Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
Here we describe a molecular approach to assess conspecific identity that relies on the comparison of an evolved mutated transposable element sequence and its genomic insertion site in individuals from closely related species. This was explored with the IFP2 piggyBac transposon, originally discovered in Trichoplusia ni as a 2472 bp functional element, that was subsequently found as mutated elements in seven species within the Bactrocera dorsalis species complex. In a B. dorsalis [Hendel] strain collected in Kahuku, Hawaii, a degenerate 2420 bp piggyBac sequence (pBacBd-Kah) having ~ 94.5% sequence identity to IFP2 was isolated, and it was reasoned that common species, or strains within species, should share the same evolved element and its precise genomic insertion site. To test this assumption, PCR using primers to pBacBd-Kah and adjacent genomic sequences was used to isolate and compare homologous sequences in strains of four sibling species within the complex. Three of these taxa, B. papayae, B. philippinensis, and B. invadens, were previously synonymized with B. dorsalis, and found to share nearly identical pBacBd-Kah homologous elements (> 99% nucleotide identity) within the identical insertion site consistent with conspecific species. The fourth species tested, B. carambolae, considered to be a closely related yet independent species sympatric with B. dorsalis, also shared the pBacBd-Kah sequence and insertion site in one strain from Suriname, while another divergent pBacBd-Kah derivative, closer in identity to IFP2, was found in individuals from French Guiana, Bangladesh and Malaysia. This data, along with the absence of pBacBd-Kah in distantly related Bactrocera, indicates that mutated descendants of piggyBac, as well as other invasive mobile elements, could be reliable genomic markers for common species identity.
The production of oil palm (Elaeis guineensis) in Southeast Asia is vital to the economies of Indonesia and Malaysia. Both fertilisers and pesticides used in palm production can contain elevated concentrations of Trace Elements (TEs) which may accumulate in soils and leaf tissues of plants. We hypothesised that leaves from oil palms may be deficient in essential elements, while containing elevated concentrations of non-essential TEs commonly found in agrichemicals. Samples of plant materials (leaves and fruitlets) were collected from active and former plantations in Sumatra, Indonesia, and analysed for essential and non-essential elements. Indonesian palm oil samples were sourced in New Zealand and their elemental concentrations determined. Leaf materials from both active and abandoned production sites were deficient in N, K, S and Mo, while leaf materials from abandoned sites were deficient in P. These deficiencies may have been a contributing factor to the abandonment of production at these sites. Concentrations of non-essential elements were below or comparable to average plant concentrations and no evidence of contamination was found in plant tissues. Palm oil contained low concentrations of TEs, which did not pose any toxicity risks. However, Na and Al were present in concentrations of 1198 and 159 mg kg-1 respectively, which were higher than have been previously reported. Tropical oil palm production could benefit from the determination of bioaccumulation factors for fertiliser contaminants in E. guineensis, to limit the transfer of contaminants to plants and products if increased fertiliser applications were used to correct nutrient deficiencies.
The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO .
The fashion industry has a significant impact on the environment, and sustainable fashion consumption (SFC) has become a pressing concern. This study aimed to investigate the factors influencing sustainable fashion consumption behavior (SCB) among Chinese adults, specifically the role of values, attitudes, and norms in shaping such behavior, using the value-belief-norm framework. The study used an online cross-sectional survey design to collect data from 350 participants recruited through a convenience sampling method using social media platforms and email invitations, and the obtained data were analyzed using partial least squares structural equation modelling. The results of the study showed that biospheric (BV), altruistic (AV), and egoistic (EV) values significantly influenced the New ecological paradigm (EP), which, in turn, positively affected awareness of consequences (AC). Personal norms (PN) were positively influenced by EP, AC, and ascription of responsibility (AR). Social norms (SN) and trust in recycling (TR) were also found to positively influence sustainable fashion consumption intentions (SCI). Finally, the study found that SCI and TR were significant predictors of SCB, whereas the moderating effect of TR not statistically significant. The study's originality lies in its comprehensive investigation of the interplay between various factors (particularly using norms in two facets; PN and SN) in shaping SCB, using a structural equation modeling approach, and exploring the moderating effect of TR. The findings of this study suggest that interventions aimed at promoting SFC should focus on fostering values and beliefs that prioritize the environment, encouraging individuals to take responsibility for their actions, creating an environment in which SFC is normalized, and increasing TR.
Evaluation of genotypes to identify high-yielding and stable varieties is crucial for chilli production sustainability and food security. These analyses are essential, particularly when the breeding program aims to select lines with great adaptability and stability. Thirty chilli genotypes were evaluated for yield stability under four soilless planting systems viz; fertigation, HydroStock (commercial hydrogel), BioHydrogel (biodegradable hydrogel), and hydroponic to study the influence of genotype by environment interaction. The research used a split-plot randomized complete block design (RCBD) with two cropping cycles and five replications. The GGE biplot analysis was employed to assess the mean versus stability perspective in explaining the variation in genotypic and genotype-by-environment effects on the yield-related attributes for yield per plant, fruit number, fruit length, and width. Stability analysis denoted genotypes G26 and G30 as the most stable for yield per plant, while G16, G22, and G30 were stable for the number of fruits per plant. Among the four planting systems evaluated, HydroStock and BioHydrogel outperformed the others in yield per plant, demonstrating the highest level of informativeness or discrimination. These findings offer critical insights for future crop breeding programs and the optimization of agricultural practices.
The objective of this study was to examine the relationships among environmental and health values, ecological worldview, perception of consequences, the ascription of responsibility, and personal norms in the context of the value-belief-norm (VBN) model and how compatibility influences the intentions and behaviors of Chinese youth regarding the use of hydroponic farming technology. The study employed a survey questionnaire to collect data from the target population. The sample size was determined through a power analysis to ensure sufficient statistical power for the analysis. A total of 727 potential respondents' responses were analyzed using SmartPLS (4.0) to perform structural equation modeling. The results confirmed that environmental, emotional, and health values significantly associated with individuals' ecological worldviews. There was an interconnection between ecological worldview, awareness of consequences, and ascription of responsibility, and all three significantly influenced personal norms. The key determinants of the intentions and behaviors to adopt hydroponic farming technology are personal norms and technology compatibility. Therefore, to promote and motivate the interest and intention to use hydroponics among unemployed youth, government agencies, and related companies should focus on providing technology-related and pro-environmental information and training. This is expected to increase the acceptance and awareness of hydroponics among this group, thus increasing the adoption rate of hydroponics.
Polymeric based composites have gained considerable attention as potential candidates for advanced radiation shielding applications due to their unique combination of high-density, radiation attenuation properties and improved mechanical strength. This study focuses on the comprehensive characterisation of polymeric based composites for radiation shielding applications. The objective of this study was to evaluate the physical, mechanical and microstructural properties of tungsten carbide-based epoxy resin and tungsten carbide cobalt-based epoxy resin for its efficiency in shielding against gamma-rays ranging from 0.6 up to 1.33 MeV. Polymeric composites with different weight percentages of epoxy resin (40 wt%, 35 wt%, 30 wt%, 25 wt%, 20 wt%, 15 wt% and 10 wt%) were fabricated, investigated and compared to conventional lead shield. The attenuation of the composites was performed using NaI (Tl) gamma-ray spectrometer to investigate the linear and mass attenuation coefficients, half value layer, and mean free path. High filler loadings into epoxy resin matrix (90% filler/10% epoxy) exhibited excellent gamma shielding properties. Mechanical properties, such as hardness were examined to assess the structural integrity and durability of the composites under various conditions. The fabricated composites showed a good resistance, the maximum hardness was attributed to composites with small thickness. The high loading of fillers in the epoxy matrix improved the microhardness of the composites. The distribution of the filler powder within the epoxy matrix was investigated using FESEM/EDX. The results revealed the successful incorporation of tungsten carbide and cobalt particles into the polymer matrix, leading to increased composite density and enhanced radiation attenuation. The unique combination of high-density, radiation attenuation, and improved mechanical properties positions polymeric based composites as promising candidates for radiation protection field.
It is well known in the field of materials science that a substance's longevity is significantly influenced by its environment. Everything begins with the initial contact on a material's surface. This influence will then deteriorate and have an extended negative impact on the strength of the material. In this study, the effect of natural weathering in tropical climates on magnetorheological elastomer (MRE) was investigated through microstructural evaluation to understand the aging behavior of the environmentally exposed MRE. To understand and elucidate the process, MREs made of silicone rubber and 70 wt% micron-sized carbonyl iron particles were prepared and exposed to the natural weathering of a tropical climate for 90 days. The MRE samples were then mechanically tensile tested, which revealed that Young's modulus increased, while elongation at break decreased. Surface degradation due to weathering was suspected to be the primary cause of this condition. Using scanning electron microscopy (SEM), the degradation of MRE was investigated as a function of morphological evidence. Upon examination through SEM, it was noted that the weathering effects on the morphology of the exposed samples showed distinct characteristics on the degraded surfaces of the MRE, including numerous microvoids, cavities, and microcracks. While these features were not prominent for the MRE itself, they bear resemblance to the effects observed in similar materials like rubber and elastomer. An atomic force microscope (AFM) is used to investigate the surface topography and local degradation conditions. This observation revealed a distinctive degradation characteristic of the MRE in connection to natural weathering in tropical climates. The surface damage of the MRE samples became severe and inhomogeneous during the environmental aging process, and degradation began from the exposed MRE surface, causing the mechanical characteristics of the MRE to significantly change.
The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.
Physicochemical and phytochemical assessment of leaf mustard (Brassica juncea L.) grown in different agroclimatic conditions is essential to highlight their compositional variability and evaluate the most suitable bunch of agroclimatic and agronomic practices. B. juncea is one of the important leafy vegetables that serve as source of vitamin A and C and iron, and plenty of antioxidants. This in situ research was executed to assess the quality variability of B. juncea grown in different agroecosystems. Leaves' samples of B. juncea were procured from 15 farmers' fields exhibiting different agroclimatic conditions i.e., elevation, nutrient management, temperature, irrigation, and tillage practices. Leaves' samples were subjected to physicochemical and phytochemical analysis, i.e., moisture, pH, TSS, ascorbic acid, carotenoids, phenolics, flavonoids, and antioxidant potential. In the leaves' samples of B. juncea, the target properties were found to vary significantly (P ≤ 0.05) in different agroclimatic conditions. The moisture content, ascorbic acid, phenolic content, carotenoids, and antioxidants were found in the range of 62.7-79.3%, 74-91 mg/100 g, 49.2-49.2 mg GAE/100 g, 436.3-480 mg β carotene/100 g, 32.7-46.67%, respectively. This study elaborates the significant variation of physicochemical and phytochemical attributes of B. juncea due to the prevailing agroclimatic conditions. This necessitates the appropriate choice of B. juncea concerning its composition and ecological conditions of its cultivation in the prospective health benefits.
Mangiferin (MF) is a natural C-glucosylxantone compound that has many substantial curative potentials against numerous illnesses including cancers. The present study's goal is to appraise the chemo preventive possessions of MF on azoxymethane (AOM)-mediated colonic aberrant crypt foci (ACF) in rats. Rats clustered into 5 groups, negative control (A), inoculated subcutaneously with normal saline twice and nourished on 0.5% CMC; groups B-E injected twice with 15 mg/kg azoxymethane followed by ingestion of 0.5% CMC (B, cancer control); intraperitoneal inoculation of 35 mg/kg 5-fluorouracil (C, reference rats) or nourished on 30 mg/kg (D) and 60 mg/kg (E) of MF. Results of gross morphology of colorectal specimens showed significantly lower total colonic ACF incidence in MF-treated rats than that of cancer controls. The colon tissue examination of cancer control rats showed increased ACF availability with bizarrely elongated nuclei, stratified cells, and higher depletion of the submucosal glands compared to MF-treated rats. Mangiferin treatment caused increased regulation of pro-apoptotic (increased Bax) proteins and reduced the β-catenin) proteins expression. Moreover, rats fed on MF had significantly higher glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT), and lower malondialdehyde (MDA) concentrations in their colonic tissue homogenates. Mangiferin supplementation significantly down-shifted pro-inflammatory cytokines (transforming growth factor-α and interleukine-6) and up-shifted anti-inflammatory cytokines (interleukine-10) based on serum analysis. The chemo-protective mechanistic of MF against AOM-induced ACF, shown by lower ACF values and colon tissue penetration, could be correlated with its positive modulation of apoptotic cascade, antioxidant enzymes, and inflammatory cytokines originating from AOM oxidative stress insults.
In this study, the potential neuroprotective ability of coriander seeds (Coriandrum sativum L.) ethanolic extract (CSES) as a neuroprotectant agent in the brains of high-fat diet-induced obese rats was analyzed. The study investigated how CSES impacts oxidative stress markers (i.e., malondialdehyde/MDA, glutathione/GSH and catalase), inflammation marker (i.e., Interleukin-6/IL-6), cellular senescence markers (i.e., senescence-associated β-galactoside/SA-β-Gal activity and p16), brain damage marker (i.e., Neuron-specific Enolase/NSE), and neurogenesis markers (i.e., mature Brain-derived Neurotropic Factor/BDNF, pro-BDNF, and mature/pro-BDNF ratio). Male adult Wistar rats were fed a high-fat diet and given CSES once daily, at 100 mg/kg body weight, for 12 weeks. CSES significantly reduced MDA concentration (p =
Obesity and type 2 diabetes mellitus (T2DM) is an alarming problem globally and a growing epidemic. Metabolic surgery has been shown to be successful in treating both obesity and T2DM, usually after other treatments have failed. This study aims to compare Roux-Y gastric bypass and sleeve gastrectomy in determining early diabetic outcomes in obese Malaysian patients with T2DM following surgery. A total of 172 obese patients with T2DM who were assigned to either laparoscopic Roux-en-Y gastric bypass (LRYGB) or laparoscopic sleeve gastrectomy (LSG) were analysed up to a year post-procedure. The patients' T2DM severity were stratified using the Individualized Metabolic Surgery (IMS) score into mild, moderate and severe. Remission rates of diabetes were compared between surgical techniques and within diabetic severity categories. T2DM remission for patients who underwent either surgical technique for mild, moderate or severe disease was 92.9%, 56.2% and 14.7% respectively. Both surgical techniques improved T2DM control for patients in the study. Comparing baseline with results 1 year postoperatively, median HbA1c reduced from 7.40% (IQR 2.60) to 5.80% (IQR 0.80) (p
This research compares the momentum, thermal energy, mass diffusion and entropy generation of two shear thinning nanofluids in an angled micro-channel with mixed convection, nonlinear thermal radiation, temperature jump boundary condition and variable thermal conductivity effects. The [Formula: see text] approach was used to solve the Buongiorno nonlinear governing model. The effect of different parameters on the flow, energy, concentration, and entropy generating fields have been graphically illustrated and explained. The hyperbolic tangent nanoliquid has a better velocity than the Williamson nanofluid. The Williamson nanofluid has higher thermal energy and concentration than the hyperbolic tangent nanoliquid in the microchannel. The Grashof number, both thermal and solutal, increases the fluid flow rate throughout the flow system. The energy of the nanoliquid is reduced by the temperature jump condition, while the energy field of the nanoliquid is enhanced by the improving thermal conductivity value. The nanoliquids concentration rises as the Schmitt number rises. The irreversibility rate of the channel system is maximized by the variable thermal conductivity parameter.
Heavy metal pollutant is a serious problem in environmental pollution, and it is very difficult to eradicate once it enters the soil. As heavy metal adsorption has been proven to occur, the heavy metal's behaviour can be modeled as a transport equation with adsorption. Previous adsorption term mostly due to the concentration alone, while in here, the desorption effect given by the rate of change of the concentration is also included. Also, the heavy metals are frequently considered to enter the soil after being dumped into the soil for a certain period of time. But, quick dumping onto the soil can introduce heavy metal instantaneously. Heavy metals entering the soil through leaching or when their concentration in the soil is influenced by chemical reactions, can all lead to the exponential decay of heavy metals entering the soil. Based on two-dimensional advection diffusion equation (ADE) with the new adsorption term, analytical solutions are obtained for the cases of instantaneous and exponential attenuation of heavy metals emission to soil by the method of Laplace transform. The results highlight the significant influence of emission type on the peak concentrations. If heavy metals are instantaneously enter the soil, the peak occurs in the range of 1-3 m radius from the point of emission on the first day, while for exponential attenuation the peak occurs close to the point of emission. Furthermore, there exists a correlation between retardation factors and heavy metal concentrations, where a decrease in retardation factors leads to an increase in heavy metal concentration. It is essential to investigate both types of heavy metals emission to provide valuable information for proper pollution management, effective environmental regulations and enforcement.
Malaysia's government's decision to reopen schools during the COVID-19 outbreak, especially for students taking important exams, has alarmed the public. However, the Ministry of Education has implemented a COVID-19 Standard Operating Procedure (SOP) for educational institutions. The school management's ability to protect children from COVID-19 rests on their understanding, attitudes, and practices regarding COVID-19 SOP compliance. This study investigated Selangor, Kuala Lumpur, and Putrajaya school management's COVID-19 SOP compliance determinants. Multistage sampling was used to sample 740 school management from Kuala Lumpur, Putrajaya, and Selangor. A self-administered questionnaire collected sociodemographic, occupational, and lifestyle data, knowledge, attitude, and practice of COVID-19 SOP compliance. The school management had good knowledge, attitude, and practice toward COVID-19 SOP. Monthly income, school location, smoking status, and physical activity differed significantly from KAP (p
The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.
The aetiology of schizophrenia is multifactorial, and the identification of its risk factors are scarce and highly variable. A cross-sectional study was conducted to investigate the risk factors associated with schizophrenia among Malaysian sub-population. A total of 120 individuals diagnosed with schizophrenia (SZ) and 180 non-schizophrenic (NS) individuals participated in a questionnaire-based survey. Data of complete questionnaire responses obtained from 91 SZ and 120 NS participants were used in statistical analyses. Stool samples were obtained from the participants and screened for gut parasites and fungi using conventional polymerase chain reaction (PCR). The median age were 46 years (interquartile range (IQR) 37 to 60 years) and 35 years (IQR 24 to 47.75 years) for SZ and NS respectively. Multivariable binary logistic regression showed that the factors associated with increased risk of SZ were age, sex, unemployment, presence of other chronic ailment, smoking, and high dairy consumption per week. These factors, except sex, were positively associated with the severity of SZ. Breastfed at infancy as well as vitamin and supplement consumption showed a protective effect against SZ. After data clean-up, fungal or parasitic infections were found in 98% (39/42). of SZ participants and 6.1% (3/49) of NS participants. Our findings identified non-modifiable risk factors (age and sex) and modifiable lifestyle-related risk factors (unemployment, presence of other chronic ailment, smoking, and high dairy consumption per week) associated with SZ and implicate the need for medical attention in preventing fungal and parasitic infections in SZ.