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

This research replaced the traditional hole source layers in a conventional LED with a tunnel junction. In conventional light-emitting diodes (LEDs) two hole injecting layers (p-GaN and p-AlxGa1-xN) have been used, in which holes have to surpass greater energy barriers that hindered holes injection. A heavily doped GaN tunnel junction (TJ) has been used as a replacement for conventionally used hole injection layers, which has reduced the energy barriers for holes and increased the electron confining ability of wells. As a result, the internal quantum efficiency (IQE) of the LED has increased from 52% to 72%. Instead of GaN TJ, Al0.10Ga0.90N – GaN hybrid tunnel junction (HTJ) has been introduced to lower the efficiency droop and raise the efficiency further to 81% simultaneously. Using TJ and HTJ, we have demonstrated that the conventional hole’s source layer can be replaced by TJs, which results in better hole injection because of reduced barrier height, increased electron confinement, greater electron–hole radiative recombination, and higher spontaneous emission intensity.

Abstract Sub-10 µm microplastics (MPs) in aquatic environments pose significant ecological and health risks due to their mobility and potential to carry harmful microcontaminants. Our effluent analysis from a Hong Kong Sewage Treatment Works shows that traditional treatment often fails to effectively remove these MPs. These small-sized MPs are commonly neglected due to challenges in accurate quantification, analysis, and removal. This study introduces a nanobubble-assisted flotation process that enhances the removal efficiency of both regular and irregular small-sized MPs from wastewater. The proposed process outperforms the traditional flotation process by fostering a more effective interaction between bubbles and MPs, increasing removal rates of MPs from 1 µm to 10 µm by up to 12% and providing a total efficiency boost of up to 17% for various particle sizes. Improvements are attributed to enhanced collision and adhesion probabilities, hydrophobic interactions, as well as better floc flotation. Supported by empirical evidence, mathematical models, and Molecular Dynamics simulations, this research elucidates the nanoscale mechanisms at play. The findings confirm the nanobubble-assisted flotation technique as an innovative and practical approach to removing sub-10 µm MPs in water treatment processes.

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

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

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

Abstract Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Abstract The nexus between women leadership and corporate innovation in the context of developing world is key objective of this. To evaluate such narrative, firm-level data is acquired using World Bank data, and a sample of approximately 20,000 observations from 29 developing nations was collected over a four-year period (2015–2018). The findings support the hypothesis and verify significantly positive relationship between women in leadership roles and corporate innovation. These findings verify the Upper Echelons (UE) and Critical Mass (CM) theories which state that gender-based diversity among business leaders/top management impacts strategic decision-making, such as corporate innovation. Moreover, female representation in top management can bring diverse viewpoints, capabilities, knowledge, and customs which improve the functions and outcomes of the organization.

Abstract Developing economical and high‐performing sensitizers is crucial in advancing dye‐sensitized solar cells (DSSCs) and optoelectronics. This research paper explores the potential of novel red light‐absorbing organic dyes based on Indolo[3,2‐b]carbazole (ICZ) as the donor applied in co‐sensitizer‐free DSSCs for breakthroughs in photovoltaic (PV) applications. DFT and TD‐DFT based computational methods were employed to calculate the conduction band levels, electron injection capabilities, and power conversion efficiency (PCE) of metal‐free organic dyes (ICZ1–ICZ9) having D‐A‐π‐A architecture. Comprehensive analyses included NBO, DOS, FMO, ICT, MEP, binding energy, and TDM analysis. Quantum chemical calculations of the structural, photochemical, and electrochemical properties, as well as the key parameters, reveals that all the designed dyes could be an excellent candidate for high‐efficiency DSSCs due the small energy gap (2.130–1.947 eV), longer wavelength absorption (759.47–520.63 nm), longer lifetimes (15.65–6.67 ns), a lower ΔGreg (0.29–0.14 eV), a significant dipole moment changes (31.489–16.195D), LHE (0.95‐0.46), the large qCT (0.962–0.689), small DCT (7.657, 4.897 Å), and VOC (1.13–0.86 eV). This quantum simulation showed that, when compared to reference D8, the photovoltaic dyes ICZ8, ICZ2, and ICZ7 are recognized as being eye‐catching. Furthermore, dye@(TiO2)9 cluster model results demonstrate promising prospects for enhancing the photovoltaic (PV) performance of ICZ1–ICZ9 dyes by electron injection and conduction band (CB) engineering. This study will help the experimentalists for developing ICZ‐based PVs as more efficient and sustainable energy solutions.

Abstract Joule and photothermal heating offer promising avenues for enhancing oil recovery, desorption, and reusability by reducing the viscosity of highly viscous oils, thereby facilitating their mobility on sorbent surface. This study developed a heatable reduced graphene oxide nanoribbon (r-GONR)-coated polyvinylidene fluoride (PVDF) oil sorbent to address these viscosity challenges. The application of heating increased oil desorption by approximately 50%, significantly outperforming the conventional PVDF mat. The r-GONR sorbent, leveraging the photothermal effect, demonstrated exceptional reusability, maintaining 40% oil desorption efficiency up to the 10th cycle. Furthermore, its high oil desorption and reusability translated into considerable economic benefits, with a revenue potential of 6.4–29.0 $/m2, alongside significant environmental impact reduction. This study introduces a novel, sustainable approach to oil desorption and reuse, underscoring the practical applications of heatable materials in enhancing oil recovery.

Abstract The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model’s applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.

Abstract Over the last 20 years, the global production of dimension stones has grown rapidly. Today, seven countries—China, India, Turkey, Iran, Italy, Brazil, and Spain—account for around two-thirds of the world's output of dimension stones. Each one has annual production levels of nine to over twenty-two million tons. Mining operation in general is one of the most hazardous fields of engineering. A large amount of dimensional stone quarries require a special scheme of risk assessment. Risk Breakdown Structure is one of the major stages of risk assessment. In this paper, a detailed structure of risks of the dimension stone quarrying is formed, and divided into 17 main levels and 128 sublevels. The complexity of identifying different parameters made it requisite to use multi-attribute decision-making methods for prioritizing associated risks. As a case study, the main risks of the Ghasre Dasht marble mine are evaluated using the VIKOR method considering 10 major parameters under a Fuzzy environment. The results showed that the economic, Management, and Schedule risks are the most threatening risks of dimensional stone quarrying.

Abstract Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the T ransparent R FID T ag Wall (TRT-Wall), a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of $$95.6\%$$ 95.6 % under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.

The Chinese government is focusing on Artificial Intelligence (AI) techniques for numerous sectors, such as healthcare. The achievement of AI initiatives depends on the adoption and attractiveness of AI by way of physicians, nurses, decision-makers, and patients. This study aims to explore essential achievement factors for implementing AI initiatives inside the healthcare industry. The research analyzes the Technology Acceptance Model (TAM) and identifies the broadly applied factors, together with the perceived ease of use, perceived usefulness, attitude towards use, and behavioral intention to use. A systematic evaluation of 34 related studies among 2020 and 2023 was carried out to discover the most popular elements. The authors also used Partial Least Squares Structural Equation Modelling (PLS-SEM). The data was collected through surveys from 154 employees within the health and IT sectors in China. The findings demonstrated that perceived usefulness and ease of use of AI projects are definitely influenced by management, organizational, operational and IT infrastructure factors.

Abstract The present study deals with the production of cellulase-free endoxylanase by Aspergillus niger ISL-9 using wheat bran as a solid substrate. Endoxylanase was produced under a solid-state fermentation. Various growth parameters were optimized for the improved production of the enzyme. The Substrate level of 15 g was optimized as it provided the fungus with balanced aeration and nutrition. Among the six moisture contents investigated, Moisture Content 5 (MC5) was optimized (g/l: malt extract, 10; (NH4)2HPO4, 2.5; urea, 1.0) and 10 mL of MC5 was found to give the highest production of endoxylanase. The pH and time of incubation were optimized to 6.2 and 48 h respectively. The Inoculum size of 2 mL (1.4 × 106 spores/mL) gave the maximum enzyme production. After optimization of these growth parameters, a significantly high endoxylanase activity of 21.87 U/g was achieved. Very negligible Carboxymethylcellulase (CMCase) activity was observed indicating the production of cellulase-free endoxylanase. The notable finding is that the endoxylanase activity was increased by 1.4-fold under optimized conditions (p ≤ 0.05). The overall comparison of kinetic parameters for enhanced production of endoxylanase by A. niger ISL-9 under Solid State Fermentation (SSF) was also studied. Different kinetic variables which included specific growth rate, product yield coefficients, volumetric rates and specific rates were observed at 48, 72 and 96 h incubation time and were compared for MC1 and MC5. Among the kinetic parameters, the most significant result was obtained with volumetric rate constant for product formation (Qp) that was found to be optimum (1.89 U/h) at 72 h incubation period and a high value of Qp i.e.1.68 U/h was also observed at 48 h incubation period. Thus, the study demonstrates a cost-effective and environmentally sustainable process for xylanase production and exhibits scope towards successful industrial applications.

The push for sustainability in nanomaterials has catalyzed significant advancements in the green synthesis of carbon dots (CDs) from renewable resources. This review uniquely explores recent innovations, including the integration of hybrid techniques, such as micro-wave-assisted and ultrasonic-assisted hydrothermal methods, as well as photocatalytic synthesis. These combined approaches represent a breakthrough, offering rapid production, precise control over CD properties, and enhanced environmental sustainability. In addition, the review emphasizes the growing use of green solvents and bio-based reducing agents, which further reduce the environmental footprint of CD production. This work also addresses key challenges, such as consistently controlling CD properties—size, shape, and surface characteristics—across different synthesis processes. Advanced characterization techniques and process optimizations are highlighted as essential strategies to overcome these hurdles. Furthermore, this review pioneers the integration of circular economy principles into CD production, proposing novel strategies for sustainable material use and waste reduction. By exploring innovative precursor materials, refining doping and surface engineering techniques, and advocating for comprehensive life cycle assessments, this work sets a new direction for future research. The insights provided here represent a significant contribution to the field, paving the way for more sustainable, efficient, and scalable CD production with diverse applications in optoelectronics, sensing, and environmental remediation.

Abstract Background Potassium nitrate poisoning is a rare but potentially serious condition that can result in methemoglobinemia and subsequent cyanosis. This case report presents a unique instance of rapid-onset methemoglobinemia resulting from the ingestion of a traditional medicine preparation containing potassium nitrate, known as “kalmi shora.” Case presentation A 14-year-old Pakistani boy reported to the emergency department with a history of sudden-onset headache, drowsiness agitation, irritability, and generalized cyanosis. Pulse oximetry showed a concerning oxygen saturation level of 58%, whereas arterial blood gas analysis revealed a normal partial pressure of oxygen (90 mmHg). The profile of abrupt onset of symptoms, generalized cyanosis, and the discrepancy between the partial pressure of oxygen and oxygen saturation readings necessitated a comprehensive assessment including inquiries into potential toxins. The peculiar appearance of the blood, resembling chocolate in color, further indicated the possibility of methemoglobinemia. The patient was successfully treated with methylene blue, leading to a prompt resolution of symptoms. Conclusion This case highlights the significance of considering toxin exposures, such as traditional-medicine-induced poisoning, in emergency settings. The report contributes to the medical literature by highlighting the potential risks associated with traditional remedies and emphasizes the critical role of prompt diagnosis and intervention in optimizing patient outcomes. Recognition of the specific etiology of methemoglobinemia, in this case, traditional medicine ingestion, is essential for effective management in emergency medicine.

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.

Abstract Wheat is an important staple crop not only in Pakistan but all over the globe. Although the area dedicated to wheat cultivation expands annually, the quantity of wheat harvested is declining due to various biotic and abiotic factors. Global wheat production and output have suffered as a result of the drought, which is largely driven by a lack of water and environmental factors. Organic fertilizers have been shown to reduce the severity of drought. The current research was conducted in semi-arid climates to mitigate the negative effects of drought on wheat during its critical tillering (DTS), flowering (DFS), and grain filling (DGFS) stages through the application of three different abscisic acid treatments: ABA0 (0 mgL−1) control, ABA1 (100 mgL−1) and ABA2 (200 mgL−1). Wheat growth and yield characteristics were severely harmed by drought stress across all critical development stages, with the DGFS stage being particularly vulnerable and leading to a considerable loss in yield. Plant height was increased by 24.25%, the number of fertile tillers by 25.66%, spike length by 17.24%, the number of spikelets per spike by 16.68%, grain count per spike by 11.98%, thousand-grain weight by 14.34%, grain yield by 26.93% and biological yield by 14.55% when abscisic acid (ABA) was applied instead of the control treatment. Moreover, ABA2 increased the more physiological indices (water use efficiency (36.12%), stomatal conductance (44.23%), chlorophyll a (24.5%), chlorophyll b (29.8%), transpiration rate (23.03%), photosynthetic rate (24.84%), electrolyte leakage (− 38.76%) hydrogen peroxide (− 18.09%) superoxide dismutase (15.3%), catalase (20.8%), peroxidase (− 18.09%), and malondialdehyde (− 13.7%)) of drought-stressed wheat as compared to other treatments. In the case of N, P, and K contents in grain were maximally improved with the application of ABA2. Through the use of principal component analysis, we were able to correlate our results across scales and provide an explanation for the observed effects of ABA on wheat growth and production under arid conditions. Overall, ABA application at a rate of 200 mgL−1 is an effective technique to boost wheat grain output by mitigating the negative effects of drought stress.

Abstract Omniphobic membranes, due to their exceptional properties, have drawn significant attention for overcoming the bottleneck in membrane distillation (MD) technology. This study demonstrates an innovative method for fabricating an omniphobic membrane that is simple and facile compared to other methods such as wet/dry etching and photolithography. The surface morphology of springtails was imitated using electrospraying technique to coat a polyvinylidene fluoride substrate with concave-shaped polystyrene beads that were successfully developed by controlling the electrical traction (voltage) and air resistance (humidity). Then, the lipid coating of springtail surfaces was mimicked by dip-coating the membrane in a low-toxicity short-chain perfluoropolyether lubricant. The concave structure’s tiny air pockets increased membrane hydrophobicity significantly, indicated by the fact that the first round of water bouncing took only 16.3 ms. Finally, in MD treatment of seawater containing 1.0 mM sodium dodecyl sulfate, the optimized omniphobic membrane maintained a stable 99.9% salt rejection rate.

In this investigation, the behavior of a chemically reactive flow of micropolar Eyring–Powell ferrofluids over stretchable surface was examined. The study also considers heterogeneous and homogeneous chemical reactions, as well as thermal radiation and magnetic dipole impacts. By applying suitable similarity variables, the nonlinear partial differential governing equations, which describe the flow and energy dynamics of the micropolar fluid, were transformed into ordinary differential equations. The resultant equations were then numerically solved by using the shooting method with a Runge–Kutta update. The model equations are presented and discussed in dimensionless form through numerical approximations. A variety of numerical tests were performed for the dimensionless parameters to provide a comprehensive understanding. The MATLAB version 2023 was used for the numerical computations as well as graphical illustrations. It was concluded that the velocity field decreases with greater values of the ferro-magnetic parameter ß as well as for other fluid material parameters H, and ß. Whereas the temperature field increases by increasing the thermal radiation R while decreases with a higher dissipation parameter λ1 and Prandtl number Pr. The numerical simulation for these parameters were shown graphically, and the values were tabulated for clear illustration. Moreover, the current work was also compared with the findings of existing studies from recent literature to validate and contextualize the findings.

The present research investigated the effectiveness of cumin and black cumin extracts as natural antioxidants in uncooked chicken nuggets stored at refrigeration temperature (4°C) for 12 days and compared the results with a control treatment. Seven treatments were formulated, including a control group (Tc), T1 (0.5% cumin), T2 (1% cumin), T3 (1.5% cumin), T4 (0.5% black cumin), T5 (1% black cumin), and T6 (1.5% black cumin). The product was evaluated for several physicochemical parameters on days 0, 3, 6, 9, and 12 of storage. The data was analyzed using a two-way factorial design under a completely randomized (CRD) design. Results showed that both treatments and storage significantly impacted all physicochemical properties, including DPPH, TPC, pH, POV, and TBARS (p 0.05). Sensory evaluation showed that T3 (1.5% cumin) was the most pleasant and preferred treatment. Conclusively, both cumin and black cumin have good antioxidant properties and can be used as natural antioxidants to increase the shelf life of food products.

AbstractElectrochromic (EC) thin films have received considerable attention due to their potential applications in various fields such as smart windows, electrochromic displays, and energy storage devices. This review highlights various methods used for the fabrication and functionalization of EC films for various applications. Various techniques for EC thin film deposition ranging from solution-processable, low-temperature approaches such as sol-gel, spin coating, dip coating, and spray pyrolysis, to advanced techniques for deposition such as physical vapor deposition, chemical vapor deposition, and sputtering are summarized in this review. In addition, various applications of EC thin films and the outcome of different deposition approaches on the opto-electrochromic properties of EC thin films have been discussed elaborately. This review has the potential to spark the interest of researchers from a broad range of disciplines, including photocatalysis, electrocatalysis, nanotechnology and materials science.

Abstract This paper investigates the disparity between matter and antimatter in the universe with the help of gravitational baryogenesis. This phenomenon commenced shortly after the big bang, resulting in a predominance of matter over antimatter. We analyze the mechanism of gravitational baryogenesis (baryon to entropy ratio) under the framework of f(Q, C) gravity, where Q indicates non-metricity scalar and C denotes the boundary term. This Phenomenon depends on the charge parity violation interaction and for this paper we produce it with the coupling between baryon matter current ( $$j^{\nu }$$ j ν ) and $$\partial _{\nu }(Q+C)$$ ∂ ν ( Q + C ) . In the present work, we evaluate he baryon to entropy ratio ( $$\frac{\eta _{_B}}{S}$$ η B S ) by proposing two models of f(Q, C) with the assumption of power-law scale factor for each model and the universe contains perfect fluid throughout. We find that under optimal choice of model parameters, the results of $$\frac{\eta _{_B}}{S}$$ η B S of propose models in f(Q, C) are compatible with the observational bound. The crux of the current work is that the outcomes of our propose models for generalized case of gravitational baryogenesis are consist with its observational constraint in different eras of the Universe.

Abstract Anion exchange membrane water electrolysis (AEMWE) has seen rapid advancements over the past decade due to its promising role in green hydrogen production. Ensuring long-term functionality is as crucial as optimizing performance to achieve commercial viability and industrial integration. However, few studies have systematically discussed the degradation issues of this technology. Therefore, a thorough understanding of AEMWE degradation is needed to guide the design, assembly, operation, and maintenance of the device over its lifetime. To address this gap, this review systematically overviewed the heterogeneous degradation of AEMWE across different material and interface levels, focusing on several key components including catalysts, ionomers, membranes, and gas diffusion layers. The influences of these components and their interfaces on the catalytic efficiency, active site density, and mass and electron transfer capabilities were discussed. Moreover, the impacts of operation conditions, including temperature, electrolyte composition, and clamping pressure, on the stable operation of AEMWE were assessed. Accordingly, current mitigation strategies to resolve these degradation phenomena were rigorously evaluated. By offering insights into optimizing operations, designing materials, and improving assessment protocols for AEMWE, this work will contribute to enhancing its stability for large-scale hydrogen production.

Abstract Background Trifarotene is effective for treating acne and other skin issues. To ensure its quality and meet regulatory standards, a reverse phase liquid chromatography (RP-LC) stability-indicating method was developed and validated for its quantification in pure and topical dosage forms. An isocratic elution chromatographic method was employed, using an octadecylsilyl silica gel-packed column (150 mm × 4.6 mm, 3 µm particle size). The mobile phase was a mixture of phosphate buffer and acetonitrile (40:60 v/v). Chromatographic conditions included a flow rate of 0.5 mL/min, column temperature of 40 °C, detection at 265 nm, and injection volume of 20 µL. Results The developed analytical method reports the retention time of trifarotene 11.2 min, higher theoretical plate count, asymmetric peak, and good resolution between the peaks of trifarotene, phenoxyethanol, and environmentally generated impurities. Conclusion The analytical method has been found to be linear, accurate, robust, specific, and selective for impurities produced during forced degradation studies. The proposed analytical method can be utilized for routine pharmaceutical analysis of trifarotene to judge its quality and safety.

Abstract QSPR mathematically links physicochemical properties with the structure of a molecule. The physicochemical properties of chemical molecules can be predicted using topological indices. It is an effective method for eliminating costly and time-consuming laboratory tests. We established a QSPR between mev-degree and mve-degree-based indices and the physical properties of benzenoid hydrocarbons. To compute these indices, we designed a program using Maple software and the correlation between indices and physical properties was developed using the SPSS software. Our study reveals that the mve-degree-based sum-connectivity $$(\chi ^{mve})$$ ( χ mve ) and atom bond connectivity ( $$ABC^{mve}$$ A B C mve ) index, mev-degree-based Randić ( $$R^{mev}$$ R mev ) and Zagreb ( $$M^{mev}$$ M mev ) index are the three most significant parameters and have good prediction ability for the physicochemical properties. We examined that $$R^{mev}$$ R mev predicts the molar refractivity and boiling point, $${\chi }^{mve}$$ χ mve predicts the LogP and enthalpy, $$ABC^{mve}$$ A B C mve predicts the molecular weight, $$M^{mev}$$ M mev predicts the Gibb’s energy, Pie-electron energy and Henry’s law. Moreover, we computed the indices for the linear [n]-phenylen.

Abstract Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject’s data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be $$26\%$$ 26 % higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique.

Abstract Transportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Abstract This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.

Abstract This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding $$96.5\%$$ 96.5 % . This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.

Abstract The demand for modern electronics and semiconductors has increased throughout the years, which has enabled the innovation and exploration of solution-processed deposition. Solution-based processes have gained a lot of interest due to the low-cost fabrication and the large fabrication areas without the need for high-vacuum equipment. In this study, we utilized the ZnO ink for inkjet printer ink to fabricate a thin film via Electrohydrodynamic printing. Three different ink solutions were prepared for experimentation. The EHD printing technique demonstrated the ink’s compatibility with and without the modifications. The outcomes of the EHD printed materials were comparable with the spin-coated thin films. The EHD-printed films demonstrated better results in comparison to spin-coated films. Ra and Rq of the EHD film measured at 3.651 nm and 4.973 nm, respectively. It improved the absorbance up to two-fold at 360 nm wavelength and electrical conductivity up to 40% compared to the spin-coated films. Furthermore, the optimization of the printing parameters can lead to the improved morphology and thickness of the EHD thin films.

Abstract Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.

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 This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML – particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors – in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components – including the blue-green-red, multiple, and spatial attentions – in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.

The effects of bottom tunnel junction (TJ) on optoelectronic characteristics of green light-emitting diodes are analyzed numerically. Our proposed LED has enhanced internal quantum efficiency (IQE), radiative recombination rate, and carrier concentration in comparison to reference conventional LED. The IQE increases by 17%, droop is reduced by 28%, and emission intensity is nearly twice as high as than reference design. The improved optical properties of the proposed structure are due to excellent carrier confinement and altered spontaneous polarization in the vicinity of the active region by TJ.

Nickel-Titanium alloys or Nitinol shape memory alloys have applications in various fields like biomedical, agriculture, pharmaceutical, civil, and mechanical engineering. Ferrous-based shape memory alloys (Fe-SMAs) do not possess excellent properties like that of Nitinol. In recent times, Fe-SMAs have been under consideration in the research field to enhance their shape memory properties because of their cost-effectiveness, corrosion resistance, biocompatibility, and high mechanical strength. Fe-SMAs are limited in their applications in the human body due to high austenite start temperature () even though they are biocompatible and being considered for temporary biodegradable implant applications. Several factors affect the phase transformation temperature of Fe-SMA, like alloy composition and heat treatment conditions. Some of the techniques that are useful to reduce the are quenching, solid solution treatment, ternary element addition, alloying, and thermal-mechanical treatment. In this study, the quenching with brine solution is used to reduce of Fe-15Mn-10 Cr-8Ni-4Si (wt. %). The phase transformation is observed using Differential Scanning Calorimetry (DSC). The best result from experimentation of all the samples collected as reduced from 93oC to 39.02oC which is very close to the temperature of the human body. The results of this study exhibit significant potential for advancement in the application of Fe-SMAs in the biomedical field for permanent implants in cardiovascular and orthopaedic applications.

The purpose of this paper was to investigate the impact of tax audit on tax compliance and examine the moderating effect of religiosity on the associations between tax audit and tax compliance in Pakistan. In this study, 400 questionnaires were distributed by using convenience sampling to actual as well as potential taxpayers of Pakistan, after which a total of 247 useable questionnaires were deemed suitable for analysis. Partial least squares structural equation modelling (PLS SEM) was devised using SmartPLS 3 to validate the measurement model and structural model and the predictive relevance of the study’s model. The findings showed that tax audit was positively associated with the level of tax compliance. They also demonstrated significant moderating effect of religiosity on the relationship between tax audit and tax compliance. Tax authorities and policymakers in developing majority societies in developing countries and in other South Asian countries, especially in Pakistan may use the results to focus their interest on the formulation of policies founded on the outcomes of the study to strengthen revenue collection. This study extended the deterrence theory in the context of tax compliance by studying the effect of tax audit on tax compliance as well as the proposed moderating effect of religiosity in the purview of deterrence theory on tax compliance. The study explored the said relationships among actual as well as potential taxpayers in socio-cultural context of Pakistan. Moreover, the suitability for the use of PLS-SEM as a statistical tool in investigating the above-mentioned relationships was also discussed.

This paper investigates the performance of deep neural network (DNN)-based signal detection in multiple input, multiple output (MIMO), communication systems. MIMO technology plays a critical role in achieving high data rates and improved capacity in modern wireless communication standards like 5G. However, signal detection in MIMO systems presents significant challenges due to channel complexities. This study conducts a comparative analysis of signal detection techniques within both the single input, single output (SISO), and MIMO frameworks. The analysis focuses on the entire transmission chain, encompassing transmitters, channels, and receivers. The effectiveness of three traditional methods—maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF)—is meticulously evaluated alongside a novel DNN-based approach. The proposed study presents a novel DNN-based signal detection model. While this model demonstrates superior computational efficiency and symbol error rate (SER) performance compared to more conventional techniques like MLD, MMSE, and ZF in the context of a SISO system, MIMO systems face some challenges in outperforming the conventional techniques specifically in terms of computation times. This complexity of MIMO systems presents challenges that the current DNN design has yet to fully address, indicating the need for further developments in wireless communication technology. The observed performance difference between SISO and MIMO systems underscores the need for further research on the adaptability and limitations of DNN architectures in MIMO contexts. These findings pave the way for future explorations of advanced neural network architectures and algorithms specifically designed for MIMO signal-processing tasks. By overcoming the performance gap observed in this work, such advancements hold significant promise for enhancing the effectiveness of DNN-based signal detection in MIMO communication systems.

Abstract Drought stress (DS) is a significant abiotic stress that limits agricultural productivity worldwide. In semi-arid climates, one potential solution to alleviate the deleterious effects of drought is the use of soil amendments such as nanoparticles. The current research was conducted out to probe the sway of drought at critical growth stages (CGS) of wheat crop (D0: Control, D1: Drought at tillering stage, and D2: Drought at anthesis stage) and the application of Cu-nanoparticles (T0: 0 mg L−1, T1: 300 mg L−1, T2: 700 mg L−1, and T3: 950 mg L−1) in order to improve drought resilience. Results of the study revealed that DS considerably decreased the wheat growth and yield during CGS. However, Cu-nanoparticles application alleviated the detrimental backlash of DS and led to improvements in various aspects of wheat growth and yield, including plant height, spike length, 1000 grain weight, stomatal conductance, leaf chlorophyll content, water use efficiency, leaf turgor potential, relative water content, and ultimately the grain yield. The use of principal component analysis allowed us to integrate and interpret the diverse findings of our study, elucidating the impact of Cu-nanoparticle treatment on wheat growth and yield under drought. Overall, the study concluded that DS during the anthesis stage had the most significant negative impact on crop yield. However, applying Cu-nanoparticles at the rate of 300 mg L−1 proved to be an effective strategy for improving crop productivity by reducing the harmful effects of drought.

Abstract The control charts are frequently employed in process monitoring to assess the average and variability of a process, assuming a normal distribution. However, it is worth noting that some process distributions tend to exhibit a positively skewed distribution, such as the lognormal distribution. This article proposed a maximum exponential weighted moving average control chart for joint monitoring of mean and variance under a lognormal process. The proposed control chart is evaluated by using the run length profile such as ARL and SDRL. The Monte Carlo simulation is conducted by using the R language to find the run length profile. An application is presented to demonstrate the design of the proposed control chart.

The prevalence of cotton leaf curl disease (CLCuD) has significantly hampered chili (Capsicum spp.) production, presenting a formidable challenge in Pakistan. During a chili field survey in 2018, distinct symptoms, including stunted growth, yellowing, and severe leaf curling, were observed on several plants. Subsequently, a comprehensive sampling effort was undertaken, collecting a total of 39 symptomatic samples from diverse locations across Multan, Punjab. The DNA extraction from these samples was conducted at the plant virology laboratory at Bahauddin Zakariya University, Multan, marking a crucial step in the investigation of this debilitating disease and its impact on chili production in the region. Molecular analysis with PCR using Av/Ac Core, Beta 01/02, and CLCuMuBF11/R33 primers confirmed begomovirus infection in chili plants. Positive amplification demonstrated a 71.79% infection rate, with 579 bp, 1.4 kb, and 481 bp amplicons for Av/Ac Core, Beta 01/02, and CLCuMuBF11/R33, respectively. Sequencing identified cotton leaf curl Multan beta-satellite (MT668934) infecting the chili plant. Effectively managing these begomoviruses is crucial to curbing their multiplication and protecting vital crops like chili. Addressing the distributions of beta-satellites in agricultural fields, particularly chili, is imperative to prevent further viral spread.

Abstract An electrocardiogram is a medical examination tool for measuring different patterns of heart blood flow circle either in the form of usual or non-invasive patterns. These patterns are useful for the identification of morbidity condition of the heart especially in certain conditions of heart abnormality and arrhythmia. Myocardial infarction (MI) is one of them that happened due to sudden blockage of blood by the cause of malfunction of heart. In electrocardiography (ECG) intensity of MI is highlighted on the basis of unusual patterns of T wave changes. Various studies have contributed for MI through T wave’s classification, but more to the point of T wave has always attracted the ECG researchers. Methodology. This Study is primarily designed for proposing the combination of latest methods that are worked for the solutions of pre-defined research questions. Such solutions are designed in the form of the systematic review process (SLR) by following the Kitchen ham guidance. The literature survey is a two phase’s process, at first phase collect the articles that were published in IEEE Xplore, Scopus, science direct and Springer from 2008 to 2023. It consist of steps; the first level is executed by filtrating the articles on the basis of keyword phase of title and abstract filter. Similarly, at two level the manuscripts are scanned through filter of eligibility criteria of articles selection. The last level belongs to the quality assessment of articles, in such level articles are rectified through evaluation of domain experts. Results. Finally, the selected articles are addressed with research questions and briefly discuss these selected state-of-the-art methods that are worked for the T wave classification. These address units behave as solutions to research problems that are highlighted in the form of research questions. Conclusion and future directions. During the survey process for these solutions, we got some critical observations in the form of gaps that reflected the other directions for researchers. In which feature engineering, different dependencies of ECG features and dimensional reduction of ECG for the better ECG analysis are reflection of future directions.

Background: Measles has been a significant public health concern in Pakistan, especially in the Khyber Pakhtunkhwa (KPK) province, where sporadic and silent epidemics continue to challenge existing control measures. This study aimed to estimate the prevalence and investigate the molecular epidemiology of the measles virus (MeV) in KPK and explore the vaccination status among the suspected individuals. Methods: A cross-sectional study was conducted between February and October 2021. A total of 336 suspected measles cases from the study population were analyzed for IgM antibodies using Enzyme-Linked Immunosorbent Assay (ELISA). Throat swabs were randomly collected from a subset of positive cases for molecular analysis. Phylogenetic analysis of MeV isolates was performed using the neighbor-joining method. The vaccination status of individuals was also recorded. Results: Among the suspected participants, 61.0% (205/336) were ELISA positive for IgM antibodies, with a higher prevalence in males (64.17%) compared to females (57.04%). The majority of cases (36.0%) were observed in infants and toddlers, consistent with previous reports. The majority of IgM-positive cases (71.7%) had not received any dose of measles vaccine, highlighting gaps in vaccine coverage and the need for improved immunization programs. Genetic analysis revealed that all MeV isolates belonged to the B3 genotype, with minor genetic variations from previously reported variants in the region. Conclusion: This study provides valuable insights into the genetic epidemiology of the MeV in KPK, Pakistan. The high incidence of measles infection among unvaccinated individuals highlights the urgency of raising awareness about vaccine importance and strengthening routine immunization programs.

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

IntroductionGlioblastoma is a grade IV solid brain tumor and has a 15-month survival rate even after treatment. Glioblastoma development is heavily influenced by retinoblastoma protein (pRB) pathway changes. The blood–brain barrier, drug resistance, and severe toxicity of Temozolamide are key obstacles in treating glioblastoma. Innovative treatments targeting the pRB pathway with efficient delivery vehicles are required to treat glioblastoma.MethodsFor this purpose, a library of 691 plant extracts previously tested in vitro for anti-cancerous, anti inflammatory, and anti-proliferative characteristics was created after thorough literature investigations. Compounds were docked against pRB pathway protein ligands using molecular operating environment and chimera. Their nuclear structure and drug-like properties were predicted through Lipinski rule and density functional theory analysis. Physio-chemical characterizations of naked and drug-encapsulated SLNPs assessed size, stability, entrapment efficiency, and drug release rate. Anti-cancer potential of drug and drug- loaded SLNPs was evaluated using U87, U251, and HEK cell lines. Formulations were tested for cancer cell metastatic potential using cell migration assays.ResultsSilymarin (Sil) was identified as the most potent compound against CDK4, which was then encapsulated in stearic acid solid lipid nanoparticles (SLNP-Sil). Sil showed decreased cell viability 72 h after treatment against both U87 and U251 cell lines but had negligible cytotoxic effect on HEK-293. IC50 value of Sil was 155.14 µM for U87 and 195.93 µM for U251. Sil and SLNP-Sil effectively inhibited U87 and U251 cell migration 24 h after treatment.DiscussionOur results indicated that Sil and SLNP-Sil are promising therapeutic approaches against glioblastoma and merit in vivo experimental verification using orthotropic xenograft mouse models against glioblastoma.

Volvariella volvacea, commonly referred to as paddy straw mushroom, is renowned for its remarkable medicinal and nutritional properties. This mushroom, part of the family Pluteaceae, thrives in tropical and subtropical regions and is highly esteemed for its distinctive flavor and substantial health benefits. The fruiting body of V. volvacea is a rich source of bioactive compounds, including antioxidant enzymes, terpenes, polypeptides, sugars, phenolics, and flavonoids. These compounds exhibit an extensive range of therapeutic activities such as anti-tumor, anti-microbial, antioxidant, anti-malarial, anti-cancer, anti-inflammatory, and anti-allergic effects. Nutritionally, V. volvacea is an excellent source of carbohydrates, proteins, fibers, ascorbic acid, and essential minerals. It also boasts a comprehensive profile of amino acids, including valine, arginine, glutamine, serine, aspartic acid, leucine, isoleucine, tyrosine, asparagine, lysine, cystine, proline, glycine, tryptophan, methionine, phenylalanine, threonine, and histidine. This review emphasizes the significant medicinal and nutritional potential of V. volvacea, advocating its inclusion as a functional food to enhance human health and well-being. By highlighting its diverse bioactive compounds and therapeutic benefits, this review aims to foster greater recognition and utilization of paddy straw mushroom in both dietary and medicinal applications.

This paper investigates the behavior of fractional-order nonlinear multi-agent systems subjected to Byzantine assaults, specifically focusing on the manipulations of both sensors and actuators. We employ weighted graphs, both directed and undirected, to illustrate the system's topology. Our methodology combines algebraic graph theory with fractional-order Lyapunov techniques to develop algebraic requirements for leader-following consensus, providing a robust framework for analyzing consensus dynamics in these complex systems. We present quantitative results demonstrating the effectiveness of our approach, including two numerical examples that validate the proposed requirements for consensus evaluation. Notably, our work highlights the novelty of using fractional-order systems to enhance resilience against adversarial conditions, contributing significantly to the field of multi-agent systems. By clarifying key terms and streamlining our language, we ensure accessibility for a broader audience while emphasizing the implications of our findings for real-world applications.

The optimization of the pyrolysis of the spruce sawdust was conducted based on the yield, textural properties, and static capture capacity of the chars to generate a new micro porous char for biogas upgrade. The nature of the precursor (C: 45.68 %, H: 6.08 %, N: 0.15 %, and 0.34 % ash content) helped to tune the porosity of the char for capturing CO2 from gas streams and biogas. The optimization of the pyrolysis at 700 °C, a heating rate of 10 °C / min and holding time of 1 hour, produced a microporous char with high yield, CO2 capture capacity and CO2/CH4 selectivity. The presence of the oxygen and nitrogen-containing functional groups in the optimum char help in the selective capture of CO2. The static CO2 capture capacity of the optimum char was 1.98 mmol/g at atmospheric pressure and 25 °C (90 % CO2 stream). The CSD (700−10−60) have capture capacity of 1.50 mmol/g (at a feed gas stream of 50 % CO2 and adsorption temperature of 25 °C and 120 kPa) which is equivalent to Norit C and Calgon BPL. Hence, spruce sawdust can be used to produce efficient biochar for biogas upgrade (CO2 capture) owing to high capture capacity and CO2/CH4 selectivity (around 3) and easy regeneration of the biochar for 10 consecutive cycles.