highly active individuals (age = 27 ± 3.5 y, height = 1.75 ± 0.52 m, weight = 66.02 ± 8.63 kg) performed, in a randomized order, two afternoon test sessions following no nap (NN) and N60. Postural balance was assessed using the sensory organisation test (SOT), the unilateral stance test (UST), and the limits of Stability Test performed on NeuroCom® Smart Balance Master. The subjective rating of sleepiness before and after the nap conditions was also assessed. Compared to NN, N60 improved the composite balance score (p < 0.05, ES = 0.75, Δ = 5.3%) and the average and maximum percentage balance in the most challenging postural conditions of the SOT (p < 0.05 for SOT-4 and 5 and p < 0.0005 for SOT-6; ES range between 0.58 and 1.1). This enhanced postural balance in N60 was accompanied with improved visual (p < 0.05; ES = 0.93; Δ = 8.9%) and vestibular (p < 0.05; ES = 0.81; Δ = 10.5%) ratios and a reduced level of sleepiness perception (p 0.05). Overall, a 60 min post lunch nap opportunity may be viable for improving static balance, although further work, involving larger samples and more complex motor activities, is warranted.

The outcomes of renal transplantation have improved significantly with the use of calcineurin inhibitors (CNI). However, this improvement comes at the price of side effects. CNI-induced pain syndrome (CIPS) is a benign but disabling painful syndrome. It particularly affects the lower limbs. We present the case of a young male renal transplant recipient. He presented with worsening bilateral lower limb pain four months after transplantation. Induction therapy was basiliximab. Tacrolimus, steroids, and mycophenolate mofetil constituted maintenance immunosuppressive therapy. Pain affected the ankles and toes bilaterally. It started gradually but progressed over four weeks. The relentless pain affected his mobility to an extent that he became wheel chair dependent. Pain was unresponsive to paracetamol and codeine. No formal psychiatry assessment was done but patient-reported depression symptoms related to his reduced mobility. On examination, he had bony tenderness over the affected areas with the good range of passive movements. Neurological and vascular examinations of lower limbs were unremarkable. Inflammatory and infective causes of joint pain were excluded. Magnetic resonance imaging (MRI) feet showed the features of bone marrow edema. He was diagnosed with CIPS. Immunosuppression was changed from tacrolimus to cyclosporine. Pregabalin was also introduced after the diagnosis. Symptoms improved gradually over a month. He started to walk with a stick initially and then without any aid. Renal transplant function remained stable throughout this period. MRI feet scan, five months after the symptoms showed resolution of the bone marrow edema. CIPS is an uncommon, benign but disabling complication of CNI. Recognizing it early could limit the burden of symptoms (both physical and psychological) and loss of productivity. The management of CIPS is not evidence based and further research is required in this therapeutic area.

Abstract Background Klebsiella pneumoniae, an environmental pathogen causing mastitis in dairy cattle, is often resistant to antibiotics. K. pneumoniae was used as the host bacteria to support bacteriophage replication; 2 bacteriophages, CM8-1 and SJT-2 were isolated and considered to have therapeutic potential. In the present study, we determined the ability of these 2 bacteriophages to mitigate cytotoxicity, pathomorphological changes, inflammatory responses and apoptosis induced by K. pneumoniae (bacteriophage to K. pneumoniae MOI 1:10) in bovine mammary epithelial cells (bMECs) cultured in vitro. Results Bacteriophages reduced bacterial adhesion and invasion and cytotoxicity (lactate dehydrogenase release). Morphological changes in bMECs, including swelling, shrinkage, necrosis and hematoxylin and eosin staining of cytoplasm, were apparent 4 to 8 h after infection with K. pneumoniae, but each bacteriophage significantly suppressed damage and decreased TNF-α and IL-1β concentrations. K. pneumoniae enhanced mRNA expression of TLR4, NF-κB, TNF-α, IL-1β, IL-6, IL-8, caspase-3, caspase-9 and cyt-c in bMECs and increased apoptosis of bMECs, although these effects were mitigated by treatment with either bacteriophage for 8 h. Conclusions Bacteriophages CM8-1 and SJT-2 mitigated K. pneumoniae-induced inflammation in bMECs cultured in vitro. Therefore, the potential of these bacteriophages for treating mastitis in cows should be determined in clinical trials.

Objective: To study thrombocytopenia and elevated ALT levels in malaria patients reporting to Pak Med Level II+ Hospital XII and XIII, UN Mission Liberia, West Africa. Study Design: Cross sectional study. Place and Duration of Study: Departments of Pathology and Medicine, Pak Med Level II+ Hospital XII and XIII, UN Mission Liberia, West Africa, from Feb 2015 to Dec 2016. Methodology: In total of 100 febrile patients of both gender and all ages reported to Pak Med Level II+ Hospital XII and XIII, Liberia with clinical features of malaria and having positive malarial parasite (MP) on any of the methods of immune chromatography (ICT) or microscopic film were included. The febrile patients with typical clinical features of malaria but having negative MP both on ICT and microscopic methods were excluded. Pretreatment whole blood in EDTA was collected for testing MP on ICT and by microscopic method including MP index and platelets, while serum for ALT. Results: Out of total 100 patients, 75 were males, 25 females with mean age of 38 ± 5 years. MP was found positive on MP film in 95% and on ICT in 88% cases. Thrombocytopenia was found in 69% and ALT was elevated in 60% cases. Mean and SD for platelets count was 129 ± 72 x 109/L and of ALT 76 ± 66 U/L. Mean of MP index was 0.49 ranging from 0 to 3.5%. Correlation of MP index with platelets and ALT was found insignificant, although in malaria patients, platelets and ALT was inversely correlated significantly with each other showing low platelets along with elevated ALT levels. Conclusion: In malaria patients, thrombocytopenia and elevated ALT are frequent findings, which may not definitely correlate with MP index. So it is advisable to test for platelet count and ALT in all patients of malaria for early diagnosis and better management.