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Abstract Wheat is an important staple crop not only in Pakistan but all over the globe. Although the area dedicated to wheat cultivation expands annually, the quantity of wheat harvested is declining due to various biotic and abiotic factors. Global wheat production and output have suffered as a result of the drought, which is largely driven by a lack of water and environmental factors. Organic fertilizers have been shown to reduce the severity of drought. The current research was conducted in semi-arid climates to mitigate the negative effects of drought on wheat during its critical tillering (DTS), flowering (DFS), and grain filling (DGFS) stages through the application of three different abscisic acid treatments: ABA0 (0 mgL−1) control, ABA1 (100 mgL−1) and ABA2 (200 mgL−1). Wheat growth and yield characteristics were severely harmed by drought stress across all critical development stages, with the DGFS stage being particularly vulnerable and leading to a considerable loss in yield. Plant height was increased by 24.25%, the number of fertile tillers by 25.66%, spike length by 17.24%, the number of spikelets per spike by 16.68%, grain count per spike by 11.98%, thousand-grain weight by 14.34%, grain yield by 26.93% and biological yield by 14.55% when abscisic acid (ABA) was applied instead of the control treatment. Moreover, ABA2 increased the more physiological indices (water use efficiency (36.12%), stomatal conductance (44.23%), chlorophyll a (24.5%), chlorophyll b (29.8%), transpiration rate (23.03%), photosynthetic rate (24.84%), electrolyte leakage (− 38.76%) hydrogen peroxide (− 18.09%) superoxide dismutase (15.3%), catalase (20.8%), peroxidase (− 18.09%), and malondialdehyde (− 13.7%)) of drought-stressed wheat as compared to other treatments. In the case of N, P, and K contents in grain were maximally improved with the application of ABA2. Through the use of principal component analysis, we were able to correlate our results across scales and provide an explanation for the observed effects of ABA on wheat growth and production under arid conditions. Overall, ABA application at a rate of 200 mgL−1 is an effective technique to boost wheat grain output by mitigating the negative effects of drought stress.

Abstract Background Pakistan reported 1.57 million COVID-19 cases between 2020 and 2022, based on approximately 30.6 million SARS-CoV-2 RT-PCR (reverse-transcription polymerase chain reaction) tests conducted. This study utilized data from one of the largest in-country testing facilities, Aga Khan University Hospital (AKUH) in Karachi, Pakistan, to explore gender and age-related in RT-PCR testing patterns. Methods We conducted a retrospective review of SARS-CoV-2 RT-PCR test data extracted from AKUH clinical laboratory records between February 2020 and February 2022. Gender and age distributions were examined in the context of testing patterns across the period. Multivariate regression models assessed independent associations between COVID-19 positivity and key variables. Results We reviewed 470,249 RT-PCR tests, finding that most tests were in those aged 21–40 years (48.1%). Overall, COVID-19 test positivity was 20.6%. In all, 57.7% were performed for males, predominant amongst those tested across all age groups and waves. Females had significantly lower odds of testing positive for COVID-19 (OR: 0.9; 95% CI: 0.9-1.0). However, when adjusted for gender, age and pandemic phases, the positivity rates between males and females were the same. The odds of a positive result increased significantly with age; individuals aged > 80 years had 2.5 times higher odds of testing positive than those aged 0–10 years (aOR 2.5, 95% CI 2.3–2.7). Conclusions The analysis indicates a consistent male dominance in COVID-19 testing, with higher positivity rates in older age groups. Our study highlight the importance of examining demographic characteristics in disease associated data especially, representation of females amongst cohorts.

Abstract Background This research investigated the impact of selenium-nanoparticles (Se-NPs) supplemented in sunflower meal (SM)-based diets on digestibility, mineral absorption, and growth performance of Catla catla. Se-NPs were added to seven test meals in varying amounts (0, 0.5, 1, 1.5, 2, 2.5 and 3 mgkg− 1). A total of 315 fish were evenly allocated three replicate tanks, with 15 fish per tank, and given diets equivalent to (5%) of their wet biomass daily for a period of 90 days. Results The test diet (T-D) supplemented with 1.5 mgkg− 1 Se-NPs yielded remarkable results, including a significant increase in mean weight gain (14 g), an impressive average weight gain percentage (208%), a 100% survival rate, with no mortality observed and the highest specific growth rate (1.25). Moreover, 1.5 mgkg− 1 Se-NPs (T-D-IV) demonstrated superior nutritional digestibility (CP, 76%; CF, 79% and GE, 74%). Additionally, the diet 1.5 mgkg− 1 Se-NPs showed enhanced mineral absorption (K, P, Ca, Na, Zn, Cu and Fe) than other diets. Conclusion It was practically proven that the growth indices, nutrient absorption, and mineral status of C. catla were significantly improved by Se-NPs supplemented SM-based diets, with the optimum level of supplementation being 1.5 mgkg− 1.

This study examines the incorporation of blockchain technology into Saudi Arabia’s Intelligent Transportation Systems (ITS), concentrating on enhancing the bus permission procedure for religious mass gatherings in Makkah. The research illustrates how blockchain might improve the security and operational effectiveness of transportation management for major events. The research used a weighted significance methodology and Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to identify the critical success factors (CSFs) influencing transportation at religious events. The research illustrates the strong interrelation of the CSFs, emphasizing the notable enhancement in transparency and efficiency in the approval and management processes using blockchain-based solutions. The research examines the impact of reformulated CSFs on the proposed blockchain-based transportation framework (BTF), emphasizing key domains such as people (P1), technology (P2), the environment (P3), and organization (P4). The findings indicate that blockchain-related CSFs exhibit the greatest influence, which is 21.62, while financial CSFs demonstrate the least influence of 0.25. This research significantly addresses current system limitations and stimulates wider blockchain usage inside Intelligent Transportation Systems by developing a thorough mathematical model. This paper presents a strategy framework for the successful management of large-scale transportation difficulties with blockchain technology, assuring optimum operations in organizational and environmental contexts.

Over recent years, economic development and environmental degradation have become major challenges that pressurize organizations to adopt a green innovation culture. Therefore, green human resource management (GHRM) practices are necessary to improve sustainable performance in healthcare organizations functioning in Pakistan. This study literature on GHRM practices with green innovation supports an environment-friendly atmosphere in the business world to achieve sustainable performance. At the same time, managing risk remains limited in the health sector. Therefore, the present study intends to identify the execution of GHRM practices through green innovation on sustainable performance in healthcare organizations. Additionally, this study evaluates the moderating effect of risk management among green innovation and GHRM practices toward sustainable performance. This study scrutinizes the following relationships from a sample of 400 respondents from Pakistan’s government and non-government hospitals. The partial least structure equation modeling method (PLS-SEM) is implemented to measure the structural framework of sustainable performance. Results showed that GHRM practices positively affect sustainable performance through green innovation. Additionally, risk management significantly moderates the relationship between GHRM practices and green innovation towards sustainable performance. However, GHRM practices in an eco-friendly environment have meaningful directions for top managers and policy-makers of healthcare organizations to develop a green innovation culture to mitigate the risk and boost sustainable performance.

Diabetes mellitus (DM), a major global health concern, is a chronic metabolic disorder. Bioactive compounds sourced from numerous marine natural products recently have drawn attention as novel therapeutic approaches. Considering these chemicals and their role in cellular redox modulation by involving the nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase 1 (HO-1) pathway, the current study attempts to highlight their anti-diabetic effects and the molecular mechanisms involved. Reactive oxygen species (ROS)-mediated oxidative stress, inflammation, and cellular damage are linked to most human pathologies specifically DM. The Nrf2/HO-1 pathway is a key defense mechanism developed by the cells to combat ROS burst. Marine natural compounds have strong pharmacological potential in triggering cellular antioxidant defense mechanisms by declining oxidative damage and inflammation linked to DM. How marine natural products potentially alleviate DM specifically type 2 diabetes (T2D) and its related issues is especially focused on. The literature was thoroughly analyzed to open a discussion about specific marine compounds and their well-established anti-diabetic effects to elucidate possible therapeutic applications. Furthermore, opportunities and the pros and cons of using these marine bioactive compounds as complementary treatment for DM are also discussed. The diverse characteristics of marine natural products, specifically with regard to redox control, offer promising opportunities for drug discovery and therapeutic interventions in clinical trials.

Abstract The context of education has changed due to revolutionary developments in the information communication technology (ICT) industry in the post-COVID era. Innovative learning methods were introduced in the education sector to promote quality education. The students find it more convenient to use ICT tools to integrate their knowledge-seeking. China has recently paid more attention to developing and adopting electronic infrastructure. The study assesses the effect of technology self-efficacy (TSE) on ICT acceptance and implementation in China’s education sector. It also analyzed the role of perceived trust, perceived security, and electronic word of mouth (eWOM) in integrating digital information sharing and interaction tools. Data is collected from 382 business students at Chinese universities. The results revealed that perceived trust mediates the relationship between TSE and the actual use of ICT tools, intention to use ICT tools for information, and intention to use ICT tools for interaction. Further, perceived security and eWOM significantly moderate the relationship between TSE and perceived trust. The findings indicate that it is essential to offer assistance and instruction to students in the educational sector so they can use ICT technology more frequently. It is also crucial for organizations to establish a supportive culture and provide the necessary technological resources to facilitate the use of ICT.

Abstract Background Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country’s economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. Methods To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. Results During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. Conclusion The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.

Abstract As technology continues to advance, the integration of generative artificial intelligence tools in various sectors, including education, has gained momentum. ChatGPT, an extensively recognized language model created by OpenAI, has gained significant importance, particularly in education. This study investigates the awareness, acceptance, and adoption of ChatGPT, a state-of-the-art language model developed by OpenAI, in higher education institutions across China. This study applies the partial least squares structural equation modeling (PLS-SEM) method for examining data collected from 320 Chinese university students. The study’s conceptual framework integrates key determinants from the Technology Acceptance Model (TAM) and extends it by incorporating perceived intelligence as a critical factor in the adoption process. The study findings reveal that ChatGPT awareness significantly influences the intention to adopt ChatGPT. Perceived ease of use, usefulness, and intelligence significantly mediate the association between ChatGPT awareness and adoption intention of ChatGPT. Additionally, perceived trust significantly moderates the relationship between ChatGPT awareness and perceived ease of use, usefulness, and intelligence. Moving forward, in order to maintain students’ critical thinking skills and inventiveness in their assessment writing, assessments must promote the safe use of ChatGPT. Therefore, educators will be crucial in ensuring that artificial intelligence tools are used in assessments ethically and suitably by providing clear guidelines and instructions.

Abstract Background Hypertensive disorders of pregnancy (HDP) are a significant cause of maternal mortality worldwide. The classification and treatment of hypertension in pregnancy remain debated. We aim to compare the effectiveness of the revised 2017 ACC/AHA blood pressure threshold in predicting adverse pregnancy outcomes. Methods We conducted a secondary data analysis of the Alliance for Maternal and Newborn Health Improvement (AMANHI) biorepository study, including 10,001 pregnant women from Bangladesh, Pakistan, and Tanzania. Blood pressure was measured using validated devices at different antenatal care visits. The blood pressure readings were categorized as: normal blood pressure (systolic blood pressure (sBP)

Background: Households are considered ideal settings for studying the transmission dynamics of an infectious disease. Methods: A prospective study was conducted, based on the World Health Organization FFX protocol from October 2020 to January,2021. Household contacts of laboratory-confirmed index cases were followed up for their symptomatic history, nasal swabs for RT-PCR,and blood samples for anti-SARS CoV-2 antibodies were collected at enrollment and days 7, 14 and 28. We estimated secondary attack rate (SAR), effective household case cluster size and determinants of secondary infection among susceptible household contacts using multivariable logistic regression. Results: We enrolled 77 index cases and their 543 contacts. Out of these, 252 contacts were susceptible at the time of enrollment. There were 77 household clusters, out of which, transmission took place in 20 (25.9%) giving rise to 34 cases. The acquired secondary attack rate (SAR) was 14.0% (95% CI 9.0–18.0). The effective household case cluster size was 0.46 (95%CI 0.33,0.56). Reported symptoms of nausea and vomiting (aOR, 7.9; 95% CI, 1.4–45.5) and fatigue (aOR, 9.3; 95% CI, 3.8–22.7) were associated with SARS-CoV-2 transmission. Conclusions: We observed a low SARS-CoV-2 secondary attack rate in the backdrop of high seroprevalence and asymptomatic transmission among households in Karachi, Pakistan.

Fintech is an industry that uses technology to enhance and automate financial services. Fintech firms use software, mobile apps, and digital technologies to provide financial services that are faster, more efficient, and more accessible than those provided by traditional banks and financial institutions. Fintech companies take care of processes such as lending, payment processing, personal finance, and insurance, among other financial services. A data breach refers to a security liability when unapproved individuals gain access to or pilfer susceptible data. Data breaches pose a significant financial, reputational, and legal liability for companies. In 2017, Equifax suffered a data breach that revealed the personal information of over 143 million customers. Combining federated learning (FL) and blockchain can provide financial institutions with additional insurance and safeguards. Blockchain technology can provide a transparent and secure platform for FL, allowing financial institutions to collaborate on machine learning (ML) models while maintaining the confidentiality and integrity of their data. Utilizing blockchain technology, FL can provide an immutable and auditable record of all transactions and data exchanges. This can ensure that all parties adhere to the protocols and standards agreed upon for data sharing and collaboration. We propose the implementation of an FL framework that uses multiple ML models to protect consumers against fraudulent transactions through blockchain. The framework is intended to preserve customer privacy because it does not mandate the exchange of private customer data between participating institutions. Each bank trains its local models using data from its consumers, which are then combined on a centralised federated server to produce a unified global model. Data is neither stored nor exchanged between institutions, while models are trained on each institution’s data.

Due to organizations’ unsatisfactory and ineffective environmental performance, scholars focused on employee behavioral improvements rather than relying primarily on technological advancements. This study intended the role of strategic HR practices toward talent retention through job satisfaction and employee work engagement in Pakistan’s education sectors. Additionally, this study examined the moderating impact of psychological empowerment on job satisfaction and work engagement toward talent retention and how they affect universities’ environmental performance. Past empirical research projects and related theories have helped in the creation of the conceptual model. The relevant associations were investigated in this study using a sample of 332 employees from the education sector of Pakistan. The partial least squares structural equation modeling (PLS-SEM) method is used to evaluate these elements in the current research investigation. The analysis showed that strategic HR practices positively influence talent retention in the education sector. Work engagement and job satisfaction positively mediate the relationship between strategic HR practices and talent retention. Furthermore, psychological empowerment positively moderates the relationship between job satisfaction and work engagement toward talent retention. Executives and policy-makers of education sectors get benefit from strategic HR practices in an environmentally friendly setting by learning how to foster a culture to improve sustainability and talent retention that sets organizational environmental values.

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN's superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control.

Abstract Background Early pregnancy nutritional status can be associated with adverse birth outcomes such as small-for-gestational age (SGA) and low birth weight (LBW). BMI (Body Mass Index) and MUAC (Mid-upper arm circumference) are easy to use assessments and are indicative of the pre-pregnancy nutritional status if obtained in the first trimester. This study primarily assesses the association of maternal nutritional status using BMI and MUAC with SGA in a community-based cohort of Pakistani women. It also aims to determine the predictive ability of MUAC and BMI in predicting SGA. Secondarily, we assessed the association between maternal nutrition and large for gestational age (LGA) and LBW. Methods This study is a secondary analysis of an ongoing pregnancy cohort “Pregnancy Risk Infant Surveillance and Measurement Alliance (PRISMA)“in Ibrahim Hyderi and Rehri Goth, Karachi. PRISMA participants who were enrolled between January 2021 to August 2022 were included given they had a gestational age

Abstract Information and communication technology (ICT) has become increasingly important worldwide in education. This study aims to recognize the factors that influence the intention to use information and communication technology (ITUICT) and its acceptance in the education sector in China. A literature review was conducted to ascertain the potential factors such as perceived awareness, information acquisition, trustworthiness, social influence, and regulatory support that could affect the acceptance and ITUICT. For results, a survey was conducted on a sample of 381 university employees in China using a convenience sampling approach. The collected data were analyzed using partial least squares-structural equation modeling (PLS-SEM). The results show that individual factors such as perceived awareness, information acquisition, trustworthiness, social influence, and regulatory support significantly impact the ITUICT. Additionally, trustworthiness positively moderates the relationship among perceived awareness, information acquisition, social influence, regulatory support, and ITUICT. Policymakers and educators can use the findings of this study to enhance the adoption of ICT in China’s education sector. The results of this study suggest that it is important to provide educational sector employees with training and support to increase their use of ICT technology. Furthermore, this paper offers theoretical progression to create a supportive organizational culture and provide the educational sector with the necessary technological resources to facilitate the use of ICT.

In light of prevailing technological advancements, online shopping has become increasingly prevalent. A well-designed webpage interface, particularly in terms of visual appeal, has the potential to evoke customer emotions such as pleasure and arousal, thereby stimulating impulse buying among consumers. In contrast to global brands, local web stores have faced challenges in devising strategies to cultivate an environment conducive to enhancing brand perception and encouraging impulsive buying behavior, specifically in the realm of online local brand retailing. Previous studies have primarily focused on customers’ cognitive perspectives, including privacy concerns, content sharing, and websites credibility. However, existing research has yet to provide a comprehensive framework for understanding customers’ online impulse buying behavior. Addressing these gaps, this study aims to investigate the effect of different web store stimuli on creating consumers’ brand perception of online web stores and its impact on customers’ impulsive buying behavior. Data was collected through purposive sampling, involving 423 users of different online local web stores. The results showed interesting findings with implications for both theory and practice. Specifically, environmental stimuli, such as ambience (lighting), assortment, forum fnac and helping staff, were found to significantly enhance the brand perception of local web store outlets, consequently boosting customers’ impulse buying behavior.

Abstract Cryptocurrencies—i.e., digital or virtual currencies secured by cryptography based on blockchain technology, such as Bitcoin and Ethereum—have brought transformative changes to the global economic landscape. These innovative transaction methods have rapidly made their mark in the financial sector, reshaping the dynamics of the global economy. However, there remains a notable hesitation in its widespread acceptance and adoption, largely due to misconceptions and lack of proper guidance about its use. Such gaps in understanding create an opportunity to address these concerns. Using the technology acceptance model (TAM), this study develops a parsimonious model to explain the awareness, acceptance, and adoption of cryptocurrency. The model was assessed using partial least squares structural equation modeling (PLS-SEM) with a sample of 332 participants aged 18 to 40 years. The findings suggest that cryptocurrency awareness plays a direct, positive, and significant role in shaping cryptocurrency adoption and that this positive relationship is mediated by factors that exemplify cryptocurrency acceptance, namely the ease of use and usefulness of cryptocurrency. The results also reveal that trust is a significant factor that strengthens these direct and mediating relationships. These insights emphasize the necessity of fostering an informed understanding of cryptocurrencies to accelerate their broader adoption in the financial ecosystem. By addressing the misconceptions and reinforcing factors like ease of use, usefulness, and trust, policymakers and financial institutions can better position themselves to integrate and promote cryptocurrency in mainstream financial systems.

Abstract Background Hyperglycemia during pregnancy leads to adverse maternal and fetal outcomes. Thus, strict monitoring of blood glucose levels is warranted. This study aims to determine the association of early to mid-pregnancy HbA1c levels with the development of pregnancy complications in women from three countries in South Asia and Sub-Saharan Africa. Methods We performed a secondary analysis of the AMANHI (Alliance for Maternal and Newborn Health Improvement) cohort, which enrolled 10,001 pregnant women between May 2014 and June 2018 across Sylhet-Bangladesh, Karachi-Pakistan, and Pemba Island-Tanzania. HbA1c assays were performed at enrollment (8 to

Abstract Gamification entails integrating game design elements, including rewards, points, competition, and interactive challenges, into non-game contexts to engage and motivate individuals. In the context of green consumption, gamification can encourage individuals to acquire more sustainable consumption behaviors. The proposed study aims to examine the influence of gamification on green consumption behavior among Chinese university students. However, university students are considered an important target group for such interventions due to their technological savvy and high interest in environmental issues. A self-determination theory (SDT) was used to measure the motivating factors of gamification for adopting green consumption behavior—a convenience sampling technique in which survey-based research designs were used to collect the data. A survey was conducted on a sample of 332 university students in China, using a questionnaire with structural equation modeling (SEM) to test the hypotheses and assess the proposed relationships between the variables. The finding of this study reveals that gamification has a significant negative relation with green consumption behavior. Further, technological awareness, hedonic motivation, and perceived enjoyment significantly mediate the relationship between gamification and green consumption behavior. Additionally, virtual CSR significantly moderates the relationship between gamification and technological awareness, hedonic motivation, and perceived enjoyment. The findings of this study could have implications for the development of more effective interventions for policy makers and industrialists aimed at promoting sustainable consumption behaviors in China.

Abstract 5G technology continues to evolve, and its potential to revolutionize various aspects of society is becoming increasingly evident. However, the successful adoption and utilization of 5G technology depends on various factors, particularly among consumers expected to be early adopters and critical drivers of technological advancements. This study investigates the effect of social influence, environmental awareness, and safety affordance on Chinese university students' actual use of 5G (AU5G) technologies. It also analyzed the mediating role of trustworthiness and intention to use 5G (ITU5G) between them and the moderating role of facilitation conditions between trustworthiness and ITU5G. Data was collected from university students (n = 364) in Beijing and was examined employing the structural equation modelling (SEM) approach. The outcomes confirm that trustworthiness and ITU5G significantly mediate the relationship between social influence, environmental awareness, and safety affordance on AU5G technologies among Chinese students. Further, the supportive facilitation conditions strengthen the relationship between trustworthiness and ITU5G. These findings are backed by UTAUT2 models that support the technology acceptance and adoption among the users. The study outcomes can help policymakers design strategies to address potential barriers and encourage greater uptake of 5G services among university students.

Federated learning (FL) is a decentralized machine learning approach whereby each device is allowed to train local models, eliminating the requirement for centralized data collecting and ensuring data privacy. Unlike typical typical centralized machine learning, collaborative model training in FL involves aggregating updates from various devices without sending raw data. This ensures data privacy and security while collecting a collective learning from distributed data sources. These devices in FL models exhibit high efficacy in terms of privacy protection, scalability, and robustness, which is contingent upon the success of communication and collaboration. This paper explore the various topologies of both decentralized or centralized in the context of FL. In this respect, we investigated and explored in detail the evaluation of four widly used end-to-end FL frameworks: FedML, Flower, Flute, and PySyft. We specifically focused on vertical and horizontal FL systems using a logistic regression model that aggregated by the FedAvg algorithm. specifically, we conducted experiments on two images datasets, MNIST and Fashion-MNIST, to evaluate their efficiency and performance. Our paper provides initial findings on how to effectively combine horizontal and vertical solutions to address common difficulties, such as managing model synchronization and communication overhead. Our research indicates the trade-offs that exist in the performance of several simulation frameworks for federated learning.

Workplace harassment is considered one of the main issues in the banking sector, which is subject to major human and labor rights violations. In the current global situation, harassed employees are prone to high turnover and become less productive due to undue stress and unstable mental health. Thus, this study aims to measure the influence of workplace harassment on the turnover intentions of employees in Pakistan's banking industry. This study assessed how workplace harassment affects employees' silence, mental stress, turnover intention, and the moderating role of psychological contract breaches. The study advises significant measures to prevent assault by retaining and empowering workers to work effectively. The data were collected through an adopted questionnaire from 416 respondents from the banking sector in Lahore, Pakistan. Variance-based structural equation modeling (PLS-SEM) was used. The results reveal that workplace harassment significantly and positively impacts turnover intention. Additionally, employee silence and mental stress have significantly mediated the relationship between workplace harassment and turnover intention. However, psychological contract breaches have significantly moderated the relationship between employee silence and workplace harassment. This study aims to promote equal employment opportunities where they can raise their voices about misconduct and lower the employee turnover ratio. Doi: 10.28991/ESJ-2023-07-05-016 Full Text: PDF

Abstract As worldwide unemployment is an extensive problem with the increasing population every day, job opportunities did not increase with a similar ratio. Unemployment is increasing, affecting developing countries’ economies like Pakistan. This attempt to solve this problem is commonly acknowledged by creating new opportunities and starting new business ventures. Considering this aspect, this study inspects the aspects that create and affect entrepreneurial intention in young entrepreneurs to start entrepreneurial projects. This study explores the effect of self-efficacy, family, institutional, and peer support on entrepreneurial intention. These factors are expected to create entrepreneurial intention in young graduates to start their business ventures. All these factors and the mediating role of knowledge of entrepreneurial skills, ability to take risks, and entrepreneurial innovativeness motivate young entrepreneurs to take startups. For results, a survey method with a questionnaire has been utilized to gather data. The collected data were evaluated through descriptive and inferential statistics. SPSS and SMART-PLS 3.3 were used for the analysis of results. 716 respondents participated in the data collection process. Data have been gathered from the master's students who registered in Pakistan's top business sector universities. The results of this study showed that self-efficacy, peers support, institutional support, and family support positively impact entrepreneurial intention. Additionally, knowledge of entrepreneurial skills, the ability to take risks, and entrepreneurial innovativeness also significantly affect entrepreneurial intention. In this study, all these results have been discussed. This study also discusses various theoretical and practical factors with substantial policy-making significance.

Purpose – This study aims to examine the impact of website design, reliability and perceived ease of use as an engagement motivational factors on customer e-trust and e-retention in online shopping. Design/methodology/approach – By using deductive approach, quantitative methods and purposive sampling technique, this study has collected the data from 295 young online customers to enhance an understanding of website design, reliability and perceived ease of use in an online shopping context. Findings – The findings revealed interesting insights where reliability is the most significant predictor of customer e-trust in online shopping, followed by perceived ease of use and website design. In addition, a significant mediating effect of e-trust is found between customer e-retention, website design, reliability and perceived ease of use. Research limitations/implications – Future research is recommended to predict the antecedents of online engagement motivational factors with value co-creation and co-creation experience in online shopping context. Originality/value – This study offers fresh insights about driving elements and impediments of online customer retention. Customer engagement comprising of website design, reliability and perceived ease of use appear to influence the online customer retention through direct and indirect effect.

There are millions of people suffering from thyroid disease all over the world. For thyroid cancer to be effectively treated and managed, a correct diagnosis is necessary. In this article, we suggest an innovative approach for diagnosing thyroid disease that combines an adaptive synthetic sampling method with weighted average voting (WAV) ensemble of two distinct super learners (SLs). Resampling techniques are used in the suggested methodology to correct the class imbalance in the datasets and a group of two SLs made up of various base estimators and meta-estimators is used to increase the accuracy of thyroid cancer identification. To assess the effectiveness of our suggested methodology, we used two publicly accessible datasets: the KEEL thyroid illness (Dataset1) and the hypothyroid dataset (Dataset2) from the UCI repository. The findings of using the adaptive synthetic (ADASYN) sampling technique in both datasets revealed considerable gains in accuracy, precision, recall and F1-score. The WAV ensemble of the two distinct SLs that were deployed exhibited improved performance when compared to prior existing studies on identical datasets and produced higher prediction accuracy than any individual model alone. The suggested methodology has the potential to increase the accuracy of thyroid cancer categorization and could assist with patient diagnosis and treatment. The WAV ensemble strategy computational complexity and the ideal choice of base estimators in SLs continue to be constraints of this study that call for further investigation.

The advancement of artificial intelligence (AI) and the ubiquity of social media have become transformative agents in contemporary educational ecosystems. The spotlight of this inquiry focuses on the nexus between AI and social media usage in relation to academic performance and mental well-being, and the role of smart learning in facilitating these relationships. Using partial least squares–structural equation modeling (PLS-SEM) on a sample of 401 Chinese university students. The study results reveal that both AI and social media have a positive impact on academic performance and mental well-being among university students. Furthermore, smart learning serves as a positive mediating variable, amplifying the beneficial effects of AI and social media on both academic performance and mental well-being. These revelations contribute to the discourse on technology-enhanced education, showing that embracing AI and social media can have a positive impact on student performance and well-being.

Objective: To determine the frequency of dyslipidaemia in acute non-cardio embolic ischemic stroke patients at a tertiary care centre in Rawalpindi. Study Design: Cross-sectional study Place and Duration of Study: Neurology Department, Pak Emirates Military Hospital (PEMH), Rawalpindi Pakistan, Jul 2022 to Feb 2023. Methodology: Patients with acute non-cardio embolic ischemic stroke were included. Data was collected from the patient files and hospital records including a thorough history, physical examination information, CT/MRI brain, fasting lipid profiles with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Results: The study included 196 patients, of which dyslipidaemia was seen with a major share of high TG in 44(22.44%), followed by high LDL cholesterol in 38(19.38%), and high TC was found in 28(14.28%). Conclusion: More than 1/3rd of patients with acute non-cardioembolic ischemic stroke have dyslipidaemia.

Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior. The Involutional Neural Network (INN)-based deep learning model is used for classification, achieving an accuracy of up to 97% across various temporal segments. The proposed methodology is shown to be effective in accurately classifying the feeding intensities of Oplegnathus punctatus, enabling insights pertinent to aquaculture enhancement and ecosystem management. Future work may include additional feature extraction modalities and multi-modal data integration to further our understanding and contribute towards the sustainable management of marine resources.

Pyrroloquinoline Quinone (PQQ) is a redox-active quinone molecule with significant implications for human health. Originally identified as a bacterial cofactor, PQQ has since been lauded for its diverse biological and therapeutic activities. It serves as an essential cofactor for oxidative enzymes that are vital for mitochondrial function and ATP synthesis. PQQ exhibits superior antioxidant properties that protect against ROS-mediated oxidative stress, aging, neurodegenerative diseases, certain cancers, diabetes, and metabolic disorders. It also enhances cognitive abilities and reduces insulin sensitivity. PQQ's antioxidant nature helps mitigate oxidative stress, which is implicated in many diseases. It has been shown to target cancer cells selectively, suggesting its potential as a therapeutic agent. Clinical studies have indicated the potential benefits of PQQ supplementation, including improvements in cardiovascular health, cognitive function, weight management, insulin sensitivity, and the prevention of metabolic syndromes. The safety of PQQ has been established, with no reported toxicity or genotoxicity in various studies, and it is considered a safe nutritional supplement. Future research directions should focus on determining the optimal dosages of PQQ for specific health outcomes and assessing its long-term effectiveness and safety. The translation of PQQ research into clinical practice could offer new strategies for managing metabolic disorders, enhancing cognitive health, and potentially extending lifespan. In summary, PQQ is a promising molecule with broad potential health benefits, impacting human health from cellular metabolism to disease prevention and treatment, positioning it as a key player in nutritional science and public health.

Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

This research study aims to reveal the role of supply chain parameters approved by blockchain technology toward firm performance through trust. This study has also examined the moderating role of government support between trust and firm performance. The underlying theories reinforce the usefulness and flexibility of a supply chain in regulating daily fluctuations and uncertainties in supply and demand. Blockchain technology adoption with supply chain tactics provides a more suitable environment for better firm performance. Governments address legal and security concerns related to blockchain technology and provide clear guidelines and standards for its use in supply chains, which build trust among firms and stakeholders. A conceptual model has been developed with the assistance of past empirical research studies and associated theories. This research study has examined the following relationships from a sample of 465 employees from textile industrial firms listed on the Pakistan Stock Exchange. The current research study assesses these parameters using the partial least squares structural equation modeling (PLS-SEM) method. The analysis showed that supply chain parameters (alignment, agility, adaptability) approved by blockchain technology positively correlate with firm performance. Trust positively mediated the relationship between supply chain parameters approved by blockchain technology and firm performance. Furthermore, government support positively moderated the relationship between trust and firm performance. The study would provide directions for further research. These findings will give the global supply chain industry valuable insights into blockchain technology for firm performance. In theory, this research study would contribute to the scientific literature by answering how trust and government support affect the overall firm performance.

Purpose – Entrepreneurship is an important paradigm for enhancing the economic well-being of nations. However, despite heated debate about the significant role of entrepreneurial education (EE) in developing favourable entrepreneurial intention (EI), little is known about the role of individuals’ entrepreneurial self-efficacy (ES), entrepreneurial motivation (EM) and family support (FS), which the authors investigated in this study. Design/methodology/approach – This study has used a quantitative research design to collect data from 334 young people from various Malaysian higher education institutes using a purposive sampling technique and a deductive approach based on the theory of planned behaviour (TPB). Findings – The findings revealed interesting insights into the criticality of young people’s ES, EM and FS in learning methods, techniques and skills to start new enterprises. Moreover, EE was a significant mediator of the relationship between individual self-efficacy, FS, EM and EI. Originality/value – This study is among the few to contribute to strategic management scholarship by designing a framework based on the idea that EE relies on diverse factors, particularly ES, EM and FS. These factors encourage Malaysian young people to seek the necessary education to develop favourable EI and launch successful businesses.

Energy demand is increasing globally due to the growing human population and progressive lifestyle. The adequate use of available energy resources, including renewable, contributes to a country’s economic sustainability and future development. Optimization-based energy management and cost minimization plays a significant role in overcoming energy crises in less developed countries. In this paper, an optimization–based dynamic energy management technique for smart grids is developed based on the integration of available renewable resources and variable consumer demand, distinctive to underdeveloped countries. Consumer demand is classified into fixed, flexible, and highly variable based on population characteristics. In this work, we developed a Dynamic Multiple Knapsack DMKNS algorithm, which automatically schedules energy provision to various users by optimally accounting for the available resources (Grid and Renewable). The proposed method provides a low-cost solution by maintaining a constant energy supply while preserving consumer comfort and grid stability. The simulation results with various intermittent availability of resources using MKNS show a saving up to 50% for a variable energy demand user. The proposed method is general and can also be applied to various underdeveloped regions with similar consumer demand and statistics.

Abstract Background and Aims COVID‐19 vaccinations have reduced morbidity and mortality from the disease. Antibodies against severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) have been associated with immune protection. Seroprevalence studies revealed high immunoglobulin G (IgG) antibody levels to SARS‐CoV‐2 in the Pakistani population before vaccinations. We investigated the effect of BBIBP‐CorV vaccination on circulating IgG antibodies and interferon (IFN)‐γ from T cells measured in a cohort of healthy individuals, with respect to age, gender, and history of COVID‐19. Methods The study was conducted between April and October 2021. BBIBP‐CorV vaccinated participants were followed up to 24 weeks. Antibodies to SARS‐CoV‐2 Spike protein and its receptor‐binding domain (RBD) were measured. IFNγ secreted by whole blood stimulation of Spike protein and extended genome antigens was determined. Results Study participants with a history of prior COVID‐19 displayed a higher magnitude of IgG antibodies to Spike and RBD. IgG seropositivity was greater in those with prior COVID‐19, aged 50 years or younger and in females. At 24 weeks after vaccination, 37.4% of participants showed IFN‐γ responses to SARS‐CoV‐2 antigens. T cell IFN‐γ release was higher in those with prior COVID‐19 and those aged 50 years or less. Highest IFN‐γ release was observed to extended genome antigens in individuals both with and without prior COVID‐19. Conclusion We found that IgG seropositivity to both Spike and RBD was affected by prior COVID‐19, age and gender. Importantly, seropositive responses persisted up to 24 weeks after vaccination. Persistence of vaccine induced IgG antibodies may be linked to the high seroprevalence observed earlier in unvaccinated individuals. Increased T cell reactivity to Spike and extended genome antigens reflects cellular activation induced by BBIBP‐CorV. COVID‐19 vaccination may have longer lasting immune responses in populations with a higher seroprevalence. These data inform on vaccination booster policies for high‐risk groups.

Vaccine hesitancy is a significant public health issue globally. We aim to document the barriers toward seasonal influenza vaccine uptake among healthcare workers (HCWs) and pregnant women (PW) in Pakistan. We performed a concurrent mixed methods study in four cities (Karachi, Islamabad, Quetta, and Peshawar) across Pakistan from September to December 2021. The quantitative component consisted of independent cross-sectional surveys for PW and HCWs, and the qualitative component comprised of in-depth interviews (IDIs) and focus group discussions (FGDs) among HCWs. Simple linear regression was used to determine the association of sociodemographic variables with knowledge, attitudes, and practices. Overall, 750 PW and 420 HCWs were enrolled. Among the PW, 44% were willing to receive the vaccine if available free of cost. Only 44% of the HCWs were vaccinated; however, 86% intended to get vaccinated and were willing to recommend the vaccine to their patients. HCWs refused vaccine due to side-effects (65%), cost (57%), and allergies (36%). An education level of secondary school and above was predictive of higher attitude and knowledge scores while having received the COVID-19 vaccine was associated with higher practice scores for both PW and HCWs. Several themes emerged from the interviews: 1) HCWs’ knowledge of influenza and its prevention, 2) HCWs’ perception of motivators and barriers to influenza vaccine uptake and 3) HCWs’ attitudes towrd vaccine promotion. We report low influenza vaccine coverage among HCWs and PW in Pakistan. Educational campaigns addressing misconceptions, and improving affordability and accessibility through government interventions, can improve vaccine uptake.

The aim of this study was to provide a comparative analysis of chitosan (CH), copper oxide (CuO), and chitosan-based copper oxide (CH-CuO) nanoparticles for their application in the healthcare sector. The nanoparticles were synthesized by a green approach using the extract of Trianthema portulacastrum. The synthesized nanoparticles were characterized using different techniques, such as the synthesis of the particles, which was confirmed by UV–visible spectrometry that showed absorbance at 300 nm, 255 nm, and 275 nm for the CH, CuO, and CH-CuO nanoparticles, respectively. The spherical morphology of the nanoparticles and the presence of active functional groups was validated by SEM, TEM, and FTIR analysis. The crystalline nature of the particles was verified by XRD spectrum, and the average crystallite sizes of 33.54 nm, 20.13 nm, and 24.14 nm were obtained, respectively. The characterized nanoparticles were evaluated for their in vitro antibacterial and antibiofilm potential against Acinetobacter baumannii isolates, where potent activities were exhibited by the nanoparticles. The bioassay for antioxidant activity also confirmed DPPH scavenging activity for all the nanoparticles. This study also evaluated anticancer activities of the CH, CuO, and CH-CuO nanoparticles against HepG2 cell lines, where maximum inhibitions of 54, 75, and 84% were recorded, respectively. The anticancer activity was also confirmed by phase contrast microscopy, where the treated cells exhibited deformed morphologies. This study demonstrates the potential of the CH-CuO nanoparticle as an effective antibacterial agent, having with its antibiofilm activity, and in cancer treatment.

Objectives: Staphylococcus aureus and Streptococcus pneumoniae are common colonizers of the human nasopharynx. In this study, we describe S. aureus nasopharyngeal carriage and evaluate its association with S. pneumoniae carriage post-10-valent pneumococcal conjugate vaccine (PCV10) introduction in Pakistan. Methods: A serial cross-sectional study was undertaken from 2014 to 2018, children

Wheat (Triticum aestivum L.) belonging to one of the most diverse and substantial families, Poaceae, is the principal cereal crop for the majority of the world’s population. This cereal is polyploidy in nature and domestically grown worldwide. Wheat is the source of approximately half of the food calories consumed worldwide and is rich in proteins (gluten), minerals (Cu, Mg, Zn, P, and Fe), vitamins (B-group and E), riboflavin, niacin, thiamine, and dietary fiber. Wheat seed-storage proteins represent an important source of food and energy and play a major role in the determination of bread-making quality. The two groups of wheat grain proteins, i.e., gliadins and glutenins, have been widely studied using SDS-PAGE and other techniques. Sustainable production with little input of chemicals along with high nutritional quality for its precise ultimate uses in the human diet are major focus areas for wheat improvement. An expansion in the hereditary base of wheat varieties must be considered in the wheat breeding program. It may be accomplished in several ways, such as the use of plant genetic resources, comprising wild relatives and landraces, germplasm-assisted breeding through advanced genomic tools, and the application of modern methods, such as genome editing. In this review, we critically focus on phytochemical composition, reproduction growth, types, quality, seed storage protein, and recent challenges in wheat breeding and discuss possible ways forward to combat those issues.

The intelligent transport system (ITS) has transformed urban transportation, enhancing daily commutes with services like congestion management, vehicle crash prevention, traffic control, roadside safety, breakdown assistance, ticket booking, vehicle registration, and insurance. However, in urban bus transportation, the ITS faces security threats, such as data forgery and manipulation. To counter these challenges, a blockchain-based framework for bus transportation approval is proposed, ensuring data integrity and security. The framework’s performance is evaluated based on processing time, central processing unit (CPU), graphical processing unit (GPU), cloud usage, and memory consumption, and compared to Ethereum and Aurora testnet, in terms of gas cost, security, and performance. Stochastic algorithms, including the genetic algorithm and Tabu search, are used for time complexity analysis, to obtain an optimized solution. The decision-making trial and evaluation laboratory (DEMATEL) analysis is also performed to assess factors like transaction costs, execution time, memory consumption, and security. The results show that execution time, memory consumption, and processing time are crucial, while transaction cost, reliability, and transparency positively impact the system’s effectiveness. By reducing the risk of false data presentation and ensuring accurate records, the proposed framework contributes to a more efficient and reliable transportation system.

Wheat crops are highly sensitive to high temperatures during their reproductive and grain-filling phases. We hypothesized that potassium could increase thermotolerance in wheat during grain filling by protecting cellular organelles, particularly chlorophyll, from heat injury. Two wheat genotypes, Ujala-16 (relatively heat tolerant) and Anaj-17 (relatively susceptible) were grown in pots and were submitted to 4 and 8 days of heat stress under polythene sheets 1 week after anthesis. One day before the onset of heat stress, 2% potassium (K) as K2SO4 was sprayed on all the plants. Flag leaves from both genotypes were collected after 4 and 8 days of heat stress. Leaf physiology changes were measured to quantify heat damage and to understand the K-induced recovery mechanism. The crop was harvested 125 days after sowing, and grain yield data were collected. Increasing duration of heat stress significantly impaired leaf physiology and grain yield of both studied wheat genotypes. Compared with control (under optimum temperature), 4 and 8 days heat-stressed plants produced 11 and 19% lesser grain yield per spike (averaged across genotypes and in the second years of study), respectively. Likewise, 4- and 8-days heat-stressed plants had 15 and 37% (averaged across genotypes and in the second years of study) lower flag leaf photosynthesis, respectively, compared with control plants. Across the genotypes, 8-days heat caused significantly more grain yield loss in Anaj-17 during the second year than in Ujala-16. Foliar K significantly restored leaf chlorophyll, Pn, Fv/Fm by reducing cellular membrane damage in the heat-stressed plants. This physiological recovery and activation of the plant defensive system by K under high-temperature stress protected the growth and grain development. For example, K–treated plants produced 19% higher 1,000 grain weight in 8 days of heat stress (across genotypes and in the second years of study) compared with water-treated plants under the hot environment of the respective thermal regime. Our study suggests that wheat performance under terminal heat stress can be improved through the exogenous application of K.

Electronic marketing (eM) is a flourishing phenomenon that is gaining intense concern because of a significant impact on organizational performance. Over the past few decades, the relevance of eM has been observed in numerous fields (e.g., consumers, organizational strategy, advertisement, and overall philosophy of management to understand the insights globally). To effectively maneuver the field, all stakeholders, particularly academicians and practitioners, must comprehend the current position of the eM theory and practices for dynamic utilization. A systematic bibliometric analysis can serve this issue by providing a holistic view of the publication trend and its trajectory in terms of various themes, including citations and publication metrics. This study analyzes the bibliometric data from 2000 to 2019 to reveal the most productive countries, universities, authors, journals, and prolific publications in electronic marketing. To this end, VOS viewer software was used to visualize the mapping based on co-citation, bibliographic coupling (BC), and co-occurrence (CC). The primary addition of this research is to provide an overview of eM tendencies and paths that may help researchers know the tendencies and future research directions worldwide.

An ample quantity of water and sufficient nutrients are required for economical rice production to meet the challenges of ever-increasing food demand. Currently, slow-release nitrogenous fertilizers for efficient inputs utilization and maximum economic yield of field crops are in the limelight for researchers and farmers. In this study, we evaluated the comparative efficacy of conventional urea and coated urea (zinc and neem) on rice grown under aerobic and anaerobic regimes in greenhouse conditions. For the aerobic regime, field capacity was maintained at 80–100% to keep the soil aerated. On the other hand, for the anaerobic regime, pots were covered with a polythene sheet throughout the experimentation to create flooded conditions. All forms of urea, conventional and coated (zinc and neem), improved plant growth, gas exchange, yield, yield contributing parameters, and quality characteristics of rice crop. However, better performance in all attributes was found in the case of zinc-coated urea. Gas exchange attributes (photosynthetic rate, 30%, and stomatal conductance 24%), yield parameters like plant height (29%), tillers per plant (38%), spikelets per spike (31%), grains per panicle (42%), total biomass (53%), and grain yield (45%) were recorded to be maximum in rice plants treated with zinc-coated urea. The highest grain and straw nitrogen contents, grain protein contents, and grain water absorption ratio were also found in plants with zinc-coated urea applications. In irrigation practices, the anaerobic regime was found to be more responsive compared to the aerobic regime regarding rice growth, productivity, and quality traits. Thus, to enhance the productivity and quality of rice grown in anaerobic conditions, zinc-coated urea is best suited as it is more responsive when compared to other forms of urea.

Subjective paper evaluation is a tricky and tiresome task to do by manual labor. Insufficient understanding and acceptance of data are crucial challenges while analyzing subjective papers using Artificial Intelligence (AI). Several attempts have been made to score students’ answers using computer science. However, most of the work uses traditional counts or specific words to achieve this task. Furthermore, there is a lack of curated data sets as well. This paper proposes a novel approach that utilizes various machine learning, natural language processing techniques, and tools such as Wordnet, Word2vec, word mover’s distance (WMD), cosine similarity, multinomial naive bayes (MNB), and term frequency-inverse document frequency (TF-IDF) to evaluate descriptive answers automatically. Solution statements and keywords are used to evaluate answers, and a machine learning model is trained to predict the grades of answers. Results show that WMD performs better than cosine similarity overall. With enough training, the machine learning model could be used as a standalone as well. Experimentation produces an accuracy of 88% without the MNB model. The error rate is further reduced by 1.3% using MNB.

The research on knowledge transfer (KT) has attracted much attention from the researcher and academicians from the last decade. Bibliometric and visualization research on this topic is insufficient. This study aims to analyze the leading trends in the literature of KT and provide a holistic view. Data for this analysis is extracted from the Scopus database. VOSviewer software is used to map different bibliometric indicators such as bibliographic coupling and co-occurrence. This study identifies the most productive countries, authors, journals, and the most prolific publications in knowledge transfer research during 2010-2019. Moreover, it also identifies the most popular themes and suggests some future directions. The study also provides an overview of the trends and trajectories of KT with a visual and schematic frame, which may help the researchers and the practitioners understand the current trends and future research directions.

Abstract Rapid industrialization and urbanization have resulted in elevated global temperature over the years consequently disturbing the balance of agro-ecological systems worldwide. Therefore, new eco-friendly agricultural practices for sustainable food production are needed. Mulching could potentially serve the purpose by reducing soil evaporation, conserving moisture, controlling soil temperature, reducing weed growth, and improving microbial activities. Additionally, mulches could provide economical, aesthetic, and environmental advantages to agriculture and landscape. Moreover, in the restoration sites, mulches are widely used for the plantation of trees which need no significant care. Mulches combat with different stress conditions in agricultural lands as well as in landscapes. This review paper focuses on multiple significant impacts of mulches for the production and establishment of different crops in nature. Mulches conserve the soil moisture, enhance the nutrients status of soil, control the erosion losses, suppress the weeds in crop plants, and remove the residual effects of pesticides, fertilizers, and heavy metals. Mulches improve the aesthetic value of landscapes and economic value of crops. This paper also describes some problems associated with various mulch materials. There are contradictions about mulching materials as some researchers favor mulches and others have denoted some concerns. The selection of mulching material is important with respect to crop type, management practices, and climatic conditions. The appropriate mulching technique could provide the aforementioned benefits to the agro-ecological systems. Therefore, the impacts of low-cost, eco-friendly, and biodegradable mulching materials on soil microbes, nutrient balance, plant growth, and soil erosion should be explored in the future.

Gradually rising atmospheric temperature is the vital component of the environment, which is anticipated as the riskiest abiotic stress for crop growth. Nanotechnology revolutionizing the agricultural sectors, notably, zinc oxide nanoparticles (nano-ZnO) has captured intensive research interests due to their distinctive properties and numerous applications against abiotic stresses. Mungbean (Vigna radiata L.), being a summer crop, is grown all over the world at an optimum temperature of 28–30°C. A rise in temperature above this range, particularly during the flowering stage, can jeopardize the potential performance of the plant. Hence, an outdoor study was performed to evaluate the effect of multiple suspensions of nano-ZnO (0, 15, 30, 45, and 60 mg l–1) on physicochemical attributes and yield of mungbean crop under heat stress. Heat stress was induced by fine-tuning of sowing time as: S1 is the optimal sowing time having day/night temperatures 40/25°C during the flowering stage. In vitro studies on Zn release from nano-ZnO by inductively coupled plasma mass spectroscopy (ICPMS) disclosed that the Zn release and particles uptake from nano-ZnO were concentration-dependent. Exogenous foliar application of nano-ZnO significantly upstreamed the production of antioxidants and osmolytes to attenuate the shocks of heat stress in S2 and S3. Likewise, nano-ZnO substantially rebated the production of reactive oxygen species in both S2 and S3 that was associated with curtailment in lipid peroxidation. Adding to that, foliar-applied nano-ZnO inflates not only the chlorophyll contents and gas exchange attributes, but also the seeds per pod (SPP) and pods per plant (PPP), which results in the better grain yield under heat stress. Thus, among all the sowing dates, S1 statistically performed better than S2 and S3, although foliar exposure of nano-ZnO boosted up mungbean performance under both the no heat and heat-induced environments. Hence, foliar application of nano-ZnO can be suggested as an efficient way to protect the crop from heat stress-mediated damages with the most negligible chances of nanoparticles delivery to environmental compartments that could be possible in case of soil application.

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography.