Search

Abstract The use of wild food ingredients has been inextricably linked to each human culture; therefore, any cultural shift or transformation also mutates the knowledge. Particularly cross-cultural interactions have been playing a significant role in reshaping the knowledge within a given community. The study aimed to cross-culturally research the use of wild food plants among four different ethnolinguistic groups, i.e. Muhajir, Pathans, Punjabi, and Saraiki residing in the peri-urban area of Mianwali district, Punjab Pakistan. Data were taken through semi-structured interviews, and the results of cross-cultural comparison were visualized through Venn diagrams and statistically tested through Jaccard index. A total of 59 plants were recorded, which were used mostly as cooked vegetables (29 species) and snacks (21 species). About one-fourth of the quoted plants were commonly used by all the studied groups. High similarity on the use of the quoted plants was found among Muhajir, Punjabi, and Saraiki. Punjabi reported higher numbers of plants, and Pathans quoted comparatively lesser number. Pathans reported a comparatively high number of idiosyncratic foods uses followed by Muhajir. Punjabi, Muhajir, and Saraiki have a very close affinity and have comparatively rich knowledge after comparing those food uses which were reported by more 50% of participants. Punjabi frequently reported some plant uses which were rarely reported by the other four groups. Additionally a large number of plants were also quoted along with their medicinal uses which were prepared in the same way for both food and medicine. Despite the fact that most of the food ingredients were prepared in a simialr way but still the distinct names of several plants were retained across the studied groups. In the current context, the ethnobotanies of the studied communities are a blend as they might have learned the knowledge on the use of these plants from one another. It is concluded that since the knowledge is still in the memory of the people and has no serious threat to its extinction at hand, however it is necessary to frame policy programs in order to use this knowledge for the sustainability of future food and medical system otherwise it may be lost.

Abstract The proliferation of fake news on social media platforms has become a significant concern, influencing public opinion, political decisions, and societal trust. While much research has focused on the technological and algorithmic factors behind the spread of misinformation, less attention has been given to the psychological drivers that contribute to the creation and dissemination of fake news. Cognitive biases, emotional appeals, and social identity motivations are believed to play a crucial role in shaping user behaviour on social media, yet there is limited systematic understanding of how these psychological factors intersect with online information sharing. Existing studies tend to focus on individual aspects of fake news consumption, such as susceptibility to misinformation or partisan biases, leaving a gap in understanding the broader psychological mechanisms behind both the creation and dissemination of fake news. This systematic review aims to fill this gap by synthesizing current research on the psychological factors that influence social media users’ involvement in dissemination and creation of fake news. Twenty-three studies were identified from 2014 to 2024 following the PRISMA guidelines. We have identified five themes through critical review and synthesis of the literature which are personal factors, ignorance, social factors, biological process, and cognitive process. These themes help to explain the psychological factors contributing to the creation and dissemination of fake news among social media users. Based on the findings, it is evident that diverse psychological factors influence the dissemination and creation of fake news, which must be studied to design better strategies to minimize this issue.

Abstract The transition from conventional fossil‐fuel vehicles to electric vehicles (EVs) is critical for mitigating environmental pollution. The placement of electric vehicle charging stations (EVCS) significantly impacts the utility operator and electrical network. Inappropriately placed EVCS lead to challenges such as increased load, unbalanced generation, power losses, and reduced voltage stability. Incorporating distributed generation (DG) helps mitigate these issues by maximizing EV usage. This study focuses on optimizing EVCS and DG placement in radial distribution networks. The methodology employs a backward and forward sweep method for load flow analysis and utilizes the particle swarm optimization (PSO) algorithm to determine optimal EVCS and DG locations and sizes. This approach, validated on the IEEE‐33 bus system, outperforms existing methods. Results indicate a 2.5 times greater power loss reduction compared to simulated annealing (SA), 1.6 times better than artificial bee colony, and parity with genetic algorithm (GA). Overall, the PSO algorithm demonstrates superior optimization effectiveness and computational efficiency, showcasing 1–2.5 times better performance than other methodologies. Employing this approach yields significantly improved results, making it a promising technique for optimizing EVCS and DG placement in distribution networks.

Abstract Significant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks.

Abstract Pistacia lentiscus L. is an aromatic plant containing a significant percentage of essential oil (EO) used in fragrance, pharmaceuticals, cosmetics, and the food industry. The purpose of this work is focused on the optimization of Pistacia lentiscus L. oleo gum resin EO yield extracted by superheated steam extraction (SHSE) by response surface methodology, including extraction parameters of particle size (0. 5 − 1 mm), temperature (140–180 °C) and time (90–150 min). The optimum conditions for Pistacia lentiscus L. EO extracted by SHSE were found to be (particle size: 0.75 mm, time: 120 min and temperature: 160 ℃) which produced the highest EO yield of 5.7%. A regression model was developed, demonstrating a robust quadratic correlation with an R2 value of 0.9991, making it suitable for predictions. Furthermore, the yield of Pistacia lentiscus L. EO extracted by SHSE was compared with the conventional steam and hydro distillation techniques. The study revealed that SHSE yielded higher quantities of EO than other extraction methods. GC-MS analyzed the chemical composition of Pistacia lentiscus L. EO. The predominant compound of Pistacia lentiscus L. EO was determined to be α-pinene, while the other identified compounds include trans-verbenol, verbenol, cis-verbenone, camphene, β-myrcene, d-limonene, cymene, α-myrtenol, α-campholenal, α-copaene, and α-thujene, whose content differed according to different extraction techniques. Overall, superheated steam extraction is an efficient technique for extracting Pistacia lentiscus L. essential oil that enhances EO yield, requiring less time for extraction.

Abstract The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. Despite advancements in machine learning applications for this purpose, the specific contribution of in-silico biomarkers to toxicity risk levels has yet to be thoroughly elucidated. This study addresses this gap by implementing explainable artificial intelligence (XAI) to illuminate the impact of individual biomarkers in drug toxicity prediction. We employed the Markov chain Monte Carlo method to generate a detailed dataset for 28 drugs, from which twelve in-silico biomarkers of 12 drugs were computed to train various machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), XGBoost, K-Nearest Neighbors (KNN), and Radial Basis Function (RBF) networks. Our study’s innovation is leveraging XAI, mainly through the SHAP (SHapley Additive exPlanations) method, to dissect and quantify the contributions of biomarkers across these models. Furthermore, the model performance was evaluated using the test set from 16 drugs. We found that the ANN model coupled with the eleven most influential in-silico biomarkers namely $$\frac{dVm}{dt}_{repol}, \frac{dVm}{dt}_{max}, {APD}_{90}, {APD}_{50}, {APD}_{tri}, {CaD}_{90}, {CaD}_{50}, {Ca}_{tri}, {Ca}_{Diastole}, qInward, and qNet$$ dVm dt repol , dVm dt max , APD 90 , APD 50 , APD tri , CaD 90 , CaD 50 , Ca tri , Ca Diastole , q I n w a r d , a n d q N e t showed the highest classification performance among all classifiers with Area Under the Curve (AUC) scores of 0.92 for predicting high-risk, 0.83 for intermediate-risk, and 0.98 for low-risk drugs. We also found that the optimal in silico biomarkers selected based on SHAP analysis may be different for various classification models. However, we also found that the biomarker selection only sometimes improved the performance; therefore, evaluating various classifiers is still essential to obtain the desired classification performance. Our proposed method could provide a systematic way to assess the best classifier with the optimal in-silico biomarkers for predicting the TdP risk of drugs, thereby advancing the field of cardiac safety evaluations.

Abstract The demand for superhydrophobic fabric with self‐cleaning and antibacterial features as potential practical applications has been rising steadily with the passage of time. In this research work, the wet chemical method is used to manufacture cotton with a superhydrophobic coating. Thus, Nanoparticles of silver were produced by using an in situ method. Afterward, the fabric is treated with a solution of perfluorooctyltriethoxysilane to make it superhydrophobic. The cotton fabric performs remarkably with exceptional hydrophobicity as it had a contact angle of 157 ± 1° and less than 10° in sliding angle. Water droplet's dynamic behavior is also studied so that the superhydrophobic fabric development can be proven. The fabric shows excellent chemical durability by performing chemical tests. The antimicrobial and self‐cleaning properties are greatly enhanced by the superhydrophobic coating on cotton. Particle density on the superhydrophobic cotton before and after self‐cleaning was found 5.1 and 0.4 mg/cm2. In addition, the treated cotton fabric also exhibited superior antibacterial properties against Escherichia coli bacteria. Therefore, the developed multifunctional cotton fabric can be used in real‐life applications. Highlights Cotton fabric enables to repelling of dirt, oil, and water‐based stains more effectively. Silver nanoparticles possess potent antibacterial properties. Incorporation of PFOTS controls the release of silver into the environment. Versatile applications from healthcare and hospitality to outdoor and athletic apparel.

Abstract Background and objectives Apart from known factors such as irrational use of antibiotics and horizontal gene transfer, it is now reported that clustered regularly interspaced short palindromic repeats (CRISPR) are also associated with increased antimicrobial resistance. Hence, it is critical to explore alternatives to antibiotics to control economic losses. Therefore, the present study aimed to determine not only the association of CRISPR-Cas system with antibiotic resistance but also the potential of Zinc Oxide nanoparticles (ZnO-NPs) for avian pathogenic Escherichia coli (APEC) isolated from poultry market Lahore. Materials and methods Samples (n = 100) were collected from live bird markets of Lahore, and isolates were confirmed as Escherichia coli (E. coli) using the Remel One fast kit, and APEC was identified using PCR. The antibiotic resistance pattern in APEC was determined using the minimum inhibitory concentration (MIC), followed by genotypic confirmation of antibiotic-resistant genes using the PCR. The CRISPR-Cas system was also identified in multidrug-resistant (MDR) isolates, and its association with antibiotics was determined using qRT-PCR. The potential of ZnO-NPs was evaluated for multidrug-resistant (MDR) isolates by MIC. Results All isolates of APEC were resistant to nalidixic acid, whereas 95% were resistant to chloramphenicol and 89% were resistant to streptomycin. Nineteen MDR APEC were found in the present study and the CRISPR-Cas system was detected in all of these MDR isolates. In addition, an increased expression of CRISPR-related genes was observed in the standard strain and MDR isolates of APEC. ZnO-NPs inhibited the growth of resistant isolates. Conclusions The findings showed the presence of the CRISPR-Cas system in MDR strains of APEC, along with the potential of ZnO-NPs for a possible solution to proceed. This highlights the importance of regulating antimicrobial resistance in poultry to reduce potential health consequences.

Objective This study aimed to investigate the prevalence of paratuberculosis in cattle and buffaloes at twelve public dairy farms in Punjab, Pakistan. Methods A total of 2,181 more than two-year-old animals (1,242 cattle and 939 buffaloes) were tested by avian tuberculin, i.e., killed purified protein derivative of Mycobacterium avium paratuberculosis and indirect enzyme linked immunosorbent assay (ELISA). Blood and fecal samples were collected from tuberculin positive animals. These samples were further processed by indirect ELISA. The data were analyzed using frequency analysis and logistic analysis procedures. Results The prevalence of paratuberculosis at public dairy farms was 3.8%, as determined by tuberculin+ELISA test. It varied from 0.71% to 13.5% with a 100% herd prevalence. Multivariate logistic regression analysis revealed that species, milk production, total animals, total small ruminants, and total buffaloes were significantly associated with the occurrence of paratuberculosis. Odd ratio analysis revealed that with a one-kilogram increase in body weight, there will be a 0.006% increase in disease occurrence. With the increase in one animal in small ruminants and buffaloes, there will be 0.008% and 0.42% greater chances of developing paratuberculosis, respectively. Bivariate logistic regression analysis of cattle and buffaloes revealed that farm number, age, and total number of cattle were significantly associated with the occurrence of paratuberculosis. A one-month increase in lactation length increases the chance of tuberculosis by 0.004%; similarly, a one-liter increase in milk production increases the chance of disease by 10%. With each additional buffalo in the herd, there will be a 0.007% greater chance for the occurrence of paratuberculosis. Conclusion This study concluded that tuberculin testing can be used in conjunction with ELISA to screen animals for paratuberculosis in countries with scarce resources, such as Pakistan. The efficacy of disease diagnosis can be improved by combining multiple tests.

Abstract Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

A finite element method is employed to examine the impact of a magnetic field on the development of plaque in an artery with stenotic bifurcation. Consistent with existing literature, blood flow is characterized as a Newtonian fluid that is stable, incompressible, biomagnetic, and laminar. Additionally, it is assumed that the arterial wall is linearly elastic throughout. The hemodynamic flow within a bifurcated artery, influenced by an asymmetric magnetic field, is described using the arbitrary Lagrangian–Eulerian (ALE) method. This technique incorporates the fluid–structure interaction coupling. The nonlinear system of partial differential equations is discretized using a stable P2P1 finite element pair. To solve the resulting nonlinear algebraic equation system, the Newton-Raphson method is employed. Magnetic fields are numerically modeled, and the resulting displacement, velocity magnitude, pressure, and wall shear stresses are analyzed across a range of Reynolds numbers (Re = 500, 1000, 1500, and 2000). The numerical analysis reveals that the presence of a magnetic field significantly impacts both the displacement magnitude and the flow velocity. In fact, introducing a magnetic field leads to reduced flow separation, an expanded recirculation area near the stenosis, as well as an increase in wall shear stress.

In recent years, there has been an increasing concern regarding the impact of pesticide exposure on pollinators, particularly honeybees (Apis mellifera L.). This concern arises from their crucial role in maintaining ecological balance and global food production. Therefore, an extensive investigation has been conducted to explore the intricate relationship between pesticides and the biological development of honey bees in the Rahim Yar Khan region. This study assessed the impact of four pesticides (Emamectin benzoate, Chlorpyrifos, Imidacloprid, and Acetamiprid) on honey bee eggs, larvae, and adult bees under controlled laboratory conditions. The pesticides were applied at concentrations of 10%, 30%, and 50%, as per regional agricultural recommendations. A zero-toxicity control was also included for comparison. Toxicity evaluations were conducted through contact exposure, and Probit and regression analyses were performed using SPSS software to comprehensively assess the toxicity profiles. The study revealed significant adverse effects on the immediate behavioral responses of A. mellifera following pesticide exposure. These effects included heightened agitation, narcotic-like symptoms, audible hovering, crawling, ceased food-sharing behavior, and reduced proboscis extension. Chlorpyrifos exhibited the highest toxicity against adult bees, while Emamectin Benzoate had the least toxicity. Regarding honey bee eggs, Chlorpyrifos, Imidacloprid, and Acetamiprid were notably more toxic, whereas Emamectin Benzoate exhibited the least toxicity. The impact on larvae varied across developmental stages and pesticides, with Imidacloprid, Chlorpyrifos, and Acetamiprid causing significant mortality, while Emamectin Benzoate showed lower toxicity. The study highlights that Emamectin Benzoate demonstrates lower toxicity compared to other insecticides. This emphasizes the importance of balancing effective pest management with the preservation of pollinator health. The findings underscore the need for informed and sustainable approaches to pesticide use, taking into consideration the potential repercussions on honeybee development and behavior.

Abstract: The rapid growth in tourism has had a direct impact on the local economy and the potential for changes in land value. This study aims to analyze the influence of tourist attractions and accessibility on land value changes in Getasan District from 2017 to 2024, amidst the significant growth of tourism. The research employed a descriptive and quantitative approach to analyze the data comprehensively. Quantitative analysis was carried out using multiple linear regression to evaluate the influence of proximity to tourist attractions and road accessibility on land value. The land value zone maps for 2017 and 2021 were obtained from the Semarang Regency Land Office, while the 2024 land value zone was created through the update of the 2021 zone. This update resulted in 130 zones with 688 sample points collected through field surveys based on fair market prices. Multiple linear regression analysis was conducted to evaluate the influence of straight-line distance from the zone centroid to tourist attractions and collector roads. The results showed that the p-value in the simultaneous test was 0.03647 (? 0.05), indicating that tourist attractions and accessibility have a simultaneous and significant influence on land value zone changes, with an impact size of 7.87%. These findings highlight the urgency of considering the rapidly developing tourism sector and accessibility in regional management and development, given their effect on land values in Getasan. Keywords: Getasan District, Kopeng Tourism, Land Value Zone

This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices.

Pengembangan modul ajar P5 dalam kurikulum merdeka sangatlah penting karena menyesuaikan perkembangan teknologi dan peralihan kurikulum yang sebelumnya kurikulum 2013 menjadi kurikulum merdeka. Permasalahan saat ini di lapangan yaitu guru masih terkendala dalam menyusun modul ajar serta kurangnya penggunakan model pembelajaran interaktif dan penggunaan media pembelajaran berbasis IT sehingga hal ini berdampak pada pencapaian tujuan pembelajaran yaitu rendahnya hasil belajar siswa. Mengembangkan suatu modul ajar dibutuhkan suatu model yang dapat melibatkan siswa dalam proses pembelajaran serta penggunaan media pembelajaran berbasis IT yang dapat menyesuaikan perkembangan teknologi dan kebutuhan zaman. Sehingga tujuan dari penelitian ini adalah untuk mengetahui validitas modul, media pembelajaran bebasis IT dan instrumen penelitian, serta untuk mengetahui respons guru dan siswa dalam menggunakan modul ajar. Pengembangan modul ini yang akan diukur adalah validitas modul, media pembelajaran, instrumen penelitian serta respons siswa dan guru dalam menggunakan modul. Penelitian ini merupakan penelitian pengembangan dengan menggunakan model 4-D yaitu define, design, develop dan disseminate. Instrumen yang digunakan yaitu lembar validasi, lembar respons siswa dan guru, serta lembar tes hasil belajar siswa. Berdasarkan hasil penelitian diperoleh bahwa validitas modul ajar, media pembelajaran berbasis IT dan instrumen penelitian berada dalam kategori sangat valid sehingga dapat diterapkan dalam proses pembelajaran. Sedangkan untuk respons siswa dan guru dalam penggunakan modul ajar yaitu mereka memberikan respons sangat positif.

Abtract: Climate change; the most concerning issue throughout the globe, is now considered a major cause of heat stress which is deteriorating agricultural crops. A pot experiment was performed to examine the ameliorative role of melatonin foliar spray on growth attributes, physiological attributes and yield and quality attributes of heat-tolerant and heat-sensitive genotypes of tomatoes under heat stress mediated by field environment. The results demonstrated that all the growth parameters of all tomato genotypes such as plant fresh biomass, plant dry biomass, stem girth, leaf area, the number of viable seeds, protein contents as well as physiological attributes including photosynthetic rate, stomatal contents, transpiration rate, chlorophyll contents, water use efficiency (WUE) and synthesis of osmoprotectants including proline and glycine betain (GB) were increased significantly upon foliar application of melatonin @ 25 µM under heat stress except electrolyte leakage (EL), leaf temperature and hydrogen peroxide contents (H2O2) which were decreased in all cultivars of tomato either in heat tolerant or in heat sensitive; as compared to their respective control which remained untreated. Similarly, acivity of enzymatic and non-enzymatic antioxidants including nitric oxide synthase (NOS), glutathione reductase (GR), nitrate reductase (NR) and glutathione S transferase (GST) were also improved upon melatonin foliar application under heat stress. However, the maximum improvement in all measured attributes was observed in all types of tomato genotypes grown under both control and 25 µM melatonine foliar spray treatment. All these findings proved that melatonin spray is capable to address the deteriorative impact of high temperature on tomato growth.

COVID-19 has sparked a significant shift in education toward the use of eLearning platforms. Inclusive teachers encounter several challenges when adopting eLearning platforms for education. The purpose of this study was to examine factors influencing inclusive teachers’ acceptance of eLearning platforms according to the unified theory of acceptance and use of technology (UTAUT2) model. Overall, 128 Omanis inclusive teachers completed the survey questionnaire. The findings revealed the general acceptability of eLearning platforms. Higher performance expectancy, facilitating conditions, social influence, and service quality were all significant predictors of the eLearning platform on behavioral intention. Moreover, the findings showed that further support and training are needed for inclusive teachers to use eLearning systems.

Purpose: This study explores the impact of external auditors' ethical behavior on audit quality, with a particular focus on the perspectives of internal auditors in publicly listed Pakistani companies. It aims to address the gap in literature concerning the interplay between ethical behavior, corporate ethical values, and perceived audit quality. Design/Methodology/Approach: Using Structural Equation Modeling (SEM), the study examines the direct and indirect effects of external auditors' ethics on audit quality, with corporate ethical values as a moderating factor. Data were collected via a mail survey and Google Forms from auditors in listed Pakistani firms, yielding 232 usable responses (61% response rate). The analysis utilized SPSS and AMOS for reliability, validity, factor analysis, regression, and correlation. Findings: Results reveal a positive relationship between ethical behavior and perceived audit quality, amplified by strong corporate ethical values. Companies prioritizing ethics reported enhanced audit quality, providing valuable insights for regulators and auditors. Implications/Originality/Value: This research contributes to the understanding of ethical behavior in audit quality, offering actionable insights for improving ethical standards in audit practices. The findings are pertinent for regulatory bodies, auditors, and policymakers aiming to strengthen financial disclosure integrity in Pakistan.

Purpose: This study explores the relationship between instructor personality, grapevine communication, and organizational commitment in the public and private education sectors of Pakistan. Specifically, it investigates how faculty personalities influence informal communication (grapevine) and their organizational commitment levels. Design/Methodology/Approach: A Structural Equation Model (SEM) employed to analyze the effects of teacher personalities on grapevine communication and organizational commitment. Data collected through a comprehensive survey from university faculty members, focusing on demographics such as age, gender, experience, salary, and qualifications. The collected data then used to build and validate the SEM model, offering a robust analytical framework for evaluating the relationships between the variables. Findings: The study reveals that faculty loyalty and commitment significantly influenced by academic communication routes, particularly informal grapevine communication, which is moderated by individual personality traits. Implications/Originality/Value: This study contributes to the literature on organizational behavior, communication, and management in higher education, offering strategies to enhance job satisfaction, collaboration, and commitment among university faculty.

With the advent of smart grids, advanced information infrastructures, advanced metering facilities, bidirectional exchange of information, and battery storage home area networks have all transformed the electricity consumption and energy efficiency. There is a significant shift in the energy management structure from the traditional centralized infrastructure to the flexible demand side driven cyber-physical power systems with clean energy and energy storage system. These changes have significantly evolved the home energy management (HEM) space. Consequently, stakeholders must define their responsibilities, create efficient regulatory frameworks, and test out novel commercial strategies. P2P energy trading appears to be a feasible solution in these circumstances, allowing users to trade electricity with one another before becoming completely reliant on the utility. P2P energy trading offers a more stable platform for energy trade by facilitating the exchange of energy between prosumers and consumers. This research proposes a novel demand and generation prediction techniques of P2P HEMS for optimal energy using the Multi-Objective Optimization model. An enhanced Wild Horse Optimization technique was first used to summarize historical records' qualities. Then, the Bi-LSTM is used to predict the demand and generation values. Furthermore, a Grasshopper optimization (GHO) approach is employed to fine-tune the model's hyperparameters. The P2P HEMS demand and generation prediction framework is offered with a probabilistic and fault evaluation that upholds load flow balance between need and supply for continuous operations. It results in an intelligent community transforming cities into smart ones, opening new avenues for scientific research in terms of technological developments.

Japanese persimmon (Diospyros kaki) are a good source of vitamins A and C and dietary fiber, while also being low in calories and fat. These fruits are popular in many Asian cuisines and are enjoyed in various ways, including fresh, dried, or cooked in multiple value-added products. This publication will provide information on persimmon phenological growth stages of four established varieties grown at the UF/IFAS North Florida Research and Education Center in Quincy, Florida. The nine major persimmon growth stages are divided into two categories: vegetative and reproductive stages. This publication provides illustrative figures for each development stage. Accurate identification of phenological stages is important for making informed decisions on plant care, fertilizer, irrigation, and fruit harvesting. This information will be helpful to students, growers, homeowners, nursery workers, and Extension agents interested in growing persimmons in Florida.

Load forecasting is a crucial task, which is carried out by utility companies for sake of power grids' successful planning, optimized operation and control, enhanced performance, and guaranteed reliability and adequacy. It mainly reduces the gap between demand and supply and thus assures an uninterrupted power supply and load shedding free operation. The electricity demand can be forecasted by several Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) approaches of which hybrid methods are the most famous. This paper examines various present technologies and algorithms of load forecasting including digital twin (DT), data-mining (DM), federated learning (FL), and transfer learning (TL). In this work, a comprehensive assessment of forecasting with single and hybrid models is performed, their functions are reviewed and pros and cons are investigated. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are used for performance assessment and comparison of the different models searching for best load forecasting model. The role of different experts and intelligent systems used for optimal load forecasting in power systems will be clearly elaborated along with their pros and cons, thus providing a roadmap research path for the readers, who wants to contribute to the scientific society related to this research field.

This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.

The occurrence of cascading failures poses significant risks to the stability and reliability of modern smart grids. This article presents a novel hybrid algorithm designed to assess and mitigate these failures. The technique combines advanced clustering algorithms, specifically Affinity Propagation Graph (APG) and Self-Propagating Graph (SPG), to detect critical nodes, and Unified Power Flow Controllers (UPFCs) to provide compensation to grid networks. The algorithm first uses APG to divide the network into clusters and considers the center bus as the critical node. If the center bus is not critical, SPG is applied to identify the critical node. This hybrid approach identifies the critical node in just 0.02 s (for both APG and SPG) and with improved accuracy compared to existing methods. After identifying critical nodes, UPFCs are strategically installed to regulate power flow and reduce the probability of cascading failures, with compensation taking approximately 0.14 s. Simulation results demonstrate the effectiveness of the proposed method in enhancing grid resilience and reducing the likelihood of cascading failures. By strategically deploying UPFCs at critical nodes, this approach ensures resilient grid operation in various scenarios. This research significantly contributes to the development of smart grid technologies by providing a comprehensive framework to address cascading failures in power distribution networks. The proposed method shows potential for improving the reliability and stability of power grids amid changing system dynamics and uncertainties.

Abstract Solitary wave solutions are of great interest to bio-mathematicians and other scientists because they provide a basic description of nonlinear phenomena with many practical applications. They provide a strong foundation for the development of novel biological and medical models and therapies because of their remarkable behavior and persistence. They have the potential to improve our comprehension of intricate biological systems and help us create novel therapeutic approaches, which is something that researchers are actively investigating. In this study, solitary wave solutions of the nonlinear Murray equation will be discovered using a modified extended direct algebraic method. These solutions represent a uniform variation in blood vessel shape and diameter that can be used to stimulate blood flow in patients with cardiovascular disease. These solutions are newly in the literature, and give researchers an important tool for grasping complex biological systems. To see how the solitary wave solutions behave, graphs are displayed using Matlab.

Interactivity is among the powerful characteristic that differ social media with another kind of conventional media. Social media can facilitate any kind of communication practice, including political communication. Political communication highly needs the use of media to obtain their objectives, specifically for politician to persuade voters to choose them on election. This study aim to discover the social media strategy used by Indonesia president candidates 2024. Using internet research dan observation, Instagram feeds posts from three candidates’s account were collected and analyzed, during the start of campaign period – November 28TH 2023, until the article was written. Several aspects were assessed, including communication style, consistency, and four levels of political marketing strategy. Result shows that consistency in scheduling, writing caption, and choosing hashtag didn’t directly affect social media engagement, such as like and comment. All President Candidates manage their social media to communicate and persuade their audience or voters, but didn’t utilize the interactivity to reply the comment or build conversation via Instagram feeds. Another findings show that Instagram comment section were dominated by both fans/followers and political buzzers that interactively building conversation. Further research should be done to assess another platform and another type of political marketing content.

Abstract Background Diabetes is a leading health disorder and is responsible for high mortality rates across the globe. Multiple treatment protocols are being applied to overcome this morbidity and mortality including plant-based traditional medicines. This study was designed to investigate the ethnomedicinal status of plant species used to treat diabetes in District Karak, Pakistan. Materials and methods A semi-structured survey was created to collect data about traditionally used medicinal plants for diabetes and other ailments. The convenience sampling method was applied for the selection of informants. The collected data was evaluated through quantitative tools like frequency of citation (FC), relative frequency of citation (RFC), informant consensus factor (FIC), fidelity level (FL), and use value (UV). Results A total of 346 local informants were selected for this research. Out of them, 135 participants were men and 211 participants were women. Overall 38 plant species belonging to 29 plant families were used to treat diabetes. The most dominant plant family was Oleaceae having 11 species. Powder form (19%) was the most recommended mode of preparation for plant-based ethnomedicines. Leaves (68%) were the most frequently used parts followed by fruit (47%). The highest RFC was recorded for Apteranthes tuberculata (0.147). The maximum FL was reported for Apteranthes tuberculata (94.4) and Zygophyllum indicum (94.11) for diabetes, skin, and wounds. Similarly, the highest UV of (1) each was found for Brassica rapa, Melia azedarach, and Calotropis procera. Based on documented data, the reported ailments were grouped into 7 categories. The ICF values range between 0.89 (diabetes) to 0.33 (Cardiovascular disorders). Conclusion The study includes a variety of antidiabetic medicinal plants, which are used by the locals in various herbal preparations. The species Apteranthes tuberculata has been reported to be the most frequently used medicinal plant against diabetes. Therefore, it is recommended that such plants be further investigated in-vitro and in-vivo to determine their anti-diabetic effects.

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future.

Abstract This research endeavors to bridge a vital gap in the study of transgender individuals’ participation in the labor market within the Gulf Cooperation Council (GCC) region. It is driven by the question: “What are the underlying factors contributing to the reluctance of GCC employers to hire transgender individuals, and how does this impact the labor market participation of this group?” In pursuit of comprehensive insights, the study aligns with the Cochrane Handbook for systematic research design. The methodology used encompasses an extensive review of academic literature from 2012 to 2023, in-depth interviews with transgender individuals in the UAE and Saudi Arabia, and thematic analysis to identify critical factors. The findings reveal that transgender individuals face significant legal challenges and widespread cultural discrimination in the GCC. These issues lead to workplace biases, unequal pay, and hindered career progression. A key factor in the limited employment opportunities for transgender individuals in the GCC is the reluctance of employers, stemming from cultural stigma and legal shortcomings. This reluctance propagates employment discrimination and a hostile labor market environment for transgender individuals. The study found that employers' hesitancy to hire transgender individuals leads to higher unemployment rates within this group, perpetuating poverty, social exclusion, and hindering economic diversity and innovation in the GCC. The study underscores the urgent need for holistic strategies and policy reforms in the GCC to ensure workplace equality and full labor market integration for transgender individuals, addressing a crucial research gap and emphasizing the need for dedicated efforts in this field.

Abstract Water scarcity and its geopolitical implications have been a cornerstone of scholarly discourse. However, literature often overlooks the nuanced relationship between human traits and water management. Addressing this oversight, this study synthesized data from 149 articles (1991–2023), revealing a substantial connection between human actions and water management dynamics. From this data, a unique comprehensive framework was developed, focusing on the intricate interplay of human behaviors, leadership dynamics, economic factors, and technological advancements in water management. Unlike previous works, this framework holistically integrates these components, offering a fresh lens through which to understand the human-centric factors underpinning global water scarcity. This study underscores the framework’s vital role in guiding sustainable water management and strategy, making it an indispensable tool for stakeholders, from policymakers to environmentalists. In essence, this research not only bridges a knowledge gap but also serves as a beacon for addressing pressing water scarcity challenges in today’s world.

Abstract A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

Power systems are the most complicated networks that play a crucial role in daily lifestyles. To operate such systems in a stable condition, several control and protection strategies are required. Despite preventative measures, power systems still experience issues and breakdowns like power outages. However, severe and unanticipated conditions might cause damage to the entire system and causes blackout. In this article we studied the analysis of the significant blackouts and cascading failures that have been happened in Pakistan in last few years. The authors proposed a new infrastructure for Pakistan energy sector by introducing advanced energy storage system and cutting edge technology like Vehicle-to-Grid (V2G) technology that make the existing power system robust against these phenomena. The advanced energy storage systems include like solid- state batteries that play a vital role to balance supply and demand, while V2G enables electric vehicles to contribute to grid stability. The potential of these technologies is helpful to improve the grid stability and facilitate a sustainable energy transition. The role of various electric companies in Pakistan power infrastructure has also been discussed after an occurrence of such unplanned events in power system. It has been noted that the main cause of power outages is fluctuation in frequency and voltage collapse. Finally, various solutions have been proposed to overcome these problems and make sure the reliability and stability of power grid against blackout phenomena.

AbstractThe occlusal surfaces of posterior teeth are susceptible to caries formation due to the presence of pits and fissures. The bacteria present in the oral cavity and food particles are accumulated on these complex structures. To prevent and protect the tooth structure, different additives were added to pits and fissures sealants to improve adhesion with tooth structure, enhance the mechanical properties, and prevent bacterial growth. In this regard, cotton was coated with chlorohexidine (CHX) and incorporated in a resin-based sealant to improve its mechanical properties (compressive and flexural strength), mass change, and bacterial properties. The mean compressive strength values of CHX coated cotton modified RBS samples S1, S2 & S3 were significantly higher than the control group. The mean flexural strength value of group S1 was significantly higher as compared to the C, S2 & S3. Disc diffusion methods showed a significantly higher value of (p-value = 0.003) antibacterial potential of groups S2 and S3 as compared to group C. The mean percent mass change values of S1, S2 & S3 were higher than the control group. Hence the addition of CHX-coated cotton fillers in RBS improved its properties.

AbstractThe inconsistent pattern of precipitation, a shift in the seasonality of river flows, and the early onset of snow and glacier melt in recent decades across river basins of High Mountain Asia (HMA) has compelled us to further investigate future variations in sources of runoff under projected climate change scenarios. This will help in determining the timing and magnitude of runoff components and this will help in management of future water resources. The current study employed the University of British Columbia Watershed Model (UBC WM) to estimate the spatiotemporal variations in simulated runoff components (i.e. snowmelt, glacier melt, rainfall-runoff, and baseflow) and their relative contribution to total runoff of Gilgit River regarding the baseline period (1981–2010) in near (2021–2050) and far future (2071–2100) under low (SSP1), medium (SSP2) and high (SSP5) emission scenarios. A significant increase in the magnitude of mean annual temperature and precipitation is expected in the near future (2021–2050) than far future (2071–2100) under most SSPs. Moreover, high-altitude stations of the Gilgit River basin are expected to experience more warming in the near and far future than low altitudes under all SSPs. On average, regarding the baseline period, the simulated runoff is projected to increase in the near (27%, 30%, and 33%) and far future (30%, 53%, and 91%) under SSP1, SSP2, and SSP5, respectively. Moreover, an early onset of snow/glacier melting is predicted in the far future due to an increase in summer air temperature and a decline in winter (DJF) precipitation. Besides, the rise in high altitude temperature is expected to cause the melting of snow/glaciers even above 6000 m elevation in the far future.

Background: Healthcare workers who work in emergency departments are at a high risk of being involved in workplace violence (WPV) due to the nature of their profession which is very stressful and tense worldwide. Objective: The purpose of this study was to see the frequency of violence against healthcare workers and to identify the type of violence as well as the offender involved in this act in the hospital of Multan city. Study type, settings & duration: This cross-sectional study was conducted at Nishtar Medical College and Hospital Multan from June to August 2019. Methodology: We conducted this cross-sectional study using the World Health Organization’s tool to collect quantitative data on different aspects of workplace violence (WPV) among universally selected 164 healthcare professionals working in the emergency department of a tertiary care hospital of Multan after taking informed consent. Results: Most physicians reported exposure to at least one type of violence in the past two months. A most common form of violence was verbal violence, which accounted for about three fourth (75.6%), and physical violence about 24.4%. The perpetrators of such incidences were mostly relatives of attendants of patients. Very few cases that created such events of aggression were investigated. Conclusion: Violence in hospitals is a serious public health concern. Verbal violence is the most reported workplace form of violence, affecting mostly physicians. Higher authorities and policymakers should introduce legitimate procedures and interventions to manage this serious issue.

This literature review examines the impact of brand image on customer satisfaction and brand loyalty in the context of foreign tourism. Following a review of relevant literature, 13,302 articles were found for this study, including the keywords “brand image (BI)”, “customer satisfaction (CS)”, and “brand loyalty (BL)”. Considering the required inclusion and the quality of studies, we employed the rigorous PRISMA technique for comprehensive data synthesis and evaluated 79 articles for the final review. Our findings underscore the significant impact of brand image on shaping customer satisfaction and fostering brand loyalty within the foreign tourism sector. The study enriches the literature by incorporating self-congruity theory. In addition, factors like product quality, pricing, and advertising are identified as key determinants significantly influencing the proposed relationship.

Cadmium (Cd) contamination in agricultural soils and its accumulation in plant organs have become a global issue due to its harmful effects on human health. The in-situ stabilizing technique, which involves using organic amendments, is commonly employed for removing Cd from agricultural soils. Thus, the current study investigated the effect of vermicompost (VC) on soil properties and plant physio-biochemical attributes, leaf ultrastructure analysis, antioxidant defense mechanisms, and grain yields of two different fragrant rice cultivars, Xiangyaxiangzhan (XGZ) and Meixiangzhan-2 (MXZ-2), under Cd-stress conditions. The results showed that Cd toxicity deteriorates soil quality, the plant’s photosynthetic apparatus, and the plant’s antioxidant defense mechanism. Moreover, under Cd stress, both cultivars produced significantly lower (p

Artificial intelligence (AI) is an umbrella term for a wide range of machine intelligence systems that can replicate the behavior of humans. AI and Big Data have emerged as defining characteristics of the fourth industrial revolution (IR). AI has developed tools. Because of the novelty, the investigation of IR, AI, and their environmental effects is still in the early stages of exploration. This study investigates how IR and AI affect human and environmental health and also discusses IR, AI, machine-human ideas, innovation, and AI's environmental benefits, further examines the challenges of these innovations, and recommends additional studies to explain their progress. As a result, the application of AI technology in environmental management, particularly concerning pollution, has become a significant advancement in reshaping our approach to monitoring the environment. Numerous countries are reaping substantial advantages by integrating AI in creating, executing, and assessing measures to address environmental degradation. These innovations can yield societal advantages and contribute to achieving the Sustainable Development Goals (SDGs) 2030; unfortunately, it is important to acknowledge that these benefits may not align well with environmental sustainability objectives, and the increasing number of electronic gadgets presents an additional concern. Conducting future research is crucial to investigate the growing prevalence of electronic devices utilized for AI, its potential ramifications for the future trajectory of climate change, and the approaches being taken to address the issue. Future research should prioritize conducting lifecycle environmental impact analyses, developing sustainable AI hardware, optimizing renewable energy usage, advancing climate modeling techniques, finding effective solutions for managing e-waste, utilizing AI for environmental monitoring and protection, conducting socio-environmental impact studies, developing policies and regulations, creating energy-efficient AI algorithms, and integrating circular economy principles to ensure that AI advancements align with environmental sustainability.

Qurratulain Hayder is a renowned fiction writer of the twentieth century. Her novel Aag ka Darya (also translated as River of Fire) is considered to be a classic in the tradition of Urdu fiction writing. This novel deals with Indian civilization and wisdom emanating from there in pre-Christ era. She has presented various constituting elements of Indian civilization through this narrative. Besides that, the thoughts and the teachings of the wise of that age, namely, Gautam Buddha, Mahavir, Kapil, Panini and Chankya have also been interwoven in the story. This article explores these themes and civilzational details in the novel.

Purpose: The purpose of this study presented bibliometric analysis of earnings management (EM) research and provides information on main results and new developments in this field of study. Deliberate manipulation of financial reports, known as earnings management (EM), has received widespread attention from the academic community in recent decades. Methodology/ Design/ Approach: This study utilizes VOS viewer software for bibliometric analysis of 1975 articles archived in the Scopus database between 2001and 2023. Findings: The results show significant relationships between EM with corporate governance, performance metrics, and information quality. The study serves to trace publications and categories as a kind of context for understanding development and EM research emphasis. It also indicates the most productive authors, countries, Institutions and journals on the topic. An analysis of clusters using keywords highlighted new trends in EM research. Notably, this study identified a possible class of real activities that had never been studied in EM before, warranting future work. Research Limitations/Implications: The major limitations of this research are that it uses a single database Scopus and only one search term (“earnings management”), and the selection of journal articles, excluding other publication types, is another limitation. Originality/ value: This study identified a global perspective that is unique in the existing EM literature, and other similar bibliometric studies were limited in their focus on specific subtopics of EM. It discusses the demand for arranging this unorganized domain of study. Additionally, it is an important contribution to the scientific literature on EM research and an excellent source for studies based on the current state of knowledge.

تعد المحاسبة الإدارية أحد الاستراتيجيات المهمة لإدارة الشركات ومساعدة المؤسسات على اتخاذ القرارات الاستراتيجية الصحيحة. فلذلك يهدف البحث إلى تسليط الضوء حول مدى فاعلية المحاسبة الإدارية في تحقيق أهداف شركات قطاع الاتصالات وتحسين أدائها، ومن أجل تحقيق ذلك الهدف فقد اعتمدت الدراسة على استخدام المنهج الوصفي التحليلي، حيث يتكون مجتمع الدراسة الحالية من العاملين بشركات قطاع الاتصالات بالعراق، وقد بلغ عدد أفراد عينة الدراسة 237 فرد وهم الذين قاموا بالإجابة على اسئلة استمارة الاستبيان الالكتروني، وتوصلت الدراسة إلى العديد من النتائج منها، ارتفاع مستوى تطبيق المحاسبة الادارية في شركات قطاع الاتصالات من وجهة نظر أفراد عينة الدراسة حيث بلغت قيمة المتوسط الحسابي 4.073 بانحراف معياري 0.781، بالإضافة إلى تأكيد الدراسة على وجود ارتفاع في مستوى قدرة شركات قطاع الاتصالات على تحقيق الأهداف وتحسين الاداء من وجهة نظر أفراد عينة الدراسة، ولذلك توصي الدراسة بضرورة العمل على التوسع في تطبيق المحاسبة الإدارية في كافة الشركات العاملة في العراق، مع ضرورة تشجيع الباحثين على إجراء الدراسات التي تبين أثر استخدام الأساليب المحاسبة الإدارية على كفاءة أداء وانتاجية الشركات.

Key Clinical Message Inflammatory bowel disease is rare in infants and it includes perianal Crohn's disease, which is inflammation at or near the anus. An early diagnosis is essential to prevent the complications that may affect the development and growth of the child.

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

A key challenge in the production of high-grade automotive aluminum components through the High-Pressure Die Casting (HPDC) process is the imperative to minimize imperfection. In addressing this concern, this study utilizes friction stir processing (FSP), a widely recognized intense plastic deformation technique. FSP is applied to systematically alter the microstructure of HPDC Al-4Mg-2Fe, a prominent alloy extensively used in the die-casting sector. By using the pass strategy to incorporate both one-pass and two-pass approaches, the microstructure is selectively altered to establish a defect-free processed zone. The utilization of FSP demonstrates its efficacy in breaking aluminum dendrites and acicular silicon particles, leading to a uniformly dispersed arrangement of equiaxed silicon particles within the aluminum-based matrix. In addition, FSP eradicates porosity and disintegrates needle-like Fe particles, resulting in a more refined and homogeneously distributed structure. Subsequently, the material's mechanical properties processed by FSP were assessed in the longitudinal direction concerning the processing axis and then compared with those of the original base material.The microstructural refinement and reduction in porosity induced by FSP result in a notable enhancement in hardness, with an increase of 23 % after one pass and 37 % after two passes. The substantial improvement in mechanical properties during the FSP process is predominantly attributed to modifications in the morphology, refinement, and dispersion of intermetallic particles within the matrix. This improvement is further complemented by the ultrafine dispersion of casting defects.This study underscores the efficacy of FSP as a valuable tool for modifying microstructures and improving mechanical properties in HPDC Al-4Mg-2Fe alloys. Such advancements align with the lightweighting objectives pursued by the automotive industry.

Purpose of current study was to determine physicochemical, triglyceride composition, and functional groups of wild adlay accessions (brown, black, yellow, grey, green, off white, and purple) to find out its scope as cereal crop. Triglycerides, minerals and functional groups were determined through Gas chromatography, spectrophotometer and Fourier Transform Infrared (FTIR) spectrophotometer respectively. Results revealed variation among bulk densities, specific densities, percent empty spaces, and corresponding grain counts per 10 g of sample are useful in distinguishing brown, black, yellow, grey, green, off white, and purple wild adlay accessions. Specific density and grain count per 10 g sample was significantly related. No statistical relationship exists among the pronounced physical characteristics. Brown adlay expressed the highest protein, fat, and fiber contents 15.82%, 4.76% and 2.37% respectively. Protein, fat, ash, and fiber percent contents were found comparable to cultivated adlay. Spectrophotometric analysis revealed macro elements including phosphorus, potassium, calcium, and sodium in the range 0.3% - 2.2% and micro elements boron, iron, copper, zinc, and manganese in the range 1.6 mg/kg - 20.8 mg/kg. Gas chromatography showed polyunsaturated fatty acids (PUFA) constitute the primary fraction (39% ± 7.2) of wild adlay triglycerides. Linoleic and palmitic acids were present as prominent fatty acids, 43.5% ±1.4 and 26.3% ±1.4 respectively. Infra-red frequencies distinguished functional groups in narrow band and fingerprint region of protein in association with out of plane region leading to structural differences among adlay accessions. Comparison of major distinguishing vibrational frequencies among different flours indicated black adlay containing highest functional groups appeared promising for varietal development.

Background & Objective: Benign prostatic hyperplasia (BPH) is a prevalent condition in older males, primarily responsible for lower urinary tract symptoms (LUTS). Transurethral resection of the prostate (TURP), a frequently utilized surgical procedure for BPH management, has mixed effects on erectile function (EF). The objective of this study is to evaluate the changes in EF following TURP in patients with BPH. Methodology: This descriptive study was conducted at Bilawal Medical College for Boys, LUMHS Jamshoro, involving 261 male subjects between the ages of 50 and 70 who underwent TURP. Evaluation of EF was carried out before and after the surgical procedure using the International Index of Erectile Function (IIEF) scoring system. RESULTS: The average age of the participants was 60.47 years, and the mean duration of BPH prior to surgery was 15.83 months. The mean IIEF score prior to surgery was 16.72, which notably decreased to 11.50 after surgery. This decline was statistically significant (p < 0.001), and this trend remained consistent across variables such as age, duration of BPH, and prostate volume. Conclusion: The study concludes that TURP significantly correlates with a decline in EF among patients with BPH. These outcomes highlight the necessity for comprehensive preoperative counseling about the potential impacts on sexual health for patients considering TURP.

The home energy management (HEM) sector is going through an enormous change that includes important elements like incorporating green power, enhancing efficiency through forecasting and scheduling optimization techniques, employing smart grid infrastructure, and regulating the dynamics of optimal energy trading. As a result, ecosystem players need to clarify their roles, develop effective regulatory structures, and experiment with new business models. Peer-to-Peer (P2P) energy trading seems to be one of the viable options in these conditions, where consumers can sell/buy electricity to/from other users prior to totally depending on the utility. P2P energy trading enables the exchange of energy between consumers and prosumers, thus provide a more robust platform for energy trading. This strategy decentralizes the energy market more than it did previously, opening up new possibilities for improving energy trade between customers and utility. Considering above scenarios, this research provides an extensive insight of P2P energy trading structure, procedure, market design, trading platform, pricing mechanism, P2P approaches, forecasting techniques, scheduling topologies and possible futuristic techniques, while examining their characteristics, pros and cons with the primary goal of determining whichever approach is most appropriate in a given situation for P2P HEMs. Moreover, an optimal and robust P2P HEMs load scheduling framework simulation model is also proposed to analyze the P2P HEMs network critically, thus paving futuristic technical research directions for the scientific researchers. With this cooperation, a new age of technological advancements ushering in a more intelligent, more interconnected, and reactive urban environment are brought to life. In this sense, the path to smart living entails reinventing the urban environment as well as how people interact with and perceive their dwellings in the larger framework of a smart city. Finally, this research work also provides a comprehensive overview of technical challenges in P2P HEMs in terms of load forecasting and scheduling strategies, their possible solutions, and future prospects.