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

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

Abstract Diabetes is thought to be the most common illness in underdeveloped nations. Early detection and competent medical care are crucial steps in reducing the effects of diabetes. Examining the signs associated with diabetes is one of the most effective ways to identify the condition. The problem of missing data is not very well investigated in existing works. In addition, existing studies on diabetes detection lack accuracy and robustness. The available datasets frequently contain missing information for the automated detection of diabetes, which might negatively impact machine learning model performance. This work suggests an automated diabetes prediction method that achieves high accuracy and effectively manages missing variables in order to address this problem. The proposed strategy employs a stacked ensemble voting classifier model with three machine learning models. and a KNN Imputer to handle missing values. Using the KNN imputer, the suggested model performs exceptionally well, with accuracy, precision, recall, F1 score, and MCC of 98.59%, 99.26%, 99.75%, 99.45%, and 99.24%, respectively. In two scenarios one with missing values eliminated and the other with KNN imputer, the study thoroughly compared the suggested model with seven other machine learning techniques. The outcomes demonstrate the superiority of the suggested model over current state-of-the-art methods and confirm its efficacy. This work demonstrates the capability of KNN imputer and looks at the problem of missing values for diabetes detection. Medical professionals can utilize the results to improve care for diabetes patients and discover problems early.

Abstract Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of −6.4, −5.1, −6.6, −5.1, and − 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

الملخص: أهداف البحث: أجريت هذه الدراسة لتقييم تأثير فيتامين سي على جينات الاستماتة والتكاثر في خلايا ''هيب جي 2'' المصابة. طريقة البحث: في تحليل السيليكو تم إجراء استخدام الالتحام الجزيئي للمركبات الكيميائية مع بروتين عامل النمو البطاني الوعائي. الأدوات الحسابية المختلفة المستخدمة هي ''أوتودوك فينا'' و''بايوفيا ديسكفري ستوديو'' و ''بايمول''. تم تحليل تشابه الأدوية وسميتها بواسطة ''سويس أدميت''. تمت معالجة التقاء الخلايا – 60-70% بتركيزات مختلفة من ''اتش 2 أوه 2'' (100-2000 مايكرومولر) وحمض الأسكوربيك (30، 60، 90 ميكروغرام / مل). تم إجراء اختبار موت الخلايا ''ام تي تي'' لمقارنة الإمكانات التكاثرية لخلايا ''هيب جي 2'' المعالجة بـ ''اتش 2 أوه 2'' وحمض الأسكوربيك باستخدام 96 لوحة جيدة. النتائج: تمت ملاحظة أدنى طاقة ربط لـ بروتين عامل النمو البطاني الوعائي مع فيتامين سي -5,2 كيلو كالوري/مول وحمض الأسكوربيك-2 جليكوسيد -4.7 كيلو كالوري/مول في تحليل السيليكو. تم اختيار فيتامين سي لأنه يظهر تفاعلا عاليا مع بروتين عامل النمو البطاني الوعائي ويحقق قاعدة ليبينسكي، والقدرة على البقاء عن طريق الفم، والحركية الدوائية مقارنة بجليكوسيد حمض الأسكوربيك. أظهرت فحوصات صلاحية الخلية أن فيتامين سي كان له تأثيرات موت الخلايا المبرمج. بعد معالجة خلايا ''هيب جي 2'' بحمض الأسكوربيك، لوحظ انخفاض بروتين عامل النمو البطاني الوعائي (تكوين الأوعية) من خلال التعبير الجيني للموت المبرمج والتكاثري. أدى علاج حامض الأسكوربيك لخط خلايا ''هيب جي 2'' إلى تنظيم علامات التكاثر ''بي سي ان ايه'' و ''كي آي 67'' و تي أوه بي 2 ايه''. وقد لوحظت زيادة في موت الخلايا المبرمج بعد العلاج بفيتامين سي بسبب تنظيم ''بي53'' و ''أنيكسين في''. الاستنتاجات: بناء على النتائج، من الواضح أن فيتامين سي يثبط نمو الخلايا السرطانية وبالتالي يحمي خلايا ''هيب جي 2'' من الإجهاد التأكسدي. أظهر فيتامين سي نشاطا مضادا للتكاثر كما لوحظ في السيليكو، وفي المختبر، وكذلك النتائج المحتملة من تثبيط التعبير عن الجينات المشاركة في تخليق البروتين. Abstract: Objectives: This study was conducted to evaluate the effects of vitamin C on apoptotic and proliferative genes in injured HepG2 cells. Methods: In silico analysis was performed using molecular docking of chemical compounds with vascular endothelial growth factor (VEGF). The different computational tools used were AutoDock Vina, BIOVIA DISCOVERY studio, and PyMOL. Drug likeness and toxicity were analyzed by SWISS ADMET. Cells that were 60–70% confluent were treated with different concentrations of hydrogen peroxide (H2O2) (100–2000 μM) and ascorbic acid (30, 60, 90 μg/mL). The MTT cell proliferation assay was performed to compare the proliferative potential of HepG2 cells treated with H2O2 or ascorbic acid with untreated HepG2 cells using 96-well plates. Results: The lowest binding energy of VEGF with vitamin C –5.2 kcal/mol and L-ascorbic acid-2 glycoside −4.7 kcal/mol was observed by in silico analysis. Vitamin C was selected because it exhibited a high interaction with VEGF and fulfilled Lipinski's rule, and had better oral viability and pharmacokinetics compared to L-ascorbic acid-2 glycoside. Cell viability assays showed that vitamin C had significant apoptotic effects (P

Abstract Cystic Fibrosis Transmembrane Regulator (CFTR) is a significant protein that is responsible for the movement of ions across cell membranes. The cystic fibrosis (CF) occur due to the mutations in the CFTR gene as it produces the dysfunctional CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands selection was performed through virtual screening and top 3 ligands choose out of 65 ligands silibinins, curcumin, demethoxycurcumin were selected with a reference drug Trikafta (R*). According to docking, ADMET analyses, the natural ligands (Silibinins and Curcumin) displayed best binding energy, pharmacokinetic and free toxicity than other natural compounds and reference drug (R*). An MD simulation for 200 ns was also established to ensure that natural ligands (Silibinins and Curcumin) attached to the target protein favorably and dynamically, and that protein–ligand complex stability was maintained. It is concluded that silibinins and curcumins have a better capacity to decrease the effect of mutant CFTR protein through improved trafficking and the restoration of original function. In conclusion, in silico studies demonstrate the potential of silibinins and curcumin as therapeutic agents for cystic fibrosis, particularly for the D614G mutated protein. Their ability to increase CFTR function while reducing cellular stress and inflammation, together with their favorable safety profile and accessibility could make them valuable additions to cystic fibrosis treatment options. Further experimental and clinical validation will be required to fully realize their potential and include them into effective therapy regimens.

Abstract In order to comprehend the dynamics of disease propagation within a society, mathematical formulations are essential. The purpose of this work is to investigate the diagnosis and treatment of lung cancer in persons with weakened immune systems by introducing cytokines ( $$ IL_{2} \& IL_{12}$$ I L 2 & I L 12 ) and anti-PD-L1 inhibitors. To find the stable position of a recently built system TCD $$IL_{2} IL_{12}$$ I L 2 I L 12 Z, a qualitative and quantitative analysis are taken under sensitive parameters. Reliable bounded findings are ensured by examining the generated system’s boundedness, positivity, uniqueness, and local stability analysis, which are the crucial characteristics of epidemic models. The positive solutions with linear growth are shown to be verified by the global derivative, and the rate of impact across every sub-compartment is determined using Lipschitz criteria. Using Lyapunov functions with first derivative, the system’s global stability is examined in order to evaluate the combined effects of cytokines and anti-PD-L1 inhibitors on people with weakened immune systems. Reliability is achieved by employing the Mittag-Leffler kernel in conjunction with a fractal-fractional operator because FFO provide continuous monitoring of lung cancer in multidimensional way. The symptomatic and asymptomatic effects of lung cancer sickness are investigated using simulations in order to validate the relationship between anti-PD-L1 inhibitors, cytokines, and the immune system. Also, identify the actual state of lung cancer control with early diagnosis and therapy by introducing cytokines and anti-PD-L1 inhibitors, which aid in the patients’ production of anti-cancer cells. Investigating the transmission of illness and creating control methods based on our validated results will both benefit from this kind of research.

Abstract The issue of antibiotic resistance in pathogenic microbes is a global concern. This study was aimed to explore in silico and in vitro analysis of the antibacterial efficacy of different natural ligands against bacterial activity. The ligands included in the study were Propolis Neoflavanoide 1, Carvacrol, Cinnamaldehyde, Thymol, p-benzoquinone, and Ciprofloxacin (standard drug S*). The outcomes of molecular docking revealed that Propolis Neoflavaniode-1 showed a highly significant binding energy of − 7.1 and − 7.2 kcal/mol for the two gram-positive bacteria, as compared to the gram-negative bacteria. All ligands demonstrated acute toxicity (oral, dermal), except for Propolis Neoflavanoide 1 and S* drugs, with a confidence score range of 50–60%. Using a molecular dynamic simulation approach, we investigated Propolis Neoflavaniode-1’s potential for therapeutic use in more detail. An MD simulation lasting 100 ns was performed using the Desmond Simulation software to examine the conformational stability and steady state of Propolis Neoflavaniode-1 in protein molecule complexes. Additionally, in vitro studies confirmed the antimicrobial activity of Propolis Neoflavaniode 1 by increasing the zone of inhibition against Gram-positive bacteria, p

Abstract Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.

Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research study proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction. The proposed model analyzes streams of brain data, including behavioral and physiological signal patterns using Swell and WESAD datasets. Testing on the Swell multi-class dataset, the model achieves an impressive accuracy of 95.76%. Furthermore, when evaluated on the WESAD dataset, it demonstrates even higher accuracy, reaching 98.32%. When applied to the binary classification of stress and no stress using the Swell dataset, the model achieves an outstanding accuracy of 97.19%. Comparative analysis with other previously published research studies underscores the superior performance of the proposed approach. In addition, cross-validation confirms the significance, efficacy, and robustness of the model in brain stress level prediction and aligns with the goals of smart diagnostics for understanding neurological behaviors.

Accurate detection of skin lesions through computer-aided diagnosis has emerged as a critical advancement in dermatology, addressing the inefficiencies and errors inherent in manual visual analysis. Despite the promise of automated diagnostic approaches, challenges such as image size variability, hair artifacts, color inconsistencies, ruler markers, low contrast, lesion dimension differences, and gel bubbles must be overcome. Researchers have made significant strides in binary classification problems, particularly in distinguishing melanocytic lesions from normal skin conditions. Leveraging the “MNIST HAM10000” dataset from the International Skin Image Collaboration, this study integrates Scale-Invariant Feature Transform (SIFT) features with a custom convolutional neural network model called LesionNet. The experimental results reveal the model's robustness, achieving an impressive accuracy of 99.28%. This high accuracy underscores the effectiveness of combining feature extraction techniques with advanced neural network models in enhancing the precision of skin lesion detection.

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.

The growing concern about environmental degradation and the depletion of fossil fuel supplies has prompted experts to investigate alternate and sustainable transportation energy sources. In these circumstances, biodiesel made from renewable feedstock has emerged as a viable environmentally friendly alternative to conventional diesel. This study thoroughly examines the performance and emission characteristics of a 4-stroke diesel engine running on biodiesel blends, including reduced graphene oxide (rGO) nanomaterial. The non-edible biodiesel from the Jatropha curcas plant is utilized, and it is blended in various ratios with conventional diesel, ethanol, and nanomaterial rGO to improve performance and emissions parameters. Torque increased by up to 17 % in rGO DJE02GO25 at 3500 rpm, while Brake Power decreased by 6.3 % in rGO DJE01GO25 at 2600 rpm. Brake Thermal Efficiency decreased by 12.5 % in rGO Blend 1 at 2600 rpm, and Brake-specific fuel consumption decreased by 16.5 % in rGO DJE02GO25 at 3500 rpm. CO2 emissions decreased up to 19.71 % in rGO Blend 10 at 2600 rpm. HC emissions decreased to 94 % in rGO blend 8 at 3500 rpm. Finally, NOx emissions decreased up to 84.78 % in rGO Blend 1 at 2900 rpm. The current study reveals that after adding rGO nanomaterial in biodiesel, there is a significant decrease in NOx and HC emissions.

In order to achieve high strength in concrete composites, various fiber reinforcements in plain concrete composites (PCC) are used. Steel is widely used for this purpose but the main issue in using steel as a reinforcement member is rusting, which results into structure deterioration. So, the alternative is to use a durable, environment friendly, and cost-effective natural fiber. Jute Fiber is used in this study for the reinforcement of plain concrete composites while, the plain concrete composites were made with cement, sand, and bricks waste. The comparative study of using bricks waste as an alternative material to crush bricks is also presented in this research study. Bricks waste and its disposal is becoming an environmental challenge. This study proposes jute fibers as a reinforcement element and uses bricks waste as an alternative material to crushed stone. M20 grade concrete with various volumetric percentages (0.05%, 0.10%, 0.25%, 0.50%, and 1.00%) of 10 mm long jute fibers are produced. An increase of 39% and 37% in compressive strength and split tensile strength, respectively, was observed after the addition of jute fibers. Therefore, the use of jute fibers and bricks waste to replace crushed stone is an effective way to mitigate environmental pollution and sustainability issues.

Cold agglutinin-induced hemolytic anemia presents diagnostic and management challenges, particularly in resource-limited settings like Pakistan. A 92-year-old male presented with altered consciousness, fever, weakness, and severe asthenia. Laboratory findings revealed severe anemia, elevated mean corpuscular volume, and deranged renal function tests. The diagnosis was confirmed via a positive Direct Coombs test and elevated cold agglutinin titers. Multidisciplinary management led to symptom resolution, including blood transfusions and immunosuppressive therapy. This case report and literature review emphasize the complexities of diagnosing and managing cold agglutinin-induced hemolytic anemia in Pakistan. By enhancing awareness and understanding of this condition, clinicians can optimize patient outcomes through timely intervention and multidisciplinary care.

Abstract Modifying the coordination or local environments of single‐, di‐, tri‐, and multi‐metal atom (SMA/DMA/TMA/MMA)‐based materials is one of the best strategies for increasing the catalytic activities, selectivity, and long‐term durability of these materials. Advanced sheet materials supported by metal atom‐based materials have become a critical topic in the fields of renewable energy conversion systems, storage devices, sensors, and biomedicine owing to the maximum atom utilization efficiency, precisely located metal centers, specific electron configurations, unique reactivity, and precise chemical tunability. Several sheet materials offer excellent support for metal atom‐based materials and are attractive for applications in energy, sensors, and medical research, such as in oxygen reduction, oxygen production, hydrogen generation, fuel production, selective chemical detection, and enzymatic reactions. The strong metal–metal and metal–carbon with metal–heteroatom (i.e., N, S, P, B, and O) bonds stabilize and optimize the electronic structures of the metal atoms due to strong interfacial interactions, yielding excellent catalytic activities. These materials provide excellent models for understanding the fundamental problems with multistep chemical reactions. This review summarizes the substrate structure‐activity relationship of metal atom‐based materials with different active sites based on experimental and theoretical data. Additionally, the new synthesis procedures, physicochemical characterizations, and energy and biomedical applications are discussed. Finally, the remaining challenges in developing efficient SMA/DMA/TMA/MMA‐based materials are presented.

Steam direct contact condensation (DCC) into subcooled water may be encountered in different important industrial process applications, such as steam jet pumps, steam ejectors, pressurizers, and emergency cooling systems of nuclear reactor core. In this work, a numerical simulation study has been done for injection of steam into a tank full of subcooled water. In the simulations, the Eulerian multiphase flow in addition to a realizable k-epsilon turbulence model has been utilized. Moreover, a DCC model has been used for condensation capturing. Fluent software with a user-defined function (UDF) for the DCC model was employed for simulations. The results obtained from simulations were validated with experimental results, and a fair agreement was observed. This study considered the local Nusselt number (LNN) the most suitable parameter for investigating the heat transfer rate (HTR) at the computational cell level. Therefore, the contours of the LNN, its axial distribution, and radial distribution were studied with respect to the inlet pressure of injected steam, temperature of tank water, and location along the axis of the nozzle. The results reveal the fact that the value of the LNN reaches a maximum at the nozzle exit along the axis of the nozzle at 323 K tank water temperature. LNN decreases by increasing or decreasing the tank water temperature beyond 323 K. It is claimed that the heat transfer (HT) study at such a local scale has been conducted for the first time to the best of our knowledge, and it unfolds various crucial facts regarding steam-water interaction.

Electrolysis from biomass is a promising process for hydrogen generation from biomass and biowaste that is still unexplored. The paper used lignocellulosic biomass components and food waste (banana and cucumber peels) as feedstocks to explore hydrogen generation through an H-type proton exchange membrane electrolytic cell. Polyoxometalates (PMo12) were employed as a catalyst and charge carrier during the pre-treatment stage in the biomass degradation process. Electrochemical characterisations were conducted in a potential range of 0–1.20 V to analyse the electrochemical reaction behaviour at the anode. To assess the impact of temperature on the hydrogen yield rates, the electrolysis was conducted at both room temperature (19 °C) and a higher temperature (80 °C). Results show that the maximum hydrogen produced in the first hour was 7.8 mL per gram of biomass. With an electrical potential of 1.20 V and a temperature of 80 °C, the hydrogen yield rate of glucose was three times higher than that of the individual biomass components, i.e., cellulose, lignin, hemicellulose, and starch. The volume of hydrogen yielded from banana peel and cucumber was 49.2 mL and 39.6 mL, respectively. The overall conversion efficiency was calculated as the weight percentage ratio of the hydrogen contained in cucumber and banana peels and the hydrogen collected was 10 % and 17 %, respectively, suggesting the need to identify solutions to extract the hydrogen content from biomass material further.

To study the dynamical system, it is necessary to formulate a mathematical model to comprehend the dynamics of the diseases that are prevalent around the world by using fractional calculus. A mathematical model is developed with the hypothesis created by adding control and asymptomatic variables to observe the rate of change of pine wilt and the ABC operator is used to turn the model into a fractional ordered model for continuous monitoring. The Boundedness and uniqueness of the developed model are investigated for bounded findings by using Banach space, which are the key properties of such an epidemic model. A newly developed system is examined both qualitatively and quantitatively to determine its stable position, and the verification of flip bifurcation has been made for developed systems. Derived reproductive numbers using the next-generation technique as well as the sensitivity of each involved parameter are verified. The Atangana–Toufik scheme is employed to find the solution for the developed system using different fractional values, which are advanced tools for reliable bounded solutions. Simulations have been made to see the real behavior and effects of pine wilt disease with control and asymptomatic battels in the community. Also, identify the real situation of the spread as well as the control of pine wilt after employing control and asymptomatic battels due to treatment. Such a type of investigation will be useful in investigating the spread of disease as well as helpful in developing control strategies based on our justified outcomes.

Abstract In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression.

Allelo-chemical has been emerged as an important play to induce the abiotic stress tolerance. The experiment included three components: different levels of salinity stress (SS: control, 6 dS m-1, 12 dS m-1), seed priming with moringa leaf extract (MLE: 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, 3.0%), and saltwater-tolerant and salinity-sensitive wheat cultivars (Faisalabad-2008, Galaxy-2013). Results showed that salinity lowered photosynthetic pigments, photosynthesis, transpiration, internal carbon, and stomatal conductance, while causing poor and delayed germination, inconsistent seedling growth, and increased hydrogen peroxide accumulation. However, hydro-priming and MLE priming enhanced emergence dynamics, growth, biochemical and enzymatic characteristics, and physiological aspects. The cultivar Faisalabad-2008 (wheat) performed well, but at high salinity levels, the hormetic impact of moringa leaf extract was more obvious, enhancing the germination and growth of cultivar Galaxy-2013, which was salinity-sensitive. Wheat cultivars' germination and seedling growth improved most when primed with 2% MLE (Faisalabad-2008) and 2.5% MLE (Galaxy-2013). This demonstrated that moringa possesses growth-promoting compounds that efficiently mitigate toxic impacts of salinity.

Cadmium (Cd) toxicity is an alarming issue for our agricultural soils and serious threat to crop productivity. The concentration of Cd in our soils is continuously increasing which is posing serious threat to plants, animals and humans. Mung bean is a conventional pulse crop cultivated all over the world. Thus, this study's goal was to evaluate response of mung bean seedlings in terms of growth, physiology, and biochemistry to varying degrees of Cd stress. The investigation examined various Cd levels, including control, 5, 10 and 15 mg Cd/kg of soil. The results indicate that mungbean growth, physiological and biochemical components was negatively impacted by Cd stress. Results depicted that Cd (15 mg/kg) reduced the growth attributes photosynthetic pigments (Chl. a, b and carotenoids), total soluble proteins (TSP) and free amino acids (FAA) and increased the malondialdehyde (MDA), hydrogen peroxide (H2O2), and electrolyte leakage (EL). Interestingly, activities of all four antioxidants (ascorbic acid, catalase, ascorbate peroxidase and peroxidase) increased with increase in Cd toxicity.

Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medical treatments. Stillbirth prediction is a similar domain where artificial intelligence-based predictive modeling can alleviate this significant global health challenge. Despite advancements in prenatal care, the prevention of stillbirths remains a complex issue requiring further research and interventions. The cardiotocography (CTG) dataset is used in this research work from the UCI machine learning (ML) repository to investigate the efficiency of the proposed approach regarding stillbirth prediction. This research work adopts the Tabular Prior Data Fitted Network (TabPFN) model which was originally designed to solve small tabular classification. TabPFN is used to predict the still or live birth during pregnancy with 97.91% accuracy. To address this life-saving problem with more accurate results and in-depth analysis of ML models, this research work makes use of 13 renowned ML models for performance comparison with the proposed model. The proposed model is evaluated using precision, recall, F-score, Mathews Correlation Coefficient (MCC), and the area under the curve evaluation parameters and the results are 97.87%, 98.26%, 98.05%, 96.42%, and 98.88%, respectively. The results of the proposed model are further evaluated using k-fold cross-validation and its performance comparison with other state-of-the-art studies indicating the superior performance of TabPFN model.

Gestational diabetes mellitus (GDM) and thyroid disorders during pregnancy pose significant health concerns, impacting a substantial number of mothers globally. Globally, about 14% of pregnant women develop GDM, while thyroid disorders impact approximately 2%–3%. Both conditions contribute to adverse outcomes, including gestational hypertension, excessive fetal growth, and heightened perinatal morbidity. The central focus of this literature review is to examine the relationship between vitamin A, a crucial fat-soluble micronutrient in fetal development, and the occurrence of GDM and thyroid disorders during pregnancy. The primary research question investigates the association between vitamin A, GDM, and thyroid disorders, analyzing their combined impact on maternal, fetal, and neonatal outcomes. The review underscores the potential of vitamin A to modulate the risk and outcomes of GDM and thyroid disorders during gestation, emphasizing its role in GDM development and resolution and its influence on thyroid function in pregnancy.

Key Clinical Message Bone marrow transplantation (BMT) saves lives in Omenn syndrome, a severe immunodeficiency disorder. Timely genetic diagnosis and matched donor BMT are crucial. Emphasis on newborn screening and multidisciplinary care improves outcomes for infants with inherited disorders. Prompt intervention and comprehensive management are vital for a successful transplant outcome. Abstract Omenn syndrome represents a severe variant of combined immunodeficiency characterized by disregulated immune responses and susceptibility to recurrent infections. We present the case of a 3‐month‐old female infant initially presenting with upper respiratory infection symptoms and a diffuse rash, unresponsive to local treatment. At 4 months of age, the patient underwent allogeneic bone marrow transplantation (BMT) utilizing stem cells from a fully matched sibling donor. Pre‐transplant conditioning included antimicrobial prophylaxis and supportive therapies. Following BMT, the patient received immunosuppressive medications to prevent graft rejection and graft‐versus‐host disease. Clinical monitoring post‐transplant showed timely neutrophil and platelet engraftment, with subsequent follow‐up demonstrating stable clinical parameters and negative cytomegalovirus status. The case highlights the importance of timely diagnosis and treatment in managing severe immunodeficiency disorders, demonstrating the potential for successful outcomes with appropriate timely interventions. Regular monitoring and follow‐up appointments were crucial in ensuring the success of the treatment. This case also emphasizes the significance of multidisciplinary care and genetic testing in identifying and managing rare immunodeficiency disorders. The successful outcome in this case provides hope for improved treatment options and better patient outcomes in the future.

Introduction: Aortic pseudoaneurysms are a type of contained rupture where most of the aortic wall is breached, leaving only a thin rim of the remaining wall or adventitia to hold the blood. This condition carries a high risk of rupture and potentially fatal complications. Typically, patients present with chest pain; haemoptysis can also occur, though rarely. Case description: A 64-year-old male who presented with two episodes of haemoptysis, with no history of cardiovascular surgery or trauma. A chest computerized tomography (CT) followed by an aortogram revealed a thoracic aortic pseudoaneurysm and the patient underwent surgical aortic repair without any complications. This case underscores the rare presentation of thoracic aortic pseudoaneurysm. Discussion: Haemoptysis is a rare manifestation of thoracic aorta pseudoaneurysm and can be a warning sign of impending rupture. Haemoptysis may occur due to formation of aortopulmonary fistula or direct erosion of pseudoaneurysm into lung parenchyma. Conclusion: It is imperative for clinicians to recognise such manifestations early for prompt diagnosis and prevention of complications.

We present the case of a 63-year-old female diagnosed with atypical SSc in the setting of acute SRC. She was undergoing work-up for progressive dyspnoea in the outpatient setting when she was found to have newly diagnosed restrictive lung pathology and worsening renal function, thus prompting acute hospital admission. Given multisystem involvement of the pulmonary and renal systems, the differential diagnosis included autoimmune and connective tissue disorders. Although serologies were non-specific, renal biopsy confirmed scleroderma renal disease, and she was started on treatment with captopril. This case highlights the importance of clinical judgment and timely diagnosis, even when laboratory data might indicate otherwise.

Nowadays, thermal comfort is becoming a major problem for people due to increasing global warming and climatic changes, but it can be resolved by the concept of Heating, Ventilation, and Air Conditioning (HVAC) systems. The purpose of HVAC is to provide occupants with a comfort zone so that they can feel comfortable according to their thermal comfort. The core objective of this study is to design and propose an HVAC system as per actual design conditions for the University Auditorium located in Karachi, Pakistan. A direct Expansion (DX – Type) system is installed in the Auditorium that has exceeded the lifespan of twenty years, refrigerant R-22 which is currently being used has been obsolete due to its high GWP (Global Warming Potential) and ODP (Ozone Depletion Potential) values which are 1810 and 0.05 respectively. To achieve the objective of this study, two approaches are employed. Cooling Load Temperature Difference (CLTD) method & Hourly Analysis Program (HAP) software. The cooling load calculated from the CLTD method is 202 kW equivalent to 57.5 Ton of Refrigeration (TR). On the other side, the cooling load calculated from HAP software is 192.8 kW equivalent to 55 TR. By considering the calculated cooling load for the University Auditorium, two different HVAC systems are proposed, based on Water cooled and Air-cooled Vapor Compression Cycle. After this study, engineers will be able to design an HVAC system for any facility as per design conditions. Also, they can propose different cost-effective and energy-efficient HVAC systems for that particular space.

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.

The strain hardening fiber reinforced concrete—generally known as the Engineered Cementitious Composite (ECC)—has rapidly gained the attention of researchers in recent years. However, most of the research on ECC is limited to material and member level, leaving a gap in the understanding of its behavior at the structural scale. Therefore, this study investigates the global seismic response of ECC structures and compares their performance with conventional reinforced concrete (RC) structures. For this purpose, a case study long span building structure (an aircraft hangar having a span length of 40 m) is selected. Under the design-level gravity and lateral loads, its members are separately designed using the conventional RC and ECC. It is observed that for ECC members, the requirement of longitudinal steel is reduced by 30% when compared with the conventional RC members. Similarly, owing to an improved tensile behavior, the ECC members also exhibited a higher shear capacity than RC members, resulting in a significant reduction in the requirement of transverse reinforcement. The detailed inelastic finite element models for both design cases (RC and ECC) were subjected to the pushover analysis and nonlinear response history analysis (NLRHA) to assess their seismic performance. It is observed that (in terms of local and global seismic demands, structural damage, and ductile behavior) the performance of the ECC structure is significantly improved when compared to the conventional RC structure. The comparative cost analysis showed a reduction of 11.9% in the overall material cost of the ECC structure as compared to RC. These results show that ECC can be effectively used at the full structural level as an economic solution to ensure the ductile response and superior seismic performance.

The construction industry is responsible for around 5% of total CO2 emissions globally. As a result, recent research is focused on the sustainability aspects of construction materials. Engineered cementitious Composites (ECC) are high-performance fiber reinforced composites with improved ductility and tensile strength but utilize higher cement content, raising concerns about their sustainability. In this study, nano-lime and calcined clay are combined with cement to develop a high strength sustainable nano lime calcined clay cement (nLC3) based Engineered Cementitious Composite (ECC). Initially, the packing density model was employed to develop a high strength nLC3-based matrix by combining the particle packing models and chemical compatibility while keeping costs to a minimum. Micromechanical modeling was then applied to determine the critical volume of fibers for the novel matrix using single fiber pullout tests and matrix toughness tests. According to the micromechanical model, the critical volume fraction is found to be 1.93% for the nLC3 mix. This was further confirmed by casting dog bone samples containing 2% fibers by volume which confirmed strain hardening response. The uniaxial test results indicated a strength of around 5.85 MPa in tension and 51 MPa in compression with about 3% tensile strain. These results are comparable to conventional ECC and better than previously developed LC3 based ECC. This study reveals that nLC3-based ECC is a more sustainable composite as compared to conventional ECC mix without any compromise on strength and cost.

Aims The Central Mental Hospital is the Republic of Ireland's only secure forensic hospital and the seat of its National Forensic Mental Health Service (NFMHS). We scrutinised admission patterns in the NFMHS during the period 01/01/2018–01/10/2023; before and after relocating from the historic 1850 site in Dundrum to a modern facility in Portrane on 13/11/2022. Methods This prospective longitudinal cohort study included all patients admitted during the above period. The study initially commenced in Dundrum and continued afterwards in Portrane. Data gathered included demographics, diagnoses, capacity to consent to treatment, and the need for intramuscular medication (IM) after admission. Therapeutic security needs and urgency of need for admission were collated from DUNDRUM-1 and DUNDRUM-2 scores rated pre-admission. Hours spent in seclusion during the first day, week, and month after admission were calculated. Data were collected as part of the Dundrum Forensic Redevelopment Evaluation Study (D-FOREST). Results There were 117 admissions during the 69-month period. The majority were male (n = 98). Most were admitted from prisons (87%). Schizophrenia was the most common diagnosis (55.8%). Mean DUNDRUM-1 triage security scores were in the medium-security range (2.84–3.15) during this period. At the time of admission, 53.8% required seclusion, 25.6% required IM medication, and 79.5% lacked capacity to consent to treatment. Those who required seclusion on admission had worse scores on the DUNDRUM-2 triage urgency scale (F = 20.9, p < 0.001). On linear logistic regression, the most parsimonious model resolved with five predictors of hours in seclusion during the first day and week, which were: D1 item 8 – Victim sensitivity/public confidence issues, D1 item 10 – Institutional behaviour, D2 item 2 – Mental health, D2 item 4 – Humanitarian, and D2 item 6 – Legal urgency. 50% required IM medication during their first week of admission and these patients had significantly worse scores on: D1 item 8 – Victim sensitivity/public confidence issues, D1 item 10 – Institutional behaviour, D2 item 2 – Mental health, and D2 item 4 – Humanitarian (all p < 0.05). Conclusion There was an increase in the frequency of admissions since relocating to Portrane. The results suggest that there was no change in overall triage security and urgency needs during the time period in question. Major mental illness related factors impacted the need for seclusion early in the admission, whereas factors linked to prison behaviour or personality-related factors were more associated with an ongoing need for seclusion at month one.

Growing population, burgeoning economic growth and technological advancements have caused a surge in electricity demand worldwide. In recent years, Pakistan, contributing less than 1 % to global Greenhouse Gas (GHG) emissions, has endured severe weather, including the 2022 floods that affected nearly 30 million people. Pakistan is a developing country and signatory of Paris accord has made an international commitment to cut down its GHG emissions. To date, the total electricity generation of Pakistan is about 122,934 GWh, from which 60.9 % (744,862 GWh) is met by thermal sources (coal, oil gas), 23.7 % (29,181 GWh) by hydel, 12.4 % (15,182 GWh) by nuclear and 3 % (3709 GWh) of electricity generation is done through renewable energy sources. The demand for electricity in Pakistan is rising exponentially, and around 60.9 % is fulfilled by hydrocarbons. This study explores a novel approach by assessing the electricity sector's GHG emissions using CO2, CH4, and N2O, expanding beyond the traditional focus on CO2 alone. This work uses a time-varying carbon intensity approach to calculate the GHG emissions and, subsequently, carbon intensity from fossil fuel-driven electrical systems. One notable aspect of this approach is its capability to calculate GHG emissions and carbon intensity with a more frequent temporal resolution, frequently. The peak GHG emissions are observed in January and December at values 650gCO2-eq/kWh and 550gCO2-eq/kWh respectively, causing smog issues in the country. Results show that the carbon intensity in Pakistan is relatively higher during January, February, October, November and December due to unavailability of hydel resources.

Abstract This work examines the fractional generalized Korteweg-de-Vries-Zakharov-Kuznetsov equation (gKdV-ZKe) by utilizing three well-known analytical methods, the modified $$\left( \frac{G^{'}}{G^2}\right)$$ G ′ G 2 -expansion method, $$\left( \frac{1}{G^{'}}\right)$$ 1 G ′ -expansion method and the Kudryashov method. The gKdV-ZK equation is a nonlinear model describing the influence of magnetic field on weak ion-acoustic waves in plasma made up of cool and hot electrons. The kink, singular, anti-kink, periodic, and bright soliton solutions are observed. The effect of the fractional parameter on wave shapes have been analyzed by displaying various graphs for fractional-order values of $$\beta$$ β . In addition, we utilize the Hamiltonian property to observe the stability of the attained solution and Galilean transformation for sensitivity analysis. The suggested methods can also be utilized to evaluate the nonlinear models that are being developed in a variety of scientific and technological fields, such as plasma physics. Findings show the effectiveness simplicity, and generalizability of the chosen computational approach, even when applied to complex models.

Abstract White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

Continual learning approaches are useful to help a model learn new information or new tasks sequentially, while also retaining the previously acquired information. However, such approaches are known to be extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific task or class at test time. In this work, we propose a novel defensive framework to counter such an attack and use the attacker’s primary strength – hiding the backdoor pattern by making it imperceptible to humans – against itself. To do so we train the model to learn a random perceptible pattern as decoy data during training with the deliberate intent to overpower the attacker’s imperceptible pattern. We demonstrate the effectiveness of the proposed defensive mechanism through various commonly used replay-based (both generative and exact replay-based) continual learning algorithms using continual learning benchmark variants of CIFAR-10, CIFAR-100, and MNIST datasets. We show that the proposed defensive framework considerably improves the robustness of continual learning algorithms without having any knowledge of the attacker’s target task, attacker’s target class, shape, size, and location of the attacker’s backdoor pattern. Moreover, our defensive framework does not depend on the underlying continual learning algorithm and does not rely on detecting the attack samples (to subsequently remove them from further consideration), but instead attempts to correctly classify even the attack samples and thus ensuring robustness in continual learning models. We refer to the proposed defensive framework as adversary aware continual learning (AACL).

Top speed of an aircraft is limited by the stiffness and damping of a structure which are contributing to aeroelastic response such as divergence and flutter, respectively. This study explores stiffness tailoring of Carbon Fiber Reinforced Polymer (CFRP) spars using a proposed bi-level framework. Frequency-based aeroelastic approaches are fast but less accurate for highly flexible structures, while time-based methods utilizing Fluid-Structure Interaction offer high accuracy but at a significant computational cost. This work addresses the computational challenge by proposing a bi-level framework for efficient and accurate aeroelastic analysis. This framework employs a two-step process: Initially, a frequency-based approach in MSC NASTRAN provides an initial prediction of critical velocities. However, this method can be over-conservative for flexible structures due to limitations in capturing non-linear aerodynamics such as wake, viscous effects and induced drag. Subsequently, the framework utilizes ANSYS for more detailed analysis beyond the earlier predicted velocities. Essentially, this bi-level approach can reduce the computational cost upto 60% compared to a full-time-based analysis depending upon flight envelope, while maintaining an acceptable error margin of within 5%. Furthermore, this work investigates the impact of parametric optimization of stacking sequence on the CFRP spar. While maximizing shear modulus increases flutter speed, it comes at the expense of decreased divergence speed highlighting the importance of design trade-offs for improved aeroelastic performance. In conclusion, results of the aeroelastic tailoring showed that mass of the wing is reduced by 17.7% compared to baseline whereas divergence and flutter speed is increased by 10 and 22.8%, respectively.

Recent developments and the widespread adoption of remote sensing data (RSD) gave rise to various hyperspectral imaging (HSI) applications. With its detailed spectral information, HSI has been adopted for use in various agriculture-related applications. These applications demand more accurate classification, highlighting the significance of HSI in plant disease detection, crop classification, etc. Despite existing models for RSD-based agricultural applications, such models lack generalizability for plant classification. This task is challenging for the UNet-based architectures due to nonlinear combinations of the pixels in the hyperspectral image, reduced and diverse information of each pixel, and high dimensions. Furthermore, a shortage of labeled data makes achieving high classification accuracy challenging. This study proposes an improved 3-D UNet architecture based on a modified convolutional neural network that uses spatial and spectral information. This approach solves the limitations of existing UNet-based models, which suffer to deal with nonlinear combinations and reduced and diverse information of small pixels. The proposed model employs a semantic segmentation strategy with modified architecture for more accurate classification and segmentation. The studies employ widely recognized benchmark HSI datasets, such as the Indian Pines, Salinas, Pavia University, Honghu, and Xiong'an datasets. These datasets are assessed using average accuracy, overall accuracy, and the Kappa coefficient. The suggested model demonstrated exceptional classification accuracy, achieving 99.60% for the Indian Pines dataset and 99.67% for the Pavia University dataset. The proposed approach is further validated and proven to be superior and robust through comparisons with existing models.

Plant diseases can have profound effects on the economy, impacting both local and global scales. These diseases can lead to substantial losses in agricultural productivity, affecting crop yields and quality. In this context, deep learning algorithms are widely acknowledged as effective solutions. However, the use of these black-box approaches raises concerns about trust in interpreting and validating the decisions generated by the models. This study proposes an explainable artificial intelligence (XAI) based plant disease classification system to classify and identify distinct ailments with improved accuracy. The system correctly identifies 38 different plant diseases with accuracy, precision, and recall as 99.69%, 98.27%, and 98.26%, respectively. These predictions are subjected to additional analysis employing the local interpretable model-agnostic explanations (LIME) framework to produce visual explanations aligning with prior beliefs and adhering to established best practices in explanations. This system will serve as a promising avenue for revolutionizing disease detection, fostering informed decision-making, and ultimately contributing to global food security.

Online platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbullying involves using aggressive and offensive language including rude, insulting, hateful, and teasing comments to harm individuals on social media platforms. Human moderation is both slow and expensive, making it impractical in the face of rapidly growing data. Automatic detection systems are essential to curb trolling effectively. This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. The proposed approach is further compared with the state-of-the-art machine, deep, and transformer-based learning approaches with the FastText word embedding approach. Statistical results demonstrate that the proposed model outperforms others, achieving a 95% accuracy for detecting cyberbullying tweets. In addition, the model obtains 95%, 97%, and 96% for precision, recall, and F1 score, respectively. Results from k-fold cross-validation further affirm the supremacy of the proposed model with a mean accuracy of 95.07%.

Abstract The brain-derived neurotrophic factor (BDNF) involves stress regulation and psychiatric disorders. The Val66Met polymorphism in the BDNF gene has been linked to altered protein function and susceptibility to stress-related conditions. This in silico analysis aimed to predict and analyze the consequences of the Val66Met mutation in the BDNF gene of stressed individuals. Computational techniques, including ab initio, comparative, and I-TASSER modeling, were used to evaluate the functional and stability effects of the Val66Met mutation in BDNF. The accuracy and reliability of the models were validated. Sequence alignment and secondary structure analysis compared amino acid residues and structural components. The phylogenetic analysis assessed the conservation of the mutation site. Functional and stability prediction analyses provided mixed results, suggesting potential effects on protein function and stability. Structural models revealed the importance of BDNF in key biological processes. Sequence alignment analysis showed the conservation of amino acid residues across species. Secondary structure analysis indicated minor differences between the wild-type and mutant forms. Phylogenetic analysis supported the evolutionary conservation of the mutation site. This computational study suggests that the Val66Met mutation in BDNF may have implications for protein stability, structural conformation, and function. Further experimental validation is needed to confirm these findings and elucidate the precise effects of this mutation on stress-related disorders.

Abstract Respiratory syncytial virus (RSV) is the cause of lung infection, nose, throat, and breathing issues in a population of constant humans with super-spreading infected dynamics transmission in society. This research emphasizes on examining a sustainable fractional derivative-based approach to the dynamics of this infectious disease. We proposed a fractional order to establish a set of fractional differential equations (FDEs) for the time-fractional order RSV model. The equilibrium analysis confirmed the existence and uniqueness of our proposed model solution. Both sensitivity and qualitative analysis were employed to study the fractional order. We explored the Ulam–Hyres stability of the model through functional analysis theory. To study the influence of the fractional operator and illustrate the societal implications of RSV, we employed a two-step Lagrange polynomial represented in the generalized form of the Power–Law kernel. Also, the fractional order RSV model is demonstrated with chaotic behaviors which shows the trajectory path in a stable region of the compartments. Such a study will aid in the understanding of RSV behavior and the development of prevention strategies for those who are affected. Our numerical simulations show that fractional order dynamic modeling is an excellent and suitable mathematical modeling technique for creating and researching infectious disease models.

Quora is an expanding online platform, that contains a growing collection of questions and answers generated by users. The content on this platform is managed by its users which involves creating, editing, and organization. Due to the vast number of users, it is not uncommon to find multiple questions with similar intents, leading to the problem of duplicate and identical questions. Detection of these duplicates could effectively lead to a more efficient search for high-quality answers, ultimately improving the user experience for both readers and writers on Quora. This study utilizes the dataset of Question Pairs for Quora obtained from Kaggle for identifying questions that are duplicates or identical. To vectorize the questions and for model training, six types of word embeddings are implemented including GoogleNewsVector, FastText crawl, FastText crawl sub-words, bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and embeddings from language models (ELMO) containing 100 dimensions. The Siamese Manhattan long short-term memory (MaLSTM) neural network model, where Ma is Manhattan distance, is applied with ELMO word embedding to predict duplicate questions in the dataset. Experimental results demonstrate that the proposed model attained an accuracy of 95.68% which surpasses the state-of-the-art models.

Unmanned aerial vehicles (UAVs) substantially rely on the utilization of global positioning systems (GPS) to navigate. A simulator for commercial GPS applications with false GPS signals can lead to the deviation of a GPS-guided drone from its planned path. As a result, an anti-spoofing technology is required to assure UAV operating safety. Several approaches have been developed to detect GPS spoofing, however, predominantly such methods rely on additional hardware. Using additional hardware might not be an ideal solution for small and low-capacity UAVs. Detecting signal spoofing attacks in small UAVS has significant importance. This study presents a stacked ensemble approach to detect GPS signal spoofing within the context of small UAVs. The initial phase involves outlining the sequential procedures for obtaining and preparing the GPS signal dataset, including details about the UAV hardware, blocker, data collection timing, environmental factors, and the utilization of z-score normalization for preprocessing. Then controlled simulation tests with varying experimental conditions are conducted and the model is built using a support vector machine and convolutional neural network. Additionally, a comprehensive comparative assessment is conducted to analyze the efficacy of the proposed model against traditional machine learning models. Experimental results demonstrate notably good performance by the proposed model with a 99.74% accuracy, showing its superior performance in the context of GPS signal spoofing in small UAVs.

Software Defect Prediction (SDP) reduces the overall cost of software development by identifying the code at a higher risk of defects at the initial phase of software development. SDP helps the test engineers to optimize the allocation of testing resources more effectively. Traditional SDP models are built using handcrafted software metrics that ignore the structural, semantic, and contextual information of the code. Consequently, many researchers have employed deep learning models to capture contextual, semantic, and structural information from the code. In this article, we propose the DP-CCL (Defect Prediction using CodeBERT with Contrastive Learning) model to predict the defective code. The proposed model employs supervised contrastive learning using this CodeBERT Language model to capture semantic features from the source code. Contrastive learning extracts valuable information from the data by maximizing the similarity between similar data pairs (positive pair) and meanwhile minimizing the similarity between dissimilar data pairs (negative pair). Moreover, The model combines the semantic features with software metrics to obtain the benefits of both semantic and handcrafted features. The combined features are input to the logistic regression model for code classification as either buggy or clean. In this study, ten PROMISE projects were utilized to conduct the experiments. Results show that the DP-CCL model achieved significant improvement i.e., 4.9% to 14.9% increase in F-Score as compared to existing approaches.

Passive cooling of photovoltaics (PV) using phase change materials (PCM) may be extremely effective owing to their enormous implicit specific heat. However, the low rate of heat transfer, high solar radiation, and ambient temperature drop its efficiency by 0.45%/ᵒC. Only such a small fraction of solar irradiance is turned into electrical energy by PV cells; most of the irradiation is transformed into heat; hence, cells start operating above ambient temperature. Therefore, the exterior heat of photovoltaic panels is controlled by applying artificial cooling to enhance their efficiency. The current research aims on the significant benefits of using PCM to reduce panel surface temperature in terms of boosting energy efficiency and maintaining thermal comfort. This study demonstrates a successful design of PV controlled-temperature module using phase change materials for hot climate, especially for south Asian regions. The surface temperature of solar panels has been reduced using computational analysis and experimental study on paraffin wax. In this work, Paraffin wax used, which reduced the panel surface temperature by 5-7ᵒC, generating a 29% increase of the modified PV panel relative efficiency compared to its standard value.

Abstract Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety.

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.