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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.

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.

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.

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts—with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks—significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Abstract Introduction Acne vulgaris, a chronic inflammatory condition, is associated with significant physical and psychosocial burden. Since 2019, three new topical agents for acne vulgaris have been approved in the USA and Canada. We performed a systematic review and meta-analysis to compare the efficacy between twice-daily clascoterone cream 1%, once-daily trifarotene 0.005% cream, and once-daily tazarotene 0.045% lotion for acne treatment. Methods Randomized controlled trials (RCTs) comparing clascoterone, trifarotene, or tazarotene with vehicle in patients with moderate-to-severe acne were identified from a systematic literature review and included in a meta-analysis. Primary outcomes were percentage reduction in inflammatory and noninflammatory lesion count (ILC and NILC, respectively) and treatment success rate (≥ 2-grade improvement in Investigator’s Global Assessment or Evaluator’s Global Severity Score and a rating of clear or almost clear) at week 12. DerSimonian and Laird random-effects models with the inverse variance method were used to calculate the mean difference (MD) for percentage reduction in ILC and NILC, and odds ratios (ORs) for the rate of treatment success. Results Six Phase 3 RCTs were included in the meta-analysis. The analyses showed robust differences favoring the interventions for ILC (MD: − 11.5; 95% confidence interval [CI]: − 14.39, − 8.62), NILC (MD: − 12.25; 95% CI: − 15.21, − 9.29), and treatment success rate (OR: 2.14; 95% CI: 1.81, 2.53). No differences were observed between clascoterone, trifarotene, and tazarotene for ILC (MD: − 12.8, − 11.2, and − 10.1, respectively), NILC (MD: − 11.6, − 13.9, and − 12.8, respectively), or treatment success rate (OR: 2.9, 1.9, and 2.1, respectively (all P > 0.05). Conclusion No significant differences in efficacy were observed between clascoterone, trifarotene, and tazarotene after 12 weeks of treatment in patients with moderate-to-severe acne. Differences in application frequency and safety profile should also be taken into consideration when making treatment decisions.

Abstract The utilization of the Lie group method serves to encapsulate a diverse array of wave structures. This method, established as a robust and reliable mathematical technique, is instrumental in deriving precise solutions for nonlinear partial differential equations (NPDEs) across a spectrum of domains. Its applications span various scientific disciplines, including mathematical physics, nonlinear dynamics, oceanography, engineering sciences, and several others. This research focuses specifically on the crucial molecule DNA and its interaction with an external microwave field. The Lie group method is employed to establish a five-dimensional symmetry algebra as the foundational element. Subsequently, similarity reductions are led by a system of one-dimensional subalgebras. Several invariant solutions as well as a spectrum of wave solutions is obtained by solving the resulting reduced ordinary differential equations (ODEs). These solutions govern the longitudinal displacement in DNA, shedding light on the characteristics of DNA as a significant real-world challenge. The interactions of DNA with an external microwave field manifest in various forms, including rational, exponential, trigonometric, hyperbolic, polynomial, and other functions. Mathematica simulations of these solutions confirm that longitudinal displacements in DNA can be expressed as periodic waves, optical dark solitons, singular solutions, exponential forms, and rational forms. This study is novel as it marks the first application of the Lie group method to explore the interaction of DNA molecules.

Abstract A comprehensive mathematical model is proposed to study two strains of dengue virus with saturated incidence rates and quarantine measures. Imperfect dengue vaccination is also assumed in the model. Existence, uniqueness and stability of the proposed model are proved using the results from fixed point and degree theory. Additionally, well constructed Lyapunov function candidates are also applied to prove the global stability of infection-free equilibria. It is also demonstrated that the model is generalized Ulam-Hyers stable under some appropriate conditions. The model is fitted to the real data of dengue epidemic taken from the city of Espirito Santo in Brazil. For the approximate solution of the model, a non-standard finite difference(NSFD) approach is applied. Sensitivity analysis is also carried out to show the influence of different parameters involved in the model. The behaviour of the NSFD is also assessed under different denominator functions and it is observed that the choice of the denominator function could influence the solution trajectories. Different scenario analysis are also assessed when the reproduction number is below or above one. Furthermore, simulations are also presented to assess the epidemiological impact of dengue vaccination and quarantine measures for infected individuals.

Abstract Large amount of wastes are burnt or left to decompose on site or at landfills where they cause air pollution and nutrient leaching to groundwater. Waste management strategies that return these food wastes to agricultural soils recover the carbon and nutrients that would otherwise have been lost, enrich soils and improve crop productivity. The incorporation of liming materials can neutralize the protons released, hence reducing soil acidity and its adverse impacts to the soil environment, food security, and human health. Biochar derived from organic residues is becoming a source of carbon input to soil and provides multifunctional values. Biochar can be alkaline in nature, with the level of alkalinity dependent upon the feedstock and processing conditions. This study conducted a characterization of biochar derived from the pyrolysis process of eggplant and Acacia nilotica bark at temperatures of 300 °C and 600 °C. An analysis was conducted on the biochar kinds to determine their pH, phosphorus (P), as well as other elemental composition. The proximate analysis was conducted by the ASTM standard 1762-84, while the surface morphological features were measured using a scanning electron microscope. The biochar derived from Acacia nilotica bark exhibited a greater yield and higher level of fixed carbon while possessing a lower content of ash and volatile components compared to biochar derived from eggplant. The eggplant biochar exhibits a higher liming ability at 600 °C compared to the acacia nilotica bark-derived biochar. The calcium carbonate equivalent, pH, potassium (K), and phosphorus (P) levels in eggplant biochars increased as the pyrolysis temperature increased. The results suggest that biochar derived from eggplant could be a beneficial resource for storing carbon in the soil, as well as for addressing soil acidity and enhancing nutrients availability, particularly potassium and phosphorus in acidic soils.

Abstract Phosphorus (P) is a crucial macronutrient for plant growth and development. Basic metabolic processes regulate growth; however, the molecular detail of these pathways under low phosphorous (LP) in wheat is still unclear. This study aims to elucidate the varied regulatory pathways responses to LP stress in wheat genotypes. Phenotypic, physiological, and transcriptome analyses were conducted on Fielder (P efficient) and Ardito (P inefficient) wheat genotypes after four days of normal phosphorous (NP) and LP stress. In response to LP, Fielder outperformed Ardito, displaying higher chlorophyll content-SPAD values (13%), plant height (45%), stem diameter (12%), shoot dry weight (42%), and root biomass (75%). Root structure analysis revealed that Fielder had greater total root length (50%), surface area (56%), volume (15%), and diameter (4%) than Ardito under LP. These findings highlight Fielder’s superior performance and adaptation to LP stress. Transcriptome analysis of wheat genotype roots identified 3029 differentially expressed genes (DEGs) in Fielder and 1430 in Ardito, highlighting LP-induced changes. Key DEGs include acid phosphatases (PAPs), phosphate transporters (PHT1 and PHO1), SPX, and transcription factors (MYB, bHLH, and WRKY). KEGG enrichment analysis revealed key pathways like plant hormones signal transduction, biosynthesis of secondary metabolites, and carbohydrate biosynthesis metabolism. This study unveils crucial genes and the intricate regulatory process in wheat’s response to LP stress, offering genetic insights for enhancing plant P utilization efficiency.

Abstract Crop residue-based biochar (CRB) has shown great potential for removing trace elements (TEs) from aquatic matrices. Despite the increasing interest in this area, no review has focused specifically on the efficacy of CRB for TEs removal in aquatic environments. This comprehensive review examines the global TEs water contamination status with an emphasis on their sources, compositional metrics for crop residue feedstock (proximate, ultimate, and lignocellulosic properties), and the potential use of CRB for TEs removal in aquatic media. It also evaluates the factors that affect the ability of CRB to remove TEs, such as feedstock type, production conditions, water pH, background electrolytes, water temperature, CRB/water ratio, and underlying pollutant sorption mechanisms. This review also discusses the practical applications of CRB in real water samples and engineering considerations for designing CRB with improved physicochemical properties, treatment efficiencies, and regeneration abilities. Additionally, the cost–benefit and economic assessment of CRB, challenges, and future research directions related to CRB are highlighted to promote research on this sustainable source of biochar. By elucidating the prospects of CRB as an adsorbent, this review emphasizes the need for continued research on its practical implications for environmentally relevant pollutant concentrations. Graphical Abstract

Abstract The use of saline water under drought conditions is critical for sustainable agricultural development in arid regions. Biochar is used as a soil amendment to enhance soil properties such as water-holding capacity and the source of nutrition elements of plants. Thus, the research was carried out to assess the impact of biochar treatment on the morphological and physiological characteristics and production of Solanum lycopersicum in greenhouses exposed to drought and saline stresses. The study was structured as a three-factorial in split-split-plot design. There were 16 treatments across three variables: (i) water quality, with freshwater and saline water, with electrical conductivities of 0.9 and 2.4 dS m− 1, respectively; (ii) irrigation level, with 40%, 60%, 80%, and 100% of total evapotranspiration (ETC); (iii) and biochar application, with the addition of biochar at a 3% dosage by (w/w) (BC3%), and a control (BC0%). The findings demonstrated that salt and water deficiency hurt physiological, morphological, and yield characteristics. Conversely, the biochar addition enhanced all characteristics. Growth-related parameters, such as plant height, stem diameter, leaf area, and dry and wet weight, and leaf gas exchange attributes, such rate of transpiration and photosynthesis, conductivity, as well as leaf relative water content were decreased by drought and salt stresses, especially when the irrigation was 60% ETc or 40% ETc. The biochar addition resulted in a substantial enhancement in vegetative growth-related parameters, physiological characteristics, efficiency of water use, yield, as well as reduced proline levels. Tomato yield enhanced by 4%, 16%, 8%, and 3% when irrigation with freshwater at different levels of water deficit (100% ETc, 80% ETc, 60% ETc, and 40% ETc) than control (BC0%). Overall, the use of biochar (3%) combined with freshwater shows the potential to enhance morpho-physiological characteristics, support the development of tomato plants, and improve yield with higher WUE in semi-arid and arid areas.

Abstract Environmental behavior needs a keen intervention to avoid the upcoming worse outcomes of the energy utilization. Global warming is transitioning towards the global boiling stage and the reason behind the faster growth of renewable energy resources exploration. The present study empirically investigates the circularity impact of the renewable energy on the environment. Two different models of the environment were estimated by applying biased corrected method on 28 low income countries from 1990 to 2019. In this study, we develop a circularity index on the basis of renewable energy sources and found that in low income economies circularity brings a positive impact on the environment in both models. The study also concludes that digitization has also positive implication in terms of environmental quality. In the sample study trade shows negative as well as positive association with environment while economic growth, financial development and energy use have positive impact on the environment. The study suggested policies based on the circular practices for energy generation to achieve the sustainable development goals.

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

In recent years, numerous articles documenting bioactive components derived from diverse food sources have been published. Plant-based bioactive substances hold significant prospects for use as dietary supplements and functional foods because of their potential advantages for human health as antimicrobial, anticancer, anti-inflammatory, and antioxidant agents. Utilizing plant by-products as raw materials can also lower production costs and lessen environmental impacts. Thus, this review covered the bioactive substances found in plants and their by-products. The health benefits of bioactive compounds obtained from plant origins were also highlighted in this review. Furthermore, we concentrated on both conventional extraction techniques (e.g., Soxhlet, heat reflux, and maceration) and innovative extraction strategies for bioactive substances, including pulsed electric field (PEF), pressurized liquid, microwave-assisted, ultrasonic-assisted, and subcritical fluid methods. Higher yields obtained by novel extraction methods were found to be of primary interest, considering immediate beneficial economic outcomes. The potential applications of those bioactive substances in the food industry have been studied. Additionally, this investigation handled concerns regarding the challenges and limitations related to bioactive compounds. It is anticipated that the information covered in this review will prove to be a useful resource for the plant food processing sector in suggesting a cost-effective and environmentally friendly extraction technique that would turn plant wastes into a functional product with a high added value.

The coffee business especially in the coffee shop sector to be superior to the competition is something that must be carried out in a sustainable manner by PT XYZ. Realizing this goal, PT XYZ must formulate its business development strategy. This study aims to determine internal factors (strengths and weaknesses) and external factors (opportunities and threats) which are used as the basis for formulating coffee shop business development strategies. The basic research method is descriptive with data collection techniques through in-depth interviews, focus group discussions (FGD), observation, documentation. Information was obtained through key informants who were determined purposively with the consideration of knowing the coffee shop business, namely directors, managers, supervisors, baristas, consumers, competitors, suppliers and coffee experts. Data analysis uses internal factor evaluation matrix, external factor evaluation, Grand Strategy matrix, SWOT matrix, and participatory prospective analysis. The results of the study show that the position of the grand strategy of PT XYZ is in quadrant I, which means that the strategy concentrates on market and product sustainability, product diversification, and aggressiveness. The main strategy prioritized based on the driving strategy is to increase the effectiveness and efficiency of departmental performance and improve the quality of human resources.

Objectives The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.

Abstract Biochar is a promising solution to alleviate the negative impacts of salinity stress on agricultural production. Biochar derived from food waste effect was investigated on three plant species, Medicago sativa, Amaranthus caudatus, and Zea mays, under saline environments. The results showed that biochar improved significantly the height by 30%, fresh weight of shoot by 35% and root by 45% of all three species compared to control (saline soil without biochar adding), as well as enhanced their photosynthetic pigments and enzyme activities in soil. This positive effect varied significantly between the 3 plants highlighting the importance of the plant-biochar interactions. Thus, the application of biochar is a promising solution to enhance the growth, root morphology, and physiological characteristics of plants under salt-induced stress.

Abstract Resveratrol, a natural polyphenol in various plants, has gained significant attention for its potential health‐promoting properties. It has been demonstrated, after reviewing various clinical and in vitro studies, that resveratrol possesses potent antioxidant potential. Resveratrol demonstrates cellular component protection by directly neutralizing free radicals (FRs) and enhancing the expression of natural antioxidant enzymes, thereby mitigating oxidative damage to proteins, lipids, and nucleic acids. Clinical trials have shown promising results, indicating that resveratrol supplementation can enhance antioxidant defenses and reduce oxidative damage markers in various populations. In addition to its antioxidant effects, resveratrol exhibits potent anti‐inflammatory properties. It can modulate key inflammatory pathways, such as nuclear factor‐kappa B (NF‐κB) and mitogen‐activated protein kinases (MAPKs), thereby suppressing the production of pro‐inflammatory cytokines and chemokines. Furthermore, resveratrol's multimodal effects extend beyond its antioxidant and anti‐inflammatory properties. It has been discovered to exert regulatory effects on various cellular processes, including apoptosis, cell cycle progression, angiogenesis, and immunological responses. The primary aim of this review paper is to provide a thorough overview of the current knowledge on resveratrol, including its chemical composition, bioaccessibility, clinical effectiveness, and utilization in nanotechnology to enhance its bioavailability. From future perspectives, revising the administration methods for certain contexts and understanding the underlying systems responsible for resveratrol's effects will require further inquiry. For the highest potential health results, advanced trial‐based research is necessary for combinational nano‐delivery of resveratrol.

Abstract Diabetes mellitus (DM) is a chronic metabolic disorder characterized by an elevated level of blood glucose due to the absence of insulin secretion, ineffectiveness, or lack of uptake of secreted insulin in the body. The improperly diagnosed and poorly managed DM can cause severe damage to organs in the body like the nerves, eyes, heart, and kidneys. This study was aimed at investigating the effect of Clostridium butyricum (probiotic) with magnesium supplementation to evaluate the effect on gut microbial dysbiosis and blood glucose levels. In the laboratory, 6–8 weeks old 24 male albino rats weighing 200–250 g were given free access to water and food. Diabetes was induced using streptozotocin (60 mg/kg) in overnight fasted rats. Diabetic rats were randomly divided into four groups (n = 6, 6 replicates in each group). Metformin (100 mg/kg/day) with a standard basal diet was provided to control group (G0), Clostridium butyricum (1.5 × 105 CFU/day) with standard basal diet was provided to treatment group (G1), magnesium (500 mg/kg/day) was provided to group (G2). Clostridium butyricum (1.5 × 105 CFU/day) and magnesium (300 mg/kg/day) in combination with a standard basal diet was provided to group (G3). Blood Glucose, Magnesium blood test and microbial assay were done. Random blood glucose levels were monitored twice a week for 21 days and were represented as mean of each week. The results conclude that Clostridium butyricum (1.5 × 105 CFU) is very effective in balancing random blood glucose levels from 206.6 ± 67.7 to 85.1 ± 3.8 (p = 0.006) compared to other groups (p > 0.005). The results of stool analysis showed that Clostridium butyricum as probiotic restores microbial dysbiosis as evident by the 105 CFU Clostridium butyricum load in G1, which was higher than G0, G2 and G3 which were 103 and 104 CFU respectively. The findings of this study conclude that Clostridium butyricum supplementation improved blood glucose levels and intestinal bacterial load in type II diabetes mellitus.

Abstract With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

Smart contracts are becoming increasingly popular for managing transactions or activities in fog computing environments. However, the use of smart contracts for registration and resource access granting is vulnerable to various types of attacks that can compromise their security. Detecting these attacks can be challenging, as attackers can use sophisticated techniques to evade detection. This research uses a machine learning-based approach for detecting different attacks on smart contracts used for registration and resource access granting in fog computing. Data is collected from online Ethereum’s official site “etherscan.io”. Different feature extraction methods and machine learning models are tested. Using accuracy, precision, recall, F1 score, cross-validation, and computational time, the performance of models is evaluated. Results indicate that extreme gradient boosting (XGB) and random forest (RF) provide the highest accuracy of 80% using the term frequency-inverse document frequency (TF-IDF) approach. The light gradient boost classifier provides the highest accuracy of 81% with the Bag of Word (BoW) approach. Similarly, the extra tree provides the highest accuracy of 83% using the N-gram technique. Furthermore, performance using TF-IDF is slightly poorer than BoW and N-gram, however, it has less computational complexity.

Introduction:The Rh blood group system is one of the most polymorphic and immunogenic blood group systems in humans. The expression of its antigens is complex, among that Rh-D antigen is the most important antigen because of its high immunogenicity. Molecular genetic of RHD gene revealed that weak D antigen is a Rh-D phenotype that possesses less numbers of D antigen epitopes on surface of red cells. These individuals usually labeled RhD -ve by conventional testing but when transfused to RhD –ve person, it can elicit antibody production. Variableincidence of weak D worldwide,lack of awareness, proper data & multi-ethnic population of our country propelled to analyze it. Material and Methods:A cross-sectional study conducted from August 2012 to August 2014. Around 48,228 healthy blood donors were tested for RhD factor. Commercially available monoclonal anti-D sera were used to detect Rh-D factor status. Individuals found negative with saline anti-D, were further investigated for weak D antigen by using indirect Coomb’s technique (IAT). Results:Among 48,228 healthy blood donors, 44853 (93%) were Rh-D factor positive while 3375 (7%) were Rh-D factor negative. Among these, 3375 Rh-D factor negative individuals 27 (0.8%) were found to be weak D positive. Conclusion: Although frequency of weak D does not came high among our donors but is still significant enough to advocate testing of weak D in routine for all Rh –ve donors & pregnant women in order to avoid consequences of anti-D allo-immunization which can lead to serious hemato-pathological problem.

Depletion of oil and gas resources is a major concern for researchers and the global community. Researchers are trying to develop a way to overcome these issues using the Fischer–Tropsch synthesis (FTS) process. The FTS reaction converts a mixture of hydrogen and carbon monoxide gases into a liquid fuel. The reactions are performed in the reactor and in the presence of a catalyst. A series of catalysts, such as iron, cobalt, nickel, and ruthenium, have been used for the FTS process. In iron-based catalysts, the Fe5C phase is the active phase that produces C5+ hydrocarbons. At higher conversion rates, the presence of water in the products is a problem for cobalt catalysts because it can trigger catalyst deactivation mechanisms. Ni-based catalysts play key roles as base catalysts, promoters, and photothermal catalysts in FTS reactions to produce different useful hydrocarbons. Ruthenium catalysts offer not only high activity but also selectivity toward long-chain hydrocarbons. Moreover, depending on the Ru particle size and interaction with the oxide support, the catalyst properties can be tuned to enhance the catalytic activity during FTS. The detailed reaction pathways based on catalyst properties are explained in this article. This review article describes the issues and challenges associated with catalysts used for the FTS process.

The BBX gene family encodes transcription factors that regulate several biological processes, such as plant growth, development, and stress response. They have been identified and thoroughly studied in numerous plant species. The functional role of the BBX gene family against UV-B stress in soybean has not been well documented. Herein, this study focused on a comprehensive investigation of the GmBBX genes in soybean and their expression patterns in response to UV-B exposure. A total of 184 BBX genes were identified and analyzed to determine the conserved domain. Single and double B-box and CCT domains were found across these proteins, indicating potential functional diversity. The protein physicochemical properties showcased diverse characteristics, highlighting molecular size, stability, and hydrophobicity. Phylogenetic analysis revealed five distinct clades, showcasing a non-uniform distribution of GmBBX genes. Chromosomal mapping showed a non-uniform distribution of GmBBX genes across the 20 chromosomes of soybean. The highest number of genes (26) were found on chromosome 13, whereas chromosomes 5 and 15 displayed the lowest number of genes (3 genes each). Gene structure analysis revealed variations in exon-intron patterns, while motif composition analysis unveiled conserved motifs among GmBBX proteins. Gene duplication indicated that the expansion of the GmBBX genes was linked with tandem and dispersed duplications. Gene ontology analysis revealed enrichment of terms associated with light stimulus, response to abiotic stress, and molecular functions such as zinc ion binding. The promoter region reveals cis-acting elements related to hormones, light, and stress responses. The GmBBX genes exhibited differential expression in response to UV-B stress, suggesting their possible role in plant defense against UV-B stress. This study will provide a solid foundation for further analysis of the molecular mechanisms of GmBBX genes in soybean against UV-B stress.

Stability in process parameters is required to ensure the quality of the finished item. Control charts, as one of the critical parts of statistical process monitoring (SPM), have seen widespread use across many disciplines for detecting and responding to shifts in process parameters. The adaptive EWMA (AEWMA) control chart is a well-known scheme that detects both small and large process shifts by integrating the features of the Shewhart and classical EWMA charts. In this study, we propose an enhanced AEWMA chart, termed the EAEWMA chart, that efficiently monitors small and large process mean shifts simultaneously. The proposed EAEWMA chart enhances the existing AEWMA chart using the shift estimator, based on the hybrid EWMA (HEWMA) statistic. The Monte Carlo simulation approach is employed as the computational method to obtain the numerical findings for the various performance metrics. The EAEWMA chart is compared with various existing charts, including AEWMA, HEWMA, EWMA, ACSUM, and IACCUSUM, in zero- and steady-state scenarios. Conclusively, two practical applications of the EAEWMA chart are presented, demonstrating its value for practitioners and engineers and illustrating its efficacy in real-world scenarios.

The precast industry offers slab panels of different geometries according to the field conditions. These slab panels are popular in temporary constructions and beneficial in sustainability but have some financial limitations and local constraints. For a long time, the construction industry has used the arch action method, which restricts the stresses to the compression zone in concrete members and develops the required load-carrying capacity. For the same motives, industrial buildings have preferred semi-circular precast roofs, but the morphology was not suitable. For the proposed slab in this research, firstly, the typical industrial precast slab panel was doubled in width to minimize the time and efforts required for its casting, curing, and placement. Secondly, that doubled-in-width slab was provided with the arch action to confine the stresses to compression and benefit from the section entirely. Lastly, the top of the slab was kept flat to take advantage of the roof space. All these changes aimed for structural stability, reduced material's weight, improved load-carrying capacity, appropriate mobilization, and financial viability. A numerical approach and practical testing were adopted using the finite element modeling software, ABAQUS, to analyze load–deflection responses of both slabs through the concrete damage plasticity model. The proposed slab exhibited better performance as its capacity enhanced by about 1.5 times that of a typical slab. Although the volume of the material in the proposed slab increased slightly from 0.040 m3 to 0.045 m3, the reductions in joint filler materials, reinforcements, and efforts required for mixing and lifting machinery compensated for this increase significantly. Hence, the slab can be recommended for the industry to save the costs while taking heavier loads efficiently.

Objective: To compare the diagnostic performance of bone scintigraphy and 18F-NaF-PET/CT in detection of bone metastasis in newly diagnosed high-grade prostate cancer patients. Study Design: Comparative cross-sectional study. Place and Duration of Study: Nuclear Medical Centre, Armed Forces Institute of Pathology and Armed Forces Institute of Radiology and Imaging, Rawalpindi Pakistan, from Jul 2020 to Dec 2022. Methodology: Newly diagnosed high-grade prostate cancer patients with Gleason score ≥8 who underwent bone scintigraphy followed by 18F-NaF-PET/CT (Sodium Fluoride-Positron Emission Tomography/Computerized Tomography) for staging during study duration were included. We compared the diagnostic accuracy of both imaging modalities in detecting bone metastasis using two-point scale scoring system i.e., metastasis present or not present on per patient basis. Results: A total of 45 patients with mean age 69.66±9.38 years and mean Prostatic Surface Antigen level 36.21±65.07 ng/ml were included. 18F-NaF-PET/CT detected bone metastasis in all 25 patients with bone metastasis (100%) compared to 20(80%) by bone scan. 4 patients had false-positive bone scan while 2 had false-positive 18F-NaF-PET/CT scan. Overall 18F-NaF-PET/CT demonstrated a higher sensitivity, specificity, positive predictive value, negative predictive value and accuracy compared to bone scan (100% vs 80%, 90% vs 80%, 92.59% vs 83.33%, 100% vs 76.19%, 95.56% vs 80% respectively). Conclusion: 18F-NaF-PET/CT has better diagnostic performance than bone scintigraphy for the detection of bone metastasis in newly diagnosed high-grade prostate cancer patients.