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This paper deals with the study of the reaction–diffusion epidemic model perturbed with time noise. It has various applications such as disease in population models of humans, wildlife, and many others. The stochastic SIR model is numerically investigated with the proposed stochastic backward Euler scheme and proposed stochastic implicit finite difference (IFD) scheme. The stability of the proposed methods is shown with Von Neumann criteria and both schemes are unconditionally stable. Both schemes are consistent with systems of the equations in the mean square sense. The numerical solution obtained by the proposed stochastic backward Euler scheme and solutions converges towards an equilibrium but it has negative and divergent behavior for some values. The numerical solution gained by the proposed IFD scheme preserves the positivity and also solutions converge towards endemic and disease-free equilibrium. We have used two problems to check our findings. The graphical behavior of the stochastic SIR model is much adjacent to the classical SIR epidemic model when noise strength approaches zero. The three-dimensional plots of the susceptible and infected individuals are drawn for two cases of endemic equilibrium and disease-free equilibriums. The results show the efficacy of the proposed stochastic IFD scheme. [ABSTRACT FROM AUTHOR]

Introduction and Objectives: Clinical data for older patients with advanced liver disease are limited. This post hoc analysis evaluated the efficacy and safety of terlipressin in patients aged =65 years with hepatorenal syndrome using data from 3 Phase III, randomized, placebo-controlled studies (OT-0401, REVERSE, CONFIRM). Patients and Methods: The pooled population of patients aged =65 years (terlipressin, n = 54; placebo, n = 36) was evaluated for hepatorenal syndrome reversal--defined as a serum creatinine level =1.5 mg/dL (=132.6 mmol/L) while receiving terlipressin or placebo, without renal replacement therapy, liver transplantation, or death--and the incidence of renal replacement therapy (RRT). Safety analyses included an assessment of adverse events. Results: Hepatorenal syndrome reversal was almost 2-times higher in terlipressin-treated patients compared with patients who received placebo (31.5% vs 16.7%; P = 0.143). Among surviving patients, the need for RRT was significantly reduced in the terlipressin group, with an almost 3-times lower incidence of RRT versus the placebo group (Day 90: 25.0% vs 70.6%; P = 0.005). Among 23 liver-transplant-listed patients, significantly fewer patients in the terlipressin versus placebo group needed RRT by Days 30 and 60 (P = 0.027 each). Fewer patients in the terlipressin group needed RRT post-transplant (P = 0.011). More terlipressin-treated patients who were listed for and received a liver transplant were alive and RRT-free by Day 90. No new safety signals were revealed in the older subpopulation compared with previously published data. Conclusions: Terlipressin therapy may lead to clinical improvements in highly vulnerable patients aged =65 years with hepatorenal syndrome. [ABSTRACT FROM AUTHOR]

Curcuma mangga val. (Zingiberaceae) is one of the plants that used as traditional medicine by Indonesian. Several studies have been reported on the content of compounds of C. mangga, but it is not yet known which compounds have medicinal properties. In this study, two labdane diterpenes were isolated from the extract of rhizome of C. mangga. Determination of the structure conducted by NMR (¹H, 13C, HSQC and HMBC) that obtained two compounds, calcaratarin A (1) and labda-8(17),12-diene-15,16-dial (2). Both compounds have an aldehyde functional group. However, both compounds did not show antibacterial activity on Escherichia coli. [ABSTRACT FROM AUTHOR]

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.

This research proposes a text data security method using a combination of Linear Congruential Generator (LCG), Advanced Encryption Standard (AES) Cipher Block Chaining (CBC) mode, and Least Significant Bit (LSB) steganography technique on digital images. The message scrambling process using LCG produces ASCII characters as noise that is inserted in the original message. After that, the message is encrypted using AES-256 CBC to provide additional security. The encryption result is then hidden in the digital image through LSB steganography technique. Tests were conducted on images with JPEG and BMP formats to measure the visual quality after the data insertion process, as measured by PSNR (Peak Signal-to-Noise Ratio). The test results show a PSNR value of 56.60 dB for JPEG images and 70.84 dB for BMP images. In addition, the insertion process in JPEG images degrades the image quality, mainly due to lossy compression, compared to the lossless BMP format. This study concludes that the proposed combination of methods is effective in hiding messages in images, but is susceptible to compression on lossy formats such as JPEG. The use of lossless image formats such as BMP or PNG is recommended to maintain data integrity.

Abstract Based on sociocultural theory and pragmatism philosophical underpinnings, this study explores the classroom assessment practices of tertiary EFL practitioners, focusing on their alignment with contemporary constructivist assessment trends, methods, and approaches, rather than psychometrical assessments. The study extends knowledge on language teachers’ preparedness for implementing classroom assessment literacy. Participants were tertiary EFL instructors from four higher educational institutions in Saudi Arabia. Adopting an explanatory sequential mixed-methods research design, data were collected through self-reported questionnaires, classroom observations, and a review of assessment documents and artifacts. Descriptive statistics were used to analyze quantitative data and thematic analysis qualitative data. The dominance of traditional over alternative assessment methods was demonstrated, indicating gaps in teachers’ classroom assessment literacy. The findings highlight the contradiction, discrepancy, and complexity of the relationship between teachers’ articulated and exercised assessment practices. They provide baseline data for L2 classroom assessment policy, a classroom-based assessment framework, and a guide for teachers’ professional development in language assessment literacy.

Abstract The mung bean crop (Vigna radiata (L.) R. Wilczek) is widely recognized as a key source of pulse food worldwide. However, this crop suffers substantial yield losses due to humid environments, particularly from infestations by the fungal pathogen Macrophomina phaseolina, which causes charcoal rot disease. This infestation results in significant agronomic losses, affecting both the crop’s growth characteristics and overall yield. Previous research suggests that these losses can be mitigated through environmentally friendly soil amendments, such as biochar, as well as by applying various nanofungicides. This study aims to explore the potential of biochar and zinc oxide nanoparticles (ZnONPs) to reduce the severity of charcoal rot disease and enhance the agronomic traits and yield of mung bean plants affected by this disease. The experiment was conducted in triplicate, applying ZnONPs at three concentrations (5, 10, and 20 mg. L− 1) via foliar spraying, combined with two levels of biochar (20 g and 40 g per pot). Positive and negative control treatments were also included for comparison. The results demonstrated that applying 40 g of biochar per pot and 20 mg. L− 1 of foliar-applied ZnONPs increased the activities of the anti-oxidative defence enzymes. Additionally, this treatment strategy boosted the plants’ disease resistance mechanisms, leading to lower mortality rates and reduced levels of malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) by 61.7% and 49.23%. Moreover, the treatment positively impacted key growth parameters, increasing total chlorophyll content by 43%, plant height by 47%, and legume count per plant by 80.4%. The application of biochar and ZnONPs also improved seed protein content, reflecting an enhancement in nutritional quality. This study supports the use of biochar and ZnONPs as biostimulants to manage yield losses in mung bean crops affected by charcoal rot disease. The future prospects of using ZnONPs and biochar as treatments in agriculture are promising, as they offer innovative, eco-friendly solutions to enhance crop productivity, improve soil health, and reduce reliance on synthetic chemicals, paving the way for more sustainable and resilient agricultural systems.

Abstract Glutathione Peroxidase 1 (GPx1) gene has been reported for its role in cellular redox homeostasis, and the dysregulation of its expression is linked with the progression of diverse cancers. Non-synonymous single nucleotide polymorphism (nsSNPs) have been emerged as the crucial factors, playing their role in GPx1 overexpression. To understand the deleterious mutational effects on the structure and function of GPx1 enzyme, we delved deeper into the exploration of possibly damaging nsSNPs using in-silico based approaches. Eight widely utilized computational tools were employed to roughly shortlist the deleterious nsSNPs. Their damaging effects on structure and function of the genes were evaluated by using different bioinformatics tools. Subsequently, the three final proposed deleterious mutants including mutations rs373838463, rs2107818892, and rs763687242, were docked with their reported binder, TNF receptor-associated factor 2 (TRAF2). The lowest binding affinity and stability of the docked mutant complexes as compared to the wild type GPx1 were validated by molecular dynamic simulation. Finally, the comparison of RMSD, RMSF, RoG and hydrogen bond analyses between wild-type and mutant’s complexes validated the deleterious effects of proposed nsSNPs. This study successfully identified and verified the possibly damaging nsSNPs in GPx1 enzyme, which may be linked the progression of various types of cancer. Our findings underscore the value of in-silico approaches in mutational analysis and encourage further preclinical and clinical trials.

Abstract With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection.

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

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

Abstract Background 17.9 million deaths worldwide were attributable to cardiovascular diseases. Basic life support is one of the crucial strategies that could increase chances of cardiac arrest victims’ survival rate by nurses and other healthcare providers. Aim The aims of this study was to examine the retention of the BLS knowledge among nurses in Palestine. Methods A descriptive cross-sectional design was used to collect data from 108 nurses between February 2022 and April 2022 from two AHA-ITCs in Palestine. The instrument consisted of two sections; demographics and knowledge test which was developed by the researcher and contain a written examination containing 25 multiple-choice questions. Results Out of 160 distributed questionnaires, 108 were completed by nurses as a convenience sampling technique. Over half of the participants were male (54.6%), and the majority had a bachelor’s degree in nursing (75%). Analysis identified that there was a significant difference (t (107) = 18.02, p

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values.

Abstract Coal Mining enterprises deploy numerous monitoring devices to ensure safe and efficient production using target detection technologies. However, deploying deep detection models on edge devices poses challenges due to high computational loads, impacting detection speed and accuracy. A mining target detection dataset has been created to address these issues, featuring key targets in coal mining scenes such as miners, safety helmets, and coal gangue. A model is proposed to improve real‐time performance for edge mining detection tasks. Detection performance is enhanced by incorporating a Pixel‐wise Normalization Spatial Attention Module (PN‐SAM) into the MobileNet‐v3 bneck structure and replacing the h‐swish activation function with Mish, providing more prosperous gradient information transfer. The proposed model, YOLO‐v4‐LSAM, shows a 3.2% mAP improvement on the VOC2012 dataset and a 2.4% improvement on the mining target dataset compared to YOLO‐v4‐Tiny, demonstrating its effectiveness in mining environments. These enhancements enable more accurate and efficient detection in resource‐constrained edge environments, contributing to safer and more reliable monitoring in coal mining operations.

This study aims to analyze the Hibryd Counseling Service (Islamic counselling) strategy to effectively prevent Quarter Life Crisis and prepare students who become agents of change for a better direction in college. Using interview, observation, and documentation methods, this study seeks to obtain data on the phenomenon of quarter-life crisis and the new approach. The data sources include critical stakeholders in the guidance and counselling laboratory. Data analysis was carried out through data transcription, coding, and categorization. The study results indicate that Islamic counselling is valuable in guiding individuals to overcome quarter-life crises by aligning psychological and spiritual aspects. Islamic counselling significantly contributes to helping individuals overcome quarter-life crises with a holistic approach involving spiritual, psychological, and social factors. Integrating Islamic values in counselling forms a solid framework to support young individuals facing life challenges. These findings provide important insights related to the effectiveness of the Islamic counselling approach in overcoming quarter-life crises through the integration of spiritual, psychological, and social values.

Topological indices play a crucial role as molecular descriptors in QSAR/QSPR research. Graph entropy measurements are of great importance in various fields like chemistry, discrete mathematics, and biology. Information-theoretic values derived from topological indices, influenced by Shannon’s entropy, are used to analyze the structural characteristics of chemical graphs and complex networks. We discuss the analysis of graph entropies obtained from a new information function. It is equivalent to both the total number of edges and the various degrees of vertices. The information function is also employed to compute the entropies of the system and build a connection between connectivity indices and degrees. This study examines the chemical graphs of Carboxylate-terminated Zinc Phthalocyanine (CtZP) and Nanostar (NS), using the function to connect degree-based topological indices like First Zegrab index, Second Zagreb index, Hyper Zagreb index, Forgotten index, First Redefined Zagreb index, The Second Redefined Zagreb index, The Third Redefined Zagreb index, and Somber index, to calculate the entropies of these structures.

Objective This study aims to evaluate the immunomodulatory effects of coconut oil extract (COE) in broilers experimentally infected with velogenic Newcastle disease virus (vNDV). Methods A total of 150 broiler birds (day-old) were equally divided into five study groups i.e., negative control, positive control, COE-1, COE-2, and COE-3. On day 10, broilers of groups COE-1, COE-2, and COE-3 were supplemented with 1, 2, and 3 mL of COE respectively per liter of drinking water for 15 days. On day 13, 0.1 mL/bird (10−5.25 ELD50) of vNDV was inoculated in broilers of positive control, COE-1, COE-2, and COE-3 groups intramuscularly. During this study, growth performance, morbidity, and mortality rates of each study group were recorded. The antibody titer against NDV was determined on days 7, 14, 21, 28, and 35. The levels of immunoglobulins (IgY and IgM) were also determined on the 7th, 14th, and 21st days post-sheep red blood cells (SRBCs) inoculation. On day 33, avian tuberculin was injected between the 1st and 2nd toes of the left side (intradermally) to measure lymphoproliferative responses. On day 35, the phagocytic activity in the blood was assessed through a carbon clearance assay by injecting carbon black ink into the right-wing vein. The visceral organs having gross lesions were also collected for histopathology. Results The COE significantly improved the growth performance, and lowered the morbidity and mortality rates of broilers. There was a significant rise in antibody titers against NDV and levels of IgY and IgM antibodies against SRBC in COE-supplemented broilers. The lymphoproliferative response and phagocytic activity were also enhanced. Among COE-supplemented groups, the broilers of the COE-3 group showed a significant increase in growth performance and boosted immune defense. Conclusion Coconut oil extract has the potential to boost the growth performance and immune status of broilers. It can be used effectively as a feed additive and alternative to antibiotics to prevent the spread of infectious poultry pathogens.

Root system architecture plays an essential role in water and nutrient acquisition in plants, and it is significantly involved in plant adaptations to various environmental stresses. In this study, a panel of 242 cotton accessions was collected to investigate six root morphological traits at the seedling stage, including main root length (MRL), root fresh weight (RFW), total root length (TRL), root surface area (RSA), root volume (RV), and root average diameter (AvgD). The correlation analysis of the six root morphological traits revealed strong positive correlations of TRL with RSA, as well as RV with RSA and AvgD, whereas a significant negative correlation was found between TRL and AvgD. Subsequently, a genome-wide association study (GWAS) was performed using the root phenotypic and genotypic data reported previously for the 242 accessions using 56,010 single nucleotide polymorphisms (SNPs) from the CottonSNP80K array. A total of 41 quantitative trait loci (QTLs) were identified, including nine for MRL, six for RFW, nine for TRL, 12 for RSA, 12 for RV and two for AvgD. Among them, eight QTLs were repeatedly detected in two or more traits. Integrating these results with a transcriptome analysis, we identified 17 candidate genes with high transcript values of transcripts per million (TPM)≥30 in the roots. Furthermore, we functionally verified the candidate gene GH_D05G2106, which encodes a WPP domain protein 2 in root development. A virus-induced gene silencing (VIGS) assay showed that knocking down GH_D05G2106 significantly inhibited root development in cotton, indicating its positive role in root system architecture formation. Collectively, these results provide a theoretical basis and candidate genes for future studies on cotton root developmental biology and root-related cotton breeding.

The purpose of this paper is to introduce the class of large MR-Kannan contractions on Banach space that contains the classes of Kannan, enriched Kannan, large Kannan, MR-Kannan contractions and some other classes of nonlinear operators. Some examples are presented to support the concepts introduced herein. We prove the existence of a unique fixed point for such a class of operators in Banach spaces.

Abstract Invasive species such as Xanthium strumarium L., can disrupt ecosystems, reduce crop yields, and degrade pastures, leading to economic losses and jeopardizing food security and biodiversity. To address the challenges posed by invasive species such as X. strumarium, this study uses species distribution modeling (SDM) to map its potential distribution in Pakistan and assess how it might respond to climate change. This addresses the urgent need for proactive conservation and management strategies amidst escalating ecological threats. SDM forecasts a species’ potential dispersion across various geographies in both space and time by correlating known species occurrences to environmental variables. SDMs have the potential to help address the challenges posed by invasive species by predicting the future habitat suitability of species distributions and identifying the environmental factors influencing these distributions. Our study shows that seasonal temperature dependence, mean temperature of wettest quarter and total nitrogen content of soil are important climatic factors influencing habitat suitability of X. strumarium. The potential habitat of this invasive species is likely to expand beyond the areas it currently colonizes, with a notable presence in the Punjab and Khyber Pakhtunkhwa regions. These areas are particularly vulnerable due to threats to agriculture and biodiversity. Under current conditions, an estimated 21% of Pakistan’s land area is infested by X. strumarium, mainly in upper Punjab, central Punjab and Khyber Pakhtunkhwa. The range is expected to expand in most regions except Sindh. The central and northeastern parts of the country are proving to be particularly suitable habitats for X. strumarium. Effective strategies are crucial to contain the spread of X. strumarium. The MaxEnt modeling approach generates invasion risk maps by identifying potential risk zones based on a species’ climate adaptability. These maps can aid in early detection, allowing authorities to prioritize surveillance and management strategies for controlling the spread of invasive species in suitable habitats. However, further research is recommended to understand the adaptability of species to unexplored environments.

Abstract The industrial sector of Pakistan is currently facing severe load-shedding, which ultimately affects its unit production. The greater dependency on conventional energy resources (Thermal, Nuclear, etc.) results in higher production costs and environmental pollution. A sustainable, cost-effective, and environment-friendly solution can help the industrial growth of Pakistan. This article proposes an optimal hybrid energy system (HES) for the industrial sector of Pakistan to overcome the mentioned challenges. The proposed HES is developed in HOMER Pro. Three different energy cases (Case I: Existing energy system including a utility grid and diesel generator, Case II: On-grid Biogas system, and Case III: On-grid PV system with batteries) are considered for the Gourmet food Industry in the Sundar Industrial estate, Pakistan. The Load profile of the selected site was calculated through on-site visits and data provided by the designated utility grid feeder. The analysis shows that Case III is more effective than other cases, indicating reduced Net Present Cost (NPC), Cost of Energy (COE), and Operating Cost (OC) to $ 19.2 million, $0.034/kWh, and $ 573,371/year respectively. Moreover, the On-grid PV system with batteries (Case III) provides an environmentally friendly solution by reducing 63.82% $$\:C{O}_{2}$$ by and 62.22% $$\:{NO}_{x}$$ . Comparing the sensitivity analysis for various grid sell-back prices ($0/kWh, $0.043/kWh, $0.061/kWh, and $0.09/kWh), Case III is more cost-effective than Case II. The revenue generation in Case III is $128,499.41/yr, considering the supply of excess electricity into nearby small industrial loads at $0.065/kWh, this indicates that installing optimal HES in industries will not only help in overall cost reduction but also support in mitigating environmental pollution and load shedding.

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 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 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 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 Considering the challenges of low multi‐object detection accuracy and difficulty in identifying small targets caused by challenging environmental conditions including irregular lighting patterns and ambient noise levels in the mining environment with autonomous electric locomotives. A new network model based on SOD−YOLOv5s−4L has been proposed to detect multi‐objects for autonomous electric locomotives in underground coal mines. Improvements have been applied in YOLOv5s to construct the SOD−YOLOv5s−4L model, by introducing the SIoU loss function to address the mismatch between real and predicted bounding box directions, facilitating the model to learn target position information more efficiently. This research introduces a decoupled head to enhance feature fusion and improve the positioning precision of the network model, enabling rapid capture of multi‐scale target features. Furthermore, the detection capability of the model has been increased by introducing the small target detection layer which is developed by increasing the number of detection layers from three to four. The experimental results on multiple object detection dataset show that the proposed model achieves significant improvement in mean average precision (mAP) of almost 98% for various types of targets and an average precision (AP) of nearly 99% for small targets on the other hand it achieves 5.19% (mAP) and 9.79% (AP) compared to the YOLOv5s model. Furthermore, comparative analysis with other models like YOLOv7 and YOLOv8 shows that the proposed model has superior performance in terms of object detection.

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.

Non-Orthogonal Multiple Access (NOMA) has already proven to be an effective multiple access scheme for 5th Generation (5G) wireless networks. It provides improved performance in terms of system throughput, spectral efficiency, fairness, and energy efficiency (EE). However, in conventional NOMA networks, performance degradation still exists because of the stochastic behavior of wireless channels. To combat this challenge, the concept of Intelligent Reflecting Surface (IRS) has risen to prominence as a low-cost intelligent solution for Beyond 5G (B5G) networks. In this paper, a modeling primer based on the integration of these two cutting-edge technologies, i.e., IRS and NOMA, for B5G wireless networks is presented. An in-depth comparative analysis of IRS-assisted Power Domain (PD)-NOMA networks is provided through 3-fold investigations. First, a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems, and parallels are drawn with conventional network configurations, i.e., conventional NOMA, Orthogonal Multiple Access (OMA), and IRS-assisted OMA networks. Followed by this, a comparative analysis of these network configurations is showcased in terms of significant performance metrics, namely, individual users' achievable rate, sum rate, ergodic rate, EE, and outage probability. Moreover, for multi-antenna IRS-enabled NOMA networks, we exploit the active Beamforming (BF) technique by employing a greedy algorithm using a state-of-the-art branch-reduce-and-bound (BRB) method. The optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques, i.e., minimum-mean-square-error, zero-forcing-BF, and maximum-ratio-transmission. Furthermore, we present an outlook on future envisioned NOMA networks, aided by IRSs, i.e., with a variety of potential applications for 6G wireless networks. This work presents a generic performance assessment toolkit for wireless networks, focusing on IRS-assisted NOMA networks. This comparative analysis provides a solid foundation for the development of future IRS-enabled, energy-efficient wireless communication systems.

Bioconvective non-Newtonian fluids with motile microorganisms have diverse applications in biology and medicine, offering numerous research opportunities. This article presents a comprehensive investigation into the non-similar analysis of bio-convective micropolar flow induced by a Darcy-Forchheimer exponentially stretchable sheet, considering the effects of suction/injection and thermal radiation in a three-dimensional framework. The fluid under scrutiny is a viscoelastic tangent hyperbolic nanofluid, the impact of Cattaneo-Christov heat subjected to Riga plate is considered for novelty. The role of activation energy and heat source are also computed. The governing equations of viscoelastic tangent hyperbolic nanofluids are transformed into couple of ordinary differential equations via similarity approximations. The resulting ODEs are tackled numerically via bvp4c tool of MATLAB. The impression of appropriate parameters like magnetic parameter, bio-convection parameter, viscoelasticity parameter suction/injection parameter etc. on involved profiles are computed via graphical and tabulated trends. It is observed that velocity layer f′ is dwindled for growing value of Forchheimer number Fr, while reverse relation in velocity curve is noted for developing value of mixed convection parameter δ. Micropolar fluids find biomedical engineering applications in modeling blood flow in microvessels, understanding cerebrospinal fluid dynamics, and optimization of biomedical devices and therapies and simulating drug delivery in tissues.

Abstract Background In this report, we describe an uncommon instance of fungating synovial sarcoma affecting the posterior aspect of the neck. Although the existing literature has documented a limited number of cases, this particular case contributes to the knowledge about it, which is scarce. Case presentation A total of 5 months before the examination, a Pakistani-Asian male, age 20 years, complained of a malodorous fungating swelling on the posterior aspect of his neck. An examination revealed a foul-smelling, 10 × 13 cm fungating enlargement surrounded by maggots and hemorrhaging at the site of the incision. A hemoglobin level of 6 and a total leukocyte count (TLC) of 23,000 indicated the patient’s disoriented and pallid appearance. He was expeditiously admitted, and preoperatively, the general well-being of the patient was optimized. After a comprehensive discussion with the medical team, a strategy for marginal excision and coverage with a latissimus dorsi (LD) flap and grafting was devised. The tumor was successfully excised, and an LD flap with graft was conducted on the patient during surgery; however, the infection caused the failure of half of the graft. Following that, the lesion was debrided, and re-grafting was performed. The patient was subsequently administered 5 cycles of chemotherapy and 32 cycles of radiotherapy. He was diagnosed with pulmonary metastasis 2 years later. Sadly, the patient died during a follow-up visit 3.5 years later. Conclusions The patient’s unfavorable prognosis after surgical intervention, radiotherapy, and chemotherapy, despite undergoing all-encompassing treatments, underscores the importance of early detection and intervention in fungating tumor cases.

Abstract The goal of this study is to learn more about social media ostracism, a stressor associated with online social networks, defined by feelings of rejection, exclusion, or ignoring. We investigate the connection between social media ostracism and worker creativity. We suggest that psychological safety and psychological rumination serve as intermediaries in this relationship. Furthermore, we investigate emotional intelligence as a relationship regulator. To verify our hypothesis, we gathered data with the help of the HR department from 244 workers of nine Chinese organizations. Our research shows that psychological rumination and social media exclusion are significantly correlated, but only in workers with low emotional intelligence. Furthermore, for individuals with strong emotional intelligence, we did not discover a statistically negative association between psychological safety and social media exclusion. Findings suggest that psychological safety and psychological rumination serve as mediating factors in the relationship between employee creativity and social media exclusion. This study illuminates the negative aspects of social media ostracism and reveals how it might hinder creativity. It also emphasizes how emotional intelligence functions as a moderator. Organizations may learn a lot from this study on how to lessen the negative impacts of social media exclusion on employee creativity.

Biochar can be a good soil amendment to reduce the soil pH, increase crop growth rate, and improve the efficient use of fertilizer. Other than that, silicon fertilizer also would promote photosynthetic ability on plant development that would help to produce high yield. In this work, a series of experiments was conducted to observe the effect of rice husk biochar and silicon fertilizer on the maize growth rate and soil pH. A 45-day pot experiment in the greenhouse with three replicates of 9 experimental treatment combinations of RHB at two rates (5 and 2.5 t.ha-1) with silicon fertilizer at three rates (125%, 100%, 75%), sole biochar (10 t.ha-1), sole silicon fertilizer (100%) and control (NPK) to observe the best rate and combination to improve growth rate and change in soil chemical in acid soil. The result showed that the co-application of sole biochar and biochar with Silicon significantly improved growth development, increased photosynthesis rate, altered soil pH, and reduced Fe concentration compared to control. The plant height increased 88.35% from T4 (5 t.ha-1 RHB + 100% Si) compared to the control and the conductance was higher in T4 (0.53) followed by T8 (0.438) while T1 (0.071) recorded the lowest conductance. The shoot fresh weight was higher in T4 (127.83 g) followed by T8 (57.14 g). However, the weight increased by 343.7% at T4 followed by T8 (2.5 t.ha-1 RHB + 75% Si) at 98.33%. The highest pH increment of 1.24 units (T1 = 5.53, T4 = 6.77) of soil pH was noted from T4 (5 t.ha-1 RHB + 100% Si) compared to control (NPK), and the highest total Fe in soil was observed from T1 (442.30 mg.kg-1). The current study results showed that T4 (50% RHB + 100% Silicon) was the best treatment over the other rates of RHB and silicon increased plant height, photosynthetic rate, and biomass.