Transportation, environmental conditions, quality of human life within smart cities, and system infrastructure have all needed practical and dependable smart solutions as urbanization has accelerated in recent years. In addition, the emerging Internet of Things (IoT) provides access to a plethora of cutting-edge, all-encompassing apps for smart cities, all of which contribute significantly to lowering energy consumption and other negative environmental impacts. For smart cities to meet the challenge of using less energy, the authors of this research article suggest planning and implementing an integrated power and heat architecture that puts renewable energy infrastructure and energy-storage infrastructure at the top of the list. To address these issues, we describe a smart proposed NEOSRD architecture that uses a distributed smart area domain to optimize renewable demand energy in a smart city across a wide area network. The energy requirements of desalination procedures are negligible when compared to the total local energy consumption and transportation, a feat accomplished by the proposed NEOSRD system. Here, the computational model shows how the established system is a valuable response to our problems and a cost-effective strategy for creating smarter structural elements that cut down on overall smart cities' energy costs.

Breast cancer (BC) is the second leading cause of death in the United States in women. 1 in 8 women in their lifetime has a risk of developing breast cancer. With advances in omics technology, more data is available for diagnosis, treatment, and prognosis. The help of Machine Learning algorithms, a subdomain of Artificial Intelligence, would allow for a better clinical support tool. Machine Learning could be the key to answering a complex problem in BC. The methodology of this research question involves collecting and finding data such as RNA-Seq datasets, selecting and training appropriate machine learning algorithms, and evaluating the performance of the algorithms in predicting BC prognosis. Also, the study explores and tries to identify the most relevant genes and pathways through feature selection extraction and dimensionality reduction techniques. After applying feature extraction, the top 10 genes were extracted using the Univariate Feature Selection (UFS) method, precisely the SelectKBest technique. After employing different machine learning algorithms, such as Networks, Random Forest, Linear SVM, Logistic Regression, and Quadratic Discriminant Analysis (QDA), we found QDA was the best model in classifying the RNA-seq dataset with a 96% accuracy rate and 94% ROC AUC. This study suggests that AK2 and CD68 have a positive correlation with each other, and it could potentially be a biomarker and therapeutic target for BC.

Application of chemical pesticides to protect agricultural crops from pests and diseases is discouraged due to their harmful effects on human and environment. Therefore, alternative approaches for crop pro-tection through microbial or microbe originated pesticides have been gaining momentum. Wheat blast is a destructive fungal disease caused by Magnaporthe oryzae Triticum (MoT) pathotype, which poses a seri-ous threat to global food security. Screening of secondary metabolites against MoT revealed that antimy-cin A isolated from a marine Streptomyces sp. had significant inhibitory effect on mycelial growth in vitro. This study aimed to investigate the inhibitory effects of antimycin A on some critical life stages of MoT and evaluate the efficacy of wheat blast disease control by this natural product. Bioassay indicated that antimycin A suppressed mycelial growth, conidiogenesis, germination of conidia and formation of ap-pressoria in germinated conidia of MoT in a dose-dependent manner with minimum inhibitory concen-tration 0.005 μg/disk. If germinated, antimycin A induced abnormal germ tubes (4.8%) and suppressed the formation of appressoria. Interestingly, application of antimycin A significantly suppressed wheat blast disease in both seedling and heading stages of wheat supporting the results from invitro study. This is the first report on inhibition of mycelial growth, conidiogenesis, conidia germination, detrimental morphological alterations in germinated conidia, and suppression of wheat blast disease caused by a Triticum pathotype of M. Oryzae. Further study is required to unravel the precise mode of action of this promising natural compound for considering it as a biopesticide to combat wheat blast.

The study explored the community perception of maternal deaths influenced by natural disaster, practice of maternal complications during natural disaster among the rural population in Bangladesh. It also explored the challenges faced by the community for providing health care and referring the complicated pregnant mothers during disaster. Three focus group discussions (FGDs) and eight in-depth interviews (IDIs) were conducted in the marginalized rural communities in the flood-prone Khaliajhuri sub-district, Netrakona district, Bangladesh. Flood is one of the major risk factors for influencing maternal death. Pregnant mothers seriously suffer from maternal complication, lack of antenatal checkup and even any doctor during flood. During the time of delivery, it is difficult to find even a skilled attendant and referring the patient with delivery complications to the healthcare facility. Boat is the only mode of transport. Majority maternal deaths occur on the boats during transfer from the community to the hospital. The rural people feel that the maternal deaths influenced by natural disaster are the natural phenomena. It needs some pre-preparation to support pregnant women during the disaster. There is unawareness of maternal health, related care and complications during disaster among the local health service providers and volunteers.

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a vaccine, early detection and containment is the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets, and others, along with probable solutions with different pre-processing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

Beyond their significant contribution to the dietary and industrial supplies, marine algae are considered to be a potential source of some unique metabolites with diverse health benefits. The pharmacological properties, such as antioxidant, anti-inflammatory, cholesterol homeostasis, protein clearance and anti-amyloidogenic potentials of algal metabolites endorse their protective efficacy against oxidative stress, neuroinflammation, mitochondrial dysfunction, and impaired proteostasis which are known to be implicated in the pathophysiology of neurodegenerative disorders and the associated complications after cerebral ischemia and brain injuries. As was evident in various preclinical studies, algal compounds conferred neuroprotection against a wide range of neurotoxic stressors, such as oxygen/glucose deprivation, hydrogen peroxide, glutamate, amyloid β, or 1-methyl-4-phenylpyridinium (MPP+) and, therefore, hold therapeutic promise for brain disorders. While a significant number of algal compounds with promising neuroprotective capacity have been identified over the last decades, a few of them have had access to clinical trials. However, the recent approval of an algal oligosaccharide, sodium oligomannate, for the treatment of Alzheimer's disease enlightened the future of marine algae-based drug discovery. In this review, we briefly outline the pathophysiology of neurodegenerative diseases and brain injuries for identifying the targets of pharmacological intervention, and then review the literature on the neuroprotective potentials of algal compounds along with the underlying pharmacological mechanism, and present an appraisal on the recent therapeutic advances. We also propose a rational strategy to facilitate algal metabolites-based drug development.

We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and temperature extreme (difference between maximum and minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration after the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, temperature extreme was significantly and positively correlated with mortality in the low-and middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed effect in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggest that warm climate may reduce the mortality rate in high-income economies but in low and middle-income countries temperature extreme may increase the mortality risk.

Curcumin is one of the bioactive metabolites of turmeric (Curcuma longa), known for its pleiotropic pharmacological actions, including antioxidant and anti-inflammation, anticholinesterase, immunomodulation, and neuroprotection. Substantial evidence suggests the therapeutic benefits of curcumin against neurodegenerative disorders, including Alzheimer’s disease (AD), acting on a diverse array of brain targets that make the molecular mechanisms complicated. System biology level-investigation could potentially present a comprehensive molecular mechanism to delineate the neuropharmacological action of curcumin. In this study, we used integrated system pharmacology and molecular simulation analysis to gain insights into the underlying mechanism of curcumin action against AD. Network pharmacology study identified curcumin-targeted potential cellular pathways such as phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling, neurotrophin signaling, toll-like receptor (TLR) signaling, and autophagy, and proteins such as tropomyosin receptor kinase B (TrkB), liver X-receptor-beta (LXR-β), estrogen receptor-β (ER-β), mammalian target of rapamycin (mTOR), TLR-2, N-methyl-D-acetate receptor subunit 2B (GluN2B), β-secretase and glycogen synthase kinase-3β (GSK-3β), which are intimately associated with neuronal growth and survival, immune response, and inflammation. Moreover, the molecular modeling further verified that curcumin showed a significant binding affinity to mTOR, TrkB, LXR-β, TLR-2, ER-β, GluN2B, β-secretase, and GSK-3β, which are the crucial regulators of molecular and cellular processes associated with AD. Together, the present system pharmacology and in silico findings demonstrate that curcumin might play a significant role in modulating AD-pathobiology, supporting its therapeutic application for the prevention and treatment of AD.

Objective: This study aimed to explore potential interventions or preventive measures to reduce the incidence of falls among the elderly in the Madinah region, and to provide recommendations for healthcare professionals, policymakers, and community stakeholders to enhance fall prevention strategies and improve the overall well-being of the elderly population in this area. Methods: The study adopted a cross-sectional study design, and was carried out in the Madinah region using convenient physical assignment among of Saudi and non-Saudi local residents aged 65 years and over. Questionnaire surveys were circulated and demographic characteristics were collected, including participants’ fall experience and health status data over the past year, in order to assess the incidence of falls among the elderly population. This design allows for the collection of data at a single point in time, aiming to evaluate the prevalence, risk factors, and consequences of falls among the target population. Results: A total of 189 participants were recruited to the study, and the data indicated that the prevalence of falls among those aged 65 years and older in the past year was 41.5%. We found that 73% of the participants had experienced falls and post-fall injuries within the past 12 months. There were minimal differences in terms of physical handicapped cases found between those with and without a fall history. Logistic regression analysis revealed a significant link between fall experience and maximum bite force. Conclusion: There is a high prevalence of falls in the elderly population in the Madinah region. Our study reveals that 50.79% of our elderly participants had experienced more than one fall during the preceding year. Some preventable risk factors were identified in an analysis of their daily routines. The impacts of these risk factors have potential consequences for their healthy lives.

This study aimed to assess knowledge and practice of caregivers and its relationship to the disease and nutritional status of children under five years of age in rural areas of Sylhet, Bangladesh. A total of 110 households having 6 to 59 months aged children was selected by simple random method from ten rural communities of three Upazila of Sylhet during September 2019 to February 2020. Descriptive statistics were used to assess the WASH knowledge & practice and multivariate chi-square analyses were performed to assess associations among diseases & nutritional status with WASH following a structured questionnaire. The study found a significant association of WASH with childhood disease and nutritional status, and 65% of children were found to be in a diseased state and 35% of children were found to be in a disease-free state within the last six months. The findings sketched that mother with poor wash knowledge and practice was at greater risk for disease outbreaks, disease frequency and duration. The highest incidence of diarrhea was 17% in children aged 12 to 23 months. Significant effect of WASH was also found in children nutrition status, that was reflected in the ratio of stunted, underweight and wasted children. Integrated convergent work focusing on the provision of clean water within the household, stop open defecation, promotion of hand washing, behavior change and poverty alleviation is needed to improve the situation. Health, nutrition and livelihood programs should be uninterrupted, and mothers or caregivers should be encouraged to participate in these programs.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

During the last decade, Bangladesh has been experienced a significant climatic anomalies which lead to an increase in magnitude and frequency of various climate relate extreme events. Climate scientists substantiate that global temperature and precipitation pattern is expected to change, which may result in consequential impact on agriculture, livelihood, and ecosystem. Bangladesh is therefore likely to face substantial challenges in the coming decades. In order to adequately understand this complex, dynamic phenomena, Analyzing historical Climate change scenarios as well as projecting its future trends is a great concern for researcher. This study aims to analyzes/ historical climatic data from (1901-2020), and predict future temperature and precipitation patterns in Bangladesh using CMIP6 data. Data used in this study (Observed data is from CRU TS 4.05 and future data is from CMIP6) have been acquired from WorldClim v2.1. Various methods including correlation, regression, standard deviation, correlation matrix, percentiles, cell statistics, and IDW interpolation were performed to analyze the trends, variability and spatial patterns of temperature and rainfall. This study revealed that Over the historical study period (1901-2020) Bangladesh has been experienced a significant warming trend with an average increase in temperature 2°C and with annual decline of the total precipitation 607.26 mm along with a shift towards drier conditions despite weak correlation with hotter years. Projected climate models represents that Bangladesh minimum temperature is expected to increase from 1°C to 4.4°C as well as maximum temperatures from 1°C to 4.1°C by 2100. Moreover, projected precipitation is expected to increase by 480.38 mm, with the biggest rises during monsoon months. Regional variations in temperature and precipitation are again expected, with the Southeast (SE) likely experiencing the most significant warming and the Northeast (NE) seeing the most substantial increase in precipitation.

Micro-plastics (MPs) become significantly important to society since their commercial manufacture began in the 1950s because of their low production costs, stability, light weight, and adaptability. Plastic was produced in 400 million tons worldwide in 2020, compared to 1.5 million tons just 60 years earlier. Scientists predict that by 2050, plastic production would increase to 33 billion tons. MPs are a rising scientific and societal issue in the environment as a new type of contaminant that is widely disseminated in aquatic ecosystems and organisms. Aquatic species that come into contact with MPs for an extended period of time experience ecotoxicity, which includes oxidative stress, genotoxicity, neurotoxicity, and reproductive issues. In aquatic environments, MPs not only transport chemicals within animals but also serve as mediators for chemicals or other contaminants. As a result of co-exposure of MPs and chemical contaminants, various toxicity incidences involving aquatic organisms have been reported. The final exposure of MPs occurs in humans through biomagnification of aquatic edible products. This study presents an important overview of the literature on MP ingestion by aquatic biota. It covers topics such as the present scenario of plastic pollution worldwide, the pathways that MPs take to reach humans and aquatic species, the eco-toxicological impacts of MPs on aquatic species, the relation between MPs and other environmental pollution and the ensuing effects on aquatic organisms, and the MP pollution status in Bangladesh.

Polyhydroxyalkanoate (PHA) co-polymers show relatively higher in vivo degradation rate compared to other PHAs thus they received a great deal of attention for a wide range of medical applications. Nanoparticles (NPs) loaded with poorly water soluble anticancer drug docetaxel (DCX) were produced using poly 3-hydroxybutyrate-co-4-hydroxybutyrate, P(3HB-co-4HB), co-polymers biosynthesised from Cupriavidus sp. USMAA1020 isolated from Malaysian environment. Three co-polymers with different molar proportions of 4-hydroxybutirate (4HB) were used: 16% (PHB16), 30% (PHB30) and 70% (PHB70) 4HB-containing P(3HB-co-4HB). Blank and DCX loaded nanoparticles were then characterized for their size and size distribution, surface charge, encapsulation efficiency and drug release. Pre-formulation studies showed that an optimised formulation could be achieved through the emulsification/solvent evaporation method using PHB70 with the addition of 1.0% PVA, as stabilizer and 0.03% VitE-TPGS, as surfactant. DCX-loaded PHB70 nanoparticles (DCX-PHB70) gave the desired particle size distribution in term of average particles sizes around 150 nm and narrow particle size distribution (PDI below 0.100). The encapsulation efficiencies result showed that at 30% w/w drug-to-polymer ratio: DCX- PHB16 NPs were able to encapsulate up to 42% of DCX; DCX-PHB30 NPs encapsulated up to 46% of DCX and DCX-PHB70 NPs encapsulated up to 50% of DCX within the nanoparticles system. Approximately 60% of DCX was released from the DCX-PHB70 NPs within 7 days for 5%, 10% and 20% of drug to polymer ratio while for the 30% and 40% drug to polymer ratios, an almost complete drug release (98%) after 7 days of incubation was observed.

This study utilised an ensemble of pre-trained networks and digital mammograms to develop a supplementary diagnostic tool for radiologists. Digital mammograms and their associated information were collected from the department of radiology and pathology, Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and explored in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks based on PR-AUC, precision, and F1 score. The final ensemble model had a mean precision, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated a balanced performance across mammographic density. In conclusion, this study exhibited the good performance of the ensemble transfer learning on digital mammograms for the purpose of breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus, reducing their workloads.

This study aimed to determine the feasibility of the development of an over-the-counter (OTC) screening model using machine learning for breast cancer screening in the Asian women population. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia. Five screening models were developed based on machine learning methods; random forest, artificial neural network (ANN), support vector machine (SVM), elastic-net logistic regression and extreme gradient boosting (XGBoost). Features used for the development of the screening models were limited to information from the patients’ registration form. The model performance was assessed across the dense and non-dense groups. SVM had the best sensitivity while elastic-net logistic regression had the best specificity. In terms of precision, both random forest elastic-net logistic regression had the best performance, while, in terms of PR-AUC, XGBoost had the best performance. Additionally, SVM had a more balanced performance in terms of sensitivity and specificity across the mammographic density groups. The three most important features were age at examination, weight and number of children. In conclusion, OTC models developed from machine learning methods can improve the prognostic process of breast cancer in Asian women.

Light is an electromagnetic radiation that has visible and invisible wavelength spectrum. The visible light can only be detected by the eyes through the optic pathways. With the presence of scalp, cranium and meninges, the brain is seen as being protected from direct exposure to light. For that reason, the brain can be viewed as a black body lying inside a black box. In physics, a black body tends to be in thermal equilibrium with its environment and can tightly regulate its temperature via thermodynamics principles. Therefore, a healthy brain inside a black box should not get exposed to light. On the contrary, photobiomodulation, a form of light therapy to the brain has been shown to have beneficial effects for some neurological conditions. The proposed underlying mechanisms are multiple. Herein we present our intraoperative findings of rapid electrocorticographic brainwave changes when the brain was shone directly with different wavelengths of light during awake brain surgery. Our findings provide literature proof on light’s ability to influence human brain energy and function. Our proposed mechanism for these rapid changes is the presence of plasma-like energy inside the brain which causes fast brain activities that are akin to lightning strikes.

In this paper, we employ fuzzy Sumudu transform for solving system of linear fuzzy differential equations with fuzzy constant coefficients. The system with fuzzy constant coefficients is interpreted under strongly generalized differentiability. For this purpose, new procedures for solving the system are proposed. A numerical example is carried out for solving system adapted from fuzzy radioactive decay model. Conclusion is drawn in the last section and some potential research directions are given.

IL1RA intron 2 86-bp repeat and IL4 intron 3 70-bp repeat are VNTRs that have been associated with various diseases, but their role in obesity is elusive. The objective of this study was to investigate the association of IL1RA and IL4 VNTRs with obesity and adiposity in 315 Malaysian subjects (128 M/187 F; 23 Malays/251 ethnic Chinese/41 ethnic Indians). The allelic distributions of IL1RA and IL4 were significantly different among ethnicities, and the alleles were associated with total body fat (TBF) classes. Individuals with IL1RA I/II genotype or allele II had greater risk of having higher adiposity - even after controlling for ethnicity [Odds Ratio (OR) of I/II genotype = 12.21 (CI = 2.54, 58.79; p = 0.002); II allele = 5.78 (CI = 1.73, 19.29; p = 0.004)]. However, IL4 VNTR B2 allele was only significantly associated with overall adiposity status before adjusting for ethnicity [OR = 1.53 (CI = 1.04, 2.23; p = 0.03)]. TBF was also significantly higher in those with IL1RA II allele compared with I allele (31.79 ± 2.52 vs. 23.51 ± 0.40; p = 0.005). Taken together, IL1RA intron 3 VNTR seems to be a genetic marker for overall adiposity status in Malaysian subjects.

Chronic fatigue and fibromyalgia symptoms frequently occur in major depressive disorder (MDD). The pathophysiology of these symptoms may in part, be ascribed to activated immune pathways, although it is unclear whether muscular factors play a role in their onset. The aim of the present study is to examine the role of muscle proteins in major depression in association with symptoms of chronic fatigue and fibromyalgia. We measured serum levels of agrin, talin-2, titin, and creatine phosphokinase (CPK) as well as the FibroFatigue (FF), the Hamilton Depression Rating Scale (HAM-D) and the Beck Depression Inventory (BDI-II) in 60 MDD patients and 30 healthy controls. The results show a significant increase in agrin and talin-2 in MDD patients as compared with controls. There were highly significant correlations between agrin and HAM-D, BDI-II and FF scores. Agrin, but not talin or titin, was significantly and positively associated with all 12 items of the FF scale. We found that a large part of the variance in HAM-D (47.4%), BDI-II (43.4%) and FF (43.5%) scores was explained by the regression on agrin, smoking, female sex (positively associated) and education (inversely associated). CPK was significantly and inversely associated with the total FF score and with muscle and gastro-intestinal symptoms, fatigue, a flu-like malaise, headache and memory, autonomic and sleep disturbances. These results suggest that aberrations in neuromuscular (NMJs) and myotendinous junctions may play a role in MDD and that the aberrations in NMJs coupled with lowered CPK may play a role in symptoms of chronic fatigue and fibromyalgia in MDD. Moreover, the increase of agrin in MDD probably functions as part of the compensatory immune-regulatory system (CIRS).

The development of a novel and simple inhibition biosensor array for detection of water pollutants based on immobilized bacteria is the main goal of this work. A series of electrochemical measurements (i.e. cyclic voltammograms) were carried out on screen-printed gold electrodes with three types of bacteria, namely Escherichia coli, Shewanella oneidensis, and Methylococcus capsulatus, immobilized via poly L-lysine. For comparison purposes, similar measurements were carried out on bacteria samples in solutions,; also optical measurements (fluorescence microscopy, optical density, and flow cytometry) were performed on the same bacteria in both liquid and immobilized forms. The study of the effect of heavy metal ions (lead), pesticides (atrazine) and petrochemicals (hexane) on DC electrochemical characteristics of immobilized bacteria revealed a possibility of pattern recognition of the above inhibition agents in aquatic environment.

Background: Community-based medical education (CBME) is an essential component of medical education, where primary healthcare physicians (PHPs) play a crucial role. This study explores PHPs' awareness and engagement in CBME and investigates the factors influencing their participation. Methods: This mixed study was conducted in two phases. In the first phase, a qual-itative study was conducted using semi-structured interviews with PHPs, and thematic analysis related to their awareness and engagement in CBME. In the second phase, a quantitative survey was conducted among 72 PHPs pre- and post-training programs. Results: Primary healthcare physicians had a positive attitude toward community-based medical education. The participants have an acceptable level of awareness and engagement, which increased substantially (p-value = 0.03) and (p-value = 0.003), respectively. Logistic analysis indicated that non-Saudi physicians were likelier to participate in the CBME (p-value=0.001). Professions and academic experiences influence their willingness and engagement to participate in CBME (OR= 7.5, p-value=0.001) and (OR=0.21, p-value=0.001), respectively. Conclusion: The study findings highlighted the importance of increased awareness and the factors that enhance PHPs' engagement in CBME. This positive perspective of the PHPs helps build effective partnerships and facilitates the extension of the curriculum to apply CBME.

The laser differential equations are used to transform them into identical coupled maps. Valuable results are deduced during analytical and numerical studies on cavity loss. Phase and spatiotemporal synchronized attractors are observed via quasi-chaos under a certain range of controlling parameters, and symmetry breaking of chaotic attractors due to collision with their basin boundaries, and transpire differently from the previous attractors. During the numerical simulation, it is found that the sequence of repeated strange attractors if the coupling strength further increases, which are orthogonal mirror images (the dynamics of the system is the same at different values of controlling parameters). Moreover, it can help us to predict future problems and their solutions based on current issues, if we develop this model in more general.

Mosquito-borne diseases (MBDs) are a group of illnesses transmitted by mosquitoes and can be caused by bacteria, viruses, or parasites. These diseases represent a significant global burden of infectious diseases, including morbidity and mortality. This systematic review delves into the multifaceted factors contributing to the spread of mosquito-borne diseases (MBDs) in the Middle East and North Africa (MENA) region. Following PRISMA guidelines, a thorough analysis of peer-reviewed articles from May 1990 to Jan 2023 was conducted, highlighting the interplay of population, environmental, disease, and mosquito factors in disease transmission and prevalence. The review incorporated 31 studies that revealed a complex relationship between various risk factors and the presence of MBDs. Significant associations were observed with age, certain occupations, environmental conditions such as rainfall and temperature, sanitation practices, specific pathogen variants, clinical symptoms, and Aedes aegypti mosquitoes. Conversely, gender, socioeconomic status, educational status, and certain sanitation-related factors showed inconsistent association with the spread of MBDs. The review underscores the need for targeted interventions, including vector control, improved sanitation, and educational campaigns to mitigate the spread of MBDs in the MENA region. This review could guide research studies to address data gaps and assist in developing effective surveillance programs in the MENA region. This work emphasizes the need for region-specific public health strategies and further research to understand and curb the burden of these diseases effectively.

Alzheimer’s disease (AD) is one of the most prevailing neurodegenerative diseases, characterized by memory dysfunction and the presence of hyperphosphorylated tau and amyloid β (Aβ) aggregate in multiple brain regions, including the hippocampus and cortex. The exact etiology of AD has not yet been confirmed. However, epidemiological reports suggest that populations who were exposed to environmental hazards are more likely to develop AD than those who were not. Arsenic (As) is a naturally occurring environmental risk factor abundant in the Earth’s crust and human exposure to As predominantly occurs through drinking water. Convincing evidence suggests that As causes neurotoxicity and impairs memory and cognition although the hypothesis and molecular mechanism of As-associated pathobiology in AD are not clear yet. However, exposure to As and its metabolites leads to various pathogenic events such as oxidative stress, inflammation, mitochondrial dysfunctions, ER stress, apoptosis, impaired protein homeostasis, and abnormal calcium signaling. Evidence has indicated that As exposure induces alterations that coincide with most of the biochemical, pathological, and clinical developments of AD. Here, we overview existing literature to gain insights into the plausible mechanisms that underlie As-induced neurotoxicity and the subsequent neurological deficits in AD. Prospective strategies for the prevention and management of arsenic exposure and neurotoxicity have also been discussed.

There are few published studies assessing dental students' and faculty's perception regarding environmental sustainability in dentistry (ESD), its existence within the dental curricula, along with barriers and enablers for its integration. As far as the authors know, no published study looked into this in Saudi Arabia, and this is what the current study aims to explore. A cross-sectional survey using validated online questionnaires for dental students and faculty was carried out in 26 dental schools in Saudi Arabia. The validated questionnaire utilized 25 questions using Qualtrics (XM) software. Both descriptive statistics and thematic analysis were included in the data analysis. ESD content is not formally embedded within the dental curricula in all 26 included universities in Saudi Arabia. Although the majority of students and faculty members agreed or strongly agreed on the importance of ESD as a whole and the importance of teaching it, more than 82% and 81% of students and faculty, respectively, demonstrated a lack of basic knowledge concerning ESD. There is a high demand for incorporating ESD content in dental education in Saudi Arabia. To achieve this, resources and policy changes are necessary. A top-down approach is needed, including incorporating ESD into dental education standards, using evidence-based practices in revising infection control regulations and providing institutional support through training, materials, and incentives. National dental associations and governments should provide practical solutions and actively support dental professionals in implementing ESD.

The main objective of this paper is to introduce a further extension of extended (p, q)-beta function by considering two Mittag-Leffler function in the kernel. We investigate various properties of this newly defined beta function such as integral representations, summation formulas and Mellin transform. We define extended beta distribution and its mean, variance and moment generating function with the help of extension of beta function. Also, we establish an extension of extended (p, q)-hypergeometric and (p, q)-confluent hypergeometric functions by using the extension of beta function. Various properties of newly defined extended hypergeometric and confluent hypergeometric functions such as integral representations, Mellin transformations, differentiation formulas, transformation and summation formulas are investigated.

The rodents of Saudi Arabia consist of 20 species and 12 genera within four families (Gliridae, Dipodidae, Muridae, and Hystricidae). Details on the past and present distribution of the rodents were included along with available data on their habitat preference and biology. The eastern central part of Saudi Arabia covering Tuwiq mountains plateau, including the vicinity of Riyadh, hosts the highest number of rodent species. Analysis of the rodent fauna of Saudi Arabia revealed that they have four major zoogeographical affinities; Palaearctic-Oriental (one species), Afrotropical- Palaearctic (six species), Palaearctic (four species), endemic to Saudi Arabia and Yemen (three species), Afrotropical-Palaearctic-Oriental (three species) and three cosmopolitan species. According to the National Red List, the Euphrates Jerboa, S. euphratica, is listed as Endangered, Near Threatened for the Indian Crested Porcupine, H. indica, three species as Data Deficient, while the rest are considered as Least Concern.

Zinc oxide (ZnO) nanoparticles (NPs) were synthesized using Rosmarinus officinalis leaf extract at 80 ° C (ZnO-80) and 180 ° C (ZnO-180). The biosynthesized ZnO NPs were characterized and their photocatalytic activity was evaluated for the degradation of methylene blue (MB) and crystal violet (CV) under sunlight irradiation. The results of the characterizations by XRD, TEM and SEM showed that the size of the NPs of ZnO-80 was smaller than that of ZnO-180 which exhibited flakier agglomerated spherical structures. Photocatalytic tests showed ZnO-80 which was prepared by a cheap and easy procedure compared to ZnO-180 effectively degrades MB and CV dyes under sunlight. The superior performance of ZnO-80 over ZnO-180 can be explained by the differences in their textural properties. This is because ZnO-80 has a smaller crystallite size, a specific surface area and a higher pore volume than ZnO-180. Fourier-transform infrared spectroscopy (FTIR) analyzes revealed that both samples contained an adsorbed carboxylate group (COO-), and accordingly a mechanism was proposed for the formation of ZnO NPs that include the carboxyl group.

Significant amount of oil remains in the reservoir after primary and secondary operations and to recover the remaining oil, enhanced oil recovery (EOR) can be applied as one of the feasible options remaining nowadays. In this study new nano polymeric materials have been prepared from purple yam and cassava starches. The yield of purple yam nanoparticles (PYNPs) was 85% and that of cassava nanoparticles (CSNPs) is 90.53%. Synthesized materials were characterized through particle size distribution (PSA), zeta potential distribution, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and transmission electron microscopy (TEM). The performance of PYNPs in recovering oil was better than CSNPs as found from recovery experiments. Zeta potential distribution results confirmed the stability of PYNPs over CSNPs (-36.3 mV for PYNPs and -10.7 mV for CSNPs). The optimum concentration for these nanoparticles has been found from interfacial tension measurements and rheological properties and it was 0.60 wt.% for PYNPs and 0.80 wt.% for CSNPs. More incremental recovery (33.46%) has achieved for the polymer that contained PYNPs in comparison to the other nano-polymer (31.3%). This opens the inception for a new technology for polymer flooding that may replace the conventional method that depends on partially hydrolyzed polyacrylamide (HPAM).

Synovial sarcomas are rare soft tissue tumors that infrequently occur in the ethmoid sinus, posing diagnostic challenges. Herein, we report a case of a 38-year-old male with a 7-month history of recurrent throbbing headaches, left eye pain, and facial nerve palsy, evolving into multiple stroke episodes. Radiological studies showed extension to the cavernous sinus, raising an initial suspicion of vasculitis. Histological findings of an endoscopic biopsy, however, confirmed a monophasic synovial sarcoma. Consequently, the patient was referred to a specialized center for comprehensive management. This case underscores the rarity of ethmoid sinus synovial sarcomas, their diverse clinical presentations, and the necessity of a multidisciplinary approach for accurate diagnosis and tailored therapeutic strategies.

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore nowadays, researchers are paying attention to the vision-based algorithm, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5% 88.53%, 84.63%, and 76.31% respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model outperformed the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks (e.g., length, maximum width, area, density). The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.

Crack inspection is important to monitor the structural health of pavement structures and to facilitate an easier rehabilitation process. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. For solving the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates on the pavement and collects visual images from the surface. A deep learning-based semantic segmentation framework named RCDNet was proposed and implemented on the onboard computer of the robot to identify cracks from the visual images. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested on both indoor and outdoor environments and was observed that it can complete inspecting a 3m × 2m grid within 10 minutes and a 2.5m × 1m grid within 6 minutes. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map based on the results from visual images was also generated to provide an idea of which locations should be paid more attention to repair in a test grid.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 & 0.91, p values = 0.001, 95% CI: 0.80-0.92 & 0.87 to 0.94, sensitivity = 0.85 & 0.87, specificity = 0.73 & 0.83, Youden index = 0.58 & 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.

This paper presents a systematic literature review that identifies critical factors impacting the implementation of advanced manufacturing technology (AMT) worldwide. The study utilizes two databases, ProQuest and Compendex, as well as Google Scholar. The study identified eight dimensions that illustrate the critical factors of AMT adoption and implementation: education, planning, top management know-how, technical know-how, business, economic impact, regulations, and social impact. The results highlight a research gap in understanding the need for effective integration among these eight dimensions in developed and developing economies. Consequently, the study recommends the adoption of a broader perspective that considers the role of integration and interaction between critical factors in each category and their impact on AMT implementation. The systematic literature review conducted in this study reviews several critical factors related to the adoption and implementation of AMT.

Brain disorders pose a significant global healthcare challenge due to their complexity and profound impact on individuals and society. This review explores recent advancements in brain disease research, deepening our understanding and progress towards treatments. Technological innovations, like high-resolution imaging and optogenetics, play a pivotal role in illuminating cellular structures and dissecting neural circuits. Genetic analysis identifies dis-ease-associated genes and mutations, yet translating findings to clinical applications remains challenging. Epigenomics, transcriptomics, and proteomics provide molecular insights, un-covering disrupted pathways in conditions like Alzheimer's disease. Stem cell models elucidate developmental mechanisms, and immunotherapies, including CAR T cells, emerge as prom-ising strategies for brain cancers like glioblastoma. Genome editing tools, exemplified by CRISPR, enable precise modification of disease genes in models, deepening our understanding of pathogenesis. Multifaceted approaches integrating human studies, animal models, and bi-oinformatics propel the field forward. However, complexities persist, as disease specificity for cell types or brain regions remains an enigma, and genetic factors interact with environmental influences, highlighting disease intricacies. Ultimately, interdisciplinary efforts combining bi-ophysical and psychospiritual perspectives are essential to fully understand brain health and unlock transformative treatments for these debilitating disorders. A comprehensive under-standing of brain disorders is vital to improve the quality of life for millions and alleviate the global burden of neurological afflictions. Continued collaborative research will lead to an era of improved brain health worldwide.

This study draws on both the theory of resource-based view and the theory of stakeholders to investigate the effects of Green Human Resource Management (Green HRM) practices on Sustainability and Employee Health Orientation (EHO) through the mediating effect of Environmental Performance (EP). Based on a cross-sectional survey questionnaire, the data were collected from 318 service and manufacturing firms across India. Partial least squares structural equation modeling (PLS-SEM) was applied to determine the link among constructs. The findings of the empirical evidence reveal that Green HRM practices have positive and significant effects on EP, Sustainability and EHO. Also, the findings demonstrate that EP partially mediates the link between Green HRM practices and Sustainability. Similarly, the findings establish that EP partially mediates the link between Green HRM practices and EHO. Finally, the potential implications of this research and future research for practitioners and scholars are addressed.

This study investigates the impact of Government Consumption (GC) on Saudi Arabia’s GDP during major economic crises from 1969 to 2022, with a focus on periods marked by fluctuations in oil and non-oil revenues. By integrating these revenue streams, the research provides a more comprehensive analysis of fiscal policy effectiveness during economic downturns. Using an Autoregressive Distributed Lag (ARDL) model, the study reveals the complex role of Government Consumption (GC) in stabilizing and stimulating the Saudi economy amidst revenue volatility.Key findings indicate that while GC does not significantly influence GDP in the short term, its long-term effectiveness varies across different crises. Specifically, GC has acted as a buffer against immediate economic shocks during certain crises while providing a stimulus for economic recovery in others. For instance, during the 2020 COVID-19 pandemic, timely fiscal measures significantly boosted GDP, underscoring the importance of adaptive and proactive fiscal policies. Conversely, the 2014-2016 oil price collapse demonstrated that GC alone was insufficient to counteract economic downturns, emphasizing the need for diversified revenue strategies.These findings underscore the dual role of GC in economic stabilization and recovery. During the COVID-19 pandemic, GC played a crucial role in both mitigating negative economic impacts and supporting recovery efforts, showcasing its effectiveness in times of global disruptions. This demonstrates GC's capability as an immediate buffer against economic shocks and a stimulus for economic recovery. In contrast, during the 2014-2016 oil price collapse, GC was less effective, indicating the limitations of relying solely on government spending without broader economic diversification. This highlights the necessity of diversified revenue strategies to complement fiscal measures for long-term economic resilience.The robustness of the findings was ensured through various diagnostic tests, including checks for autocorrelation, heteroskedasticity, and stationarity of residuals. The absence of significant autocorrelation and heteroskedasticity, along with the stationarity of differenced variables, confirms the validity of the econometric models used.The study contributes to the discourse on fiscal policy in oil-dependent economies by illustrating the critical role of diversified revenue strategies and adaptive fiscal measures in enhancing economic resilience. Recommendations are offered for policymakers to optimize fiscal strategies, ensuring robust economic recovery and long-term stability in volatile markets. This research highlights the necessity for Saudi Arabia to refine its fiscal policies towards greater economic diversification and stability.

Objectives: Human herpes viruses can cause life-threatening diseases in immunocompromised children, especially leukemic patients. Therefore, the aim of this study is to detect the human herpes viruses (HHV1-7) and to investigate its clinical significance in Middle Eastern Pediatric Leukemia Patients by using 2 Independent PCR assays. Methods: Detection of human herpes virus DNA has been done in blood samples of 200 pediatric leukemia patients in addition to 90 blood donors as a control group using multiplex PCR assays. When a ‘‘positive’’ result was observed, real-time PCR was performed to measure the viral load. Results: The most frequent herpes virus infection in Middle Eastern Pediatric Leukemia cases was CMV, followed by EBV, then HHV6, VZV, HHV7, HSV1, and HSV2, where they were 92/200 (46%), 76/200 (38%), 72/200 (36%), 48/200 (24%), 12/200 (6%), 8/200 (4%), and 2/200 (1%) respectively. Also, there was a statistically significance difference between leukemic patients and their controls regarding CMV, EBV, HHV6, and VZV (P <0.05). Correlation between percentage of co-infection, and clinical parameters for the 7 herpes viruses has been studied, and there is an increase in absolute neutrophilic count (ANC), total leukocyte count (TLC) and duration of fever and neutropenia in age group 6-11 years for HHV6/CMV, then in age group 12-18 years especially for EBV/CMV and CMV/HHV6. Also, our results show that multiplex PCR assay is close to single PCR assay in relation to specificity and sensitivity which in turn prove its validity for early diagnosis of herpes viral infection. Conclusions: Adopting multiplex PCR technique is helpful in screening of virus infections. It will save time, effort, cost effective and will assist in rapid diagnosis. However, the clinical relevance of the virus infection needs to be evaluated by quantitative real-time PCR which in turn will help patient's management by using appropriate antiviral treatment.

Cloud computing is a strong and efficient technology facing some efficiency challenges attributable to its decentralized nature. The emergence of fog computing has subdivided the expansive cloud into numerous smaller units known as "cloudlets." Fog computing is one of the most recent advancements in today’s cloud computing landscape. Each cloudlet is a novel and diminutive architectural element extending the contemporary cloud computing infrastructure. The term "cloudlets" has been integrated with fog computing due to its augmenting impact on the attributes of cloud computing. F fog computing is a significant foundation and an indispensable cloud component. Consequently, this technology holds the potential to address efficiency concerns effectively. As a result, this paper focuses on enhancing the efficiency of the cloud computing environment by fine-tuning the efficiency parameters derived from fog computing. These parameters encompass response time, latency, and network utilization. The proposed solution involves developing a simulation system to minimize task redundancy and increase performance levels in the healthcare environment. The outcomes manifest a substantial improvement in the performance of the suggested system, with a clear increase depending on the three key parameters (response time, network utilization, and latency). Therefore, these findings are encouraging and carry significant implications, promoting the application of the proposed system in the real healthcare environment.

(1) Background: Work creativity, manifested in innovative workplace behaviour (IWB) and employee work engagement, is fundamental to maintain firms’ sustainability and competitiveness. In this regard, this study aims at investigating the supporting effect of innovative leadership on IWB and employee engagement through maximising employee vigour, dedication and absorption. (2) Methods: The study data were collected from questionnaires administered to 150 respondents working in mobile phone companies in southern and central Iraq. The statistical analyses were conducted through the Statistical Package for the Social Sciences (SPSS) and Smart PLS. In analysing the measurement model and testing the proposed hypotheses, the study results revealed that inclusive leadership and work engagement were intimately connected to IWB; (3) Results: Work engagement played a mediating role between inclusive leadership and IWB. The questionnaire data indicated that inclusive leadership behaviours, such as openness, accessibility and availability, motivated the subordinates to be engaged in IWB.; and (4) Conclusions: To promote IWB, company leaders then need to effectively engage their followers by taking pride and satisfaction in employee output, which might aid employee workplace and IWB engagement.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

In this present paper, we deal with the generalized (k, s)-fractional integral and differential operators recently defined by Nisar et al. and obtain some generalized (k, s)-fractional integral and differential formulas involving the class of a function as its kernels. Also, we investigate a certain number of their consequences containing the said function in their kernels.

The main aim of this paper is to establish two generalized integral formulas involving the extended Mittag–Leffler function based on the well known Lavoie and Trottier integral formula and the obtain results are express in term of extended Wright-type function. Also, we establish certain special cases of our main result.

Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical systems with inherent scale separation that impact the system dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the system resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, system dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.

Objectives: Studies on the association between non-steroidal anti-inflammatory drugs (NSAIDs) and major adverse cardiovascular events (MACE) in the Arabian Gulf are scarce. The aim of this study was to evaluate the association between NSAIDs use and MACE in acute coronary syndrome (ACS) patients in the Arabian Gulf region. Methods: Data was analyzed from 3,007 consecutive patients diagnosed with ACS admitted to 29 hospitals in four Arabian Gulf countries from January 2012 to January 2013 as well as being on prior NSAIDs use during the index admission. MACE included stroke/transient ischemic attacks (TIAs), myocardial infarction (MI), all-cause mortality and readmissions for cardiac reasons. Results: The overall mean age of the cohort was 62 ± 12 years and 9.6% (n = 290) of the patients were on prior NSAID use during the index admission. At 12-months follow-up, after adjusting for confounding factors, those on NSAIDs were significantly more likely to have had MACE (adjusted OR (aOR), 1.89; 95% confidence interval (CI): 1.44-2.48; p < 0.001). Specifically, the higher event rates observed were stroke/TIA (aOR, 2.50; 95% CI: 1.51-4.14; p < 0.001) and readmissions for cardiac reasons (aOR, 2.09; 95% CI: 1.59-2.74; p < 0.001) but not MI (aOR, 1.26; 95% CI: 0.80-1.99; p = 0.320) and all-cause mortality (aOR, 0.79; 95% CI: 0.46-1.34; p = 0.383). Conclusions: NSAIDs use was associated with significant stroke/TIA events as well as readmissions for cardiac reasons. However, NSAIDs were not associated with increased MI or all-cause mortality rates in ACS patients in the Arabian Gulf.

Helicobacter pylori is the most common human gastric infection. H. pylori stool antigen lateral flow immunochromatography assay (HpSA-LFIA) is considered one of the most cost-effective and rapid non-invasive assays (active tests). The evaluation of this test is crucial for accuracy and utility assurance. This study aimed to evaluate the polyclonal antibody-based HpSA-LFIA in comparison to a monoclonal antibody-based ELISA kit. Methodology: Stool samples were collected from 200 gastric patients for HpSA-LFIA and semi-quantitative HpSA-ELISA. Statistical analysis of the diagnostic values was performed using MedCalc software. Chi-square tests were used to determine the effects of gender and age. Results: The obtained results found that HpSA-LFIA achieved promising sensitivity (93.75%) and NPV (98.00%). However, it had poor specificity, PPV, and accuracy, respectively, 59.76%, 31.25%, and 65.31%. LR+ & LR- were 2.33% & 0.1%, respectively. Gender had no significance on the di-agnostic parameters of HpSA-LFIA. Age groups had irrelevant sensitivity; however, specificity was significantly higher in patients over 45 years. Conclusion: It was concluded that HpSA-LFIA was not accurate enough to be the sole test for di-agnosis and needs other confirmatory tests in case of positive conditions

Cytologic diagnosis of extra‐adrenal paraganglioma presenting as a peripancreatic mass is challenging with a high rate of diagnostic error. We present a case of a peripancreatic mass identified by radiology as a gastrointestinal stromal tumor. Endoscopic ultrasound‐guided fine needle aspiration (FNA) of the mass showed a moderately cellular tumor composed of small to medium sized neoplastic cells with round to oval nuclei arranged singly and in loose clusters. The cells were positive for neuroendocrine markers (synaptophysin and chromogranin) and negative for CD117. A diagnosis of a neoplasm with a neuroendocrine tumor (NET) was made on FNA cytology. The subsequent surgical resection of the tumor revealed peripancreatic paraganglioma with immunohistochemistry positive for synaptophysin, chromogranin, and S100. The latter delineates the sustentacular cells. Although paraganglioma is a well recognized tumor, a detailed comparison of peripancreatic paraganglioma versus pancreatic/gastrointestinal NET is still lacking.

Breast cancer (BC) is the most common type of cancer in both men and women alike, but it is more prevalent in women. Natural compounds that can modulate the oncogenic process can be considered a significant anti-cancer agent for treating BC. These natural compounds are more effective than synthetic drugs, which have profound side effects on the normal cell and resistance to cancer cells. Genistein is a type of dietary phytoestrogen included in the flavonoid group with a similar structure of estrogen that might provide a strong alternative and complementary medicine to existing chemotherapeutics drugs. Several research studies demonstrated that it can target the estrogen receptor (ER), Human epidermal growth factor receptor-2 (HER2), and Breast cancer gene-1 (BRCA-1) in multiple BC cell lines, as well as sensitize cancer cell lines to this compound when used at an optimal inhibitory concentration. Genistein effectively showed anti-cancer activities through apoptosis induction, arresting cell cycle, inhibiting angiogenesis with metastasis, reducing inflammation, mammosphere formation, tumor growth, up-regulating tumor suppressor gene, and downregulating oncogene in suppressing cancer progression in vitro and animal model study. In addition, research studies have also suggested that these phytochemicals synergistically reverse the resistance mechanism of chemotherapeutic drugs, increasing the efficacy of some chemoinformatics drugs. Our review article aims to unbox and validate the molecular pharmacology in breast tissue, cell-specific anti-cancer mechanism with synergistic activity, and possible pharmacokinetic parameters of Genistein as a potential alternative therapeutic option for the treatment of BC.

Being a chilling-sensitive staple crop, rice (Oryza sativa L.) is vulnerable to climate change. The competence of rice to withstand chilling stress should, therefore, be enhanced through technological tools. The present study employed chemical intervention like application of sodium nitroprusside (SNP) as nitric oxide (NO) donor and elucidated the underlying molecular mechanisms of NO-mediated chilling tolerance in rice. At germination stage, germination indicators were interrupted by chilling stress (5.0 ± 1.0°C for 8 h day‒1), while pretreatment with 100 μM SNP markedly improved the indicators. At seedling stage (14-day-old), chilling stress caused stunted growth with visible toxicity along with alteration of biochemical markers, for example, increase in oxidative stress markers (superoxide, hydrogen peroxide, and malondialdehyde) and osmolytes (total soluble sugar; proline and soluble protein content, SPC), and decrease in chlorophyll (Chl), relative water content (RWC), and antioxidants. However, NO application attenuated toxicity symptoms with improving growth performance which might be attributed to enhanced activities of antioxidants, mineral contents, Chl, RWC and SPC. Furthermore, principal component analysis indicated that water imbalance and increased oxidative damage were the main contributors to chilling injury, whereas NO-mediated mineral homeostasis and antioxidant defense were the critical determinants for chilling tolerance in rice. Collectively, our findings revealed that NO protects against chilling stress through valorizing cellular defense mechanisms, suggesting that exogenous application of NO could be a potential tool to evolve cold tolerance as well as climate resilience in rice.

A botnical survey was conducted in Kaptai reserve forests under Rangamati district in Bangladesh to study the flora of Karnaphuli range from May 2015 to October 2018. The survey was accompanied by a collection of voucher specimens enumerates 464 plant species belonging to 334 genera under 117 families from the forest range. The survey has confirmed 31 threatened forest species from this area along with many near threatened plant species.

A botnical survey was conducted in Kaptai reserve forests under Rangamati district in Bangladesh to study the flora of Karnaphuli range from May 2015 to October 2018. The survey was accompanied by a collection of voucher specimens enumerates 464 plant species belonging to 334 genera under 117 families from the forest range. The survey has confirmed 31 threatened forest species from this area along with many near threatened plant species.

Background Obesity compromises the health of a woman's pregnancy and her newborn. Aim: To describe weight-gaining changes throughout pregnancy and evaluating outcomes for the mother and the baby. Methods A retrospective review of women who delivered singletons using Large Perinatal Registry. According to their BMI, patients were categorized into four categories: underweight, normal, overweight, and obese. Mode of delivery, gestational diabetes, hypertensive diseases, obstetric cholestasis, and postpartum hemorrhage were the main maternal outcome measures. The newborn outcome indicators were birth weight, NICU hospitalization, premature birth, and stillbirth. Results The study sample included 2,352 natives (25%) and 7,058 expatriates (75%). The majority of the sample (4726) were aged 20-29 years, 43.6% were aged 30-39 years, and 2.1% and 3.9% were aged 19 years and 40 years respectively. The incidence of hypertension, obstetric cholestasis, diabetes, postpartum hemorrhage, and cesarean section varied among the entire sample. In the underweight group, the incidence of CS was 15.7%, in the normal weight group (24.9%), in the overweight group (31.3%), and in the obese group (41%). Hypertensive disorders were 3 times more prevalent in obese women than in normal-weight women (2%), and 5.3% of obese women had PPH compared to 5. 6% of women of normal weight. The underweight groups gained the most weight during pregnancy, whereas the obese gained the least. Conclusions A significant proportion of pregnant women in our sample will become obese by the time of delivery. Obesity during pregnancy is associated with negative maternal and fetal outcomes.

Internet of Things (IoT) is a massive network of tiny devices connected internally and to the in-ternet. It is uniquely identified in the network (i.e. dedicated IP) and can share the information with other devices. However, the low power and low resources that distinguish IoT devices render them unsecure and targeted by different kinds of attacks since IoT devices cannot toler-ate heavy security models. Also, due to the heavy nature of famous protocols such as HyperText Transport Protocol (HTTP), it is costly to be used with IoT devices, and alternatively, different lightweight protocols are implemented to fit IoT devices. One of the prevailing protocols used over IoT networks is the Constrained Application Protocol (CoAP). Therefore, CoAP is popular, and that makes it targeted by different types of attacks. One of the major attacks that target CoAP is distributed denial of service (DDoS) attacks. DDoS aims to overwhelm the resources of the target and make them unavailable to legitimate users. As a result, different kinds of meth-ods were used to secure CoAP against DDoS attacks such as Datagram Transport Layer Secu-rity (DTLS) and Lightweight and Secure Protocol for Wireless Sensor Networks (LSPWSN). However, the existing models suffer from two issues: DTLS is not designed for constrained devices and is considered a heavy protocol. Besides, LSPWSN is working over the network layer, not in the application layer that CoAP works on. In this paper, we build a machine learn-ing model that can detect the DDoS attacks against CoAP with an accuracy of 98%. The CIDAD dataset is extended from ~11000 to 100,000 samples using GANs because it has fewer samples of malware (less than 0.2% of the total dataset). Our model outperforms the existing models that target securing CoAP in the application layer and obtains 93% of accuracy.

Forty-five of intact three sheep breeds (Awassi, Harri, and Najdi) were used in this study (15 animals of each breed), with weight ranged from 23.40 to 25.87 kg. Five animals from each group at the end of growth trial were used for digestibility and nitrogen balance trials. The three groups of sheep had effect (p > 0.05) in the final live weight (FLW). Average daily feed intake (ADI), average daily gain (ADG) and feed conversion ratio (FCR) were significantly (p < 0.05) different among the three groups. For digestibility coefficients, the three breeds of sheep had no effect (p > 0.05) in dry matter (DM), organic matter (OM), crude protein (CP), crude fiber (CF), nitrogen free extract (NFE), neutral detergent fiber (NDF) and acid detergent fiber (ADF). In the similar approach, no significant differences were founds among three breeds of sheep in digestible organic matter, digestible of crude protein, total digestible nutrients and nitrogen retention. The current study concluded that the sheep breeds affected average daily feed intake, average daily gain and feed conversion ratio (FCR). Awassi breed showed the best of average daily gain and feed conversion ratio followed by Najdi then Harri breeds. The breed of sheep had no effect on digestibility coefficients and nitrogen retention.

Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into data-driven models. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches encode the physics into an underlying sparse basis space that spans the manifold to generate well-posedness. To achieve this, one commonly uses generic orthogonal Fourier basis or data specific proper orthogonal decomposition (POD) basis to reconstruct from sparse sensor information at chosen locations. Such a reconstruction problem is well-posed as long as the sensor locations are incoherent and can sample the key physical mechanisms. The resulting inverse problem is easily solved using $l_2$ minimization or if necessary, sparsity promoting $l_1$ minimization. Given the proliferation of machine learning and the need for robust reconstruction frameworks in the face of dynamically evolving flows, we explore in this study the suitability of non-orthogonal basis obtained from Extreme Learning Machine (ELM) auto-encoders for sparse reconstruction. In particular, we assess the interplay between sensor quantity and sensor placement for a given system dimension for accurate reconstruction of canonical fluid flows in comparison to POD-based reconstruction.

The coast of Oman is observing huge coastal developments to materialize Oman Vision 2040 covering all the strategic sectors. Historical data of coastal hydrodynamic parameters is scarce, even though its importance for a proper design of coastal structures is recognized. Acoustic Doppler Current Profilers were deployed at eleven locations along the coast of Oman. These locations were selected based on their strategic importance for the country. The duration of deployment was from 60 to 265 days. The measured data of waves and current were collected and analyzed using the equipment-specific software and Excel. The significant wave heights can reach 2m along the whole coast of Oman. The southern coast is dominated by the swells. The maximum to significant wave height ratio is approximately 1.5 which is less than a value of 2 obtained using Rayleigh distribution. The coastal currents are stronger along the southern coast than those along the northern coast. The range of current magnitude is from 0.02 m/s to 0.8 m/s. This baseline study will help the public authorities in establishing permanent hydrodynamic measurement stations along the coast of Oman. Moreover, these measurements will serve the practicing engineers in designing coastal structures along the coast of Oman.

Anemia is a well-developed discipline where the concepts of precision medicine have, in part and being studied extensively. This review discusses the treatment of EPO-deficient anemia and resistance in Chronic Kidney Disease (CKD). Traditionally, erythropoietin-stimulating agents (ESA) and iron supplementation have been used to manage anemia in CKD. However, these treatments have potential risks, including cardiovascular and thromboembolic events. Newer treatments have emerged to address these risks, such as slow-release and low-dosage intravenous iron, oral iron, and erythropoietin-iron combination therapy. Another novel approach is using hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHI). This review highlights the need for precision medicine targeting genetic components of EPO-deficient anemia in CKD and discusses individual variability of genes such as the erythropoietin gene (EPO), Interleukin-β gene (IL-β), and hypoxia-inducible factor gene (HIF). Pharmacogenomics testing aims to provide targeted therapies and interventions that are tailored to the specific characteristics of an individual, optimizing treatment outcomes and minimizing resistance and adverse effects. The article concludes by suggesting the potential of receptor modification to revolutionize treatment outcome of erythropoietin deficiency anemia through integration of the mentioned approach.

Transcutaneous injection laryngoplasty is a well-known procedure for treating paralyzed vocal fold by injecting augmentation material into it. Hence, vocal fold localization plays a vital role in the preoperative planning as the fold location is required to determine the optimal injection route. In this communication, we propose a mirror environment based reinforcement learning (RL) algorithm for localizing the right and left vocal folds in preoperative neck CT. RL-based methods commonly showed noteworthy outcome in general anatomic landmark localization problem in the recent years. However, such methods suggest training individual agent for localizing each fold, though the right and left vocal folds are located in close proximity and have high feature-similarity. Utilizing the lateral symmetry between the right and left vocal folds, the proposed mirror environment allows for a single agent for localizing both the folds by treating the left fold as a flipped version of the right fold. Thus, localization of both folds can be trained using a single training session which utilizes the inter-fold correlation and avoids redundant feature learning. Experiment with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.

Infectious diseases continue to be a significant threat to human health and civilization, and finding effective methods to combat them is crucial. In this research, we investigate the impact of awareness campaigns and optimal control techniques on infectious diseases without proper vaccines. Specifically, we develop an SIRS type mathematical model that incorporates awareness campaigns through media and treatment for disease transmission dynamics and control. The model displays two equilibria, a disease-free equilibrium and an endemic equilibrium, and exhibits Hopf bifurcation when the bifurcation parameter exceeds its critical value, causing a switch in the stability of the system. We also propose an optimal control problem that minimizes the cost of control measures while achieving a desired level of disease control. By applying the minimum principle to the optimal control problem, we obtain analytical and numerical results that show how the infection rate of the disease affects the stability of the system and how awareness campaigns and treatment can maintain system stability. This study highlights the importance of awareness campaigns in controlling infectious diseases and demonstrates the effectiveness of optimal control theory in achieving disease control with minimal cost.

Contacting bodies in extreme environments are prone to severe wear and failure due to friction and seizure, which are associated with significant thermal and mechanical loads. This phenomenon greatly impacts the economy since most essential components encounter these challenges during machining, an unavoidable step in most manufacturing processes. In machining, stress can reach 4 GPa, and temperatures can exceed 1000 °C at the cutting zone. Severe seizure and friction are the primary causes of tool and workpiece failures. Liquid lubricants are popular in machining for combatting heat and friction; however, concerns about their environmental impact are growing, as two-thirds of the 40 million tons used annually are discarded and they produce other environmental and safety issues. Despite their overall efficacy, these lubricants also have limitations, including ineffectiveness in reducing seizure at the tool/chip interface and susceptibility to degradation at high temperatures. There is therefore a push towards solid lubricants, which promise a reduced environmental footprint, better friction management, and improved machining outcomes but also face challenges under extreme machining conditions. This research aims to provide a thorough insight into solid lubricant use in machining, discussing their mechanisms, effectiveness, constraints, and potential to boost productivity and environmental sustainability.

We report the synthesis and characterization of Poly Methyl-Meth-Acrylate (PMMA)/Poly vinylalcohol (PVA) polymeric blend doped with different concentrations of Copper oxide (CuO) nanoparticles (NPs). The (PMMA-PVA)/CuO nanocomposite hybrid thin films (wt. % = 0%, 2%, 4%, 8%, and 16%) of CuO NPs are deposited on glass substrates via dip-coating technique. The transmittance (T%), reflectance (R%), the absorption coefficient (α), the optical constants [refractive index (n), extinction coefficient (k)], optical dielectric functions [ɛ',ɛ''] are investigated and interpreted. Tauc, Urbach, Spitzer-Fan, and Drude models are employed to calculate the optical bandgap energy (Eg) and the optoelectronic parameters of the nanocomposite thin films. The refractive index and optical bandgap energy of of (PMMA-PVA) polymeric thin film are found to be (1.5 to 1.85) and 4.101 eV, respectively. Incorporation of specific concentrations of CuO-NPs in (PMMA-PVA) polymeric thin films leads to a noticeable decrease in the optical bandgap energy and to an increase of the refractive index. Moreover, Fourier Transform Infrared Spectroscopy (FTIR) transmittance spectra are measured and analyzed for undoped and doped polymeric thin films to pinpoint the major vibrational modes in the spectral range (500 and 4000 cm-1), as well as, the nature of network bonding in both systems. Thermal stability of thin films is investigated by performing thermogravimetric analysis (TGA). The TGA thermograms confirm that both doped polymeric thin films are thermally stable at temperatures below 110°C which enables them to be attractive for a wide range of optical and optoelectronic applications. Our results indicate that optical, vibrational and thermal properties of both polymeric thin films can be tuned for specific applications by the appropriate corporation of particular concentrations of CuO-NPs.

Nanofluids are a novel class of heat transfer fluid that plays a vital role in industries. In mathematical investigations, these fluids are modeled in terms of traditional integer-order partial differential equations (PDEs). It is recognized that traditional PDEs cannot decode the complex behavior of physical flow parameters and memory effects. Therefore, this article intends to study the mixed convection heat transfer in nanofluid over an inclined vertical plate via fractional derivatives approach. The problem in hand is modeled in connection with Atangana-Baleanu fractional derivatives without singular and local kernel having strong memory. The human blood is considered as base fluid dispersing carbon nanotube (CNTs) (single-wall carbon nanotubes (SWCNTs) and multi-wall carbon nanotubes(MWCNTs )) into it to form blood-CNTs nanofluid. The nanofluids are considered to flow in a saturated porous medium under the influence of an applied magnetic field. The exact analytical expressions for velocity and temperature profiles are acquired using the Laplace transform technique and plotted in various graphs. The empirical results indicate that the memory effect decreases with increasing fractional parameters in the case of both temperature and velocity profiles. Moreover, the temperature profile is higher for blood-SWCNTs by reason of higher thermal conductivity whereas, this trend is opposite in case of velocity profile due densities difference.

Background: Psychological sufferings are observed among dental students during their academic years, which had been intensified during the COVID-19 pandemic. Objectives: This study assessed the levels and identified factors associated with psychological distress, fear and coping experienced by dental undergraduate students in Bangladesh. Methods: A cross sectional online survey was conducted during October-November, 2021. The Kessler Psychological Distress Scale (K-10), Fear of COVID-19 Scale (FCV-19S) and Brief Resilient Coping Scale (BRCS) were used in order to assess psychological distress, fear and coping strategies respectively. Results: A total of 327 students participated; the majority (72%) were 19-23 years old and females (75%). One in five participants were infected with COVID-19 and 15% reported contact with COVID-19 cases. Negative financial impact (AOR 3.72, 95%CIs 1.28-10.8), recent or past COVID-19 infection, contact with COVID-19 cases were associated with higher levels of psychological distress; but being a 3rd year student (0.14, 0.04-0.55) and being satisfied about current social life (0.11, 0.03-0.33) were associated with lower levels of psychological distress. Being a 3rd year (0.17, 0.08-0.39) and a 4th year student (0.29, 0.12-0.71) were associated with lower levels of fear. Health care service use and feeling positive about life were associated with medium to high resilience coping. Conclusions: This study iden-tified dental students in Bangladesh who were at higher risk of psychological distress, fear and coping during the ongoing pandemic. Development of mental health support system within dental institution should be considered in addition to the academic and clinical teaching.

As universities increasingly compete to improve students’ experience through investment in their campuses, well-designed Campus Open Spaces (COSs) become a major feature as well as a marketing tool. Well-designed and managed COSs are increasingly attractive to students and increase the number of public visits to university campuses (footfall). Current literature does not provide planners with evidence of what makes a COS add value to the student experiences. As such, this paper aims to find the nexus between the value/cost of COS and the attractiveness to - and enhanced experience of - students. This aim is approached via a three-phase integrative framework and results in a validated assessment model with a ‘COS Exp score’ that quantifies the most used/vital, best used/engaging, and most valued/beneficial COS. The data was gathered from in-depth analysis of 21 universities in the UK & USA. The conclusion offers valuable insights into improving experience-based outdoor space developments.

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real-time. Through precise prediction of the PV and wind power output, the proposed approach guarantees optimum scheduling. The precise prediction of the PV and wind power are achieved by employing a predictor of Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational cost of the hybrid power plants, and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the proposed dispatch engine feasibility. A real-time measurement data pertaining to load, wind, and PV power outputs is obtained from different locations in the Sultanate of Oman. The real-time data is utilized to predict the future output power from PV and the Wind farm over 24 hours. The predicted powers are then used in combination with a PV-Wind-DG-ESS-Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed Feed-Forward Neural Network’s (FFNN) powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce plant running expenses.

Insulin-like growth factor binding proteins (IGFBPs) are critical modulators of the metabolism. In adults, IGFBPs are associated with obesity and insulin resistance but the association of IGFBPs with metabolic homeostasis in children and adolescents is not fully characterized. In this study we investigated the association of plasma IGFBPs (IGFBP-1, 3 and 7) with weight status, central adiposity and cardiovascular disease markers Hs-CRP and Ox-LDL. A total of 420 adolescents (age 11-14 years) were randomly recruited from public middle schools in Kuwait. IGFBPs were measured using bead-based multiplexing while Hs-CRP and Ox-LDL were measured using ELISA. IGFBP-1 levels were significantly lower in obese and overweight participants compared to normal weight children. Only IGFBP-1 was negatively associated with waist circumference to height (WC/Ht) ratio. IGFBP-1 was negatively correlated with Hs-CRP while IGFBP-3 and IGFBP-7 were negatively correlated with Ox-LDL. These data demonstrate a robust negative association of IGFBP-1, but not IGFBP-3 or -7, with overweight and obesity, and the inflammation marker Hs-CRP. Central adiposity (WC/Ht ratio) was a stronger predictor of IGFBP-1 than BMI-for-age z-score. IGFBP-1 could thus be used as a sensitive predictive diagnostic tool for obesity and its subsequent effects in screening and monitoring of obesity-related metabolic complications in adolescents.

The escalating levels of atmospheric carbon dioxide (CO₂) due to anthropogenic activities have raised significant concerns regarding their far-reaching consequences, including potential impacts on human brain function. This study employs Agent-Based Modeling (ABM) and a comprehensive literature review to comprehensively examine the effects of varying atmospheric CO₂ concentrations on human brain activity. Historical and projected CO₂concentration data, along with neuroscientific insights, were integrated into the ABM framework to simulate brain activity responses under different CO₂ scenarios. The simulations revealed that elevated CO₂ levels disrupt neural activity and alter neurotransmitter profiles, indicating potential cognitive consequences. Sensitivity analysis emphasized the importance of accurate parameterization for robust simulations. The findings underscore the urgent need to address global CO₂ emissions to preserve cognitive health. Further research is essential to validate and refine the model, incorporating additional factors for a comprehensive understanding of the cognitive repercussions of rising CO₂ levels, crucial for informed policy-making and public awareness campaigns.

Groundwater (GW) flooding mechanisms differ from river flooding both spatially and temporally, and preventative methods against groundwater flooding must take this into account. Although groundwater flooding caused by river water rise occurs seldom, it can occasionally become severe and last for a long time if the river is significantly flooded. In the southwest portion of the research domain, Friedrichshafen with a few urban communities, the level of the groundwater table was discovered to be roughly 1 m below the surface. It was discovered that the urban settlement area only has one-story buildings. In the study region, it is typical for the single-story building's foundation bottom level to extend up to a depth of about 1.5 meters. Therefore, flood mitigation methods are taken into account for the southwest portion of the study region. The installation of a pumping well, drainage, and a barrier in the affected area are three different flood control strategies that are taken into consideration for the study area. From a technical and cost-benefit perspective, installing a pumping well that withdraws water and lowers the groundwater table was found to be the most effective flood control measure locally in a small region (e.g., 1km x 1km). By contrast, removing groundwater by building drainage and barriers was also shown to be ineffective to lower the groundwater table over an extended region and was significantly more expensive than the installation of wells. Additionally, when river flooding is taken into account compared to the default scenario where no intake of water from the river is included along the western border of the study area, it is discovered that the spread of pollution is significantly greater.

An improved FSM method is used in geophysical and environmental corrections to enhance the final product of the along track Jason-2 SLA data and extend it near the Red Sea borders. In this study the ionospheric correction range, wet tropospheric correction range, sea state bias correction range and dry tropospheric correction range are enhanced and improved using FSM01, which helped to retrieve three more tracks (106, 170 and 234), earlier neglected by the distribution centers, and extend the tracks towards the coast. The FSM01 SLA is compared with Jason-2 SLA and AVISO SLA for the available 5 tracks, in which the FSM01 SLA show a good agreement and higher correlation with the Jason-2 SLA compared with that of AVISO, in addition to that it fills the gaps in the times series of all tracks. The new retrieved tracks also compared with those retrieved by AVISO, both data show similar variability, with FSM01 SLA show no gaps in the time series. The FSM01 SLA also extended towards the coast and show high correlation with the coastal tide measurements.

The popularity of functional gummies has increased, which is evident from the growing line of functional gummies from almost every nutraceutical companies. Sensory evaluation serves the purpose of determining which brand of functional gummy would capture the largest market share. Texture profile analysis was used to determine the mechanical properties of functional gummies. The brands of functional gummies that came under the scope of this study were denoted as Brand A, B, C, D, E, F and G. Fourier Transform Infrared Spectroscopy was utilised to detect organic material and functional groups in the functional gummies. Texture profile analysis gave valuable insights into the gummies’ mechanical properties which are cohesiveness, springiness, hardness, gumminess, and chewiness. Amongst the gummies that were studied, Brand F gummy has the highest value of cohesiveness of 0.92. Brand A gummy has a high springiness value of 1.0. Brand B gummy possesses the highest value of hardness, gumminess and chewiness of 12 532.2 g, 7617.6 N, and 6256.8 J, respectively. Qualitative sensory evaluation reveals that Brand G gummy has the best aesthetic qualities in terms of colour and appearance. Brand B gummy tastes the best while brand A gummy claims the top spot for gumminess and chewiness. Overall, the respondents in this study preferred brand A gummy over other brands.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a decline in cognitive function with neuronal damage. Although the precise pathobiology of AD is still elusive, accumulating evidences suggest that mitochondrial dysfunction is one of the underlying causes of AD. Mutations of mitochondrial or nuclear DNA that encode mitochondrial constituents may cause mitochondrial dysfunctions. In particular, dysfunction of electron transport chain complexes along with interactions of mitochondrial pathological proteins are associated with mitochondrial dysfunctions in AD. Mitochondrial dysfunction causes an imbalance in reactive oxygen species, leading to oxidative stress (OS) and vice-versa. Neuroinflammation is another potential contributory factor to induce mitochondrial dysfunction. Phytochemicals or other natural compounds have the potential to scavenge oxygen free radicals and enhance cellular antioxidant defense system, and thereby protect against OS-mediated cellular damage. Phytochemicals can also modulate other cellular processes, including autophagy and mitochondrial biogenesis. Pharmacological intervention through neuroprotective phytochemicals can, therefore, be a potential strategy to combat mitochondrial dysfunctions as well as AD. This review focuses on the role of phytochemicals to mitigate mitochondrial dysfunction in the therapy of AD pathogenesis.

Salinity is abiotic stress, which causes adverse environmental conditions for rice cultivation. In particular, local aromatic rice cultivation is heavily influenced by soil salinity stress, which has an impact on global food security. This study aimed to screen local aromatic rice genotypes in a hydroponics experiment using Yoshida solutions to evaluate the effect of increasing NaCl concentrations on the early growth stages of rice seedlings. Genetic diversity along with phylogenetic relationship was assessed using the random amplified polymorphic DNA (RAPD) markers. Out of 20 RAPD markers, 17 markers produced reproducible polymorphic bands. Individuals of all genotypes shared 88 (89.80%) of the 98 total RAPD elements amplified. The genetic distance-focused similarity index ranged from 0.05 to 0.94. The highest genetic distance (0.94) was discovered between genotypes Nayanmoni and Kalijira Barisal, and the lowest was between Badshabhog and Kataribhog (0.05). In addition, the OPS 3(510bp) and OPA 14(1100bp) markers could be used to identify salt-tolerant genotypes. According to genetic distance, the salt stress tolerant check genotype, Pokkali was genetically related to Chinigura as well as Kalijira Barisal. This study established a simple and consistent method for evaluating variability across various aromatic rice genotypes, which will benefit in genotype selection for breeding salinity stress tolerant aromatic rice varieties in Bangladesh.

Abstract: Cleft palate is a congenital condition result from incomplete fusion of palatine shelves. The current study aimed to assess the effects of vitamin B6 on the incidence of corticosteroid-induced cleft palate model. Eighteen pregnant mice were divided into three groups. Mice of control group were injected with sterile physiological saline from 11th - 14th days of gestation. Mice of group II were injected with 125 mg/kg hydrocortisone from 11th - 14th days of gestation. Mice of group III were injected with hydrocortisone as in group II and given drinking water containing 500 µg/ml of vitamin B6 from day zero of gestation until delivery. Two days after delivery, the offspring were counted, and their head circumferences were taken for statistical analysis. The heads of the offspring were fixed and processed for paraffin sectioning and stained with hematoxylin and eosin. Cleft palates were counted and statistically analyzed using ANOVA test to determine any significances (P

Corporate governance has received increasing attention from practitioners and politicians after the collapse of large companies due to accounting errors and failure to disclose information that reflects the true financial condition of companies. Voluntary risk disclosure includes several aspects, including financial risks, including risks of interest rate, exchange rate, liquidity as well as risks of operations. In Jordan, the most common type of business is family businesses, where boards of directors usually serve the interests of the family that owns the majority stake in the company.This study identifies the moderate effect of family in the link joining the audit committee with the voluntary risk disclosure of financial institutions in Jordan. It's population is the Jordanian commercial banks registered and operating in Jordan during the period extending from 2017 to 2023.Significantly, it concluded with revealing that the characteristics of the audit committee; namely, independence, experience, and committee size, obviously impact disclosure of voluntary risk in the selected banks. However, the results made it obvious that the number of audit committee meetings did not affect the degree of voluntary risk disclosure. In addition, the results view family ownership as moderately affecting the relationship joining some audit committee characteristics with voluntary risk disclosure.

Businesses use technological and social media marketing to respond to the Covid-19 pandemic. This study aims to understand better the factors that impact the spread of new technologies, the effectiveness of social media advertising, and the longevity of businesses. The study used a quantitative strategy based on partial least squares structural equation modeling to learn about the phenomenon of interest as much as possible. Dhaka, Bangladesh, small and medium-sized businesses participated in the survey. According to the findings, the popularity of internet and e-business technologies may be attributed to their apparent usefulness and ease of use. It is connecting internet/e-business technology, social media marketing, and the long-term success of small and medium-sized enterprises. Still, the expense factor was insufficient to prove that SMEs engage in social media advertising. Mediating effects between components may be better understood via internet/e-business technology and social media marketing, as shown by this study. This study is the first of its kind in Bangladesh and sheds light on the resilience of SMEs in the face of the Covivirus-19 pandemic, notwithstanding its focus on the capital city of Dhaka.

Ag/TiO₂ thin films were prepared using the sol-gel spin coating method. The microstructural growth behaviors of the prepared Ag/TiO2 thin films were elucidated using real-time synchrotron radiation imaging, its structure determined using grazing incidence X-ray diffraction (GIXRD), its morphology imaged using the field emission scanning electron microscopy (FESEM), and its surface topography examined using the atomic force microscope (AFM) in contact mode. Cubical white spots were detected, identified as Ag, while the TiO₂ thin film resembles a porous ring-like structure with each ring coalescing and forming channels. Junction growth is directly proportional with time under continuous heating conditions (annealing), and its growth orientations and patterns were seemingly random.

Traumatic brain injury (TBI) is among the leading causes of mortality and long-term impairment globally. TBI has a dynamic pathology encompassing a variety of metabolic and molecular events that occur in two phases, primary and secondary. An external forceful blow to the brain initiates the primary phase, which is followed by a secondary phase that involves the release of calcium ions (Ca2+) and the initiation of a cascade of inflammatory processes, including mitochondrial dysfunction, rise in oxidative stress, activation of glial cells, and damage to the blood-brain barrier (BBB), resulting in paracellular leakage. There is currently no FDA-approved drug for TBI, but existing approaches rely on delivering small and macromolecular treatments, which are severely constrained by the BBB, poor retention, off-target toxicity, and complex pathology of TBI. Therefore, there is a demand for innovative and alternative therapeutics with effective delivery tactics for diagnosis and treatment of TBI. Tissue engineering and use of biomaterials is one such alternative approach. With this approach, neuronal stem cell therapy is combined with synthetically generated tissue materials such as hydrogels, self-assembling peptides, and electrospun nanofibers, which may induce neurite outgrowth, differentiation of human neural stem cells, and nerve gap bridging in TBI. This review examines tissue engineering and the use of biomaterials as potential treatments for TBI, including their synthesis, mechanisms of action, and limitations. The review also discusses challenges facing tissue engineering and biomaterial technology including survival rate of transplanted stem cells and the development of biodegradable, biocompatible, and mechanically flexible biomaterials. A better understanding of the mechanisms and drawbacks of these novel therapeutic approaches will help guide the design of future TBI therapies.

This study was conducted to investigate heavy metal contamination and potential health risk of ground water of selected municipal solid waste (MSW) disposal sites of Khyber Pakhtunkhwa (KP) Pakistan. For this purpose a total of 30 composite samples of both surface and ground water were collected from dumpsites of Abbottabad, Bannu and Peshawar. The collected samples were analyzed for physico-chemical parameters and heavy metals by following standard methods. Results indicated that pH values were found within the acceptable limits specified by national environmental quality standards (NEQS) and world health organization (WHO). Mean concentrations of heavy metals were within NEQS and WHO limits, except for Ni which exceeded the NEQS and WHO limit of 0.02 mg/L in all samples, Similarly, Mn exceeded the limit set by NEQS and WHO (0.5 mg/L) in the ground water samples collected from Bannu dumpsite. The multivariate analysis indicated that the heavy metal contamination of the groundwater of the selected dumpsites was anthropogenic. The pollution index (PI) values (PI≥100) of Mn and Ni indicated very high pollution at individual levels while collectively based on heavy metals pollution index (HPI) values (HPI ≥ 200) the ground water of all the selected dumpsites were found highly polluted. Whereas, the hazard index (HI) values (HI ≥ 5) suggested adverse health effects. It was concluded that the ground water of all the dumpsites was highly polluted with heavy metals particularly Ni and Mn thus, posing a high health risk.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

Background: Countries around the world are facing extraordinary challenges in implementing various measures to slow down the spread of the novel coronavirus (COVID-19). Guided by international recommendations, Saudi Arabia has implemented a series of infection control measures after the detection of the first confirmed case in the country. However, in order for these measures to be effective, public attitudes and compliance must be conducive as perceived risk is strongly associated with health behaviors. The primary objective of this study is to assess Saudis’ attitudes towards COVID-19 preventive measures to guide future health communication content. Methods: Naïve Bayes machine learning model was used to run Arabic sentiment analysis of Twitter posts through the Natural Language Toolkit (NLTK) library in Python. Tweets containing hashtags pertaining to seven public health measures imposed by the government were collected and analyzed. Results: A total of 53,127 tweets were analyzed. All measures, except one, showed more positive tweets than negative. Measures that pertain to religious practices showed the most positive sentiment. Discussion: Saudi Twitter users showed support and positive attitudes towards the infection control measures to combat COVID-19. It is postulated that this conducive public response is reflective of the overarching, longstanding popular confidence in the government. Religious notions may also play a positive role in preparing believers at times of crises. Findings of this study broadened our understanding to develop proper public health messages and promote stronger compliance with control measures to control COVID-19.

Coronavirus Disease 2019 (COVID-19) exhibits a significant variation in presentation of symptoms ranging from mild to severe life-threatening symptoms, including death. COVID-19 infection susceptibility has been linked with various covariates, but studies in Qatar are limited. Data from Qatar Biobank (QBB) (n = 10,000; 18- to 79-year-old adults) were analyzed for associations between sociodemographic and lifestyle factors and susceptibility to COVID-19. Logistic regression was used to assess the associations between COVID-19 and age, gender, body mass index (BMI), smoking status, education level, dietary patterns, supplement use, physical activity, history of bariatric surgery, diabetes, and hypertension. In total, 10.5% (n = 1045) of the participants had COVID-19. Compared to non-smokers, current and ex-smokers had lower odds of having COVID-19 (odds ratio [OR]= 0.55; 95% CI: 0.44-0.68 and OR= 0.70; 95% CI: 0.57-0.86, respectively). Vitamin D supplement use was associated with an 18% reduction of the likelihood of contracting COVID-19 (OR = 0.82; 95% CI: 0.69-0.97). Obesity (BMI ≥30 kg/m2), history of bariatric surgery and higher adherence to the modern dietary pattern– characterized by consumption of foods high in saturated fat and refined carbohydrates– were positively associated with COVID-19. Our findings indicate that adopting a healthy lifestyle may be helpful in the prevention of COVID-19 infection.

Peste des Petits Ruminants (PPR) is one of the major transboundary animal diseases that severely impact livestock production and trade worldwide. An investigation was carried into the seroprevalence and risk factors of PPR in small ruminants, camels, and wild ruminants in Bahrain. A total of 1240 serum samples were collected from 119 sheep flocks and 1224 serum samples from 125 goat herds. In addition, 100 sera from camels and 38 sera from wild ruminants, were collected. Serum samples were tested for the presence of PPR specific antibodies using a competitive ELISA. Flock management and health information of the flocks and herds were collected using a semi-structured questionnaire. The individual prevalence of PPR in sheep and goats was 26% and 25.5%, respectively. The flock or herd level prevalence of PPR was 22.7% and 29.6% in sheep and goats, respectively. None of the tested camels or wild ruminants were positive for PPR-specific antibodies. Sheep and goats from the Northern governorate were more likely to be seropositive to PPR than in other governorates. On the individual animal level, the univariate analysis suggested; age (&lt;12 months), sex (male), and health status (weak animals) as risk factors for PPR seropositivity. On the flock/herd level, the multivariable logistic regression analysis identified large flock/herd size and semi-intensive farming as risk factors for PPR seropositivity.

This work aims to explore the impact of side loads, drill-pipe tool-joint (DP-TJ) speed (RPM), and mud type on the austenitic stainless steel SM2535-110 casing wear characteristics. Actual field drill pipe tool joints, casings, and drilling muds are used in this study. The results of the study show that under both types of lubrication, the wear volume increased with radial load and DP-TJ speed. SM2535-110 casing specimens tested under oil-based mud (OBM) lubrication had higher casing wear volumes than those obtained under water-based mud (WBM) lubrication. This unexpected behavior is mainly due to the increase in the surface hardness of the casing specimens tested under WBM. The results also show that the specific wear factor (K) values of specimens tested under WBM are in general 2 to 4 times higher than those obtained under OBM. While K values under WBM increase with both the side load and RPM, those under OBM show a sharp decrease with RPM. This behavior under OBM is due to this lubricant’s higher viscosity and the change of lubrication regime from thin film to thick film lubrication at higher RPM. Scanning electron microscopy (SEM) and the digital microscopic imaging (DMI) of SM235-110 casing specimens show that an aggressive combination of adhesive, abrasive, and plastic deformation was observed under WBM, while the dominant wear mechanism under OBM is abrasive wear.

As part of the Earth Climate Observatory space mission concept for the direct observation from space of the Earth Energy Imbalance, we propose an advanced camera suite for the high resolution observation of the Total Outgoing Radiation of the Earth. For the observation of the Reflected Solar Radiation, we propose the use of two multispectral cameras covering the range from 400 to 950 nm, with a nadir resolution of 1.7 km, combined with a high resolution RGB camera, with a nadir resolution of 0.57 km. For the observation of the Outgoing Longwave Radiation, we propose the use of 6 microbolometer cameras, with each a spectral bandwidth of 1 μm in the range from 8 to 14 μm, and with a nadir resolution of 2.2 km.

Gender recognition is trivial for physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during the stepping exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SMO). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Nazarloo's work (90.34%) and other classifiers.

Background: We aimed to describe the patterns, management, and outcome of traumatic femoral shaft fractures. Methods: An observational descriptive retrospective study was conducted for all trauma patients admitted with femoral shaft fractures between January 2012 and December 2015 at the only level 1 trauma center and tertiary hospital in the country. Data were analyzed and compared according to the time to intramedullary nailing, mechanisms of injury and age groups. Main outcomes included in-hospital complications and mortality Results: A total of 605 cases were analyzed, with mean age of 30.7±16.2 years. The majority of fractures were unilateral (96.7%) and 91% were closed fractures. Three-fourth of fractures were treated by reamed intramedullary nailing (rIMN); antegrade in 80%. The pyriform fossa nails were used in 71.6% while trochanteric entry nails were used in 28.4%. Forty-five (8.9%) of fractures were treated with external fixator, 37 (6.1%) had conservative management. Traffic-related injuries occurred more in patients aged 14-30 years whereas; injuries related to fall were significantly higher in patients aged 31-59 years. Thirty-one patients (7.8%) had rIMN < 6 h post-injury, 106 (25.5%) had rIMN < 6-12 h and 267 (66.8%) had rIMN > 12 h. The implant type, duration of surgery, DVT prophylaxis, in-hospital complications and mortality were comparable among the three treatment groups. Conclusion: In our center, the frequency of femoral fracture is 11% which mainly affects severely injured young males due to traffic-related collisions or falls. Further multicenter studies are needed to reach a consensus for the appropriate management based on the location and timing of injury

Intercropping as a practice is very crucial in livelihood sustainability among smallholder farm-ing communities in many growing countries. However, for most cropping systems, the benefits of intercropping have yet to be optimized due to a lack of knowledge regarding spatial ar-rangements and planting densities. The objective of the study was to find out the profitability of maize-vegetable intercropping and the yields obtained from various spatial arrangements and planting densities of intercrops. From May to October 2019 and 2020 respectively, Experimental trials were set up in the rainy season in Northern, Upper West and Upper East regions of Ghana comprising of eight treatments each. A randomized complete block experimental design was used for the field layout with three to four replications. Data was collected on grain and fruit yield and land equivalent ratios was estimated (LER). At the end of the trial, important spatial arrangements and planting densities were identified that can be adopted by smallholder famers for system intensification. For okra, the optimal intercropping system under sufficient rainfalls was 2 okra rows at higher density and 2 maize rows at lower density for Upper West Region. In Upper East Region, the optimal spatial arrangement to recommend is 1 row of maize at recom-mended density: 2 rows of okra at lower density under well distributed rainfalls. For roselle, intercropping with spatial arrangement of 2 rows of maize at higher density: 1 row of okra at recommended density was recommended in Northern Region.

Most historical buildings and structures in Oman were built using unreinforced stone masonry. These structures have deteriorated due to ageing of materials, environmental degradation, and lack of maintenance. This research investigates the physical, chemical, and mechanical properties of local building materials and the results of an experimental study on the out-of-plane bending effectiveness of an innovative strengthening technique applied to existing masonry walls. The technique consists of the application on one face of the walls of a basalt textile-reinforced sarooj mortar (TRM). Four-point bending tests of full-scale masonry samples (1000 mm width, 2000 mm height, and 350 mm depth) were carried out using one unreinforced specimen and three different cases of reinforced specimens. The performance of unreinforced and reinforced specimens was analyzed and compared. The strengthened specimens were able to resist moments of out-of-plane bending 2.5 to 3 times greater than those of unreinforced specimen (160%- 233% increase). Moreover, the strengthened walls were able to sustain higher deformations (deflections) than the unreinforced specimen ranging from 20%-130%. The results showed that using TRM was effective for the out-of-plane strengthening of stone masonry using a local material (sarooj) that is compatible with existing stone masonry building materials.

Contact tracing has become an indispensable tool of various extensive measures to control the spread of COVID-19 pandemic due to novel coronavirus. This essential tool helps to identify, isolate and quarantine the contacted persons of a COVID-19 patient. However, the existing contact tracing applications developed by various countries, health organizations to trace down the contacts after identifying a COVID-19 patient suffers from several security and privacy concerns. In this work, we have identified those security and privacy issues of several leading contact tracing applications and proposed a blockchain-based framework to overcome the major security and privacy challenges imposed by the applications. We have discussed the security and privacy measures that are achieved by the proposed framework to show the effectiveness against the security and privacy issues raised by the existing mobile contact tracing applications.

Desalination accounts for 1% of the total global water consumption and is an energy-intensive process, with the majority of operational expenses attributed to energy consumption. Moreover, at present, a significant portion of the power comes from traditional fossil fuel-fired power plants and the greenhouse gas emissions associated with power production along with concentrated brine discharge from the process, pose a severe threat to the environment. Due to the dramatic impact of climate change, there is a major opportunity to develop sustainable desalination processes to combat the issues of brine discharge, greenhouse gas emissions along with a reduction in energy consumption per unit of freshwater produced. Nanotechnology can play a vital role to achieve specific energy consumption reduction as nanofluids application increases the overall heat transfer coefficient enabling the production of more water for the same size desalination plant. Furthermore, concentrated brine discharge harms the marine ecosystems, and hence, this problem must also be solved to support the objective of sustainable desalination. Several studies have been carried out in the past several years in the field of nanotechnology applications for desalination, brine treatment and the role of renewable energy in desalination. This paper aims to review the major advances in this field of nanotechnology for desalination. Furthermore, a hypothesis for developing an integrated solar thermal and nanofluid sustainable desalination system, based on the cyclic economy model is proposed.

Background: Dengue is the most common arthropod-borne sickness worldwide, impacting at least 50 million people each year. The dengue virus has four primary serotypes. Infection with one serotype confers homotypic immunity but not heterologous immunity, and secondary infections may be more severe. Although blood transfusions and organ donations have also been observed, the Aedes aegypti mosquito is the primary vector for the transmission of dengue. Infection causes a continuum of clinical illness, from asymptomatic infection to dengue fever, DHF, and dengue shock syndrome (DSS).Aim: To assess the presence of anti DENV IgG and anti DENV IgM antibodies specific to the four dengue serotypes in blood donor service donors and the importance of pre-donation screening in routine blood collection procedures.Method: 3 mL of peripheral venous blood from 507 blood donors was collected in tubes with BD vacutainer gel tube for serum separation after epidemiological records were reviewed. After that, serum was separated and tests were performed by SD Bioline Dengue Duo. Participants in the study completed a social and epidemiological questionnaire that contained information such as age, gender, and dengue diagnosis.Result: Out of the 507 blood samples that were taken, 473 (93.3%) came from male blood donors, while the remaining 34 (6.7%) belonged to female blood donors. The ratio of males to females is 13.91 to 1. The age range is 18–60 years, and the mean and standard deviation are both 27.7 and 6.5. 183 of the 507 samples produced anti DENV IgG positivity, while 324 did not. The ratio of positive to negative was 1.25:2.Conclusion: According to the findings of this study, quantitative methods for determining the presence of anti-dengue antibodies or detecting the dengue virus in blood donors in endemic areas should be devised in order to ensure the quality of blood transfusions.

COVID-19 infection is extremely dangerous for persons with comorbidities such as kidney disease and diabetes. Although many people of all ages and socioeconomic backgrounds have suffered due to this pandemic, those with underlying medical conditions like kidney disease and diabetes are more vulnerable to becoming infected with COVID-19 related to weak immunity. However, the role of vaccination in the co-infection of COVID-19 with diabetics is not available in the literature. Therefore, we examine the unique challenges presented by the co-infection of COVID-19 in individuals with diabetes, focusing on its disease transmission dynamics. A mathematical modelling technique is to be employed in this work to analyse the dynamics of COVID-19 outbreaks with seven compartments, including vaccination with comorbidities such as diabetes. We also investigated analytically by displaying the solutions' existence, boundedness, positivity and sensitivity. After calculating the basic reproduction numbers, the stability analysis of model equilibrium points is also covered. If the reproduction numbers are less than unity, the disease-free equilibrium points of the model appear to be both locally and globally stable. The findings of this study are that as the vaccination rate rises, the incidence of COVID-19 and its co-infections with diabetes decreases. Effective disease treatment strategies may be based partly on the possible impact of vaccination on the co-infection of COVID-19 related to diabetic disease insights.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

This study presents a prediction model based on Logistic Growth Curve to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depended on partial data from the COVID-19 cases in Malaysia. The model is then studied together with the effectiveness of the three phases of MCO implemented in Malaysia. Evidence from this study suggests that results of the LGC prediction model match with the progress and effectiveness of the MCO to flatten the curve, thus helped to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

Following the development of tyrosine kinase inhibitors (TKI), the survival of patients with chronic myeloid leukaemia (CML) drastically improved. With the introduction of these agents, CML is now considered a chronic disease, for some patients. Taking into consideration the side effects, toxicity, and high cost, discontinuing TKIs became a goal for patients with chronic phase CML. Patients who achieved deep molecular response (DMR) and discontinued TKI, remained in treatment-free remission (TFR). Currently, the data from the published literature demonstrate that 40-60% of patients achieve TFR, with relapses occurring within the first six months. In addition, almost all patients who relapsed regained a molecular response upon re-treatment, indicating TKI discontinuation is safe. However, there is still a gap in the understanding the mechanisms behind TFR, and whether there are prognostic factors that can predict the best candidates who qualify for TKI discontinuation with a view to keeping them in TFR. Furthermore, the information about a second TFR attempt and the role of gradual de-escalation of TKI before complete cessation is limited. This review highlights the factors predicting success or failure of TFR. In addition, it ex-amines the feasibility of a second TFR attempt after the failure of the first one, and the current guidelines concerning TFR in clinical practice.

Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to increasingly enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimizing it for large-scale applications remains to be a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin-Huxley Model, Izhikevich model, integrate-and-fire model and spike response model. Hardware implementation platforms of the chip follow three approaches: analog, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnects with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarizes the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.

The study of angiosperm flora, endemic plants in the literature, and investigations of ‘POWO Build a checklist’ were used to compile an updated list of Bangladesh's endemic plants. According to the study, at least 49 (1.24%) of Bangladesh's 3,956 plant species are endemic to the country including 5 varieties, belonging to 41 genera in 26 families. Magnoliopsida account for thirty of them, Liliopsida for the remaining sixteen, and Pteridohpytes for the final three. The four families Zingiberaceae (9 taxa), Araceae (6 taxa), Rubiaceae (4 taxa), and Euphorbiaceae (3 taxa), include the most endemic taxa. The five most numerous endemic taxa belong to the Curcuma genus. The endemic flora, which is mostly found in the districts of Chattogram, Moulvibazar, and Rangamati, consists of 13 tree species, 4 shrubs, 5 climbers, and 27 herbaceous plant species.