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Abstract Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

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 present study explores the comprehensive composite material selection methodology of a tennis racket involving coupled Multi-Criteria Decision-Making (MCDM) and numerical analysis. Initially all the possible material alternatives from the composite family were shortlisted using CES Selector 2016. A design space was developed incorporating MCDM technique naming VICKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to rank the most suitable composite material based on critical mechanical properties to identify the most suitable composite materials for applications requiring optimal combinations of rigidity, strength, and structural performance. Using finite element analysis, the structural integrity of each of the materials was evaluated on static and dynamic stress analysis modules of ANSYS. Out of all material alternatives, T300 carbon fiber epoxy was considered as the most suitable for tennis rackets. Further, a comparison between the selected material was also made with conventional materials and were found in good agreement with selected material.

Optical coherence tomography (OCT) helps identify coronary artery disease of different etiologies. Vasospasm from OCT catheter is a rarely reported complication that is more commonly seen in the right coronary artery. We report a case of OCT-catheter induced vasospasm of the left anterior descending artery that resolved with administration of nitroglycerine. Interventionalists need to weary of the occurrence of catheter-related coronary artery spasm to avoid stenting when not necessary.

Abstract Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs.

Objectives: Colistin is known as the last resort antibiotic to treat the infections caused by multidrug resistant foodborne pathogens. The emergence and widespread dissemination of plasmid-mediated colistin resistance gene mcr-1 in the Escherichia coli (E. coli) incurs potential threat to public health. Here, we investigated the epidemiology, transmission dynamics, and genetic characterization of mcr-1 harbouring E. coli isolates from poultry originated in Hebei Province, China. Methods: A total of 297 faecal samples were collected from the two large poultry farms in Hebei Province, China. The samples were processed for E. coli identification by matrix-assisted laser desorption ionization-time of flight mass spectrometry and 16S rDNA sequencing. Then, the mcr-1 gene harbouring E. coli strains were identified by polymerase chain reaction and subjected to antimicrobial susceptibility testing by broth microdilution assay. The genomic characterization of the isolates was done by whole genome sequencing using the various bioinformatics tools, and multi-locus sequence typing was done by sequence analysis of the seven housekeeping genes. The conjugation experiment was done to check the transferability of mcr-1 along with the plasmid stability testing. Results: A total of six mcr-1 E. coli isolates with minimum inhibitory concentration of 4 μg/mL were identified from 297 samples (2.02%). The mcr-1 harbouring E. coli were identified as multidrug resistant and belonged to ST101 (n = 4) and ST410 (n = 2). The genetic environment of mcr-1 presented its position on IncHI2 plasmid in 4 isolates and p0111 in 2 isolates, which is a rarely reported plasmid type for mcr-1. Moreover, both type of plasmids was transferable to recipient J53, and mcr-1 was flanked by 3 mobile elements ISApl1, Tn3, and IS26 forming a novel backbone Tn3–IS26-mcr-1– pap2-ISApl1 on the p0111 plasmid. The phylogenetic analysis shared a common lineage with mcr-1 harbouring isolates from the environment, humans, and animals, which indicate its horizontal spread among the diverse sources, species, and hosts. Conclusion: This study recommends the one health approach for future surveillance across multiple sources and bacterial species to adopt relevant measures and reduce global resistance crises.

The current investigative exploration exemplifies the conceptualization of a novel design intelligent computing paradigm based on artificial neural networks (ANNs) by utilizing radial basis function (RBF) to analyze magnetohydrodynamic (MHD) Williamson nanofluid two-dimensional flow along a stretchable sheet under the effect of chemical reaction as well as thermal radiation in a porous medium. This newly designed technique is an amalgam of a well-known reliable global solver named genetic algorithms (GAs) and a swift convergence generated local solver named sequential quadratic programming (SQP) used in ANNs by taking RBF as a kernel function i.e. ANNs-RBF-GASQP solver. The PDEs demonstrating the current nanofluid problem flow are transformed into the system of non-linear ODEs through a relevant similarity transformation and subsequently solved using ANNs-RBF-GASQP solver to investigate thermohydraulic properties by manipulating the values of various system parameters present in the ODEs. Moreover, the simulation results show that increasing the heat source parameter leads to a significant decrease in temperature. Additionally, an increase in the porosity parameter causes a decrease in the velocity of nanofluid, as a higher value of porosity increases fluid permeability and greater resistance to flow. The efficacy of the suggested solver is scrutinized through various statistical and convergence analyses.

Cardiac strangulation from leads of an epicardial pacemaker is a rarely reported life-threatening complication. We report the case of a 72-year-old female with history of supraventricular tachycardia status post sino-atrial node ablation with epicardial pacemaker placement who presented to the outpatient setting with shortness of breath. Heart catheterization was performed which showed elevated right and left heart pressures. Compression involving the mid left anterior descending artery and left circumflex artery was noted from an epicardial lead on coronary angiography consistent with cardiac strangulation. The patient was then referred to cardiothoracic surgery for epicardial pacemaker lead removal. The literature has only reported one other survival case of cardiac strangulation in an adult.

Organizations face intense competition in the commercial sector, both in developed and developing nations. Businesses that intend to remain competitive must encourage the desire to understand the function of budgeting and how it affects their financial success. The current study is conducted to study the effect of budgeting and budgetary control systems on Pakistan SMEs performance circulating an adopted questionnaire physically, considering 210 respondents as the sample size using purposive sampling technique, as a result of which 187 responses were received, with a valid response rate of 89.04%. The proposed investigation comprises descriptive statistics, correlation analysis, convergent & discriminant validity, and regression analysis. The current study's findings revealed a significant positive impact of both budgeting and budgetary control systems on SMEs performance. The study recommends senior managers be careful while preparing a long-term comprehensive budgetary plan and also implement measures to address existing budgetary control system issues. A significant limitation of this study is to consider only non-financial performance as a SMEs performance measuring tool neglecting financial performance, which potential researchers can eliminate.

Objective This study investigated the impact of supplementation of protease and organic acid on growth performance and other biological parameters in broilers fed poultry by-product meal (PBM) based diet. Methods Five hundred 1-day-old broiler chicks (Ross 308) were distributed into five treatments with 5 replicates, each pen having 20 birds, and fed each group one of five isocaloric and isonitrogenous diets in two phases: stater phase (1 to 21 days) metabolizable energy (ME) 3000 kcal/kg; crude protein (CP) 22%, and a finisher phase (22 to 35 days) ME 3,200 kcal/kg; CP 19.5%. The dietary treatments were: i) standard broiler ration (Cont); ii) The control diet with 25% of the soybean meal replaced by PBM on an equivalent protein basis (PBM); iii) PBM diet supplemented with 0.5 g/kg of protease (PBMP); iv) PBM diet supplemented with 1 g/kg organic acid (PBMO); and v) PBM diet addition with 0.5 g/kg protease and 1 g/kg organic acid (PBMPO). Results The overall data showed that feed conversion ratio was improved (p

Background: Acute cholecystitis is the most frequent illnesses that necessitate surgical procedures in an emergency. Research indicates that performing cholecystectomy at an early stage leads to improved results, reduced hospital stays, and lower healthcare expenses. Early diagnosis is crucial during the presentation of a patient in the emergency ward and can be achieved by a precise clinical examination and the use of suitable diagnostic procedures. Ultrasound is regarded as the ideal initial imaging approach for individuals suspected of acute cholecystitis clinically. Objective: To examine the diagnostic precision of emergency sonographic signs in patients with suspected acute cholecystitis, while considering histopathology as the gold standard. Methods: A cross-sectional study at the selected hospital of Karachi conducted over one year, on 103 patients who met the diagnostic criteria. Mean and standard deviation for continuous data and frequencies with percentages were calculated for categorical data. Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy were measured. A p-value of ≤0.05 is significant. Results: 47 (45.6%) were male and 56 (54.4%) were female. The mean age was 47.47 (8.57) years and the duration of symptoms was 7.19 (2.14) hours. Out of 103 patients, sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of emergency sonographic signs for the diagnosis of acute cholecystitis by keeping histopathology as the gold standard was 81.1%, 88.2%, 93.3%, 69.7%, and 83.4% respectively. Conclusion: Emergency sonographic findings are effective in the identification and diagnosis of acute cholecystitis. The histopathological diagnosis correlated well with the ultrasound diagnosis of acute cholecystitis. This methodology will enhance the quality of medical care and increase patient safety

According to the Global Burden of Disease, chronic kidney disease (CKD) was Pakistan’s eighth most common cause of death in 2017. After having end-stage renal disease, most patients prefer hemodialysis as Kidney Replacement Therapy (KRT) because of the lack of kidney donors. There are specific fears related to hemodialysis therapy, but a multidisciplinary approach is required to remove the concern. Along with this, kidney disease is an irreversible condition; the primary goal of treatment is to delay disease progression. To accomplish this goal, pre-dialysis nurses can intervene in kidney disease stage III to delay the deterioration of kidney function and prevent further complications through health education and strengthening patients’ self-care behavior. Employing predialysis nurses in the nephrology clinics will be beneficial to the healthcare system as it will reduce hospital costs by reducing the number of hospital admissions, slow the progression of kidney disease, and, therefore, defer the requirement for dialysis therapy. However, multiple challenges exist to introducing pre-dialysis nurses in the healthcare sector, but through proper planning, these challenges can be overcome, and better health outcomes can be achieved. This position aims to highlight the vital role of pre-dialysis care nurses in the healthcare sector, their responsibilities with supporting arguments, the Chronic Care Model, challenges, and recommendations to overcome those challenges

Financial engineering problems hold considerable significance in the academic realm, where there remains a continued demand for efficient methods to scrutinize and analyze these models. Within this investigation, we delved into a fractional nonlinear coupled system for option pricing and volatility. The model we examined can be conceptualized as a fractional nonlinear coupled wave alternative to the governing system of Black-Scholes option pricing. This introduced a leveraging effect, wherein stock volatility aligns with stock returns. To generate novel solitonic wave structures in the system, the present article introduced a generalized Ricatti mapping method and new Kudryashov method. Graphical representations, both in 3D and 2D formats, were employed to elucidate the system's response to pulse propagation. These visualizations enabled the anticipation of appropriate parameter values that align with the observed data. Furthermore, a comparative analysis of solutions was presented for different fractional order values. Additionally, the article showcases the comparison of wave profiles through 2D graphs. The results of this investigation suggested that the proposed method served as a highly reliable and flexible alternative for problem-solving, preserving the physical attributes inherent in realistic processes. To sum up, the main objective of our work was to conceptualize a fractional nonlinear coupled wave system as an alternative to the Black-Scholes option pricing model and investigate its implications on stock volatility and returns. Additionally, we aimed to apply and analyze methods for generating solitonic wave structures and compare their solutions for different fractional order values.

In this study, we introduce a novel tri-trophic food chain model that integrates cannibalism among basal prey and harvesting behaviors in the top predator, aiming to understand ecosystem dynamics comprehensively. Objectives encompass assessing system boundedness, computing fixed points, and determining stability characteristics using mathematical frameworks. The Routh-Hurwitz criteria and Lyapunov function are employed for local and global stability analyses of coexistence equilibrium points. Graphical interpretations elucidate relationships among pivotal parameters: prey growth rate, cannibalism intensity, and predator predation rate. Phase portraits and time series solutions illustrate parameter impacts. To enhance analytical depth and predictive capabilities, we utilize artificial neural networks (ANNs). Methods include connecting ANNs to computational proficiency for insights into the model's behavior over time. Findings demonstrate system boundedness, computed fixed points, and stability characteristics. Graphical interpretations reveal parameter impacts on system dynamics. ANNs offer predictive insights into model behavior. This study's novelty lies in integrating cannibalism and harvesting behaviors into a tri-trophic food chain model, employing mathematical analyses and ANNs to understand ecosystem dynamics comprehensively. Improvements include predictive capabilities and deeper analytical insights. Doi: 10.28991/ESJ-2024-08-04-02 Full Text: PDF

Abstract The rapid evolution of the digital landscape has catalyzed the integration of blockchain technology within the domain of cultural heritage, particularly in virtual museums within the Metaverse. This study introduces ArchaeoMeta, a novel framework designed to leverage blockchain technology to enhance security, authenticity, and visitor interaction in a virtual museum environment. Utilizing smart contracts deployed on the Ethereum Sepolia testnet, the framework manages visitor interactions and secures digital artifacts, addressing challenges associated with scalability and user experience under varying loads. The performance evaluation involved simulating user interactions, scaling up to ten thousand concurrent users, to assess the impact on transaction latency, gas usage, and blockchain size. Findings reveal significant scalability challenges, as transaction latency and blockchain size increased with the number of users, highlighting areas for optimization in managing high user traffic within the blockchain infrastructure. This study contributes to the understanding of blockchain applications in cultural heritage, suggesting that while ArchaeoMeta offers a robust platform for virtual museums, enhancements in scalability through layer-2 solutions or alternative blockchain platforms are essential for its practical implementation. The framework sets a precedent for future research in the convergence of blockchain technology and cultural heritage preservation, promising a transformative impact on how digital cultural experiences are curated and consumed.

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management.

Abstract Background There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model. Results The solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model. Conclusion The results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility.

Abstract The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Abstract Leukemia is a type of blood cell cancer that is in the bone marrow’s blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy.

Abstract Background In the intricate tapestry of food security, wild food species stand as pillars, nourishing millions in low-income communities, and reflecting the resilience and adaptability of human societies. Their significance extends beyond mere sustenance, intertwining with cultural traditions and local knowledge systems, underscoring the importance of preserving biodiversity and traditional practices for sustainable livelihoods. Methods The present study, conducted between February 2022 and August 2023 along the Line of Control in India’s Kashmir Valley, employed a rigorous data collection encompassing semi-structured interviews, focus group discussions, and specific field observations facilitated through a snowball sampling technique. Results and discussion The comprehensive inventory includes 108 edible plant and fungal species from 48 taxonomic families, with Rosaceae (N = 11) standing out. Young and soft leaves (N = 60) are an important component of various culinary preparations, with vegetables (N = 65) being the main use, followed by fruits (N = 19). This use is seasonal, with collection peaks in March–April and June–August (N = 12). The study also highlights the importance of use value (UV), with Portulaca oleracea standing out as the plant taxon (UV = 0.61), while Asyneuma thomsoni has the lowest use value (UV = 0.15). Many species such as Senecio chrysanthemoides, Asperugo procumbens, Asyneuma thomsoni, and Potentilla nepalensis were classified as new for gastronomic use. Furthermore, the study underlines the great cultural importance of mushrooms such as Morchella esculenta and Geopora arenicola in influencing social hierarchies within the community. However, the transmission of traditional knowledge across generations is declining in the region. At the same time, the conservation of endangered plant species on the IUCN Red List, such as Trillium govanianum, Taxus wallichiana, Saussurea costus, and Podophyllum hexandrum, requires immediate attention. Conclusion Conservation measures should be prioritized, and proactive remedial action is needed. Further research into the nutritional value of these edible species could pave the way for their commercial cultivation, which would mean potential economic growth for local communities, make an important contribution to food security in the area under study, and contribute to scientific progress.

Abstract During the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.

Abstract Biochar can potentially reduce heavy metals (HMs) mobility and bioavailability during composting. However, siderophores secreted by functional microbes might lead to the re-mobilization of metals like Cu and Zn. Therefore, this study intended to explore the impacts of Mikania micrantha Kunth (MM) and MM-derived biochar (MMB) in the reduction of Cu and Zn bioavailability, and siderophore-related gene abundances during composting. Compared with MM and corn straw (CS) composts, a significant decline was noticed in the extractable and reducible Cu [(2.3 mg kg−1 + 12.1 mg kg−1), and (3.3 mg kg−1 + 14.6 mg kg−1)], and Zn [(103.1 mg kg−1 + 110.1 mg kg−1), and (109.6 mg kg−1 + 117.2 mg kg−1)] in MMB and corn straw biochar (CSB) composts, respectively. Besides, the lowest relative abundance of HMs-resistant bacteria particularly Corynebacterium (0.40%), Pseudomonas (0.46%), and Enterobacter (0.47%), was noted in MMB compost. Also, a significant increase in sesquiterpenoid and triterpenoid biosynthesis abundance (5.77%) accompanied by a reduction in the abundance of clusters related to siderophore transport, and siderophore transmembrane transporter activity was detected in MMB compost. Multivariate analysis labeled temperature, moisture content, total organic carbon, Corynebacterium, and Bacillus as the primary factors significantly correlated with the Cu and Zn bioavailability (− 0.90 ≤ r ≤ 0.90, P

The surgical site infections cost 0.5 % of the annual budget of the hospitals. Therefore, present research aimed to develop and evaluate antibacterial Neem/chitosan-coated silk braided sutures to reduce the risk of hospital-acquired surgical site infections. The silk braided sutures were coated by dip-coating technique by using 5, 10, 15 and 20 % concentrations of ethanolic Neem extract and chitosan. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and tensile strength measurements were used to examine the surface morphology, chemical composition, and tensile strength of the coated sutures. At a 20 % concentration, the sutures showed strong antibacterial action against Escherichia coli and Staphylococcus aureus, with inhibition zones of 15 and 19 mm, respectively. Additionally, the sutures demonstrated low cytotoxicity in the MTT assay, good radical scavenging activity, and hemolytic activity of less than 2 % at all concentrations. The sutures for the in vivo investigation were coated at a concentration of 20 % since this concentration showed the best antibacterial action. The rats were incised on both sides of their spine, with the left side stitched with uncoated sutures, and the right side stitched with coated sutures. The wound was inoculated with E. coli and S. aureus and examined for 7 days. The results indicated complete healing of the wound at the site where the coated sutures were applied. On other hand, wound with uncoated suture was still not completely healed and revealed signs of infection. The in vivo study revealed the efficacy of Neem/chitosan-coated sutures in wound healing and infection control.

The annual production of medical waste from healthcare facilities in Pakistan is around 250,000 tons. An effective waste management system is essential for disposing of hazardous medical waste, and incineration is considered the most effective and accessible technology. Disposal of medical waste ash in landfills without proper treatment could lead to groundwater contamination due to leachate. This research paper aims to evaluate the feasibility of using hospital waste ash obtained from the National Cleaner Production Center (NCPC) in Rawalpindi as a partial replacement for cement. The primary variable in this study was the amount of hospital waste ash (0%, 3%, 7%, and 10% by weight of cement), while the amount of cementitious material, water-to-cement ratio, and fine and coarse aggregate content were kept constant. A total of 36 cubes were cast, with nine cubes for each replacement level for curing periods of 7, 14, and 28 days. The slump value and density of fresh concrete decreased with the increase in the proportion of hospital waste ash in the mix. The compressive strength of mixes with 3% hospital waste ash was higher than that of the control mix. The best results (20.13 MPa) were obtained from the 3% mix after 28 days of curing, while the result obtained with the 7% mix was nearly equal to that of the control mix.

In today’s fast-moving world of information technology (IT), software professionals are crucial for a company’s success. However, they frequently experience low motivation as a result of competitive pressures, unclear incentives, and communication gaps. This underscores the critical need to handle these internal marketing challenges such as employee motivation, development, and engagement in IT organizations. Internal marketing practices aiming at attracting, engaging, and inspiring employees to use excellent services have become increasingly important. Internal marketing is attracting, engaging, and motivating employees as internal customers to utilize their quality services. Gamification has emerged as a significant trend over recent years. Despite the expanding use of gamification in the workplace, there is still a lack of focus on internal marketing tactics that incorporate gamification approaches. Thus, addressing the challenges related to employee motivation, development, and engagement is crucial. Therefore, as a principal contribution, this research presents a comprehensive framework designed to implement gamified solutions for software teams of IT organizations. This framework has been tailored to effectively address the challenges posed by internal marketing by optimizing motivation, development, and engagement. Moreover, the framework is applied to design and implement a gamified work portal (GWP) through a systematic process, including the design of low-fidelity and high-fidelity prototypes. Additionally, the GWP is validated through a quasi-experiment involving IT professionals from different IT organizations to authenticate the effectiveness of framework. Finally, the outclass results obtained by the gamification-based GWP highlight the effectiveness of the proposed gamification approach in enhancing development, motivation, and engagement while fostering ongoing knowledge of the employees.

This paper introduces a precise method for solving time-fractal parabolic equations specifically designed to represent complex physical phenomena using fractal calculus. The suggested technique attains third-order accuracy by utilizing the fractal Taylor series and implements a concise scheme for spatial discretization. Stability and convergence analyses are offered for scalar and system fractal parabolic equations. The mathematical model analyzes fluid flow over both flat and oscillatory surfaces, considering the effects of heat and mass transfer. This involves transforming the governing equations into dimensionless partial differential equations, which are then solved using the suggested method. From the calculated results, it can be verified that the proposed scheme produces less error than the existing two schemes. This study enhances computational techniques for fractal calculus and introduces novel opportunities for comprehending non-Newtonian fluids in diverse physical contexts.

This work reports the observation of sunlight diffraction through empty spaces in between twigs and leaves in tree crowns. The diffraction patterns are dominated by red wavelength on front followed by the trail of rest of spectral bands of white light rainbow bursts. Tree crowns act as obstacles and apertures causing diffraction/ interference of solar rays. Separation between leaves and twigs (d) is a few inches and distance between crown and camera (D) is in range of a few meters. Empty spaces in tree crown are much longer than red to violet wavelengths in sunlight therefore scattering, dispersion, reflection, diffraction and interference effects cannot be ruled out. Tree crown deflects solar rays into narrow angles all around to enable interference inside camera lens to create grating structure diffracting light into regular patterns. Diffraction pattern look like red petunia flower having 3 × 3 rose petals. Motorola mobile phone camera was used to record the sunlight diffraction patterns. Mobile camera consists of small focal length (≈5 mm) lens, a fixed opening aperture and an image sensor. Lens consists of few plastic or glass elements and sensor consists of charge coupled device (CCD) or CMOS technology. These diffraction/ interference patterns have potential to increase efficiency of Next Gen solar cells. This paper describes possible theoretical background of light diffraction and multiple ray interference through tree twigs and leaves.

Abstract Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Abstract Current harmonics are generated at the switching frequency and its multiples when the traction converters are modulated. To address this, multi-trap filters are introduced, which are capable of selectively eliminating these specific harmonics to the limits set by IEEE 519-2014. This targeted removal significantly reduces the need for high total inductance, thereby allowing for a more compact filter design. Comparatively, to traditional inductor-capacitor-inductor (LCL) filters, more magnetic cores are needed for trap inductors. Furthermore, the traction systems have not been examined in conjunction with multi-trap filters. To reduce the filter size and investigate its application in traction converters, this paper presents an integrated double-trap LCL (DTLCL) filter. A tiny capacitor is connected in parallel with the grid-side inductor to form one LC-trap. In addition, another LC-trap is formed by connecting the equivalent trap inductor, introduced through the magnetic coupling between inverter-side and grid-side inductors, in series with the filter capacitor. The presented filters' features are thoroughly analyzed, and the design method has been developed. Finally, the simulation and hardware-in-the-loop (HIL) experiment results validate the proposed method's viability and efficacy. Compared to the discrete windings, the integrated ones enable a size decrease of two cores. Furthermore, the proposed filters can meet IEEE 519-2014 criteria with 0.3% for all the current switching harmonics and total harmonic distortion (THD) of 2.36% of the grid‐side current.

Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.

Objective: The present research was conducted to evaluate the negative effects of nicotine powder on the blood physiology, and biochemical and histological alterations of Labeo rohita. Materials and Methods: Fish were divided into four groups (1–4). Fish groups 2, 3, and 4 were exposed to different concentrations of nicotine, such as 0.75, 1.25, and 1.75 mg/l, while group 1 acted as a control. To find out the long-term impact of nicotine on body physiology, we conducted a 42-day experiment. After the completion of the experiment, hematology, biochemical assays, and histology were done. Results: Results revealed a considerable increase in HGB, red blood cells, WBCs, hematocrit, mean corpuscular volume, red cell distribution width -SD, procalcitonin, neutrophils, lymphocytes, monocytes, triglycerides, total cholesterol, low-density lipoprotein, very low-density lipoprotein, alanine transaminase, aspartate aminotransferase, globulin, thyroid stimulating hormone, BUN, creatinine, and blood glucose levels, whereas mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, RDW, platelet, high-density lipoprotein, albumin, total proteins, and T3 levels were significantly (p ≤ 0.05) decreased in exposed fish as compared to control group fish. Histological alterations showed that exposure to smokeless nicotine causes deleterious and degenerative effects in the liver, kidney, and gills of exposed fish. Conclusion: Nicotine administration in fish results in adverse effects on different biochemical and hematological parameters and causes histological alterations in some vital organs of exposed fish. [J Adv Vet Anim Res 2024; 11(2.000): 463-473]

Abstract Background Early blight (EB) of Tomatoes, caused by Alternaria solani, is a serious fungal disease that adversely affects tomato production. Infection is characterized by dark lesions on leaves, stems, and fruits. Several agrochemicals can be used to control infection, these chemicals may disrupt environmental equilibrium. An alternative technology is needed to address this significant fungal threat. This study was designed to control the growth of EB in tomatoes caused by A. solani, using green-fabricated silver nanoparticles (Ag-NPs). Results Ag-NPs were synthesized through an environmentally friendly and cost-effective approach using leaf extract of Quercus incana Roxb. (Fagaceae). The physico-chemical characterization of the Ag-NPs was conducted through UV-visible spectroscopy, scanning electron microscopy, X-ray diffraction analysis, and Fourier transform infrared spectrometry. The Ag-NPs produced were round with a mean diameter of 27 nm. The antifungal activity of these Ag-NPs was assessed through in vitro Petri plate and in vitro leaflet assays against A. solani. The green fabricated Ag-NPs exhibited excellent antifungal activity in vitro at a concentration of 100 mg/l against A. solani, inhibiting growth by 98.27 ± 1.58% and 92.79 ± 1.33% during Petri plate and leaflet assays, respectively. Conclusion In conclusion, this study suggests the practical application of green-fabricated Ag-NPs from Q. incana leaf extract against A. solani to effectively control EB disease in tomatoes.

Abstract Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh–Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if $${R}_{0}1$$ R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if $${R}_{0}1$$ R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter $$\alpha$$ α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.

Enhancement in the visible light absorption and efficient interfacial charge transfer is crucial for optimizing photocatalytic efficiency in the degradation of pollutants such as methyl orange (MO) and formaldehyde. This study focuses on the properties of a TiO2/C/g-C3N4 hybrid efficient photocatalyst, which is developed using an air calcination method to deposit graphitic nitride (g-C3N4) onto a carbon-modified TiO2 surface. The characterization techniques, including high-resolution transmission electron Microscopy (HRTEM), X-ray diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), were used to provide a comprehensive understanding of the material’s structural, morphological, thermal, and chemical properties. This hybrid catalyst is specifically engineered for the efficient decomposition of methyl orange (MO) and formaldehyde, demonstrating a significant increase in photocatalytic activity. The TiO2/C/g-C3N4 photocatalyst also exhibits an enhanced specific surface area of 181.2 m2/g, which facilitates increased physical adsorption and photo-catalytic active sites. Experimental results confirm that this catalyst effectively adsorbs MO physically even in the dark without degradation. Combining physical and photo-catalytic functions, this catalyst degrades 94 % of MO within 180 min with the initial concentration 0.2 mol/L of MO, and achieves almost 100 % decolorization of MO under visible light irradiation. Notably, the catalyst retains its high activity after 4 cycles of MO degradation, underscoring its durability and consistent performance. Additionally, the hybrid catalyst features a staggered type-II energy level configuration, which effectively enhances charge separation and boosts photocatalytic efficacy. The incorporation of an ultrafine carbon layer further augments electron mobility towards the surface, crucial for effective catalytic reactions. This study paves the way for future development of highly efficient photocatalytic materials for environmental purification.

Type II diabetes prevalence rate is increasing globally. That’s why recently, our research group has worked on functional yogurts made from milk of different species by using functional ingredients to explore their α-amylase inhibitory activity relevant to management of type II diabetes. Sixteen yogurt treatments were made from milk of four species, cow, goat, buffalo, and camel by incorporating Stevia rebudiana (5 ml) and Cinnamomum verum (10 ml) extract. The physicochemical attributes and α-amylase inhibition rate of prepared functional yogurts were evaluated on 1st, 7th, 14th and 21st days of storage. Significant changes in physicochemical properties were observed in cinnamon and stevia camel milk yogurt. All yogurts showed increase in inhibition rate till 14th day and decline on 21st day of storage period. However, cinnamon camel milk yogurt showed 63% α-amylase inhibition on 1st day,cinnamon and stevia camel milk yogurt showed highest α-amylase inhibition (61.15%) on 14th day.

The ESKAPE pathogens and E. coli are of significant public health concern and are listed among the top priorities of the World Health Organization. These pathogens are equally important for animal and public health, but their interaction with animal, human, and environmental aspects needs to be sufficiently studied. We investigated the comprehensive molecular characterization of three ESKAPE pathogens and E. coli recovered from milk, farm workers, and environmental settings in Hainan province. Minimum inhibitory concentration (MIC), extended-spectrum β-lactamase production, and biofilm production were performed per set standard protocols. The isolates were further processed by PCR and sequencing to detect resistome, virulome, and mobilome. The study found four Enterobacteriaceae species (E. coli, n=32; K. pneumoniae, n=13; E. cloacae, n=3; E. aerogenes, n=1) among n=49 bacterial isolates from the study area. Most of the strains exhibited resistance against tetracycline (71.4%), ampicillin (61.2%), florfenicol (44.9%), and ciprofloxacin (42.9%), while none of the strains was resistant against meropenem and amikacin. The MDR and ESBL-production percentages were 55.1% and 34.7 %, respectively. The resistance determinants for β-lactams (blaTEM, blaOXA-2, blaSHV, blaCTX-M), aminoglycosides (aac(3ʹ)-IIa), quinolones (qnrB, qnrS), tetracyclines (tetA, tetD), florfenicol (floR), sulfonamides (sul1, sul2), trimethoprim (dfrA14), and MDR efflux pumps (oqxA, oqxB) were detected along with diverse plasmid replicon types and integrons (intl3, 55.1%; intl1, 18.4%). The detection of virulence determinants was noted at a higher rate, and most of the strains were identified as biofilm producers by genotype (85.7%). The current study findings revealed diversified resistance and virulence determinants along with mobilome in ESKAPE pathogens and E. coli, which may pose a significant threat to human health through the food chain and environmental exposure and need immediate attention.

Objective: To determine the outcome of laparoscopic cholecystectomy in males versus females. Study Design: Cross-sectional analytical study. Place and Duration of Study: Department of General Surgery, Combined Military Hospital Rawalpindi Pakistan, from Mar to Aug 2022. Methodology: A total of 200 participants were enrolled in the study as Group–A (Males) and Group – B (Females). Patients were selected based on inclusion and exclusion criteria and variables analyzed were demographic profile, past abdominal surgery history, total duration of surgery computed from the insertion of the telescopic port till the extraction of the gall bladder, post-operative hospitalization in terms of the number of days, event of laparoscopic cholecystectomies to open cholecystectomies along with the reasoning of this decision, and perioperative/postoperative complications. Results: A total of (200) elective laparoscopic cholecystectomies cases were included in the study with 35(17.5%) males (Group-A) and 165(82.5%) females (Group-B). A relatively higher frequency of 46(27.9%) (p-value 0.01) of lower abdominal surgeries were recorded in female participants. No statistically significant difference in duration of surgery was recorded (p-value 0.9). The thin-walled gallbladder was mainly represented by the female candidates 105(63.6%). Laparoscopic cholecystectomies was converted to open cholecystectomies in 2(1.2%) females however it was not encountered in any of the male participants (p-value 0.51). Conclusion: Gender-based frequency of cholecystitis and laparoscopic cholecystectomies is greater in females as compared to males with a ratio of 4.7:1 with no significant difference in the preoperative, intraoperative, and postoperative outcomes between the genders.

Background: Lupus patients frequently exhibit specific and nonspecific skin lesions and lesions associated with skin infections. This study aimed to determine the frequency of lupus-specific and non-specific skin lesions and the incidence of skin infections. Methods: This study was conducted in the Department of Rheumatology at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh, from 2014 to 2016. After obtaining ethical clearance from the Institutional Review Board of BSMMU, 136 consecutive patients fulfilling the ACR criteria for SLE were enrolled and followed up for at least one year. A dermatologist confirmed lupus-specific, non-specific, and skin lesions related to infections and noted them in a datasheet. Relevant investigations were performed at baseline and during subsequent follow-up visits. Results: One hundred thirty-one patients completed their follow-up period. The mean (standard deviation) follow-up period was 13.3 (2.0) months. The patients' mean (standard deviation) age was 28.8 (8.2) years. Skin lesions and skin infections were present in 71.8% and 26.7%, respectively of patients. Common lupus-specific lesions were malar rash (75.4%) and DLE (12.3%). Photosensitivity (72.6%), non-scarring alopecia (67.9%), mucosal ulcers (47.6%), Raynaud’s phenomenon (23.8%), and hyperpigmentation (23.8%) were the prevalent lupus non-specific skin lesions. The common skin infections were tinea (42.9%), herpes infection (34.3%), paronychia (20%), and scabies (17%). Conclusions: Skin lesions related to infections were common, along with lupus-specific and nonspecific lesions skin lesions. Tinea and herpes infections were more common skin infections.

Solar photovoltaic (PV) panels suffer from a reduction in performance due to dirt and environmental pollutants accumulating on their surface. As the number of solar panels installed grows, boosting their efficiency becomes more crucial to optimizing electricity output while decreasing the need for additional panels. Cleaning the PV panels can increase their efficiency, and an automated cleaning system with cutting-edge technologies can improve cleaning effectiveness. This article proposes a system that utilizes the Messaging Queuing Telemetry Transport protocol to enhance the efficiency of solar panels. Also, the system harnesses the power of IoT technology to automate the cleaning process. The system incorporates IoT-enabled sensors, actuators, and communication modules, all controlled by ESP-32. We programed the microcontroller using the software Arduino and Visual Studio Code. The Adafruit IO controls and monitors the system. Users can switch between fully automatic and manual modes using the Adafruit dashboard. The system utilizes DC motors, nylon brushes, a metal frame, a relay module, a buck converter, and a boost converter. A 10W solar panel powers the system with the help of a 20 A charge controller and a 12 V/2.5 Ah battery. The Adafruit dashboard fully controls and monitors the developed system. By automating the cleaning process and leveraging real-time data, the system maximizes solar power generation efficiency, minimizing downtime. The system increases the efficiency of tested 30 W solar panels by 30%.

Herein, five Schiff’s base analogs (designated as AK1 to AK5) were synthesized and screened for their analgesic and anti-inflammatory efficacies. All compounds exhibited analgesic activity at selected doses from 45.31 % to 75.75 %, while the standard drug diclofenac sodium produced result as 86.33 %. Compounds AK1, AK3, AK4 and AK5 were found significantly P

This article presents the notion about Slow Invariant Manifold (SIM) and their fundamental role in model reduction techniques (MRTs) for challenges encountered in mechanical engineering within dissipative systems of chemical kinetics. Focusing on the reaction routes of complex mechanisms, we construct and compare primary approximations of the SIM through MRTs, including the Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). These methods effectively transform high-dimensional complex problems into lower dimensions, solving them without compromising crucial information about the complex systems modified for homogeneous reactive systems. Employing the sensitivity analysis by using the MATLAB's toolbox, we present the numerical findings in a tabular format obtained through MRTs. This study provides the understanding about the accessible exploration of numerical solutions, improving insights of the complex variation within the system.

It is imperative to investigate the proposed computational scheme for the resolution of two-dimensional partial differential equations that result from non-Newtonian nanofluid flow over flat and oscillatory sheets, taking into account the effects of the magnetic field and chemical reactions, as it is designed to address stochasticity, unstable flow conditions, and Maxwellian behaviour. Consequently, it offers valuable insights into the dynamics of nanofluids. A stochastic computational scheme is suggested to address the two-dimensional partial differential equations (PDEs) caused by non-Newtonian nanofluid flow over flat and oscillatory sheets in the presence of a magnetic field and chemical reaction effects. The Taylor series analysis is employed to construct a scheme. The two-stage approach can be employed to discretize the time derivative in the differential equations. The stability study results and the consistency by mean square measure are also included. The continuity equation in the given partial differential equations (PDEs) is discretized using the first-order finite difference approach. The remaining equations, including the energy equation, the nanoparticle volume fraction, and the Navier-Stokes equation, are discretized using the second-order difference in space scheme that is proposed. Three separate figures show how the various parameters affect the nanoparticle volume fractions, velocity, and temperature. Through a focus on a specific computational technique designed to handle the challenges given by stochasticity, unstable flow conditions, and the Maxwellian behaviour shown by these nanofluids, this study introduction serves as a portal into the involved domain of nanofluid dynamics.

For the growth of sustainable energy, increasing the efficiency of photovoltaic/thermal (PV/T) systems is still essential. Particularly ternary hybrid nanofluids have shown promise as a means of improving the systems' electrical and thermal performance. This study delves deeply into the exploration of a three-dimensional annulus-type Photovoltaic Thermal (PV/T) system, centering on its cooling function through the utilization of ternary hybrid nanofluids featuring diverse nanomaterial shapes. The system structure involves a circular cylinder enveloped within glass, silicon, and copper layers, forming the core of the PV panel. The investigation employs COMSOL Multiphysics 6.0 with boundary conditions like radiative heat flux on the upper domain. Nanofluids with varied shapes (spherical, brick-like, cylindrical, platelet, and blade-shaped) comprising copper, aluminum oxide, and micro-wall carbon Nanotubes suspended in water are scrutinized as the base fluid. Using COMSOL Multiphysics, our simulation employs a conjugate heat transfer interface to solve the intricate three-dimensional Navier-Stokes equations and energy equations, addressing the combined convection challenges in the system. Analyzing parameters such as Reynolds number (100–1500), aspect ratios (0.1, 0.15, 0.2), shape factors (3–8.9), and total volume fractions of nanomaterials (1%–10 %), we noted a significant decline (21–28 %) in the average Nusselt number as nanoparticle concentration increased. This trend highlights strengthened convection processes, notably observable with increased flow pipe diameter or aspect ratio. This decrease suggests that the system's convective heat transfer efficiency has decreased. Nonetheless, because the PV cells are better cooled, the decrease in convective heat transfer is accompanied by an increase in the electrical efficiency of the system. The practical implication of this trend is that even though the thermal performance in terms of heat removal may decline, the overall cooling effect still helps to keep the PV cells' temperature down, thereby raising their electrical output. The study revealed enhanced electrical efficiency (up to 9.3834 %) with blade-shaped particles under specific conditions: Re = 1500, 10 % volume fraction, and an aspect ratio of 0.1. Additionally, the investigation suggests a potential for achieving a maximum thermal efficiency of 85.62 %. These findings underline the promising impact of nanomaterial manipulation on both electrical and thermal efficiencies within the PV/T system while the maximum temperature at the outlet reaced to 5.470 C.

The uneven spatial and temporal distribution of light resources and water scarcity during the grain-filling stage pose significant challenges for sustainable crop production, particularly in the arid areas of the Loess Plateau in Northwest China. This study aims to investigate the combined effects of drought and shading stress on winter wheat growth and its physio-biochemical and antioxidative responses. Wheat plants were subjected to different drought levels— full irrigation (I100), 75% of full irrigation (I75), 50% of full irrigation (I50), and 25% of full irrigation (I25), and shading treatments — 12, 9, 6, 3 and 0 days (SD12, SD9, SD6, SD3, and CK, respectively) during the grain-filling stage. The effects of drought and shading treatments reduced yield in descending order, with the most significant reductions observed in the SD12 and I25 treatments. These treatments decreased grain yield, spikes per plant, 1000-grain weight, and spikelets per spike by 160.67%, 248.13%, 28.22%, and 179.55%, respectively, compared to the CK. Furthermore, MDA content and antioxidant enzyme activities exhibited an ascending trend with reduced irrigation and longer shading durations. The highest values were recorded in the I75 and SD12 treatments, which increased MDA, SOD, POD, and CAT activities by 65.22, 66.79, 65.07 and 58.38%, respectively, compared to the CK. The Pn, E, Gs, and iCO2 exhibited a decreasing trend (318.14, 521.09, 908.77, and 90.85%) with increasing shading duration and decreasing irrigation amount. Drought and shading treatments damage leaf chlorophyll fluorescence, decreasing yield and related physiological and biochemical attributes.

Studying compressible inviscid flow over symmetric and supercritical airfoils is important because it is useful to understand and maximize aerodynamic performance in a range of industrial contexts. This study addresses the analysis of symmetric and supercritical airfoils for compressible inviscid flow for both time-dependent and stationary scenarios. The primary goal of these simulations’ is to look at how different velocity variations affected certain airfoil parameters. Air is utilized as the material above airfoils. The best fit for these simulations is the Spalart-Allmaras Turbulence model, which shows good agreement over a wider velocity range with published experimental data from other studies. The COMSOL Multiphysics software is used for all the analysis, for angle of attack 0°, temperature 288K, pressure 1bar and the airfoils that are constructed by importing airfoil data file in COMSOL in the form of excel file. On the comparison of compressible inviscid flow, the supercritical airfoil has better results on the basis of velocity magnitude, pressure, vorticity magnitude, shear rate and rotation rate. The results are converging faster in supercritical airfoil than symmetric airfoil. At a zero-degree angle of attack, velocity increases more on the suction side while decreases on the pressure side (bottom). The final solution error for supercritical airfoil is 1.6E-05, while final residual error for “velocity u pressure p” is 1.2E-05. For symmetric airfoil, final solution error is 0.15, although final residual error is 2.6. Based on the error plot, it decreases rapidly in case of supercritical with a range from 100 to 10-4. Additionally, the wall resolution is constructed for both cases i.e., symmetric airfoil and supercritical airfoil. The main result is that ,with the passage of time, the flow is becoming to stationary state. In the end, model validation via comparison with experimental data highlights the validity and applicability of the results.

Prof. Dr. Muhammad Aslamkhan died peacefully on February 20, 2023, in Lahore, Pakistan. Prof. Aslamkhan was the founding head of the Human Genetics and Molecular Biology Department of the University of Health Sciences Lahore (UHS). He was also a Director of Sports and cocurricular activities at the UHS. His services to UHS were brought by the Higher Education Commission’s project entitled “strengthening of basic medical sciences at UHS, ” commonly known as SBMS as a project director. This project not only brought a lot of funding for the establishment of research facilities at the UHS but also employment opportunities for young people. Prof. Aslamkhan received his Doctorate of Science (D.Sc.) in Genetics and Anthropology from Johannes Gutenberg University in Mainz, Germany, in 1963, after his master's degree from Punjab University (1953). He was among the founding fellows of the Biological Society of Pakistan (1956) and the Zoological Society of Pakistan (1968). In 1968, he was awarded the Fellowship of the Royal Society of Tropical Medicine and Hygiene, London. Later, the Pakistan Academy of Medical Sciences awarded him a fellowship in 1985. His professional services include academic and research affiliations with the provincial bodies of Punjab, Khyber Pakhtunkhwa, and East Pakistan (now Bangladesh). He served as Deputy Chief for the Division of Medical Entomology of the International Center for Medical Research and Training (ICMRT) at the Pakistan Medical Research Center, Lahore. Later, he became the Chief of Malaria Vector Research at ICMRT. This was the time when he studied the bionomics of mosquito species in Pakistan, which serves as a reference (1). While affiliated with the University of Maryland School of Medicine, he extended his research towards vector-borne diseases of public health importance (2-10). After retirement from the University of Maryland, he worked as an independent consultant for several national and international bodies. Later, he joined the Higher Education Commission’s project SBMS as Project Director. During this affiliation, he also launched the first department of Human Genetics in the country at UHS Lahore. This department is still operational and produces many PhDs in human genetics, working at various reputable organizations in and outside the country. The department’s objective was to study the basics of genetic diseases in the country, and it has been doing very well until today. Prof. Aslamkhan’s research on genetic diseases has mainly focused on the role of consanguinity in gene pooling in the Pakistani population (11-13). Over the past several years, he has also voluntarily offered genetic counselling to patients with hemoglobinopathies at Sundas Foundation Lahore. He also studied the public health dynamics of blood donors at the Sundas Foundation in addition to genetic counselling (14, 15). He was also recognized as an Eminent Professor of Genetics by Higher Education Commission. In 2017, he founded The International Journal of Frontier Sciences and became its first editor-in-chief until he died last year. I am publishing this obituary at his first death anniversary on February 20, 2024, praying for his eternal peace. I wish him to rest in peace in his life in hereafter. Ameen!

Background: Childhood Recurrent Abdominal Pain results in the one of the adulthood complications i.e., irritable bowel syndrome, which is a gastro-intestinal disease. The stomach’s acidic environment is the growth site for Helicobacter Pylori, a pathogenic Gram-negative bacterium with rod shape. It is the main reason for chronic gastritis, gastric adenocarcinoma, peptic ulcers, non-ulcer dyspepsia and mucosa-associated lymphoid tissue (MALT) lymphoma. According to one estimate, 50% of people in world are infected with Helicobacter Pylori. Currently the developing countries are on the hit list of Helicobacter Pylori. The objective of this study was to determine the frequency of Helicobacter pylori infection in the children having recurrent abdominal pain. Materials and Methods: It was a cross-sectional study. This research was carried out during 1.1.2018 to 1.6.2018 in the D.H.Q hospital Mirpur AJK. The sample was consisted of 200 participants among them the proportion of Helicobacter was 8% in patients with Recurrent Abdominal Pain. Results: The mean age of the patients was 12.43 ± 1.23 years. Mostly the age of the sample group was between 8 to16 years. The 4.23 ± 0.47 months was the mean time period of abdominal pain. Helicobacter pylori was found in 47% of patients. Conclusion: In paediatric population, Helicobacter pylori is very common with Recurrent Abdominal Pain. It is a severe problem in children and further study is recommended to explore the risk element associated with this increase trouble of Helicobacter Pylori.