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Increasing incidence of central nervous system (CNS) disorders has become a major challenge for both basic and clinical scientific society to develop novel therapeutic models for treatment. The knowledge of stem cells has added a new dimension in the research towards finding more appropriate targets responsible for the disease fate determination. As stem cell research is progressing day by day in routine research laboratories there is great hope to find suitable therapeutic targets for complete cure of the CNS disorders. Discovery of ABC transporters in animal tissues has emerged as new spot for several disease prognosis and therapeutic target. ABC transporters are membrane proteins expressed in various organs like liver, kidney, blood-brain barrier, blood-testis barrier etc. It is involved in various important cellular processes such as absorption, distribution and excretion of drugs, xenobiotics and endogenous compounds showing their role in tissue defense and organ regeneration. The current review explains about the role of ABC transporters in CNS pathogenesis and defense adding stem cells therapeutic strategies.

Background: The correlation of adiponectin and serum tumor necrosis factor alpha (TNFα) with glucometabolic parameters in diabetes mellitus (DM) needs further studies. We aimed in this study to evaluate the relationship between adiponectin and TNFα with glucometabolic parameters in patients with type 2 DM (T2DM). Methods: We conducted a cross-sectional study in the Department of Physiology, College of Medicine, King Saud University, Saudi Arabia. The sample size was 117 from the diabetes clinic of King Abdul-Aziz University hospital through the convenience sampling technique. Subjects were grouped into control (healthy) subjects (53) with no chronic diseases and the diabetic group (64) with confirmed T2DM. Socio-demographic data were collected along with the serum blood sample to analyze the variables. Results: Adiponectin was significantly high in healthy subjects compared to the diabetic group (control: 14.4 ± 4.3, T2DM: 11.0 ± 4.1, P = 0.000), while TNFα was higher in the T2DM group (7.8 ± 2.7) than in the control group (6.6 ± 2.9, P = 0.024). TNFα was negatively correlated with adiponectin in the control group (-0.279) and in diabetic subjects (-0.311) and positively correlated with HbA1c in the diabetic group (0.319) and triglycerides (0.252). Adiponectin was positively correlated with HDL in the control group (0.252) and in diabetic subjects (0.326). There was an inverse correlation between TNFα and adiponectin. Conclusion: Adiponectin is higher in healthy subjects than in diabetic patients, while TNFα is higher in diabetic patients. In addition, adiponectin is positively correlated with HDL in healthy as well as diabetic patients. TNFα is positively correlated with HbA1c and triglycerides.

The road construction industry aims to contribute to the protection of already compromised environment. Cold mix asphalt (CMA) is a measure initiated by the road industry to protect the environment and preserve energy. Despite having additional benefits, CMA has attracted little attention due to its inferior performance. CMA's performance is enhanced using a sustainable binder bio-modifier, natural cup lump rubber (CLR) is one of them. This study evaluated the tensile properties, rutting, moisture susceptibility, and adhesion properties of CLR-modified CMA (CMA-CR). The tensile property was enhanced by 26% due to CLR modification. CMA-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles, showing 70% improvement compared with conventional CMA. Moisture susceptibility evaluation indicated that CMA-CR had tensile strength ratio (TSR) value of 104%, satisfying the minimum 80% requirement of AASHTO T283. It also retained more than 96% bitumen coating. The moisture damage resistance was improved by 12% and 10% in terms of TSR and stripping, respectively. The durability results revealed that the CMA-CR mixture prevented higher mass loss, representing 14% improvement compared with conventional CMA.

Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions.

Abstract A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

Background Patient-related factors and limited medication adherence in patients with chronic diseases, are associated with poor clinical outcomes, long-term complications, and increased overall disease costs. Many methods have been tested with mixed results, and innovative approaches are needed to encourage patients to adhere to their prescribed drug regimens. Methods This randomised controlled trial examined a new multifactorial pharmacist-led intervention protocol (MPIP), including a medication therapy management (MTM) program with face-to-face counselling, patient-specific medication booklets, and a mobile application, from July 2021 to September 2022 in the Oud Al Touba diagnostic and screening ambulatory centre in 192 patients with type 2 diabetes in the United Arab Emirates. Medication adherence was assessed using the fixed medication possession ratio of medication refills and the medication adherence questionnaire. Results At 12 months follow-up, participants in the MPIP showed significant improvement in overall medication adherence with total (composite) medication possession ratio (MPRt) of mean (±SD) 0.95 (±0.09) compared to 0.92 (± 0.09) in the control group with mean difference of 0.03 (95%, CI 0.01–0.06), P =0.02. In addition, improvement trend was evident in the MPIP group for all medication regimens with P value

In statistical inferences, the estimation of population parameters using information obtained from a sample is an important method. This involves choosing an appropriate sampling method to collect data. An efficient sampling method used for data collection is Ranked Set Sampling (RSS). In this study, we investigate the reproducibility of four well-known mean estimators under RSS using parametric predictive bootstrapping. These estimators are called conventional, ratio, exponential ratio, and regression estimators. Reproducibility is the ability of a statistical technique to obtain results similar to those based on the original experiment if the experiment is repeated under the same conditions. We conduct a simulation study to compare the reproducibility of mean estimators for varying sample sizes when sampling is based on perfect and imperfect rankings. We consider data on abalone in our simulations to demonstrate real-world applications. This study concludes that the regression estimator is the best reproducible estimator, while the conventional estimator is the worst in this regard.

The study aims to examine the advantages of utilizing big data analytics (BDA) on circular economy (CE) dual systems, which are closed-loop supply chain (CLSC) and the reverse omnichannel (ROC), aiming to enhance sustainable firm performance (SFP) of the small and medium based enterprises (SMEs). The product return knowledge (PRK) can reinforce the proposed network of relationships and facilitate the approach of active returns in CE. Using the structural equation model (SEM) in AMOS v24, the researchers examined the hypotheses using data from a simple random sample of 232 SMEs in Pakistan. Though ROC solutions provide rare performance-related benefits, however, BDA makes the CE system more efficient. CLSC gets strengthened when the PRK is developed. The improved PRK facilitates the CLSC network and enhances its performance. This study adopts a novel approach to studying CE by considering the dual system of CE in the forms of CLSC and ROC. The research investigates whether the BDA facilitates improved product return processes by improving CLSC operations and achieving service capabilities using ROC. Finally, the proposed framework is the first to investigate the benefits evolving from the PRK, which enhance the capability to sense, seize, and reconfigure the process and facilitate the approach of active return in CE. The findings suggest that firms must decide carefully between CLSC and ROC. Choosing CLSC requires higher operational capabilities, whereas deciding on ROC requires higher service capabilities. Lastly, PRK is necessary for achieving the firm's performance objectives.

Global warming and endocrine disorders are intertwined issues posing significant challenges. Greenhouse gases emanating from human activities drive global warming, leading to temperature rise and altered weather patterns. South Asia has experienced a noticeable temperature surge over the past century. The sizable population residing in the region heightens the susceptibility to the impact of global warming. In addition to affecting agriculture, water resources, and livelihood, environmental changes interfere with endocrine functioning. Resulting lifestyle changes increase the risk of metabolic and endocrine disorders. Individuals with diabetes face heightened vulnerability to extreme weather due to impaired thermoregulation. A high ambient temperature predisposes to heat-related illnesses, infertility, and nephropathy. Additionally, essential endocrine drugs and medical devices are susceptible to temperature fluctuations. The South Asian Federation of Endocrine Societies (SAFES) calls for collaboration among stakeholders to combat climate change and promote healthy living. Comprehensive approaches, including the establishment of sustainable food systems, promotion of physical activity, and raising awareness about environmental impacts, are imperative. SAFES recommends strategies such as prioritizing plant-based diets, reducing meat consumption, optimizing medical device usage, and enhancing accessibility to endocrine care. Raising awareness and educating caregivers and people living with diabetes on necessary precautions during extreme weather conditions are paramount. The heat sensitivity of insulin, blood glucose monitoring devices, and insulin pumps necessitates proper storage and consideration of environmental conditions for optimal efficacy. The inter-connectedness of global warming and endocrine disorders underscores the necessity of international collaboration guided by national endocrine societies. SAFES urges all stakeholders to actively implement sustainable practices to improve endocrine health in the face of climate change.

Abstract Background Bladder leiomyomas are rare benign growths in the bladder, comprising less than 0.5% of bladder tumors with only 250 cases reported globally. They are more common in women. This case involves a 70-year-old woman with recurrent leiomyoma, presenting with lower urinary tract symptoms and painless hematuria. A recurrent bladder leiomyoma is rarely reported, making its presence exceptionally rare. Case presentation A 70-year-old Pakistani woman with hypertension and diabetes presented with lower urinary tract symptoms (LUTS) and painless hematuria. She had a history of similar symptoms in 2010, leading to a diagnosis of bladder leiomyoma via cystoscopy and biopsy. Imaging studies revealed a substantial 3.7 × 4 × 4.0 cm isodense mass with calcifications at the bladder base, along with bladder wall thickening and diverticula. Pathological examination during Transurethral Resection of Bladder Tumor (TURBT) confirmed the presence of bladder tissue with smooth muscle, ruling out malignancy. Immunohistochemical studies supported the diagnosis. A successful TURBT was performed, and the patient recovered well. Discussion Recurrent bladder leiomyoma is a rarely-discussed topic in medical literature. This article primarily aims to review existing studies and present a detailed case study, shedding light on this rare phenomenon.

Introduction: The commonly used Glasgow Coma Scale (GCS) score for assessing consciousness has several shortcomings, especially in intubated patients. Recently, the Full Outline UnResponsive Score (FOUR) has been validated as an alternative to the GCS due to its additional benefits in evaluating brainstem reflexes and respiratory patterns. The use of the FOUR score can overcome the shortcomings of the GCS and aid in prognosticating patients with altered sensorium. Aim: To compare the FOUR score with GCS score to find a better scoring system for predicting outcomes among children aged 3-12 years with non-traumatic causes of Altered Level Of Consciousness (ALOC) in the hospital. Materials and Methods: A prospective cohort study was conducted on a total of 100 children with ALOC in the Paediatric Intensive Care Unit (PICU) of Gandhi Medical College and Hospital, Telangana, from December 2020 to November 2021. FOUR and GCS assessments were performed simultaneously within two hours of admission. For statistical analysis, continuous variables are expressed as mean±standard deviations. The predictive values of the GCS score and FOUR score were established using the Receiver Operating Characteristic (ROC) curve, by calculating the Area Under the Curve (AUC) with a 95% Confidence Interval (CI). Results: The mean age of the study population was 7.3±5.2 years, and the mean duration of hospital stay was 7.5±6.74 days. In-hospital mortality was 34%, and the survival rate was 66%. The mean FOUR score for in-hospital mortality and survival was 8.47±3.01 and 12.24±1.46, respectively (p-value 10 (p

This review provides a comprehensive analysis of the latest development of hydrogen (H2)-selective metallic membranes for pre-combustion carbon dioxide (CO2) capture. Highlighting the essential role of these membranes in CO2 capture and storage technologies, we detail the membrane performance through the measurement of H2 permeability under different operating conditions (i.e., temperature and pressure). Our assessments cover the advancement in alloy compositions, surface treatments, and manufacturing techniques to achieve improved membrane performance. Apart from this, in this review, we discuss the challenges encountered in fabricating metallic membranes, such as embrittlement, sulfur contamination, and high production costs, while suggesting potential solutions to these issues. Last but not least, future research direction for metallic membranes is proposed to emphasize the important strategies in developing these membranes in a scalable and cost-effective manner.

The study evaluates the impacts of livelihood intervention programs implemented by the Balochistan Rural Support Program (BRSP) on poverty reduction in Balochistan. The estimation of livelihood impacts is gauged through several indicators, including the Income Generating Grant (IGG), Community Investment Fund (CIF), and Skill Development (TVET). To this end, data from 1500 households segregated into 950 IGG, 250 CIF, and 300 TVET are selected using a two-stage stratified sampling technique in 8 selected districts of Balochistan. This study mainly adopted mixed research approaches, including the descriptive analysis of the data to reflect the role of these interventions in poverty reduction in Balochistan. In addition, Poverty Score Cards (PSC) values have been used to determine the poverty levels. After implementing livelihood support intervention, it was obtained that Households (HHs) monthly income of IGG, CIF, and TVET beneficiaries increased by 17%, 20%, and 39%, respectively, on average. The meaningful tabulation of livelihood interventions positively impacts multifaceted aspects of rural HHs. The results also give evidence of employment generation and income-earning prospects in rural Balochistan. The results also show that gender equality and support for women's empowerment are positively changed in rural Balochistan. Therefore, the tabulated results of community and HHs’ specific funding programs and human capital formation via skills provisions to ultra-poor and poor rural people justify Balochistan Rural Development and Community Empowerment (BRACE) as a livelihood support initiative for poverty reduction in rural Balochistan. The government and non-government organizations (NGOs) may get insightful policy interventions from this study to bring effective socioeconomic development in rural Balochistan.

The purpose of this paper is to analyze the digitalization of Islamic knowledge with respect to advantages and limitations with particular reference to the computerization of religious texts and education. Moving from manuscripts to the e-formats has reinvented the way Muslims and Muslim scholars use, preserve and practice, sacred texts including the Qur’an, the Hadith and Fiqh / Islamic law, governance. However, while this was an encouraging trend that made Islamic scholarship more approachable and protect the youth, it also poses some ethical issues. The introduction of Islamic education for a fee such as Islamic Studies and the use of subscription-based platforms has brought the question of whether religious information is still sacred or if it can be put willing in a monetary exchange system. This paper concentrates on the issues of censorship highlighting the desire of the audience to develop such content while there are concerns of its moderation. Moreover, the emergence of new digital channels brings up important concerns regarding safety and confidentiality. The paper explores the danger of breaching privacy and the abuse of personal data pointing to the necessity and effectiveness of a policy against such issues. The role of Artificial Intelligence (AI) and Machine Learning (ML) towards improving efficiency of translations or automated validation of Hadiths is also discussed. There is no doubt that all these innovations can contribute immensely to the development of knowledge and research in the area of Islam. However, there is a need to use them in just or right manner to avoid trustworthiness, correctness and purity of Islam. At the end of the day, being exposed to globalization influences should not incite alteration in the fundamentals and values of Islamic principles and practices. The longstanding concerns with regard to ethical issues and the use of technology do not mean that the digitization of Islamic knowledge cannot protect the vast intellectual and spiritual heritage of Islam for the coming generations, making it genuine and relevant in a fast developing digital society.

Key Clinical Message This case emphasizes the significance of COVID‐19 in pediatric patients presenting with unusual hepatic manifestations, urging clinicians to broaden their diagnostic lens. The unexpected elevation of SARS‐CoV‐2 antibodies and the effective use of N‐acetyl cysteine highlight the importance of adaptability in treatment strategies. Abstract This case report presents a unique manifestation of severe hepatic involvement in a 4‐year‐old girl with thalassemia minor and COVID‐19. Despite the absence of prominent respiratory symptoms, the patient exhibited jaundice, elevated liver enzymes, and coagulopathy. Initial suspicion of viral hepatitis was replaced by the discovery of significantly elevated SARS‐CoV‐2 antibodies. A multidisciplinary approach, including gastroenterology consultation and an extensive workup, was pivotal in ruling out alternative etiologies. Unconventional use of N‐acetyl cysteine contributed to clinical improvement, highlighting the need for adaptable treatment strategies. This case underscores the importance of heightened awareness in recognizing atypical presentations of COVID‐19 in pediatric patients, especially those with underlying health conditions. Further exploration into nuanced manifestations and treatment approaches is warranted for comprehensive clinical management.

Currently 1.3 billion individuals globally engage in smoking, leading to significant morbidity and mortality, particularly among diabetic patients. There is urgent need for a better understanding of how smoking influences antidiabetic treatment efficacy. The review underscores the role of cigarette smoke, particularly polycyclic aromatic hydrocarbons (PAHs), in modulating the metabolic pathways of antidiabetic drugs, primarily through the induction of cytochrome P450 (CYP450) enzymes and uridine diphosphate (UDP)-glucuronosyltransferases (UGTs), thus impacting drug pharmacokinetics and therapeutic outcomes. Furthermore, the review addresses the relatively uncharted territory of how smoking cessation influences diabetes treatment, noting that cessation can lead to significant changes in drug metabolism, necessitating dosage adjustments. Special attention is given to the interaction between smoking cessation aids and antidiabetic medications, a critical area for patient safety and effective diabetes management. This scoping review aims to provide healthcare professionals with the knowledge to better support diabetic patients who smoke or are attempting to quit, ensuring tailored and effective treatment strategies. It also identifies gaps in current research, advocating for more studies to fill these voids, thereby enhancing patient care and treatment outcomes for this at-risk population.

Synchronous reluctance motors offer promising advantages for industrial applications, including improved efficiency and reduced maintenance requirements. However, their widespread adoption is hindered by design complexities and performance limitations. This study focuses on addressing these challenges by developing a customized 24kW synchronous reluctance motor for industrial use, with a specific emphasis on centrifugal pumps. The research introduces a novel rotor design incorporating carefully designed flux barriers and flux carriers. Comprehensive machine analysis is conducted using the finite element method and Motor Solve software, while particle swarm optimization techniques are employed to enhance performance. Extensive simulations explore various machine parameters, and practical testing is conducted to validate the results and determine the optimized torque achievement. The results demonstrate significant improvements in the motor's performance. Compared to conventional induction motors used in similar applications, the optimized synchronous reluctance motor exhibits enhanced efficiency and smoother operation. The motor achieves notable advancements in terms of speed, efficiency, and torque characteristics, making it a valuable solution for industrial applications. The findings highlight the potential of the developed synchronous reluctance motor for industrial use. The motor design showcases excellent thermal performance, consistent steady-speed operation, and improved efficiency. This study contributes to the advancement of synchronous reluctance motor technology, showcasing its effectiveness and viability in diverse industrial settings. By addressing design complexities and optimizing performance, this research bridges the gap between the potential of synchronous reluctance motors and their practical implementation in industrial applications. Valuable insights are provided for the development of efficient and reliable synchronous reluctance motors in industrial systems.

This research explores the effectiveness of two hybrid composites, pattern-1 (K-M-G-K-M-G-G-M-K) and pattern-2 (K-M-G-K-M-G-K-M-G), in experimental and simulation tests to create a cost-efficient hybrid composite. The composite includes Mulberry (M) silk (Bombyx mori) fibers reinforcing an epoxy resin-matrix hybrid with Kevlar (K) and Glass fiber (G). Both pattern-1 and pattern-2 are evaluated through experimental tests, and simulation tests are conducted to validate the findings. ANSYS Workbench is used for simulation tests to determine the best stacking sequence for protective gears, aerospace components, automotive parts, sport equipment and marine applications. The structural morphology of the hybrid composites is examined using scanning electron microscope (SEM). The results show that pattern-1 performs better than pattern-2 in various experimental and simulation tests, with a wider plastic zone in the tensile test stress-strain curve, greater tensile strength (73 MPa and 67.923 MPa), higher load-bearing capacity, and enhanced ductility. Pattern-1 also demonstrates improved performance in flexural strength (45.35 MPa and 71.474 MPa) and impact strength (6 J and 5.4898 J). Simulation results confirm that pattern-1 is superior to pattern-2 in terms of energy absorption and resistance to bullet penetration.

Non-response in surveys is a common problem faced by surveyors, this results in missing data. Missing values are often omitted when doing any statistical analysis, but this reduces the sample size and consequently decreases the precision of estimates. In such situations, imputation is a commonly used method to deal with missing data, this involves estimating the missing values based on the observed data. In this paper, we propose two new estimators for the finite population mean, formulated using two suggested sampling methods and their associated imputation strategies. We derive the variance of the proposed estimators and obtain conditions under which these estimators are more efficient than existing estimators. We conduct a simulation study to assess the relative efficiency (RE) of the proposed estimators for varying sample sizes, response rates, and ranking criteria. For real-world application, we consider data on measuring the characteristics of abalone. The simulation results demonstrate that the proposed mean estimators based on the suggested imputation methods are more efficient than the existing methods in estimating the mean of the finite population.

Currently, a significant challenge revolves around the release of harmful wastewater burdened with synthetic dyes from various industries such as textiles, paper, cosmetics, food and beverages, and pharmaceuticals. These synthetic dyes carry considerable risks to human health, organisms, and the environment. The environmentally friendly production of SnO2 nanoparticles (SnO2 NPs) is gaining recognition for its costeffectiveness and reduced environmental impact compared to traditional chemical and physical methods, which are both costly and hazardous. In this study, Pandan extract was utilized in the biosynthesis process due to its containing compatible phytochemicals that can act as capping and reducing agent to prepare SnO2 NPs. The degradation of methylene blue dyes through the photocatalytic process was observed to reach 79.8% within a span of 70 minutes. Additionally, the dependence on catalytic loading revealed that only a moderate amount of catalyst is necessary for effective photodegradation.

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. Donor–acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes are explored by replacing long‐chain oleylamine ligands with short‐chain butylamines. Through postsynthesis ligand exchange, the nanocrystals are functionalized with butylamine ligands while their intrinsic properties are maintained. In solution, butylamine‐capped nanocrystals exhibit reduced photoluminescence intensity with increasing TiO2 concentration but without any change in photoluminescence lifetime. Intriguingly, the Stern–Volmer plot depicts different slopes at low and high TiO2 concentrations, suggesting donor‐acceptor interaction through mixed static photoluminescence quenching and quenching sphere of action mechanism . Oleylamine‐capped nanocrystals in solution, on the other hand, show no interaction with TiO2, as indicated by consistent photoluminescence intensities and lifetimes before and after TiO2 addition. In films, both types exhibit decreased photoluminescence lifetime with TiO2, indicating enhanced donor–acceptor interaction, which is discussed in terms of electron transfer. TiO2 nanotubes enhance nonradiative recombination more in butylamine‐capped CsPbBr3 perovskite nanocrystals, emphasizing the role of ligand chain length.

The control and state estimation of Unmanned Aerial Vehicles (UAVs) provide significant challenges due to their complex and nonlinear dynamics, as well as uncertainties arising from factors such as sensor noise, wind gusts, and parameter fluctuations. Neural network-based methods tackle these problems by accurately approximating unknown nonlinearities through training on input-output data. This paper proposes an adaptive Multi-layer Neural Network (MLNN) Luenberger observer-based control for altitude and attitude tracking of a quadrotor UAV. The MLNN observer, employing a modified back-propagation algorithm, is used for the quadrotor’s state estimation. The adaptive nature of the proposed observer helps mitigate the effects of parameters such as wind gusts, measurement noise, and parameter variations. Subsequently, a sliding mode controller is designed based on the observed states to track the reference trajectories. Lyapunov stability is ensured by using the modified back-propagation weight update rule for the proposed MLNN observer. Simulation results demonstrate superior tracking performance of the proposed observer compared to the Sliding Mode Observer (SMO) and a Single Hidden Layer Neural Network (SHLNN) observer, even in the presence of the aforementioned parameters.

This research delves deeply into the analysis of the sanad network of K.H. Muhammad Arwani (1905-1994 AD) and his role in spreading of Qiraat Sab’ah in Indonesia. Utilizing historical methods and document analysis, this study elucidates the dissemination process of Qiraat Sab’ah knowledge propelled by the contributions of K.H. Muhammad Arwani and his sanad network. The research highlights interpersonal relationships, education, as well as social and cultural factors that facilitated the dissemination and acceptance of Qiraat Sab’ah knowledge among the Indonesian society at that time. The findings of this study provide profound insights into the intellectual legacy of K.H. Muhammad Arwani and how his sanad network played a pivotal role in enriching the tradition of Qiraat Sab’ah in Indonesia. The results of this research hold significant relevance in the context of Indonesia's scholarly history and make a significant contribution to our understanding of the role of scholars in supporting the dissemination of religious knowledge in society.

A wild elephant calf of 6-8 months of age was brought to the Centre for Wildlife Rehabilitation and Conservation (CWRC), Bokakhat, Assam, India, having symptoms of diarrhea and debility. The fecal sample was collected and studied for isolation and identification of bacteria. Eosin Methylene Blue (EMB) agar media showed moderate growth of bacterial colonies with a characteristic green metallic sheen. The isolates were subjected to Gram stain and biochemical tests (lactose, sucrose, and sorbitol). Polymerase chain reaction (PCR) amplification with universal primers showed an amplicon of 300 bp. The PCR amplified product after nucleotide BLAST showed 98.95 % similarity with the sequence of enteropathogenic Escherichia fergusonii.

In this paper, the reference tracking and switching loss minimization problem of grid-tied inverters has been formulated as an integer least square (ILS) problem. It is considered an NP-hard and combinatorial problem due to the binary nature of switching status. A novel modification to the traditional non-recursive sphere decoding algorithm is being proposed in this research to address the implementation of long-horizon control with reduced computational burden. The proposed algorithm demonstrates a significant reduction in computation cost for long horizons resulting in energy minimization. The proposed method exploits the state-space model of the system to predict the sector in the space vector two-dimensional plane in which reference voltage could exist, thereby reducing the search space and time to search for the optimal solution. The proposed algorithm optimizes the utilization of controller resources compared to the conventional approach. The algorithm’s performance has been validated through simulations and Hardware in loop verification, for the performance optimization of a three-phase grid-tied inverter coupled using RL filter.

In this paper, we discuss an epidemic switched system. A susceptible–infected–treated model is considered. The course of an epidemic is profoundly influenced by the allocation of resources. If these resources are limited, then we need to devise an optimal distribution strategy. One significant case to study is when the drug supply is insufficient. We study a control problem that minimizes the total outbreak size of the epidemic and optimizes the rate of vaccination/isolation control by minimizing the suitable functional subject to resource constraints. In the end, simulations are performed for illustrations.

Background: Tobacco smoking exacerbates diabetes-related complications; its prevalence is notwithstanding substantial. Persons with diabetes face a number of barriers and challenges to quitting such as multiple lifestyle restrictions; tailored interventions are required for smoking cessation. Objective: To identify research on behavioral interventions for smoking cessation in diabetes. Methods: Studies had to be randomized controlled trials, quasiexperimental or systematic reviews. The behavioral interventions included were: the 5As, Cognitive-Behavioral Therapy, Motivational Interviewing, Contingency Management, Health Coaching and Counselling, as compared to standard care. The outcomes were self-reported and/or biochemically verified smoking cessation. CINAHL Complete, MEDLINE Complete, the Cochrane databases of systematic reviews and randomized controlled trials, PsychInfo and PubMed Central were searched until July, 2023. Keywords used included diabetes, smoking cessation and each of the behavioral interventions included. Results: 1615 papers were identified. Three studies on the 5As/brief advice, 4 on Motivational Intervention and 1 on counseling were retained. The results on the 5As and Motivational Interviewing were conflicting. More intensive interventions appear to be more successful in achieving smoking cessation in smokers with diabetes. Conclusions: Future research should focus on the continued development and evaluation of structured smoking cessation interventions based on the 5As, Motivational interviewing and Cognitive Behavioral Therapy.