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

Abstract In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.

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

Abstract In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce VirtuDockDL, which is a streamlined Python-based web platform utilizing deep learning for drug discovery. This pipeline employs a Graph Neural Network to analyze and predict the effectiveness of various compounds as potential drug candidates. During the validation phase, VirtuDockDL was instrumental in identifying non-covalent inhibitors against the VP35 protein of the Marburg virus, a critical target given the virus’s high fatality rate and limited treatment options. Further, in benchmarking, VirtuDockDL achieved 99% accuracy, an F1 score of 0.992, and an AUC of 0.99 on the HER2 dataset, surpassing DeepChem (89% accuracy) and AutoDock Vina (82% accuracy). Compared to RosettaVS, MzDOCK, and PyRMD, VirtuDockDL outperformed them by combining both ligand- and structure-based screening with deep learning. While RosettaVS excels in accurate docking but lacks high-throughput screening, and PyRMD focuses on ligand-based methods without AI integration, VirtuDockDL offers superior predictive accuracy and full automation for large-scale datasets, making it ideal for comprehensive drug discovery workflows. These results underscore the tool’s capability to identify high-affinity inhibitors accurately across various targets, including the HER2 protein for cancer therapy, TEM-1 beta-lactamase for bacterial infections, and the CYP51 enzyme for fungal infections like Candidiasis. To sum up, VirtuDockDL combines user-friendly interface design with powerful computational capabilities to facilitate rapid, cost-effective drug discovery and development. The integration of AI in drug discovery could potentially transform the landscape of pharmaceutical research, providing faster responses to global health challenges. The VirtuDockDL is available at https://github.com/FatimaNoor74/VirtuDockDL .

Abstract Automatic analysis of facial expressions has emerged as a prominent research area in the past decade. Facial expressions serve as crucial indicators for understanding human behavior, enabling the identification and assessment of positive and negative emotions. Moreover, facial expressions provide insights into various aspects of mental activities, social connections, and physiological information. Currently, most facial expression detection systems rely on cameras and wearable devices. However, these methods have drawbacks, including privacy concerns, issues with poor lighting and line of sight blockage, difficulties in training with longer video sequences, computational complexities, and disruptions to daily routines. To address these challenges, this study proposes a novel and privacy-preserving human behavior recognition system that utilizes Frequency Modulated Continuous Wave (FMCW) radar combined with Machine Learning (ML) techniques for classifying facial expressions. Specifically, the study focuses on five common facial expressions: Happy, Sad, Fear, Surprise, and Neutral. The recorded data is obtained in the form of a Micro-Doppler signal, and state-of-the-art ML models such as Super Learner, Linear Discriminant Analysis, Random Forest, K-Nearest Neighbor, Long Short-Term Memory, and Logistic Regression are employed to extract relevant features. These extracted features from the radar data are then fed into ML models for classification. The results show a highly promising classification accuracy of 91%. The future applications of the proposed work will lead to advancements in technology, healthcare, security, and communication, thereby improving overall human well-being and societal functioning.

Abstract The study of genotype × environment interaction is crucial for plant breeders to introduce new cultivar(s) with improved and stable yield performance. The productivity of chickpea crops is very low in Pakistan. It requires the selection of genotypes with optimal productivity for diverse environmental conditions. Fourteen different chickpea genotypes were assessed using the linear mixed model to evaluate genotypes across four diverse chickpea growing regions, including Attock (Punjab), Bhakkar (Punjab), Karak (Khyber Pakhtunkhwa), and Larkana during the year 2017–19. The environmental effect was pronounced and contributed significantly to variation (25.8%) in grain yield. Analysing genotype × environmental interactions at multiple locations facilitates ranking superior genotypes that excel in specific or diverse environments. Notably, the genotypes, viz., Fakhr-e-Thal and Bhakkar-2011, demonstrated superior performance in terms of overall grain yield. Utilising a multitrait stability index, Bittal-2016 and Thal-2006 exhibited the most stable genotypes across various environments and were observed suitable for diverse growing regions. While, for specific environmental conditions, genotypes, i.e., KK-1, Noor-2013 and Fakhr-e-thal, exhibited higher yields and stability. This showed their adaptability to the particular environment. The present study revealed that Larkana as the mega environment was conducive to higher yield, while Attock, Bhakkar, and Karak sites were less favorable for KK-3, DG-89, and Dasht. The BLUP outperformed the AMMI in our analysis of evaluating multi-location trials. The integration of WAASBY and MTSI indices proved useful in differentiating between high yielding genotypes and those with stable performance. These tools are essential for breeders aiming to select genotypes that will perform consistently across diverse environmental conditions.

Abstract In plants, the BAX inhibitor-1 (BI-1) gene plays a crucial part in controlling cell death under stress conditions. This mechanism of Programmed Cell Death (PCD) is genetically regulated and is crucial for the elimination of unwanted or damaged cells in a controlled manner, which is essential for normal development and tissue maintenance. A study on cucumber identified and characterized five BI-1 genes: CsBI1, CsBI2, CsBI3, CsBI4, and CsBI5. These genes share conserved domains, indicating common evolutionary history and function. Physicochemical analysis revealed their molecular weights and isoelectric points, while subcellular localization showed their presence in different cellular compartments. The phylogenetic analysis highlighted evolutionary relationships with related crops. Chromosomal distribution and synteny analysis suggested segmental or tandem duplications within the gene family. Protein-protein interaction analysis revealed extensive interactions with other cucumber proteins. Cis-regulatory elements in the promoter regions provided insights into potential functions and transcriptional regulation. miRNAs showed diverse regulatory mechanisms, including mRNA cleavage and translational inhibition. The CsBI3, CsBI4 and CsBI5 genes exhibit elevated expression levels during cold stress, suggesting their vital involvement in cucumber plant defense mechanisms. The application of chitosan oligosaccharides externally confirms their distinct expression patterns. The qRT-PCR confirms the upregulation of CsBI genes in ToLCNDV-infected plants, indicating their potential to mitigate biotic and abiotic stresses. The comprehensive genome-wide exploration provides opportunities for the development of cold-tolerant and virus-resistant cucumber variants by traditional breeding or gene.

Abstract Background Chronic subdural hematoma (CSDH) is an important medical condition characterized by the accumulation of blood in the subdural space. Several methods have been explored, including surgical intervention and administration of tranexamic acid, which is a conservative treatment option. This meta-analysis aimed to assess the effectiveness and safety of tranexamic acid in CSDH management. Methods A systematic literature search was performed according to the PRISMA and MOOSE guidelines. This study included cohort and randomized controlled trials involving adults with chronic subdural hematoma (CSDH) who were treated with intravenous tranexamic acid. The primary outcome measures were recurrence and adverse events. Results Six studies encompassing 2024 participants were included. Studies have shown that tranexamic acid reduced recurrence rates (OR, 0.32; 95% CI: 0.27–0.38, p = 0.16; I2 = 0%), although the result was non-significant. However, the analysis of adverse events indicated a moderate level of heterogeneity (OR, 1.14; 95% CI: 0.60–2.15, p = 0.07; I2 = 57%), suggesting a potential safety concern. Conclusion In conclusion, this meta-analysis suggests tranexamic acid (TXA) may reduce chronic subdural hematoma (CSDH) recurrence, though significance varied across studies. Adverse effects with TXA were similar to controls. Larger trials are needed to confirm TXA’s role in CSDH management.

Abstract Wheat (Triticum aestivum L.) is one of the most significant crops and the backbone of food security worldwide. However, low wheat production remains a substantial concern in global agricultural systems. It can be attributed to several factors, including adverse climatic conditions, plant disease and poor soil quality. Recent efforts have explored bioinoculant applications as a promising approach to enhance wheat yield, trying to mitigate constraints essential for future wheat production and global food security. This study tested talc powder, wheat biochar, sugarcane bagasse biochar, and farmyard manure as carriers with two endophytic bacterial strains, Burkholderia phytofirmans PsJN and Bacillus spp. MN54 was applied to three wheat varieties (Ujala-16, Zincol-16, and Fathejang-16). The data was recorded at the seedling and maturity growth stages of plants. A pot experiment revealed significant improvements in plant growth following bioinoculant application compared to controls. Notably, the combination of sugarcane bagasse biochar with Bacillus sp. MN54 exhibited the most pronounced effects, promoting internodal length, spike length, tiller number per plant, grain yield per plant, and spikelets per spike. Additionally, talc powder with Bacillus sp. MN54 increased peduncle length, tiller number per plant, and spike length in Fathejang-16. These findings offer valuable insights into optimizing bioinoculant formulations for improved agricultural practices, adapting to climate change, and contributing to ensuring global food security.

Abstract This study examines the impact of leaching with dilute hydrochloric acid solution on the reduction of ash content and the thermal degradation behavior of sugarcane bagasse. Response surface methodology (RSM) was used to statistically design the experiments and investigate the effect of three independent variables: treatment time, solid-to-liquid ratio, and reagent concentration. The leaching conditions were further optimized and experimentally validated for maximum ash reduction for suitability of treated biomass as feedstock for thermochemical conversion technologies. Reagent concentration and treatment time directly affected ash reduction, while the solid-to-liquid ratio inversely influenced it. Concentration had the highest impact, and treatment duration had the least. The maximum 78.2% ash reduction was achieved by treating the biomass with 1 M HCl for 80 min at a solid-to-liquid ratio of 50:1 (wt/vol). This ash reduction also resulted in a 9.82% increase in higher heating value (HHV). Hemicellulose hydrolysis during leaching was observed through chemical composition and Fourier-transform infrared spectroscopy (FTIR). Ash fusion temperatures increased, indicating more thermally stable biomass. Thermogravimetric analysis (TGA) showed elevated maximum degradation temperature and activation energy.

Abstract In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

Abstract In the current age of chemical science, chemical graph theory has significantly advanced our understanding of the characteristics of chemical compounds. To simulate the mathematical, chemical, and physical aspects of networks, a topological index, a numerical measure obtained from the graph of a chemical network, employed. Recent work has explored the topological properties of boron oxide using chemical graph theory. In this work, we conduct a Pearson correlation analysis of boron oxide to assess the correlations between the Van and S indices and entropy metrics. We analyze the Pearson correlation coefficients between the entropy values and the calculated indices using a heatmap. In this article, a significant positive correlation between the Van, and S indices, and entropy values, which is represented by the heatmap of the strong linear correlations. To avoid duplication, a dimensionality reduction technique should be used for highly connected variables. Additionally, this study gives a detailed explanation of the link between the indices and entropy, which will form the basis of further statistical investigations.

Abstract The mixed metric dimension of a graph is an important parameter in characterizing its structural complexity, specifically in nanoscale networks where precision is paramount. In this article, we calculate the mixed metric dimension of hexagonal nano-network, a modal with significant applications in nanotechnology and material science. Through rigorous analysis, we set up that the mixed metric dimension of the hexagonal nano-network is exactly three, highlighting its minimal but enough resolving set that uniquely identifies all vertices. Furthermore, we check out the exchange property within this context, demonstrating the robust adaptability of the hexagonal network’s resolving sets. Our findings display that the alternate assets aren’t the handiest preserved but stronger in these nano-networks, allowing for flexible adjustments in resolving sets without compromising the network’s integrity. This examination offers critical insights into the fundamental properties of hexagonal nano-networks, offering a theoretical foundation for future research in nanomaterial design and optimization. The results underscore the potential of leveraging mixed metric dimensions and exchange properties to achieve efficient and scalable solutions in nano-network applications.

Abstract Vaccine acceptance is a crucial component of a viable immunization program in healthcare system, yet the disparities in new and existing vaccination adoption rates prevail across regions. Disparities in the rate of vaccine acceptance result in low immunization coverage and slow uptake of newly introduced vaccines. This research presents an innovative AI-driven predictive model, designed to accurately forecast vaccine acceptance within immunization programs, while providing high interpretability. Primarily, the contribution of this study is to classify vaccine acceptability into Low, Medium, Partial High, and High categories. Secondly, this study implements the Feature Importance method to make the model highly interpretable for healthcare providers. Thirdly, our findings highlight the impact of demographic and socio-demographic factors on vaccine acceptance, providing valuable insights for policymakers to improve immunization rates. A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. The association rules suggest that higher SES, region, parents’ occupation, and mother’s education have an association with vaccine acceptability.

Abstract Fimbristylis complanata is an aquatic halophytic sedge that thrives in salt-affected land, marshes, and water channels. Two ecotypes (HR-Rasool headworks ECe 19.45; SH- Sahianwala 47.49 dS m−1) of F. complanata were collected from two salt-affected wetlands of Punjab, Pakistan. Five rhizomes of each ecotype were grown in plastic pots in the Botanical garden research area and treated with three intensities of salt [0 mM (control), 200 mM (moderate), 400 mM (high) NaCl for three durations (0, 15 and 30 days). The pots were arranged using a completely randomized block design (CRD) with three replications. After each duration, sampling was done. The HR ecotype optimally performed better under moderate salt incubation and moderate to higher salt exposure. This ecotype had improved growth traits, including shoot fresh weight (SFW), shoot dry weight (SDW), leaf area (LA), root length (RL), leaf mass fraction (LMF), relative growth rate (RGR), and unit leaf area (ULA) at higher NaCl (400 mM) in comparison with control NaCl (0 mM). This improvement in growth occurs due to the accumulation of photosynthetic pigments, better photosynthesis, and water use efficiency (A/E). The leaf microstructure increased in HR ecotype as midrib (MrT), leaf blade (LTh), bulliform cells (BTh), and cortical cells (CcT) thicknesses to prevent water loss under salinity, increase aerenchymatous area (ArA) for efficient gas movements at moderate salt levels and less exposure time concerning absolute control (0 mM NaCl). The SH ecotype affirmed more tolerance to salt by securing higher biomass (SFW, SDW), increased growth traits (LA, RL, LMF, ULA), photosynthetic pigments (Chl a, b, Car), and maximum photosynthetic performance at high salt regimes and prolonged duration in comparison to control (0 mM NaCl). Additionally, increased MrT, LTh, BTh, ECA, abaxial and adaxial stomatal area, and density, broadened metaxylem and phloem area, large aerenchyma, more cortical cell thickness under moderate to high salt regimes under moderate to high salt levels and time. Overall, changes in morpho-physiological traits and leaf microstructures in both ecotypes are linked to salt tolerance under temporal salt regimes. Our findings suggest that both ecotypes of F. complanata can potentially rehabilitate the salt-affected wetlands.

The optical and thermal properties of nanoparticles have attracted much interest due to their extinction effectiveness in industries like electronics, medical devices, photonics, materials sciences, drug delivery, hyperthermia, etc. In this work, the optical response of eight different geometrical nanostructures, namely Solid-CuNS, Au–Ag-coated-Solid-CuNS, Hollow-CuNS, Au–Ag-coated-Hollow-CuNS, Solid-CuNR, Au–Ag-coated-Solid-CuNR, Hollow-CuNR, and Au–Ag-coated-Hollow-CuNR, were studied for hyperthermia. Finite element analysis was performed in COMSOL Multiphysics to simulate the optical and thermal responses of the nanostructures. The size range of nanostructures was selected differently based on the hyperthermia condition to attain a temperature between 42 °C and 46 °C. An appropriate temperature distribution of 43.8 °C was achieved in the 500 nm liver domain using an Au–Ag-coated-Solid-CuNS with Dsphere = 20 nm. The minimum thermal response was 42.1 °C for both Solid-CuNS and Solid-CuNR when Dsphere was 35 nm and Lcylinder was 35 nm. The highest electric field distribution of 11.2 V/m in the surrounding area was seen for Au–Ag-coated-Solid-CuNR, with an extinction resonance peak at 475 nm. On the other hand, the Au–Ag-coated-Solid-CuNS showed the least electric field distribution of 2.22 V/m with a corresponding resonance peak of 480 nm when Dsphere was 15 nm.

Wheat yellow rust disease poses a significant threat to global wheat yield and grain quality. Early detection of this disease will help to minimize the loss caused by its effects. Existing models work well on images taken in a controlled environment, whereas a uniform background is placed behind the leaf, but these models fail to produce good results in natural settings. Previous research also involves manual interventions in the pipeline to achieve good classification results such as cropping the images, using uniform backgrounds, etc. These systems are not practical to use in natural environments where there will be a lot of background noise to the image and manual cropping becomes an extra step for the farmer. Moreover, the unavailability of the dataset in which images of leaves are taken in a natural setting became another challenge. In this research, a dataset is curated and leaves are annotated for object detection, object segmentation further the leaves are classified into 3 classes ie healthy, resistant, and susceptible. A novel unsupervised image rotation algorithm is proposed that takes input from YOLOv8 to align the leave in such a way that maximum background can be removed by a rectangular bounding box . Then the comparison between multiple state-of-the-art segmentation models ie. UNET, Segment-Anything (SAM), Segnet, LinkNet, PSPNet, FPN, Deep-Labv3+ (Xception), and DeepLabv3+ (Mo-bileNet) has shown that UNET has outperformed all the other segmentation models with an IOU score of 0.9563. Lastly for classification, the performance of multiple convolution neural networks ie. VGG16, Resnet 101(v2), Xception, Mo-bileNetV2, and Transformer-based models ie. Swin trans-former and MobileVit have been compared. Swin transformer has outperformed the state-of-the-art CNN models with an accuracy of 95.8%. This paper proposes a complete robust pipeline that can be deployed in natural environment and does not need any manual intervention to produce good results. This research shows that good localization of leaves and removal of unwanted background noise at the earliest stage of the pipeline will assist the segmentation model to effectively segment the leaf from the background which will enable classification models to achieve high classification accuracy, even when dealing with very small datasets.

This study focuses on revealing closed-form solutions for traveling waves in the nonlinear dynamical model of dust-acoustic waves in multispecies of dusty plasma. Plasma consists of cold dust grains, along with isothermal electrons and positive ions for balance. Fluid equations simplify through reductive perturbation, yielding the modified Korteweg–de Vries equation for analyzing weak dust-acoustic solitons. Applying this method to a specific scenario involving negatively charged dust grains and protons with all electrons bound to the dust grains shows support for rarefactive supersonic solitons, contrasting with typical compressive ion-acoustic solitons. Introducing streaming reveals a subtle soliton amplitude decrease. Using the Generalized exp(-S(φ)) expansion method effectively derives various soliton solutions, including kink, anti-kink, singular, hyperbolic functions, and diverse solitary waves. Furthermore, the equation’s dynamic behaviors are explored through equilibrium point bifurcations. Employing a Galilean transformation, the model is transformed into a planar dynamical system, and qualitative inspection is performed. Phase portraits depict bifurcation outcomes. Our methodology is significant for tackling the novel problem and applying previously untested approaches in this context, resulting in the development of multiple exact and novel optical soliton solutions. This highlights the efficiency and broad application of our new strategy to addressing nonlinear challenges in astrophysics, laboratories, space, and technology.

Linguistic complex intuitionistic fuzzy aggregation operators are a novel idea for the description of intuitionistic fuzzy information, where linguistic complex concepts are used to describe membership and non-membership. This can convey the hazy information more clearly, and some findings on linguistic complex intuitionistic fuzzy aggregation operators have been attained. Nonetheless, several linguistic complex intuitionistic fuzzy aggregation operators in the literature are founded on conventional operational principles, which have certain limitations when used to multi-attribute group decision-making (MAGDM). In this study, we presented some improved operating rules based on linguistic complex intuitionistic fuzzy variables (LCIFVs) and changed their features to address these issues. Next, we created a few aggregation operators, such as the enhanced linguistic complex intuitionistic fuzzy weighted average (LCIFWA) and the linguistic complex intuitionistic fuzzy ordered weighted averaging (LCIFOWA) operator, to fuse the decision information represented by LCIFVs. We also demonstrated that they had a few favorable qualities. We introduced many novel approaches to address the MAGDM issues in the context of the linguistic complex intuitionistic fuzzy environment, based on the LCIFOWA and linguistic complex intuitionistic fuzzy ordered weighted geometric (LCIFOWG) operators. In short, we employed few real-world scenarios to demonstrate the viability and soundness of the suggested techniques through comparison with other approaches. This unique technique has been applied to plastic waste management selection, and the results are more accurate than the previously used materials and methods.

Abstract This research addresses the critical need to optimize the Energy Efficiency (EE) for Ultra-Dense HetNets amid the ever-increasing demands for high-speed data networks. The rapid increase in high-speed devices highlights the urgent necessity for a transformative change in upcoming 5G cellular networks. According to Ericsson's 2022 report, mobile data traffic volume is expected to double by 2027, with mobile video traffic anticipated to rise by nearly 30% each year. In response to these challenges, researchers have identified essential technologies that facilitate 5G networks, including massive Multiple-input-Multiple-output (m-MIMO) systems and HetNets. Although both promise enhanced coverage and throughput, HetNets emerges as a cost-effective solution, surpassing m-MIMO in implementation cost and coverage. However, achieving maximum EE in HetNets necessitates careful consideration of various constraints, including delay, coverage probability, and Signal-to-Interference-plus-Noise Ratio (SINR) thresholds. This research marks a significant milestone in adopting the Distributed Dynamic Opportunistic Sleep Strategy (D-DOSS) approach. The D-DOSS method organizes small clusters throughout the network and evaluates key Quality of Service (QoS) parameters including Energy Utilization Efficiency (EUE), Coverage Probability, Data Throughput, and Success Probability using Monte Carlo simulations. This research analyzes Distributed Sleep (DS) and Centralized Schemes (CS) concerning given QoS parameters. While DS methodologies often exhibit performance trade-offs compared to CS, they provide significant advantages in terms of ease of implementation and management. CS, though representing the most commonly used method in ultra-dense HetNets involves high computational costs that complicate its management. By integrating the D-DOSS and addressing various constraints, this research not only advances HetNet technologies but also makes a significant contribution to optimizing EE while preserving network performance and QoS. The innovative D-DOSS approach offers a promising solution to the challenges of energy efficiency in wireless communication networks and paves the way for future advancements in HetNet deployments. The results and analysis show that D-DOSS effectively addresses the limitations of DS and outperforms existing CS techniques.

Abstract Salt stress is becoming a major issue for the world’s environment and agriculture economy. Different iron [Fe] sources can give an environmentally friendly alternative for salt-affected soil remediation. In this study the effects of Iron sulfate on Luffa cylindrica (Sponge gourd) cultivated in normal and saline water irrigated soil were examined. When FeSO4 (0.01, 0.025, 0.05, 0.1 ppm) were applied to salt affected soil, the length, fresh and dry biomass of sponge gourd plant roots and shoots inclined by an average of 33, 28, 11, 21, 18 and 22%, respectively. In plants irrigated with saline water, leaf count was raised successively (23–115%) with increasing concentration of FeSO4 (0.025-0.1 ppm) compared to stress only plants. The use of FeSO4 boosted sponge gourd growth characteristics in both normal and salt-affected soils compared to respective controls. The application of Iron sulfate under salt stress boosted photosynthetic indices such as chlorophyll a (22%), chlorophyll b (34%), carotenoids (16%), and total chlorophyll levels (22%). Iron sulfate application also exhibited incline in primary (total free amino acids, 50%; total soluble proteins, 46%) and secondary (total phenolics, 9%; flavonoid content, 51%) metabolites in salt-affected soils. Oxidative enzymatic activities such as catalase (CAT), peroxidase (POD), polyphenol oxidase (PPO) and DPPH scavenging activity (36%) were also increased by foliar spray of FeSO4 in control and salt stressed L. cylindrica plants. FeSO4 had a considerable impact on the growth and development of Luffa cylindrica in normal and salt-affected soils. It is concluded that FeSO4 application can effectively remediate salt affected soil and improve the production of crop plants.

Abstract The major challenge for the construction industry is to design and produce sustainable construction materials that are efficient in their performance and can be affordable for construction projects. The objective of this study is to determine the viability of incorporating waste products such as sugarcane bagasse ash (SCBA) to produce concrete with polypropylene (PP) fibers. SCBA is an industry waste that has certain pozzolanic properties, however, it shows limited mechanical properties when used as a cement replacement. To improve the mechanical and durability performance, metakaolin (15%) and PP fiber (0.5%, 1%, and 1.5%) were incorporated in the SCBA blend as a ternary additive. Some of the important characteristics explored include compressive strength, tensile strength, density, water absorption, acid resistance, and sorptivity. The study reveals that the incorporation of 15% metakaolin in the composite enhanced the compressive strength by 7%, 8.2%, and 9.1% for PP fiber additions of 0.5%, 1%, 1.5%, while the acid resistance enhanced by 4%, 6% and 8% relative to the control mix for the same value of pp fiber. Furthermore, cost evaluation confirms that the overall costs of concrete with 15% metakaolin and 5% SCBA were 12.2% less than the control concrete, thus making this option economically feasible. This research proves that the inclusion of SCBA, metakaolin, and PP fibers into the concrete mixture brings a sustainable approach that enhances mechanical properties and durability while assisting sugar manufacturing plants in the proper disposal of wastes.

Abstract This study investigates the projected impact of air pollution on mortality and Disability-Adjusted Life Years (DALYs) across SAARC countries. Utilizing Time Series and Machine Learning methodologies such as Autoregressive Integrated Moving Average, Exponential Smoothing, and Neural Network, the research aims to accurately forecast the mortality and DALYs attributed to air pollution from 2020 to 2030. Statistical analyses reveal a consistent upward trend in deaths and DALYs during the forecasting period, primarily driven by Ambient Particulate Matter Pollution (APM) and Ambient Ozone Pollution (AOP). Comparing the predictive accuracy of the models, Neural Network outperformed other methods, as indicated by Root Mean Square Error (RMSE) values. Specifically, the study finds that deaths and DALYs due to Ambient Particulate Matter pollution are least prevalent in the Maldives, while India and Pakistan exhibit the highest rates, and deaths and DALYs due to Ambient Ozone pollution are lowest in the Maldives and highest in Bangladesh and Pakistan. Moreover, deaths and DALYs attributed to Household Air Pollution (HAP) are lowest in Pakistan and highest in Nepal. These findings underscore the urgent need for air pollution control measures and informed policymaking in SAARC countries to mitigate the escalating health burden associated with air pollution.

Abstract Purple blotch disease is a major fungal disease of Allium cepa L. plants which is caused by the fungus Alternaria porri. The best conditions for the growth of Alternaria porri are temperatures between 22 °C and 25 °C and relatively high humidity. The Hydrotime, Thermal Time, and Hydrothermal Time models were used to measure different parameters of seed germination; therefore, we used them to measure the interactive effects of temperature and water potential on the germination conidia of Alternaria porri. The laboratory experiments were carried out at five constant temperatures, between 5 and 30 °C, and five different water potentials between 0 MPa and − 6 MPa. The germination of Alternaria porri conidia was highest at 25 °C and 0 MPa and lowest at 5 °C and − 6 MPa. The percentage of conidia germination decreased rapidly after 25 oC. Conidia germination was also affected by different water potentials, decreasing at lower water potential. Models based on HTT showed a reasonable fit to the germination and growth rate datasets. The best fitting model for conidia germination (R2 = 0.98491) was based on variable base and maximum temperature as a function of water potential. Based on the TT, HT, and models, the highest and lowest values for θT1 were observed at -6.0 MPa at 30 °C, and 0 MPa at 5 °C and the highest and lowest θT2 values were recorded at -6.0 MPa at 5 °C and 0 MPa at 30 °C while the lowest and highest θH values were recorded at -6.0 MPa at 5 °C and 0 MPa at 25 °C, respectively, for the HTT model, the predicted θHTT average value is 16.32 (MPa°Ch-1). Based on the statistical analysis, the cardinal hydrothermal time constant (θHTT) accurately explains the interactive effect of T and Ψ on the germination of Alternaria porri conidia under different environmental conditions.

Abstract The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Abstract Cream cheese analogue is a soft cheese with a slightly sweet and pleasantly tangy taste. It is prepared using the substitutes of dairy fats and proteins partially or completely. Cream cheese analogue ingredients are purchased carefully keeping in mind the required specifications. For the above-mentioned research, different samples of cream cheese (T0, T1, T2 and T3) were manufactured with standardized fat and protein and the moisture was maintained at 50–55% during these trials. All ingredients were added in a large container for mixing and cooking treatment for 1–2 min at 75 °C and homogenized for 5–6 min with the help of a hand mixture until completely homogenous sample was obtained. Samples were then transferred in to another container and allowed them to attain temperature between 20 and 25 °C. After that, samples were incubated with the addition of mesophilic culture until desired pH (4.6–4.7) was achieved. Later samples were heated at 50–55 °C for 2–3 min and then placed in a muslin cloth and left overnight for whey drainage. Physicochemical, microbial analysis were performed while sensory evaluation was performed using different parameters and scales. Sample prepared using 75% dairy cream and 25% cocoa butter substitute fat (T1) showed good results. T1 had better consistency which was considered desirable characteristics of cream cheese. This research proved the cost effective as cream cheese prepared with plant-based fat which made its production possible at industrial level. Cream cheese which was made from dairy fat (75%) and cocoa butter substitute fat (25%) had more springiness, smoothness, gumminess, more pleasant taste, color, flavor and aroma than other samples. This dairy product can be used in future in Pakistan.

Papaya (Carica papaya) fruit has a short shelf life and suffers significant postharvest losses due to climacteric, instant ripening and perishable nature. Pre-storage hot water treatment (HWT) has significant potential to delay decay incidence in horticultural commodities. The present study was designed to evaluate effectiveness of HWT on ‘Red lady’ papaya fruit for minimizing the fruit decay and maintaining the quality during storage. Physiologically mature unripe ‘Red lady’ papaya fruit harvested from a commercial papaya orchard. Selected fruits were treated at three different hot water temperatures (50 °C, 52 °C and 54 °C) along with a control (room temp) for 10 minutes thereafter stored at 12 °C and 85–90% RH for four weeks. Different sensory evaluation, fruit quality scales and enzymatic activities were observed at weekly intervals. Fruit stored after 54 °C HWT showed lower weight loss (4.63%), decay incidence (5.1%) and higher fruit firmness (12.92 N) relative to control. Fruit treated at 52 °C HWT improved the fruit acidity, but total soluble solids decreased than control. The results revealed that ascorbic acid (33.33 mg/100 g), total phenolic contents (35 mg/g FW) total antioxidants (47.50% DPPH inhibition), and antioxidative enzyme activities were improved by 52 °C HWT. It is concluded that pre storage papaya fruit treatment (HWT @ 52 °C) preserves the quality of fruit throughout storage on the other hand at 54 °C HWT helps in decreased fruit decaying. It can be summarized that papaya fruit can be treated with hot water in the range of 52–54 °C to support the fruit shelf life and suppress fruit decay along with maintaining fruit quality.Practical applications: Papaya (Carica papaya L.) is a climacteric fruit which is highly susceptible to fungal attacks after harvest. Due to its perishable nature, it becomes decayed and huge amount of highly nutritious and medicinally important fruit is wasted. Recently people are more concerned about food safety which limits the use of fungicides for controlling the fruit decay in papaya. However, heat treatment has tremendous potential to control microbial decay, and keep food commodities safe from hazardous chemicals. In this study, the hot water treatment has successfully suppressed the fruit decay and maintained fruit quality of papaya in the course of entire period of cold storage. These findings indicated that use of hot water treatment is a practical approach for increasing the shelf life of papaya fruit.

Inequalities are essential in solving mathematical problems in many different areas of mathematics. Among these, problems involving coefficient combinations that occurred in the Taylor–Maclaurin series of the inverse of complex-valued analytic functions are the challenging ones to solve. In the current article, our aim is to study certain coefficient-related problems that construct from coefficients of the inverse of specific analytic functions. These problems include the Zalcman and Fekete–Szegö inequalities, as well as sharp estimates of the second and third-order Hankel determinants with inverse function coefficients. Also, one of the obtained results gives an improvement of the problem that has been recently published in the journal "AIMS Mathematics".

Abstract Gladiolus, a widely cultivated cut flower known for its aesthetically pleasing multicoloured spikes, has earned significant commercial popularity. A comprehensive understanding of the rhizosphere bacterial community associated with gladiolus is imperative for revealing its potential benefits. Molecular characterization is considered an effective method to gain insights into the structural and functional aspects of microbial populations. The soil characteristics and bacterial communities in the rhizosphere are typically influenced by quorum sensing (QS) and quorum quenching (QQ) mechanisms. This study aims to explore the niceties and diversity of rhizospheric bacterial populations linked with gladiolus corms, with a specific focus on understanding the dynamics of QS and QQ mechanisms in their complex interactions. The isolation of bacterial strains was achieved through the serial dilution method on nutrient agar (NA) media. The identification of the isolates was accomplished by amplifying 16 S rRNA gene sequences via polymerase chain reaction (PCR) via the use of universal primers. Sequence analysis was conducted via BLAST on the National Center for Biotechnology Information (NCBI) database. The characteristics of the isolated bacteria were elucidated via biosensors. This study identified three QS strains and five QQ strains. A consortium of quenchers was formulated utilizing five strains that demonstrated efficacy in mitigating the impact of disease on gladiolus and fostering growth. Among the three treatments—Scale, Descale, and Descale and Cut Half (DSC)—the DSC treatment emerged as the most effective. This treatment exhibited a broader range of variation in biological parameters over time, aligning with prevailing trends in the local market.

The multiband, multifunctional chiral metasurface with asymmetric transmission exhibits significant potential for diverse applications in modern communication systems, ranging from enhanced signal modulation and polarization control to advanced beam steering and compact antenna design. This research presents a versatile and advanced chiral metasurface operating at multiple bands with diverse functionalities, including asymmetric transmission. The proposed metasurface effectively transforms an incoming Linearly Polarized (LP) wave into a Circularly Polarized (CP) wave. Additionally, it functions as a 90° polarization rotator for the incident LP wave. The design starts with an element of a 2 × 2 supercell comprising a Square Split Ring Resonator (SSRR) and an I-shaped resonator. The right diagonal elements of a supercell undergo scaling down, giving rise to a rotational asymmetry. Chirality is introduced into the design, and cross polarization conversion is enhanced by rotating all four elements by 90° relative to each other. On the back side of the substrate, each element undergoes a 90° rotation compared to its counterpart on the front side, realizing the asymmetric transmission feature. The incorporation of multiband and multifunctional features within a single supercell equips the subject chiral metasurface to be utilized in various engineering applications.

Abstract Road corridor vegetation plays a vital role in maintaining ecosystem stability and providing essential ecological services, particularly in semi-arid regions where environmental conditions are challenging. In this study, we investigated the functional traits of native and non-native plant species along the N5 highway corridor in the semi-arid region of Punjab, Pakistan. The methodology involved extensive field surveys and systematic sampling of herbaceous vegetation, followed by detailed measurements of functional traits diversity. We classified 38 plant species into native and non-native categories and analyzed their distribution, life forms, leaf spectra, and flowering phenology. Our results revealed distinct patterns in the functional traits of native and non-native species, with non-native species exhibiting larger plant heights, leaf sizes, and leaf surface areas compared to native species. Additionally, native species displayed greater root and stem biomass, indicative of adaptations to nutrient-poor soils and water-limited environments. The findings suggest that non-native species possess traits associated with rapid growth and resource acquisition, enabling them to outcompete native vegetation and establish dominance in roadside ecosystems. These results provide valuable insights for understanding the ecological implications of non-native species and designing effective management strategies to mitigate their impacts on native biodiversity and ecosystem resilience in semi-arid regions.

Abstract Linguistic term fuzzy sets provide an intuitive way to express preferences, enhancing understanding and communication among decision-makers. In this article, we introduce the novel concept of p,q-quasirung orthopair fuzzy linguistic sets (p,q-QOFLSs), which merge the principles of p,q-quasirung orthopair fuzzy sets (p,q-QOFSs) with linguistic fuzzy sets. This new framework offers a more robust approach to handle uncertain and imprecise information in decision-making processes, characterized by linguistic membership and non-membership degrees. We establish several fundamental operational laws, alongside score and accuracy functions, to facilitate the comparison of p,q-quasirung orthopair fuzzy linguistic numbers. Leveraging these operational laws, we propose a series of weighted averaging and geometric operators under p,q-QOFLSs. Furthermore, we formulate a multi-attribute decision-making methodology using these operators. The significance of the proposed method lies in its ability to model complex decision-making scenarios with enhanced precision. A numerical example validates the practicality and adaptability of the methodology, supported by sensitivity analyses and comparative evaluations, highlighting the innovation and efficiency of the approach.

Abstract In the fields of mathematics, chemistry, and the physical sciences, graph theory plays a substantial role. Using modern mathematical techniques, quantitative structure-property relationship (QSPR) modeling predicts the physical, synthetic, and natural properties of substances based only on their chemical composition. For a chemical graph, the temperature of a vertex is a local property introduced by Fajtlowicz (1988). A temperature-based graphical descriptor is structured based on temperatures of vertices. Involving a non-zero real parameter $$\beta$$ β , the general F-temperature index $$T_{\beta }$$ T β is a temperature index having strong efficacy. In this paper, we employ discrete optimization and regression analysis to find optimal value(s) of $$\beta$$ β for which the prediction potential of $$T_{\beta }$$ T β and the total $$\pi$$ π -electron energy $$E_{\pi }$$ E π of polycyclic hydrocarbons is the strongest. This, in turn, answers an open problem proposed by Hayat & Liu (2024). Applications of the optimal values for $$T_{\beta }$$ T β are presented a two-parametric family of carbon nanocones in predicting their $$E_{\pi }$$ E π with significantly higher accuracy.

Abstract The threat of antibiotic resistance is escalating, diminishing the effectiveness of numerous antibiotics due to the rapid development of resistant bacteria. In response, the use of green-synthesized nanoparticle, alone or combined with antimicrobial agents, appears promising. This study explores the effectiveness of zinc oxide nanoparticles (ZnONPs) synthesized using Loranthus cordifolius leaf extracts and subsequently coated with anethole. The fabrication of these nanoparticles was confirmed via UV-Vis, FTIR and TEM analyses, ensuring the nanoparticles were produced as intended. Utilizing a nanoprecipitation process that excludes evaporation and drying, a high drug loading capacity of 16.59% was accomplished. The encapsulation efficiency for anethole was recorded at 88.23 ± 4.98%. Antibacterial efficacy was assessed by com paring the green-synthesized ZnONPs (average size: 14.47 nm), anethole-loaded ZnONPs (average size: 14,75 nm), and commercially sourced ZnONPs. The ZnONPs with anethole demonstrated superior inhibition against all tested bacterial strains, including Gram-negative species like Pseudomonas aeruginosa and Escherichia coli, and Gram-positive species like Bacillus subtilis and Staphylococcus aureus, outperforming the commercially available ZnONPs. Additionally, anethole-coated ZnONPs showed the greatest inhibition of Gyr-B activity (IC50 = 0.78 ± 0.2 M), better than both green-synthesized and commercially available ZnONPs. These findings emphasize the enhanced antimicrobial properties of ZnONPs, particularly when combined with green synthesis and anethole loading, highlighting their potential in various biomedical applications.

Abstract The root architecture, more seminal roots, and Deeper roots help the plants to uptake the resources from the deeper soil layer to ensure better growth. The Gibberellic acid-sensitive (GA-sensitive) Rht genes are well known for increasing drought tolerance in wheat. Much work has been performed on the effect of these genes on the plant agronomic traits and little work has been done on the effect of Rht genes on seminal roots and root architecture. This study was designed to evaluate 200 wheat genotypes under normal and osmotic stress. The genotypes were sown in the solution culture and laid under CRD factorial arrangement with three replications and two factors i.e., genotypes and treatments viz. normal and osmotic stress (20% PEG-6000) applied one week after germination. The data was recorded for the root traits. Results demonstrated that out of 200 genotypes, the GA-sensitive Rht13 gene was amplified in 21 genotypes with a fragment length of 1089 bp. In comparison, the GA-insensitive Rht1 gene was amplified in 24 genotypes with a band size of 228 bp. From 200 wheat genotypes, 122 genotypes produced 5 seminal roots, 4 genotypes 4 seminal roots, and 74 genotypes 3 seminal roots. The genotypes G-3 (EBW11TALL#1/WESTONIA-Rht5//QUAIU#1), G-6 (EBW01TALL#1/SILVERSTAR-Rht13B//ROLF07) and G-8 (EBW01TALL#1/SILVERSTAR-Rht13B//NAVJ07) produced 5 seminal roots and have longer coleoptile (> 4.0 cm), root (> 11.0 cm) and shoot (> 17 cm) under normal and osmotic stress. Furthermore, Ujala 16, Galaxy-13, and Fareed-06 produced 3 seminal roots and have short coleoptile (

Background and Aim: Avian influenza is a global threat to avian species, particularly in developing countries. Recombinant vaccines, including virus-like particles (VLPs), are promising strategies for preventing the spread of the disease. VLPs produced through the self-assembly of viral structural proteins without genomic material mimic native virions and are promising platforms for new vaccines. VLPs have been shown to elicit protective antibodies and are effective and safe vaccines against influenza. This study aimed to optimize the protocol for the production and characterization of H9N2 VLPs and their evaluation as a vaccine in broiler birds. Materials and Methods: Low-pathogenic influenza virus (LPAI) H9N2 was isolated and characterized through whole-genome sequencing, and a VLP-based vaccine for LPAI H9N2 was prepared using a baculovirus expression system. Codon-optimized hemagglutinin (HA), neuraminidase (NA), and M1 were successfully cloned in pFastbac1 and expressed in SF9 cells. Proteins were characterized using sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), western blotting, and electron microscopy after purification. Semi-purified proteins were tested as a vaccine in broiler chickens challenged with LPAI H9N2. Results: Recombinant Bacmid DNA from positive clones was extracted and confirmed using a polymerase chain reaction. The transfection showed cytopathic effects, and the proteins were confirmed through western blotting and SDS-PAGE, which showed the sizes of HA = 62–64 KD, NA = 52 KD, and M1 = 25 KD. The shape and morphology were confirmed through transmission electron microscopy which revealed 100–150 nm size particles. As a result, the semi-purified VLPs (HA assay: 256) were tested as a vaccine for specific-pathogen free broiler birds; administered through subcutaneous and intranasal routes. The birds were challenged on the 28th day after vaccination with the H9N2 strain, and the birds showed significant cross-reactivity with the H9N2 strain. Conclusion: The semi-purified VLP-based vaccine induced a significant immune response in vivo. This vaccine formulation has the potential to control avian influenza outbreaks in Pakistan’s poultry industry.

Abstract Valvular Heart Disease (VHD) is a globally significant cause of mortality, particularly among aging populations. Despite advancements in percutaneous and surgical interventions, there are still uncertainties that remain regarding the risk factors that significantly contribute to this condition within the domain of cardiovascular disease. This study investigates these uncertainties and the role of machine learning in categorizing VHD based on cardiovascular risk factors. It follows a two-part investigation comprising feature extraction and classification phases. Feature extraction is initially performed using a wrapping approach and refined further with binary logistic regression. The second phase employs five classifiers: Artificial Neural Network (ANN), XGBoost, Random Forest (RF), Naïve Bayes, and Support Vector Machine (SVM), along with advanced methods such as SVM combined with Principal Component Analysis (PCA) and a majority-voting ensemble method (MV5). Data on VHD cases were collected from DHQ Hospital Faisalabad using simple random sampling. Various statistical measures, such as the ROC curve, F-measure, sensitivity, specificity, accuracy, MCC, and Kappa are applied to assess the results. The findings reveal that the combination of SVM with PCA achieves the highest overall performance while the MV5 ensemble method also demonstrates high accuracy and balance in sensitivity and specificity. The variation in VHD prevalence linked to specific risk factors highlights the importance of a comprehensive approach to reduce this disease’s burden. The Exceptional performance of SVM + PCA and MV5 highlights their significance in diagnosing VHD and advancing knowledge in biomedicine.

Abstract The performance of various geogrid earth-retaining walls integrated with a non-cohesion granular backfill for reducing earth pressure has been investigated through small-scale shaking table tests and full-scale 3D finite element analysis. This purpose involved a series of physical modeling tests involving different earth-retaining walls (0.83 cm, height 7.5 cm, thickness, and length 1 m) and arrangements of full-scale 3D finite element analysis (5 m, height, 0.3 m, thickness, and length 6 m). This research investigates and designs hollow prefabricated concrete panels, gravity-type stone masonry, and reinforcement concrete (GRE) walls. It also displays comparative studies such as the top displacement of the wall, deflection of the wall, lateral pressure of the wall, settlement of the backfill, and vertical settlement of the foundation across the height of the (GRE) walls. The understanding and findings based on shaking table experiments and FE simulations have been used to develop a critical model for estimating earthquake-induced displacement (GRE) walls. The validity of the proposed FE simulation model has also been examined in the shaking table experiment and the FE simulation results. Based on the findings, the hollow prefabricated concrete panels were the most practical alternative due to their lower deflection and displacement. The observation also found that the hollow prefabricated (GER) wall is the most viable option, as the backfill surface settlement and lateral pressure decreased with the inclusion of geogrid reinforcement.

Abstract For a sustainable ecology, slaughterhouse sludge must be managed effectively in preview of the parasitic or coliforms’ spill over to the community. In order to determine the effectiveness of a customized biological decomposer solution in lowering the parasitic eggs and coliform bacteria, three composting units (Unit 1, Unit 2, and Unit 3) were treated with its different amounts. Over a period of 60 days, pH, temperature, humidity, number of the parasitic eggs per gram (EPG) of faecal material, viability of eggs, and coliform counts were evaluated. By the fifth day of the composting process, pH had significantly (P 45 °C), which persisted for 20 days for Unit 3 and 15 days for Units 1 and 2. Humidity levels initially increased significantly (P 0.05) 12.5% decline in EPG in Unit 2 and Unit 3 was recorded. After that, a significant (P

Abstract Selective and sensitive sarcosine detection is crucial due to its recent endorsement as a prostate cancer (PCa) biomarker in clinical diagnosis. The reduced graphene oxide-cobalt nickel tungsten boron oxides (CoNiWBO/rGO) nanocomposite is developed as a non-enzymatic electrochemical sensor for sarcosine detection in PCa patients’ serum. CoNiWBO/rGO is synthesized by the chemical reduction method via a one-pot reduction method followed by calcination at 500 °C under a nitrogen environment for 2 h and characterized by UV-Vis, XRD, TGA, and SEM. CoNiWBO/rGO is then deposited on a glassy carbon electrode, and sarcosine sensing parameters are optimized, including concentration and pH. This non-enzymatic sensor is employed to directly determine sarcosine in serum samples. Differential pulse voltammetry (DPV) and linear sweep voltammetry (LSV) are employed to monitor the electrochemical behavior where sarcosine binding leads to oxidation. Chronoamperometric studies show the stability of the developed sensor. The results demonstrate a wide linear range from 0.1 to 50 µM and low limits of detection, i.e., 0.04 µM and 0.07 µM using DPV and LSV respectivel. Moreover, the calculated recovery of sarcosine in human serum of prostate cancer patients is 78–96%. The developed electrochemical sensor for sarcosine detection can have potential applications in clinical diagnosis.

Abstract The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall.

Abstract Background Pressure faced by dental students from academic activities, clinical skills training, and patient care may lead to high stress and potential burnout, negatively impacting their well-being and patient safety. Aim The study aimed to explore the burnout level of dental students at Qassim University, Saudi Arabia and to identify the factors that are associated with the level of burnout. Methods The descriptive cross-sectional study was carried out at Dental College, Qassim University, Saudi Arabia, from January to February 2024. The study was conducted to evaluate burnout levels among first to final-year undergraduate dental students enrolled at Qassim University using a pre-validated questionnaire; Burnout Clinical Subtype Questionnaire, via Google Forms. Analysis was performed with IBM SPSS-24, utilizing descriptive statistics and non-parametric tests, Mann-Whitney-U and Kruskal-Wallis. A multiple linear regression analysis was conducted to predict Burnout from demographic and academic factors. Results 151 participants responded to the survey, with 49.7% male and 50.3% female. Results showed that burnout scores increased significantly (p

Abstract Despite the rapid advancements in optimization algorithms for economical reinforced concrete (RC) structures, their application to RC slabs has been significantly limited. The primary constraint is slab depth, governed only by deflection control criteria, which impedes further depth reduction—a key factor in cost optimization. The deflection check relies solely on one design parameter—span length, which is often fixed. This constraint limits the effectiveness of even advanced algorithms, which have consistently failed to deliver substantial cost savings in slab design. To address this issue, this study presents a detailed parametric investigation of three key cost-reducing factors in RC one-way slabs: steel strength, concrete strength, and reinforcing bar sizes, in accordance with the ACI 318–19 code. By bypassing algorithmic constraints, the study formulates optimal design strategies through extensive manual design iterations, providing novel trend-based solutions for cost-effective slab design. The findings indicate that for lower live loads, grade 280 steel offers a 20–30% cost reduction compared to higher-grade steel, despite the latter's strength advantage. However, for higher loads, grade 420 or 500 is recommended. Additionally, optimal bar sizes and concrete strengths are recommended for varying load conditions, offering practical design strategies. This approach provides designers with actionable insights to achieve cost-efficient designs without relying on traditional optimization algorithms.

Abstract Background There are reported studies of Hepatitis C and chronic kidney disease association. However, how this liver virus infection affects the general population’s susceptibility to the onset of the kidney disease is still unknown. Methods To determine if a positive anti-HCV serologic status is linked to a greater incidence of chronic kidney disease in the general adult population, a systematic evaluation of the published medical literature since 2015 was conducted. A summary estimate of the relative risk of chronic kidney disease with HCV was produced using a random-effects model. Moreover, stratified analysis and meta-regression were performed. Results Twelve studies (n = 605858 patients) were filtered and included. Meta-analyses were conducted according to the outcome. Pooling results of longitudinal studies (n = 06 studies, n = 347120 unique patients) demonstrated an association between positive anti-HCV serologic status and increased incidence of CKD. The summary estimate for adjusted hazard ratio was 1.21 with (95% confidence interval 1.13; 1.29, P = 0.001), and between studies heterogeneity was noted (P value by Q test

Abstract Antimicrobial resistance (AMR) poses a significant global health threat, primarily stemming from its misuse and overuse in both veterinary and public healthcare systems. The consequences of AMR are severe, leading to more severe infections, increased health protection costs, prolonged hospital stays, unresponsive treatments, and elevated fatality rates. The impact of AMR is direct and far-reaching, particularly affecting the Sustainable Development Goals (SDGs), underscoring the urgency for concerted global actions to achieve these objectives. Disproportionately affecting underprivileged populations, AMR compounds their vulnerabilities, pushing them further into poverty. Moreover, AMR has ramifications for food production, jeopardizing sustainable agriculture and diminishing the livelihoods of farmers. The emergence of antibiotic-resistant bacteria in underprivileged areas heightens the risk of complications and mortality. Climate change further contributes to AMR, as evidenced by increased instances of foodborne salmonellosis and the development of antibiotic resistance, resulting in substantial healthcare costs. Effectively addressing AMR demands collaboration among governments, entrepreneurs, and the public sector to establish institutions and policies across all regulatory levels. Expanding SDG 17, which focuses on partnerships for sustainable development, would facilitate global antimicrobial stewardship initiatives, technology transfer, surveillance systems, and investment in vaccine and drug research. The World Bank's SDG database, tracking progress towards sustainable development, reveals a concerning picture with only a 15% success rate till 2023 and 48% showing deviation, underscoring a global gap exacerbated by the COVID-19 pandemic. Tackling AMR's global impact necessitates international cooperation, robust monitoring, and evaluation methods. The five priorities outlined guide SDG implementation, while impoverished countries must address specific challenges in their implementation efforts. Addressing AMR and its impact on the SDGs is a multifaceted challenge that demands comprehensive and collaborative solutions on a global scale.

Abstract Background It is well-known that serum uric acid (SUA) can increase the risk of hypertension, diabetes, obesity and dyslipidemia. However, its independent association with the risk of cardiovascular diseases (CVD) is controversial particularly in different populations. Hence, this study was aimed to assess an independent association of SUA with CVD risk in a Punjabi Pakistani cohort. Methods This is a retrospective observational study in which 502 human subjects having CVD, hypertension and/or diabetes were grouped based on SUA levels as normouricemia (n = 266) and hyperuricemia (n = 236). Role of SUA was assessed in increasing the risk of CVD independent of other key confounding factors (i.e. age, gender, dyslipidemia, hypertension, diabetes, dietary and life-style habits). All clinical and biochemical data were analyzed in SPSS (ver. 20). Results Subjects aged 55 ± 13 years were of both genders (males: 52%). SUA levels were significantly different among clinical subtypes of CVD [i.e. acute coronary syndrome (ACS), myocardial infarction (MI) and heart failure (HF)]. Spearman correlation showed a significantly positive association between CVD and SUA (rho = 0.149, p

Abstract Chitosan (CTS) is recognized for enhancing a plant’s resilience to various environmental stresses, such as salinity and drought. Moreover, salicylic acid (SA) is acknowledged as a growth regulator involved in addressing metal toxicity. However, the effectiveness of both compounds in mitigating Cr-induced stress has remained relatively unexplored, especially in the case of Aconitum napellus, a medicinally and floricultural important plant. Therefore, the primary objective of this study was to investigate the potential of CTS and SA in alleviating chromium (Cr)-induced stress in A. napellus. To address these research questions, we conducted a controlled experiment using potted plants to evaluate the individual and combined impacts of CTS and SA on plants exposed to Cr stress. Foliar application of CTS (0.4 g/L) or SA (0.25 mmol/L) led to significant improvements in the growth, chlorophyll content, fluorescence, and photosynthetic traits of A. napellus plants under Cr stress. The most notable effects were observed with the combined application of CTS and SA, resulting in increases in various morphological parameters, such as shoot length (2.89% and 7.02%) and root length (27.75% and 3.36%) under the Cr 1 and Cr 2 treatments, respectively. Additionally, several physiological parameters, such as chlorophyll a (762.5% and 145.56%), chlorophyll b (762.5% and 145.56%), carotenoid (17.03% and 28.57%), and anthocyanin (112.01% and 47.96%) contents, were notably improved under the Cr 1 and Cr 2 treatments, respectively. Moreover, the combined treatment of CTS and SA improved the fluorescence parameters while decreasing the levels of enzymatic antioxidants such as catalase (27.59% and 43.79%, respectively). The application also notably increased osmoprotectant parameters, such as the total protein content (54.11% and 20.07%) and the total soluble sugar content (78.17% and 49.82%) in the leaves of A. napellus in the Cr 1 and 2 treatments, respectively. In summary, these results strongly suggest that the simultaneous use of exogenous CTS and SA is an effective strategy for alleviating the detrimental effects of Cr stress on A. napellus. This integrated approach opens promising avenues for further exploration and potential implementation within agricultural production systems.

Abstract Solid supported catalysts have several synthetic applications. Herein, finely ground eggshells were used as a solid support for the preparation of transition metal (Ni, Zn, Cu, Sn and Co) based catalysts to synthesize 2,4-dinitrophenylhydrazone (3) and dihydropyrimidinones (7 and 8). The effect of catalyst load, time and temperature on product yield was studied. Box Behnken Model was employed, and three predictors named catalyst amount (A), reaction time (B), and reaction temperature (C) were used to find the correlation of the predictors with the yield. Second order polynomial equation was used to estimate the effects of these factors. According to the statistical model, about 12% increase in yield was observed as a result of one-unit increase in reaction time while all other terms were kept constant. The values of S (18.1616) and R2 (71.2%) indicate that the statistical model gave an adequate fit to data. Quadratic model for the response surface was used for the analysis of variance (ANOVA) results, the larger F-values, and smaller p-values indicated that the predictors are in good agreement. The linear model terms of predictors were found to be significantly effective for yield (P