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Abstract Phosphorus (P) is a crucial macronutrient for plant growth and development. Basic metabolic processes regulate growth; however, the molecular detail of these pathways under low phosphorous (LP) in wheat is still unclear. This study aims to elucidate the varied regulatory pathways responses to LP stress in wheat genotypes. Phenotypic, physiological, and transcriptome analyses were conducted on Fielder (P efficient) and Ardito (P inefficient) wheat genotypes after four days of normal phosphorous (NP) and LP stress. In response to LP, Fielder outperformed Ardito, displaying higher chlorophyll content-SPAD values (13%), plant height (45%), stem diameter (12%), shoot dry weight (42%), and root biomass (75%). Root structure analysis revealed that Fielder had greater total root length (50%), surface area (56%), volume (15%), and diameter (4%) than Ardito under LP. These findings highlight Fielder’s superior performance and adaptation to LP stress. Transcriptome analysis of wheat genotype roots identified 3029 differentially expressed genes (DEGs) in Fielder and 1430 in Ardito, highlighting LP-induced changes. Key DEGs include acid phosphatases (PAPs), phosphate transporters (PHT1 and PHO1), SPX, and transcription factors (MYB, bHLH, and WRKY). KEGG enrichment analysis revealed key pathways like plant hormones signal transduction, biosynthesis of secondary metabolites, and carbohydrate biosynthesis metabolism. This study unveils crucial genes and the intricate regulatory process in wheat’s response to LP stress, offering genetic insights for enhancing plant P utilization efficiency.

Abstract Crop residue-based biochar (CRB) has shown great potential for removing trace elements (TEs) from aquatic matrices. Despite the increasing interest in this area, no review has focused specifically on the efficacy of CRB for TEs removal in aquatic environments. This comprehensive review examines the global TEs water contamination status with an emphasis on their sources, compositional metrics for crop residue feedstock (proximate, ultimate, and lignocellulosic properties), and the potential use of CRB for TEs removal in aquatic media. It also evaluates the factors that affect the ability of CRB to remove TEs, such as feedstock type, production conditions, water pH, background electrolytes, water temperature, CRB/water ratio, and underlying pollutant sorption mechanisms. This review also discusses the practical applications of CRB in real water samples and engineering considerations for designing CRB with improved physicochemical properties, treatment efficiencies, and regeneration abilities. Additionally, the cost–benefit and economic assessment of CRB, challenges, and future research directions related to CRB are highlighted to promote research on this sustainable source of biochar. By elucidating the prospects of CRB as an adsorbent, this review emphasizes the need for continued research on its practical implications for environmentally relevant pollutant concentrations. Graphical Abstract

Abstract The use of saline water under drought conditions is critical for sustainable agricultural development in arid regions. Biochar is used as a soil amendment to enhance soil properties such as water-holding capacity and the source of nutrition elements of plants. Thus, the research was carried out to assess the impact of biochar treatment on the morphological and physiological characteristics and production of Solanum lycopersicum in greenhouses exposed to drought and saline stresses. The study was structured as a three-factorial in split-split-plot design. There were 16 treatments across three variables: (i) water quality, with freshwater and saline water, with electrical conductivities of 0.9 and 2.4 dS m− 1, respectively; (ii) irrigation level, with 40%, 60%, 80%, and 100% of total evapotranspiration (ETC); (iii) and biochar application, with the addition of biochar at a 3% dosage by (w/w) (BC3%), and a control (BC0%). The findings demonstrated that salt and water deficiency hurt physiological, morphological, and yield characteristics. Conversely, the biochar addition enhanced all characteristics. Growth-related parameters, such as plant height, stem diameter, leaf area, and dry and wet weight, and leaf gas exchange attributes, such rate of transpiration and photosynthesis, conductivity, as well as leaf relative water content were decreased by drought and salt stresses, especially when the irrigation was 60% ETc or 40% ETc. The biochar addition resulted in a substantial enhancement in vegetative growth-related parameters, physiological characteristics, efficiency of water use, yield, as well as reduced proline levels. Tomato yield enhanced by 4%, 16%, 8%, and 3% when irrigation with freshwater at different levels of water deficit (100% ETc, 80% ETc, 60% ETc, and 40% ETc) than control (BC0%). Overall, the use of biochar (3%) combined with freshwater shows the potential to enhance morpho-physiological characteristics, support the development of tomato plants, and improve yield with higher WUE in semi-arid and arid areas.

Abstract Environmental behavior needs a keen intervention to avoid the upcoming worse outcomes of the energy utilization. Global warming is transitioning towards the global boiling stage and the reason behind the faster growth of renewable energy resources exploration. The present study empirically investigates the circularity impact of the renewable energy on the environment. Two different models of the environment were estimated by applying biased corrected method on 28 low income countries from 1990 to 2019. In this study, we develop a circularity index on the basis of renewable energy sources and found that in low income economies circularity brings a positive impact on the environment in both models. The study also concludes that digitization has also positive implication in terms of environmental quality. In the sample study trade shows negative as well as positive association with environment while economic growth, financial development and energy use have positive impact on the environment. The study suggested policies based on the circular practices for energy generation to achieve the sustainable development goals.

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Objectives The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Abstract Biochar is a promising solution to alleviate the negative impacts of salinity stress on agricultural production. Biochar derived from food waste effect was investigated on three plant species, Medicago sativa, Amaranthus caudatus, and Zea mays, under saline environments. The results showed that biochar improved significantly the height by 30%, fresh weight of shoot by 35% and root by 45% of all three species compared to control (saline soil without biochar adding), as well as enhanced their photosynthetic pigments and enzyme activities in soil. This positive effect varied significantly between the 3 plants highlighting the importance of the plant-biochar interactions. Thus, the application of biochar is a promising solution to enhance the growth, root morphology, and physiological characteristics of plants under salt-induced stress.

Abstract Resveratrol, a natural polyphenol in various plants, has gained significant attention for its potential health‐promoting properties. It has been demonstrated, after reviewing various clinical and in vitro studies, that resveratrol possesses potent antioxidant potential. Resveratrol demonstrates cellular component protection by directly neutralizing free radicals (FRs) and enhancing the expression of natural antioxidant enzymes, thereby mitigating oxidative damage to proteins, lipids, and nucleic acids. Clinical trials have shown promising results, indicating that resveratrol supplementation can enhance antioxidant defenses and reduce oxidative damage markers in various populations. In addition to its antioxidant effects, resveratrol exhibits potent anti‐inflammatory properties. It can modulate key inflammatory pathways, such as nuclear factor‐kappa B (NF‐κB) and mitogen‐activated protein kinases (MAPKs), thereby suppressing the production of pro‐inflammatory cytokines and chemokines. Furthermore, resveratrol's multimodal effects extend beyond its antioxidant and anti‐inflammatory properties. It has been discovered to exert regulatory effects on various cellular processes, including apoptosis, cell cycle progression, angiogenesis, and immunological responses. The primary aim of this review paper is to provide a thorough overview of the current knowledge on resveratrol, including its chemical composition, bioaccessibility, clinical effectiveness, and utilization in nanotechnology to enhance its bioavailability. From future perspectives, revising the administration methods for certain contexts and understanding the underlying systems responsible for resveratrol's effects will require further inquiry. For the highest potential health results, advanced trial‐based research is necessary for combinational nano‐delivery of resveratrol.

Abstract Diabetes mellitus (DM) is a chronic metabolic disorder characterized by an elevated level of blood glucose due to the absence of insulin secretion, ineffectiveness, or lack of uptake of secreted insulin in the body. The improperly diagnosed and poorly managed DM can cause severe damage to organs in the body like the nerves, eyes, heart, and kidneys. This study was aimed at investigating the effect of Clostridium butyricum (probiotic) with magnesium supplementation to evaluate the effect on gut microbial dysbiosis and blood glucose levels. In the laboratory, 6–8 weeks old 24 male albino rats weighing 200–250 g were given free access to water and food. Diabetes was induced using streptozotocin (60 mg/kg) in overnight fasted rats. Diabetic rats were randomly divided into four groups (n = 6, 6 replicates in each group). Metformin (100 mg/kg/day) with a standard basal diet was provided to control group (G0), Clostridium butyricum (1.5 × 105 CFU/day) with standard basal diet was provided to treatment group (G1), magnesium (500 mg/kg/day) was provided to group (G2). Clostridium butyricum (1.5 × 105 CFU/day) and magnesium (300 mg/kg/day) in combination with a standard basal diet was provided to group (G3). Blood Glucose, Magnesium blood test and microbial assay were done. Random blood glucose levels were monitored twice a week for 21 days and were represented as mean of each week. The results conclude that Clostridium butyricum (1.5 × 105 CFU) is very effective in balancing random blood glucose levels from 206.6 ± 67.7 to 85.1 ± 3.8 (p = 0.006) compared to other groups (p > 0.005). The results of stool analysis showed that Clostridium butyricum as probiotic restores microbial dysbiosis as evident by the 105 CFU Clostridium butyricum load in G1, which was higher than G0, G2 and G3 which were 103 and 104 CFU respectively. The findings of this study conclude that Clostridium butyricum supplementation improved blood glucose levels and intestinal bacterial load in type II diabetes mellitus.

Abstract With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

People residing in arid and semi-arid regions face significant challenges because of water scarcity and floods. These challenges become more severe in downstream areas of hill torrents that experience highly unpredictable periods of droughts and floods due to highly variable rainfall. Constructing small dams in hill torrents to capture rainwater from watersheds, mitigate flash floods, and recharge artificial groundwater is one of the solutions to address these issues in South Punjab. However, the success of rainwater harvesting (RWH) systems depends on their technical design and suitable site selection. To identify appropriate sites for small dams for rainwater harvesting, a combination of Geographical Information Systems (GIS), Remote Sensing (RS), and Multi-Criteria Decision Analysis (MCDA) has been utilized in the catchment of the Vidore Hill Torrent, Punjab, Pakistan. The primary goal of this study was to evaluate the use of RS and MCDA in developing a suitability map, in a data-scarce region that can be useful for hydrologists, decision-makers, and planners in quickly identifying areas with the highest potential for RWH. The model incorporated several factors, including slope, runoff depth, land use, soil texture, drainage density, and lineament density. Implementing this method could also support policy changes toward widespread adoption of RWH. HIGHLIGHTS Management of water scarcity in dry period and floods in wet days.; Optimal solutions of Rainwater harvesting (RWH).; Application of GIS and remote sensing Approach.; Ranking of water conservation sites.; Guiding principles for the Policy implications.;

Abstract The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations.

Members of the lichen-forming fungal genus Oxneriaria are known to occur in cold polar and high altitudinal environments. Two new species, Oxneriaria crittendenii and O. deosaiensis, are now described from the high altitude Deosai Plains, Pakistan, based on phenotypic, multigene phylogenetic and chemical evidence. Phenotypically, O. crittendenii is characterised by orbicular light-brown thalli 1.5–5 cm across, spot tests (K, C, KC) negative, apothecia pruinose, hymenium initially blue then dark orange in response to Lugol’s solution. Oxneriaria deosaiensis is characterised by irregular areolate grey thalli 1.5–2 cm across, K test (light brown), KC test (dark brown), apothecia epruinose, hymenium initially blue then dark blue in response to Lugol’s solution. Both species share the same characters of thalli with black margins and polarilocular ascospores. The closest previously reported species, O. pruinosa, differs from O. crittendenii and O. deosaiensis in having non-lobate margins, thin thalline exciple (45–80 μm thick), short asci (55–80 × 25–42 μm) and K positive (yellow) and KC negative tests and divergent DNA sequence in the ITS, LSU and mt SSU regions. The newly-described Oxneriaria species add to growing evidence of the Deosai Plains as a region of important arctic-alpine biodiversity.

Long non-coding RNAs (lncRNAs) have attracted significant scientific attention due to their central role in regulating gene expression and their profound impact on the intricate mechanisms of ovarian function. These versatile molecules exert their influence through various mechanisms, including the coordination of transcription processes, modulation of post-transcriptional events, and the shaping of epigenetic landscapes. Furthermore, lncRNAs function as competitive endogenous RNAs (ceRNAs), engaging in intricate interactions with microRNAs (miRNAs) to finely adjust the expression of target genes. The intricate lncRNA-miRNA-mRNA network serves as a crucial determinant in governing the multifaceted physiological functions of the ovaries. It holds substantial potential in unraveling the causes and progression of reproductive disorders and, importantly, in identifying new therapeutic targets and diagnostic markers for these conditions. A comprehensive comprehension of lncRNAs and their ceRNA activities within the domain of ovarian biology could potentially lead to groundbreaking advancements in clinical interventions and management strategies. This exploration of lncRNAs and their intricate involvement in the regulatory framework provides an extensive platform for deciphering the complex nature of ovarian physiology and pathology. The ongoing progress in this field, which encompasses in-depth investigations into the functional roles of specific lncRNAs, the elucidation of their complex interactions with miRNAs, and the comprehensive profiling of their expression patterns, holds the promise of making significant contributions to our understanding of ovarian biology and reproductive disorders. Ultimately, these breakthroughs will have wide-ranging translational implications, paving the way for the development of precision therapies and personalized medicine strategies to address the myriad challenges in the realm of reproductive health.

Smart contracts are becoming increasingly popular for managing transactions or activities in fog computing environments. However, the use of smart contracts for registration and resource access granting is vulnerable to various types of attacks that can compromise their security. Detecting these attacks can be challenging, as attackers can use sophisticated techniques to evade detection. This research uses a machine learning-based approach for detecting different attacks on smart contracts used for registration and resource access granting in fog computing. Data is collected from online Ethereum’s official site “etherscan.io”. Different feature extraction methods and machine learning models are tested. Using accuracy, precision, recall, F1 score, cross-validation, and computational time, the performance of models is evaluated. Results indicate that extreme gradient boosting (XGB) and random forest (RF) provide the highest accuracy of 80% using the term frequency-inverse document frequency (TF-IDF) approach. The light gradient boost classifier provides the highest accuracy of 81% with the Bag of Word (BoW) approach. Similarly, the extra tree provides the highest accuracy of 83% using the N-gram technique. Furthermore, performance using TF-IDF is slightly poorer than BoW and N-gram, however, it has less computational complexity.

Data temperature is a response to the ever-growing amount of data. These data have to be stored, but they have been observed that only a small portion of the data are accessed more frequently at any one time. This leads to the concept of hot and cold data. Cold data can be migrated away from high-performance nodes to free up performance for higher priority data. Existing studies classify hot and cold data primarily on the basis of data age and usage frequency. We present this as a limitation in the current implementation of data temperature. This is due to the fact that age automatically assumes that all new data have priority and that usage is purely reactive. We propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their movement. We identify new metadata variables and user-defined variables to extend the current data temperature value. We further establish rules and conditions for limiting unnecessary movement of the data, which helps to prevent wasted input output (I/O) costs. We also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data temperature. The proposed system provides higher accuracy, increases performance, and gives greater user control for optimal positioning of data within multi-tiered storage solutions.

Structures are recommended to be designed and constructed with the integration of structural health monitoring techniques to ensure that they can dissipate a large amount of energy without considerable damage when subjected to earthquakes. Hysteretic (H), friction (F), viscous (V), and viscoelastic (VE) dampers were employed in this study to observe the response of buildings using the commercially available software ETABS. The effect of different dampers along with configurations on three prototype concrete buildings (3, 5, and 10-storey) was studied by performing a time history analysis. Initially, the response of the buildings was observed in terms of storey drifts, base shear, and displacement without using dampers, while gradually increasing the damping ratio from 0 to 40%. Subsequently, the response of the buildings was evaluated in terms of displacements and base shear using various types of dampers with different configurations. The analysis results demonstrated that the effectiveness of viscous and viscoelastic dampers is higher for 3 and 5-storey buildings, while friction and hysteresis dampers are more suitable for 10-storey buildings. This information enables informed decisions regarding the performance and maintenance of dampers, contributing to the overall resilience and durability of structures in seismic events.

Abstract Background The 2022 Canadian Cardiovascular Society (CCS) cardiorenal guideline provided clinical recommendations on sodium-glucose co-transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) use. Since then, additional trials of relevance for SGLT2i have been published. This update re-evaluates the clinical recommendations for using SGLTi and their indirect comparison with existing evidence on GLP-1RA as compared to the standard of care to reduce cardiorenal morbidity and mortality. Methods We updated our existing search and screening of the literature from September 2021 to April 2023 for randomized controlled trials of SGLT2i and GLP-1RA with placebo control. We conducted risk of bias assessment, data extraction and updated our meta-analysis of studies with similar interventions and components. The certainty of the evidence was determined using GRADE. Results Evidence from three new trials and additional results from an updated existing trial on SGLT2i met our inclusion criteria after an updated search. Across all the included studies, the total sample size was 151,023 adults, with 90,943 in SGLT2i trials and 60,080 in GLP-1 RA trials. The mean age ranged from 59.9 to 68.4 years. Compared with standard care, the use of SGLT2i and GLP-1 RA showed significant reductions in the outcomes of cardiovascular (CV) mortality (14% & 13%), any-cause mortality (12% & 12%), major adverse CV events (MACE) (11% & 14%), heart failure (HF) hospitalization (30% & 9%), CV death or HF hospitalization (23% & 11%), and kidney composite outcome (32% & 22%). In participants with T2D, both classes demonstrated significant cardiorenal protection. But, only GLP-1RA showed a reduction in non-fatal stroke (16%) and only SGLT2i showed a reduction in HF hospitalization (30%) in this population of people living with T2D. Conclusions This updated and comprehensive meta-analysis substantiates and strengthens the clinical recommendations of the CCS cardiorenal guidelines.

Abstract Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1–3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%–90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.

Background: COVID-19 has killed over 6 million people worldwide, making it the worst global health disaster since the 1918 influenza pandemic. Experts have worked to establish the source, track and analyse the disease, and produce treatment and preventative guidelines. Inactivated vaccines have little evidence of efficacy compared to mRNA and adenoviral vector vaccines; however, three doses of both mRNA and inactivated vaccines appear to provide significant and lasting protection against severe disease and mortality. This study examines inactivated vaccine effectiveness data by disease status, age, gender, primary immunisation, booster doses, and SARS-CoV2 virus types. Methods: We conducted a quantitative epidemiological meta-analysis study to assess the vaccine effectiveness of inactivated COVID-19 vaccines. Data extraction was performed on the selected studies, and data analysis was conducted using a random-effects model to determine consolidated assessments of vaccine effectiveness. Subgroup analyses were conducted for gender, age, disease level, and vaccine status, and sensitivity analyses were conducted to assess the robustness of the results. Results: The overall effect size of inactivated COVID-19 vaccinations was statistically significant (p-value

Objective: Purpose of the study was to find out the prevalence of dyslipidemia in patients of newly diagnosed type 2 diabetes mellitus (T2DM) attending Federal Government Polyclinic (PGMI) Islamabad. Study type, settings & duration: A cross sectional study was carried out at Federal Government Poly Clinic (PGMI), Islamabad from November 2022 to January 2023. Methodology: The participants consisted of 125 individuals (60 males and 65 females) who had been recently diagnosed with type 2 diabetes mellitus. Individuals who had previously been diagnosed with diabetes or were taking lipid-lowering medication were not included in the study. The levels of blood glucose fasting, total cholesterol, triglycerides, LDL and HDL were documented by standard methods for analysis. Data was entered, cleaned and analyzed by using SPSS (Version 20.0). Results: Of the total, 69 (55.2%) patients had hypercholesterolemia, 73 (58.4%) had hypertriglyceridemia, 63 (50.4%) had increased levels of LDL and 90 (72%) had reduced HDL. The incidence of dyslipidemia was more in female participants of diabetes mellitus as compared to male participants of diabetes mellitus. Conclusion: This study found a high incidence of dyslipidemia among recently diagnosed patients of T2DM regardless of gender. The risk of dyslipidemia was increased by factors such as body mass index, age, waist circumference, hypertension, lack of physical activity and tobacco use. It is important to increase awareness regarding dyslipidemia and its related risk factors. Screening for dyslipidemia should be conducted early in patients with recently diagnosed type 2 diabetes mellitus, as they are more prone for developing cardiovascular diseases. It is also important to regularly monitor the lipid profile of these patients in addition to their blood glucose levels. Early intervention should be initiated once dyslipidemia is confirmed.

The rapid surge in internet-driven smart devices and bandwidth-hungry multimedia applications demands high-capacity internet services and low latencies during connectivity. Cloud radio access networks (C-RANs) are considered the prominent solution to meet the stringent requirements of fifth-generation (5G) and beyond networks by deploying the fronthaul transport links between baseband units (BBUs) and remote radio heads (RRHs). High-capacity optical links could be conventional mainstream technology for deploying the fronthaul in C-RANs. But densification of optical links significantly increases the cost and imposes several design challenges on fronthaul architecture which makes them impractical. Contrary, Ethernet-based fronthaul links can be lucrative solutions for connecting the BBUs and RRHs but are inadequate to meet the rigorous end-to-end delays, jitter, and bandwidth requirements of fronthaul networks. This is because of the inefficient resource allocation and congestion control schemes for the capacity constraint Ethernet-based fronthaul links. In this research, a novel reinforcement learning-based optimal resource allocation scheme has been proposed which eradicates the congestion and improves the latencies to make the capacity-constraints low-cost Ethernet a suitable solution for the fronthaul networks. The experiment results verified a notable 50% improvement in reducing delay and jitter as compared to the existing schemes. Furthermore, the proposed scheme demonstrated an enhancement of up to 70% in addressing conflicting time slots and minimizing packet loss ratios. Hence, the proposed scheme outperforms the existing state-of-the-art resource allocation techniques to satisfy the stringent performance demands of fronthaul networks.

Industrial Internet of Things (IIoT) applications consist of resource constrained interconnected devices that make them vulnerable to data leak and integrity violation challenges. The mobility, dynamism, and complex structure of the network further make this issue more challenging. To control the information flow in such environments, access control is critical to make collaboration and communication safe. To deal with these challenges, recent studies employ attribute-based access control on top of blockchain technology. However, the attribute-based access control frameworks suffer due to high computational overhead. In this paper, we propose an improved role-based access control framework using hyperledger blockchain to deal with IIoT requirements with less computational overhead making the information control process more efficient and real-time. The proposed framework leverages a layered architecture of chaincodes to implement the improved access control framework that handles the permission delegation and conflict management to deal with the dynamism of the IIoT network. The system uses a Policy Contract, Device Contract, and Access Contract to manage the workflow of the whole access control process. Each chaincode in the proposed framework is isolated in terms of its responsibilities to make the design low coupled. The integration of improved access control with blockchain enables the proposed framework to provide a highly scalable solution, tamper-proof, and flexible to manage conflicting scenarios. The proposed system outperforms the recent studies significantly in computational overhead in extensive simulation results. To verify the scalability and efficiency, the proposed is evaluated against a large number of concurrent virtual clients in simulation and statistical analysis proves that the proposed system is promising for further research in this domain.

With the increase in the growing rate of municipal solid waste throughout the world and due to the high moisture and organic components of the organic fraction of municipal solid waste, dry anaerobic digestion has become the future direction to cope with this waste while reducing the impact on the environment, including climate change. Dry anaerobic digestion has become a promising technology that converts the organic fraction of municipal solid waste into combustible biogases, which can be used as an alternative energy source. However, the technology faces several challenges that must be addressed to enhance its performance and adoption. This paper provides a comprehensive analysis of the current technologies used for dry anaerobic digestion in OFMSW and delves into the various factors that influence the performance of these technologies. This review paper also identifies and discusses the challenges faced in optimizing and scaling up these technologies, such as feedstock pretreatment requirements, characteristics of inoculum, and other crucial parameters.

This paper addresses the challenging problem of Unit Commitment (UC), which involves the optimal scheduling of power generation units while adhering to numerous network operational constraints called security-constrained UC (SCUC). SCUC problem aims to minimize costs subject to turning on economically efficient generators and turning off expensive ones. These operational constraints include load balancing, voltage level at buses, minimum up and down time requirements, spinning reserve, and ramp up and down constraints. The SCUC problem, subject to these operational constraints, is a complex mixed-integer nonlinear problem (MINLP). There has been a growing interest in using evolutionary algorithms (EAs) to tackle large-scale multi-objective MINLP problems in recent two decades. This paper introduces a novel approach to address the SCUC problem, which is further complicated by including network constraints. They are pioneering the integration of single and multi-objective EAs to solve the SCUC problem while incorporating AC network constraints through hybrid binary and real coded operators. The development of an ensemble algorithm that combines mixed real and binary coded operators, extended by a bidirectional coevolutionary algorithm to tackle multi-objective SCUC problems. The paper implements a new formulation based on three conflicting objective functions: cost of energy supplied, startup and shutdown costs of generators, energy loss, and voltage deviation to solve the SCUC problem. Implementing a new formulation also addresses the solution of single and multi-objective SCUC problems using a combination of proposed technical and economic objective functions. The proposed algorithm is rigorously tested on a 10-unit IEEE RTS system and a 6-unit IEEE 30-bus test system, both with and without security constraints, addressing week-ahead and day-ahead SCUC scenarios. Simulation results show that the proposed algorithm finds near-global optimal solutions compared to other state-of-the-art EAs. Additionally, the research demonstrates the effectiveness of the proposed search operator by integrating it with a multi-objective coevolutionary algorithm driven by both feasible and infeasible solutions, showcasing superior performance in solving multi-objective SCUC problems. These results are compared with various recently implemented Multi-Objective Evolutionary Algorithms (MOEAs), demonstrating the superiority of

Diabetic retinopathy (DR) can be defined as visual impairment caused by prolonged diabetes affecting the blood vessels in the retina. Globally, it stands as the primary contributor to blindness, impacting approximately 191 million individuals. While prior research has addressed DR classification using retinal fundus images, existing methods often focus on isolated lesion detection, lacking a comprehensive framework for the simultaneous identification of all lesions. Previous studies concentrated on early-stage features like exudates, aneurysms, hemorrhages, and blood vessels, sidelining severe-stage lesions such as cotton wool spots, venous beading, very severe intraretinal microvascular abnormalities (IRMA), diffuse intraretinal hemorrhages, capillary degeneration, highly activated microglia, and retinal pigment epithelium (RPE) damage. In this study, a deep learning approach is proposed to classify DR fundus images by severity levels, utilizing GoogleNet and ResNet models based on adaptive particle swarm optimizer (APSO), for enhanced feature extraction. The extracted features from the hybrid model are further used with different machine learning models like random forest, support vector machine, decision tree, and linear regression models. Experimental results showcased the proposed hybrid framework outperforming advanced approaches with a remarkable 94% accuracy on the benchmark dataset. This method demonstrates potential enhancements in precision, recall, accuracy, and F1 score for different DR severity levels.

Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The district-level simulated yield data set was analyzed with three ML models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance ( $R^{2}$ : 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and $R^{2}$ of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.

One of the challenges in image processing involves single image deraining (SID). Existing methods do not exploit images at multi-scales and often overlook spatial and channel information. These methods fail to account for different rain conditions, leading to challenges in effectively removing a wide range of rain streak patterns, such as diverse directional or dense streaks. Moreover, they often result in the loss of fine texture details or a blurred background in the process of eliminating rain streaks from images captured in heavy rain. Mostly state-of-the-art (SOTA) deraining models achieve higher performance in removing rain from rainy images but at the expense of a high number of parameters, which results in computational complexity and memory requirements. Furthermore, they also do not consider high-level visioned evaluation metrics to perform deep evaluations of the proposed models. In this paper, a simple lightweight network with few parameters and relatively shallow depth is proposed by fusing the traditional Gaussian-Laplacian pyramid technique with the attention module. We propose a novel attention-based lightweight pyramid network for image deraining and detection (LPN-IDD) to achieve better deraining performance. The proposed model includes a dual attention module integrated with the Gaussian-Laplacian pyramids network. In LPN-IDD, different levels of Laplacian pyramids can extract multi-scale features to adapt to different shapes and types of rain streaks. Residual and recursive blocks are used in each subnetwork along with dual attention blocks to resist the occlusion or texture feature while suppressing unnecessary features. Extensive experimentation performed on the SID synthetic and real-world datasets demonstrate the effectiveness of the proposed model in image deraining tasks. For deep evaluation of the proposed methodology, object detection models including faster-RCNN, YOLO-V3, YOLO-V7 are used along with full reference evaluation metrics.

This paper presents a robust subject-wise sequential dictionary learning (swsDL) algorithm named rswsDL for functional magnetic resonance imaging (fMRI) data where the negative impact of dimensionality reduction in the form of information loss and sensitivity to anomalous observations due to the assumption of Gaussian prior has been resolved by rotating the reduced dimensions to optimal direction and replacing the quadratic loss in the data fidelity term with $\alpha $ -divergence based loss function to counter the outliers, respectively. While dimensionality optimization guaranteed robustness to model order by maximizing signal intensity and smoothness, the robust loss function guaranteed decomposition stability against deviations from the Gaussian noise model. The proposed algorithm was derived by deploying spatial and temporal bases from the computationally fast sparse spatiotemporal blind source separation (ssBSS) method and solving a sequence of rank-1 matrix decomposition problems, where the $l_{1}/l_{0}$ -norm penalty/constraint promoted sparsity, and the estimation of sparse representation matrices was accomplished using a block coordinate descent approach. This strategy allowed the utilization of multi-subject fMRI data to enhance the subject-wise source separation in a robust manner. It, therefore, can be considered a promising alternative to the state-of-the-art robust consistent adaptive sequential dictionary learning (rACSD) algorithm. The rswsDL and existing robust dictionary learning based source separation algorithms were applied to synthetic and experimental fMRI datasets to validate its performance. The rswsDL algorithm manifested a 16.7% increase in the mean correlation value over the rACSD algorithm.

Abstract The pollution of soil and aquatic systems by inorganic and organic chemicals has become a global concern. Economical, eco-friendly, and sustainable solutions are direly required to alleviate the deleterious effects of these chemicals to ensure human well-being and environmental sustainability. In recent decades, biochar has emerged as an efficient material encompassing huge potential to decontaminate a wide range of pollutants from soil and aquatic systems. However, the application of raw biochars for pollutant remediation is confronting a major challenge of not getting the desired decontamination results due to its specific properties. Thus, multiple functionalizing/modification techniques have been introduced to alter the physicochemical and molecular attributes of biochars to increase their efficacy in environmental remediation. This review provides a comprehensive overview of the latest advancements in developing multiple functionalized/modified biochars via biological and other physiochemical techniques. Related mechanisms and further applications of multiple modified biochar in soil and water systems remediation have been discussed and summarized. Furthermore, existing research gaps and challenges are discussed, as well as further study needs are suggested. This work epitomizes the scientific prospects for a complete understanding of employing modified biochar as an efficient candidate for the decontamination of polluted soil and water systems for regenerative development.

Abstract Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. In this study, we introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an "H&E stain" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches. Our dataset consists of pairs of unstained and H&E-stained images, scanned with a brightfield microscope at 20 × magnification, providing a comprehensive set of training and testing images for evaluating the efficacy of our proposed model. Two metrics, Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), were used to quantitatively evaluate virtual stained slides. Our analysis revealed that the average FID score between virtually stained and H&E-stained images (80.47) was considerably lower than that between unstained and virtually stained slides (342.01), and unstained and H&E stained (320.4) indicating a similarity virtual and H&E stains. Similarly, the mean KID score between H&E stained and virtually stained images (0.022) was significantly lower than the mean KID score between unstained and H&E stained (0.28) or unstained and virtually stained (0.31) images. In addition, a group of experienced dermatopathologists evaluated traditional and virtually stained images and demonstrated an average agreement of 78.8% and 90.2% for paired and single virtual stained image evaluations, respectively. Our study demonstrates that the proposed three-stage dual contrastive learning-based image-to-image generative model is effective in generating virtual stained images, as indicated by quantified parameters and grader evaluations. In addition, our findings suggest that GAN models have the potential to replace traditional H&E staining, which can reduce both time and environmental impact. This study highlights the promise of virtual staining as a viable alternative to traditional staining techniques in histopathology.

Abstract The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits.

In recent times, digital transformations and Industry 4.0 have revolutionized real-time bridge monitoring and its inspection. The use of smart Structural Health Monitoring (SHM) techniques is becoming powerful with the competencies of Building Information Modeling (BIM) tools, Artificial Intelligence (AI), Internet of Things (IoT), and Virtual/Augmented (VR/AR) technologies. However, the lack of interconnectivity between these tools limits their functionality. This research has addressed this problem by developing an integrated framework to assess serviceability and implement a smart SHM for a newly constructed extradosed bridge. Using Finite Element Analysis (FEA), the study proposes an integrated SHM system that utilizes various IoT sensors, including Wired Strain Gauges (WSG), Liquid Levelling Sensors (LLS), MEMS accelerometers, and a Weather Monitoring Station (WMS) to monitor concrete deformations, vertical displacements, structural vibrations, and weather conditions. BIM tool is used to develop the virtual replica of the proposed SHM system which is then used in the 3D Game Engine (GE) to develop an AR application. This application is then successfully deployed and tested in the AR headset (HoloLens) where its capabilities for onsite bridge health monitoring are discovered. This approach overcomes the limitations of HoloLens devices by providing real-time access to SHM data through a web platform, enabling on-site or remote AR-based bridge health monitoring. Conclusively, this paper emphasizes the numerical modeling of bridges for the design of a health monitoring system, that highlights the importance of robust SHM techniques in assessing bridge conditions. Moreover, it introduces a novel approach for smart bridge inspection and onsite visualization of structural defects in an AR environment.

Aluminum (Al) and manganese (Mn) toxicity are the top two constraints of crop production in acid soil. Crops have evolved common and specific mechanisms to tolerate the two stresses. In the present study, the responses (toxicity and tolerance) of near-isogenic wheat lines (ET8 and ES8) and their parents (Carazinho and Egret) to Al and Mn were compared by determining the physiological parameters and conducting transcriptome profiling of the roots. The results showed the following: (1) Carazinho and ET8 exhibited dual tolerance to Al and Mn compared to Egret and ES8, indicated by higher relative root elongation and SPAD. (2) After entering the roots, Al was mainly distributed in the roots and fixed in the cell wall, while Mn was mainly distributed in the cell sap and then transported to the leaves. Both Al and Mn stresses decreased the contents of Ca, Mg, and Zn; Mn stress also inhibited the accumulation of Fe, while Al showed an opposite effect. (3) A transcriptomic analysis identified 5581 differentially expressed genes (DEGs) under Al stress and 4165 DEGs under Mn stress. Among these, 2774 DEGs were regulated by both Al and Mn stresses, while 2280 and 1957 DEGs were exclusively regulated by Al stress and Mn stress, respectively. GO and KEGG analyses indicated that cell wall metabolism responds exclusively to Al, while nicotianamine synthesis exclusively responds to Mn. Pathways such as signaling, phenylpropanoid metabolism, and metal ion transport showed commonality and specificity to Al and Mn. Transcription factors (TFs), such as MYB, WRKY, and AP2 families, were also regulated by Al and Mn, and a weighted gene co-expression network analysis (WGCNA) identified PODP7, VATB2, and ABCC3 as the hub genes for Al tolerance and NAS for Mn tolerance. The identified genes and pathways can be used as targets for pyramiding genes and breeding multi-tolerant varieties.

Abstract Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical–chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft’s persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

Abstract Drought stress is a worldwide threat to the productivity of crops, especially in arid and semi-arid zones of the world. In the present study, the effect of selenium (Se) seed priming on the yield of quinoa under normal and drought conditions was investigated. A pot trial was executed to enhance the drought tolerance in quinoa by Se seed priming (0, 3, 6, and 9 mg Se L−1). The plants were exposed to water stress at three different growth stages of quinoa, viz. multiple leaf, flowering, and seed filling. It was noticed that drought significantly affected the yield components of quinoa, however, Se priming improved the drought tolerance potential and yield of quinoa by maintaining the plant water status. Se priming significantly increased main panicle length (20.29%), main panicle weight (26.43%), and thousand grain weight (15.41%) as well as the gas exchange parameters (transpiration rate (29.74%), stomatal conductance (35.29%), and photosynthetic rate (28.79%), total phenolics (29.36%), leaf chlorophyll contents (35.97%), water relations (leaf relative water contents (14.55%), osmotic potential (10.32%), water potential (38.35%), and turgor potential (31.37%), and economic yield (35.99%) under drought stress. Moreover, Se priming markedly improved grain quality parameters i.e., phosphorus, potassium, and protein contents by 21.28%, 18.92%, and 15.04%, respectively. The principal component analysis connected the various study scales and showed the ability of physio-biochemical factors to describe yield fluctuations in response to Se seed priming under drought conditions. In conclusion, a drought at the seed-filling stage has a far more deleterious impact among other critical growth stages and seed priming with Se (6 mg L−1) was found more effective in alleviating the detrimental effects of drought on the grain yield of quinoa.

Background: Gastroparesis is a serious condition that can be caused by diabetes, surgery or infection, or can be idiopathic. When there is no mechanical obstruction, gastroparesis is characterized by delayed stomach emptying. Itopride, a prokinetic drug, inhibits acetylcholinesterase activity in addition to antagonizing dopamine D2 receptors. Methods: This prospective, multicentre study is based on real-world data from 988 patients with a diagnosis of diabetic gastroparesis for index (PAGI-SYM2) evaluation at baseline and week 4 of treatment for upper gastrointestinal disorder symptoms. Results: Upper gastrointestinal symptom severity scores improved significantly after 4 weeks of treatment (p

Abstract Food bioactive compounds have a beneficial biological effect on human health, fending off cancer, cardiovascular disease, metabolic syndrome, and neurodegenerative disease, among other noncommunicable diseases and neurodegenerative disorders. Secondary metabolites like polyphenols, glucosinolates, carotenoids, vitamins, terpenoids, saponins, and fibers have been linked to these health benefits due to their antioxidant, antiatherogenic, anti‐inflammatory, antimicrobial, cardioprotective, and vasodilator properties. This review comprehensively illustrates the extraction of bioactive compounds in food formulations along plant wastes and provides insight into the efficient delivery systems for health‐promoting compounds. There is an insight into how to choose which bioactive to use, how to extract them in a bioavailable state, and how to include and deliver them. Grapes, garlic, sweet potato leaves, cactus pears, beetroot, amaranth, Picea abies bark waste, olive leaves, and black plum are discussed comprehensively in terms of extraction and health benefits. The novel and emerging technologies are discussed in detail, and a comprehensive review and comparison with conventional methods are presented. The significant health benefits associated with bioactive compounds are also discussed.

Background: Hepatitis C, caused by the Hepatitis C Virus (HCV), is the second most common form of viral hepatitis. The geographical distribution of HCV genotypes can be quite complex, making it challenging to ascertain the most prevalent genotype in a specific area. Methods: To address this, a review was conducted to determine the prevalence of HCV genotypes across various provinces and as a whole in Pakistan. The scientific literature regarding the prevalence, distribution, genotyping, and epidemiology of HCV was gathered from published articles spanning the years 1996–2020. Results: Genotype 1 accounted for 5.1% of the patients, with its predominant subtype being 1a at 4.38%. The frequencies of its other subtypes, 1b and 1c, were observed to be 1.0% and 0.31% respectively. Genotype 2 had a frequency of 2.66%, with the most widely distributed subtype being 2a at 2.11% of the patients. Its other subtypes, 2b and 2c, had frequencies of 0.17% and 0.36% respectively. The most prevalent genotype among all isolates was 3 (65.35%), with the most frequent subtype being 3a (55.15%), followed by 3b (7.18%). The prevalence of genotypes 4, 5, and 6 were scarce in Pakistan, with frequencies of 0.97%, 0.08%, and 0.32% respectively. The prevalence of untypeable and mixed genotypes was 21.34% and 3.53% respectively. Estimating genotypes proves to be a productive method in assisting with the duration and selection of antiviral treatment. Different HCV genotypes can exhibit variations in their response to specific antiviral treatments. Different genotypes may have distinct natural histories, including variations in disease progression and severity. Some genotypes may lead to more rapid liver damage, while others progress more slowly. Conclusions: This information can guide screening and testing strategies, helping to identify individuals at higher risk of developing severe complications. Studying the distribution of HCV genotypes in a population can provide valuable insights into the transmission dynamics of the virus.

The objective of this study was to examine the mediating role of psychological safety in the relationship between high performance work systems (HPWS) and employee voice behavior. Moreover, this study examined the moderating effect of supportive leadership on the indirect effect of HPWS on voice behavior through psychological safety. Data were gathered in a survey of middle and lower level bank employees (N = 261). Data validity/reliability and estimations were performed by applying the principles of partial least squares structural equation modeling (PLS-SEM). The findings indicate that psychological safety mediates the link between HPWS and voice behavior, and supportive leadership enhances voice behavior by strengthening the effect of HPWS on psychological safety. This study contributes to organizational psychology literature by explaining how supportive leadership affects the indirect effect of HPWS on voice behavior through psychological safety. Study limitations are related to external validity and cross-sectional testing of data.

Abstract Okra (Abelmoschus esculentus L.) is the most consumed vegetable worldwide with the potential for diverse ecological adaptation. However, increasing salinization and changing climatic conditions are posing serious threats to the growth, yield, and quality of okra. Therefore, to mitigate increasing soil salinization and ensure sustainable okra production under rapidly changing climatic conditions, evaluation of new okra germplasm to develop salt tolerant cultivars is direly needed. The present study was designed to evaluate the genetic resources of okra genotypes for salt tolerance at growth and reproductive phases. Based on mophological and physio-biochemical responses of plants under stress condition, genotypes were divided into salt tolerant and succeptible groups. The experiment was comprised of 100 okra genotypes and each genotype was grown under control conditions and 6.5 dS m−1 NaCl concentration in a pot having 10 kg capacity. The experiment was conducted in a completely randomized design and each treatment was replicated three times. The results showed vast genetic variability among the evaluated okra germplasm traits like days to emergence, pod length, pod diameter, plant height, stem girth, and other yield-related parameters. Correlation analysis showed a highly significant positive association among the number of leaves at first flower and plant height at first flower.Likewise, pod weight also revealed a highly significant positive relationship for pod weight plant-1, pod length, and K+: Na+. Principal Component Analysis (PCA) revealed that out of 16 principal components (PCs), five components showed more than one eigenvalue and the first six PCs contributed 67.2% of the variation. Bi-plot analysis illustrated that genotypes 95, 111, 133, 99, and 128, under salt stress conditions, exhibited both high yield per plant and salt-tolerant behavior in other yield-related traits. On the basis of all studied traits, a salt susceptible group and a salt-tolerant group were formed. The salt tolerant group comprised of 97, 68, 95, 114, 64, 99, 111, 133, 128, and 109 genotypes, whereas, the salt susceptible group contained 137, 139, 130, 94, and 125 genotypes. Salt-tolerant okra genotypes were suggested to be used in further breeding programs aimed to develop salt tolerance in okra. These insights will empower precision breeding, underscore the importance of genetic diversity, and bear the potential to address the challenges of salt-affected soils while promoting broader agricultural resilience, economic prosperity, and food security.

Antibiotics are gradually becoming less effective against bacteria worldwide, and this issue is of particular concern in economically-developing nations like Pakistan. We undertook a scoping review in order to review the literature on antimicrobial use, prescribing, dispensing and the challenges associated with antimicrobial resistance in primary care (PC) settings in Pakistan. Furthermore, this review aims to identify potential solutions to promote appropriate use of antimicrobials in Pakistan. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist, a comprehensive scoping review was conducted to review the literature of antimicrobials used, prescribed and dispensed in PC settings in Pakistan. Google Scholar and Pub-Med were searched for the period 2000–2023. Papers were analyzed on the basis of eligibility i.e., included antimicrobial use, prescribing and dispensing practices by general population at homes, by prescribers in outpatient departments of hospitals and by pharmacists/dispensers in community pharmacies, respectively. Two researchers analyzed the articles thoroughly and disagreements were resolved through discussion with a third reviewer. Both quantitative and qualitative research studies were eligible for inclusion. Additionally, the selected papers were grouped into different themes. We identified 4070 papers out of which 46 studies satisfied our eligibility criteria. The findings revealed limited understanding of antimicrobial resistance (AMR) by physicians and community pharmacists along with inappropriate practices in prescribing and dispensing antibiotics. Moreover, a notable prevalence of self-medication with antibiotics was observed among the general population, underscoring a lack of awareness and knowledge concerning proper antibiotic usage. Given the clinical and public health implications of AMR, Pakistan must prioritize its policies in PC settings. Healthcare professionals (HCPs) need to reduce inappropriate antibiotic prescribing and dispensing, improve their understanding of the AWaRe (access, watch and reserve antibiotics) classification and guidance, monitor current usage and resistance trends, as well as implement antimicrobial stewardship (ASP) activities starting in targeted locations.

ABSTRACTPlants are excellent food and medicinal resources because of their unique composition. Researchers have discovered that thousands of plants can help in achieving the desired health status through advanced research. This research article reveals the nutritional composition and significance of Tribulus terrestris. In addition, it also analyzed the physio-chemical evolution of nutraceutical products from Tribulus terrestris. From the proximate analysis, it was found that T. terrestris is rich in fiber and fat 24.28 ± 0.93% and 14.49 ± 1.42%, respectively. The rheological properties of the product were observed. The results reveal that water activity, stability of product, and dough development time increased 59.0 ± 0.06%, 15.0 ± 0.06 min, and 4.3 ± 0.07 min, respectively, by increasing the powder percentage. The functional behavior of the samples was examined, and significant results were depicted. Bulk density and oil absorption increased by increasing the percentage of powder 83.86 ± 1.36% and 69.16 ± 3.61%. The results of emulsifying stability were recorded as non-significant. Results regarding color analysis showed non-significant behavior of supplemented biscuits. The textural behavior of the product increased from 1.47 Nm−2 to 2.27 Nm−2. Results regarding the spread factor increased from 3.01 ± 0.04 to 3.83 ± 0.01 from T1 to T4. The sensory results indicated that T1 (2% Tribulus powder), T2 (3% Tribulus powder), T3 (4% Tribulus powder), and T4 (5% Tribulus powder) were acceptable after the control group. Thus, Tribulus terrestris can be used as a nutraceutical and functional ingredient in baked goods.

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R2 value of responses in case of RSM were close to unity and mean relative errors (MRE) were confined under specified range. A comparative study between ANN and RSM models unveiled that the ANN model should be preferred. Therefore, a joint utilization of the RSM and ANN can be more effective for reliable statistical interactions and predictions.

IntroductionNon-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas.MethodIn this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods.ResultsThe evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system.DiscussionThis study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.

Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.

Objectives We conducted an updated systematic review and meta-analysis to investigate the effect of colchicine treatment on clinical outcomes in patients with COVID-19.Design Systematic review and meta-analysis.Data sources We searched PubMed, Embase, the Cochrane Library, medRxiv and ClinicalTrials.gov from inception to January 2023.Eligibility criteria All randomised controlled trials (RCTs) that investigated the efficacy of colchicine treatment in patients with COVID-19 as compared with placebo or standard of care were included. There were no language restrictions. Studies that used colchicine prophylactically were excluded.Data extraction and synthesis We extracted all information relating to the study characteristics, such as author names, location, study population, details of intervention and comparator groups, and our outcomes of interest. We conducted our meta-analysis by using RevMan V.5.4 with risk ratio (RR) and mean difference as the effect measures.Results We included 23 RCTs (28 249 participants) in this systematic review. Colchicine did not decrease the risk of mortality (RR 0.99; 95% CI 0.93 to 1.05; I2=0%; 20 RCTs, 25 824 participants), with the results being consistent among both hospitalised and non-hospitalised patients. There were no significant differences between the colchicine and control groups in other relevant clinical outcomes, including the incidence of mechanical ventilation (RR 0.75; 95% CI 0.48 to 1.18; p=0.22; I2=40%; 8 RCTs, 13 262 participants), intensive care unit admission (RR 0.77; 95% CI 0.49 to 1.22; p=0.27; I2=0%; 6 RCTs, 961 participants) and hospital admission (RR 0.74; 95% CI 0.48 to 1.16; p=0.19; I2=70%; 3 RCTs, 8572 participants).Conclusions The results of this meta-analysis do not support the use of colchicine as a treatment for reducing the risk of mortality or improving other relevant clinical outcomes in patients with COVID-19. However, RCTs investigating early treatment with colchicine (within 5 days of symptom onset or in patients with early-stage disease) are needed to fully elucidate the potential benefits of colchicine in this patient population.PROSPERO registration number CRD42022369850.

This study investigated the habits and attitudes of individuals towards poultry consumption, utilizing primary data collected through a survey of 5 households from 285 localities in Tehsil Shakargarh, Punjab, Pakistan (n = 1425). Household selection was randomized, and personal visits were conducted for data collection via formal interviews employing a structured questionnaire. Coordinates for each site were obtained using a Garmin eTrex device, in conjunction with meteorological data, to determine global positioning system (GPS) coordinates. A notable portion of respondents (38.8 %) possessed basic knowledge, while the majority (61.2 %) demonstrated intermediate knowledge regarding commercial broilers (chickens raised for meat production). A significant proportion (70.3 %) harbored misconceptions about the inclusion of hormones/antibiotics in poultry feed, with a minority (0.2 %) misinformed about broiler chickens' leg weakness. Some respondents (17.3 %) held both misconceptions, while others (12.2 %) had none. The majority (97.6 %) favored egg consumption, with 51.7 % preferring commercial chicken eggs and 48.3 % opting for domestic chicken eggs. Preference for white-colored eggs (51.5 %) slightly outweighed that for brown-colored eggs (48.5 %). A minority (1.3 %) speculated that poultry consumption could be a potential cause of COVID-19, while the majority (65.7 %) disagreed, and a portion (33.0 %) remained uncertain. Nearly all respondents (99.9 %) believed in the immunity-boosting properties of protein intake, with 65 % associating such benefits with poultry meat and eggs. Similarly, 99.7 % did not encounter difficulties in accessing poultry products during lockdowns. Approximately half (46.3 %) of respondents believed that consuming well-cooked and safely handled poultry meat was safe during outbreaks. Poultry meat and eggs emerged as potentially efficient sources of nutrition during the COVID-19 pandemic, especially for protein-deficient populations like Pakistan. Therefore, initiatives should focus on enhancing commercial poultry production and educating the populace about its advantages.