Salmonella, major food-borne illness among human and animals where poultry being primary source of infection. Current strategies, vaccination, antibiotics, feed additives, help to reduce the infection in poultry but insufficient for long-term protection. This study's aim to compare the efficacy of three antibiotics used for salmonellosis treatment in poultry. Experimentally, rabbits (n=12) 8 weeks old, were divided into four (4) groups (A, B, C and D), three animals in each group. Animals initially kept for five days in their respective wooden cages, fed on commercial diet. The blood samples from typhoid infected human patients (n=5) were collected from Jinnah hospital Lahore to isolate bacteria by culturing on blood agar media. Culture filtrate (5 mL) of salmonella typhi was injected to experimental rabbits except control group. After 48 hours collected blood samples of three antibiotics treated groups were subjected for genomic DNA isolation, PCR amplification of flipC gene. For experiment trail, the experimental groups were subjected on three antibiotics treatments with oral dose (50mg/kg) for ten days. Group-A (control) untreated, group-B (ciprofloxacin), group-C (azithromycin) and group-D (cefotaxime). During drug delivery, feces of rabbits were collected on 1st, 4th, and 7th day for comparative analysis of drug efficacy by calculating CFU/mL grown on blood agar medium. Body weight analysis showed an increase in weight of untreated group while gradual decrease for experimental groups, which indicated the effect of infection and poor absorption of nutrients. Salmonella infection was confirmed through PCR gene mapping test which was observed in all infected animals. Lesser CFU/mL (68.33) with grey-white colonies were observed in animal of group-B, 107 CFU/mL with opaque colored colonies (group-C) and 89 CFU/mL with moist, circular, smooth convex surface colonies (group-D). Thus ciprofloxacin (group-B) revealed as most effective antibiotic against Salmonella infection with more efficacy. These findings would be helpful for the farmers to use this antibiotic at poultry flocks against salmonellosis.

Green synthesis of silver nanoparticles (AgNPs) has gained attention as an eco-friendly and sustainable approach to nanomaterial production, particularly in the search for alternatives to conventional antimicrobials amid rising resistance. This systematic review, conducted in accordance with PRISMA 2020 guidelines, identified 17 in vitro experimental studies that investigated the antimicrobial potential of green-synthesized AgNPs. Biological sources included plants (n = 11), fungi (n = 2), a polysaccharide (n = 1), a cyanobacterium (n = 1), and a succulent (n = 1). Reported nanoparticle sizes ranged from 8 to 150 nm, with smaller particles (<30 nm) generally exhibiting superior antimicrobial efficacy. Antimicrobial activity was demonstrated against Gram-positive bacteria in 15 studies, Gram-negative bacteria in 14 studies, and fungi in 5 studies, with zones of inhibition ranging from 7 mm to 37 mm. Only six studies reported minimum inhibitory or bactericidal concentrations, underscoring a lack of standardized quantitative data. The predominant mechanisms of action were attributed to reactive oxygen species (ROS) generation, oxidative stress, membrane disruption, protein inactivation, and DNA interference. Cytotoxicity was assessed in six studies, suggesting biocompatibility at lower concentrations but potential dose-dependent toxicity. Overall, green-synthesized AgNPs demonstrate consistent antimicrobial potential, but future research must focus on standardized synthesis protocols, robust MIC/MBC testing, and systematic toxicity evaluation to support clinical translation.

Critical infrastructures, such as transportation, healthcare, and energy systems, are becoming increasingly interconnected, creating an urgent need for secure and efficient data sharing between agencies. However, the complexity of inter-agency collaboration is heightened by significant challenges, including privacy concerns, regulatory constraints, and inherent security risks. To address these concerns, Federated Learning (FL), a machine learning technique that facilitates the collaborative training of models across decentralized data sources without the need to transfer sensitive data, has emerged as a highly promising solution. FL ensures that agencies can jointly leverage the power of data-driven insights while ensuring privacy preservation. This paper investigates the potential of federated learning as a means to enable secure, scalable data collaboration between agencies in critical infrastructure sectors. We propose a novel federated learning framework tailored specifically for these sectors, taking into account sector-specific data requirements, regulatory frameworks, and security needs. Additionally, we discuss the effectiveness, challenges, and limitations of the proposed framework, as well as explore its potential for future applications and advancements. This paper aims to contribute to the growing body of research on privacy-preserving machine learning solutions in high-stakes, sensitive environments.

Artificial intelligence (AI) is reshaping aesthetic dentistry by improving diagnostic precision, treatment planning, outcome predictability, and overall patient satisfaction. This review aims to systematically analyze the role of AI in aesthetic dentistry, highlighting its applications, advantages, limitations, and future directions. A comprehensive literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar, covering studies published between 2018 and 2024. Search terms included “AI in dentistry,” “aesthetic dentistry,” “machine learning,” “prosthodontics,” and “orthodontics.” The review includes 28 peer-reviewed articles encompassing systematic reviews, clinical studies, narrative analyses, and expert consensus papers. Evidence shows that AI technologies such as convolutional neural networks (CNNs), generative adversarial networks (GANs), support vector machines (SVMs), and fuzzy logic systems have enhanced dental imaging, tooth segmentation, digital smile design, implant planning, prosthetic design, and personalized treatment simulations. AI facilitates real-time visualization, streamlines CAD/CAM workflows, and improves efficiency in clinical and administrative tasks. Moreover, AI enables predictive modeling of treatment outcomes and fosters patient-centered care through individualized approaches. However, significant challenges remain, including the need for high-quality datasets, ethical concerns about privacy and bias, lack of interpretability in AI decision-making, and high costs of implementation. The findings suggest broad consensus on AI’s transformative potential, but controversies persist regarding transparency, reliability, and accessibility. Future directions include explainable AI, integration with robotics, advanced biomaterials, and interdisciplinary collaborations. Overall, AI is revolutionizing modern aesthetic dentistry, paving the way for more predictable, minimally invasive, and patient-centered treatments that align with global digital healthcare trends.

The Shakambhari Hills in the Sikar region of Rajasthan host a rich and diverse entomofauna, yet have remained largely unexplored in terms of systematic entomological studies. This study presents a comprehensive inventory of insect species recorded from three distinct locations Kalakhet, Sakarai, and Bhagova by conducting random field surveys between 2021 and 2024. A total of 8,631 individuals belonging to 148 genera across 10 orders and 61 families were identified. Coleoptera was the most diverse and abundant order, followed by Lepidoptera and Hymenoptera. Presence of large number of insects from this region clearly indicates this region to comprise of tremendous diversity of insects and quite rich in flora which serve as host plants. These findings highlight the ecological significance of the Shakambhari Hills and underscore the need for conservation and further ecological research.

The increasing unpredictability of environmental events due to climate change has amplified the need for more resilient infrastructure and supply chains. Integrating climate-aware decision intelligence into planning processes can significantly improve the ability of organizations and industries to manage these risks effectively. This paper explores the crucial role of incorporating environmental risk assessments into infrastructure and supply chain planning. We propose a decision intelligence framework that combines real-time climate data, predictive modeling, and dynamic simulation techniques to inform decision-making. This approach aims to enhance the adaptability and sustainability of infrastructure and supply chains in response to climate-related challenges. The paper also reviews existing methodologies in environmental risk management and highlights case studies that demonstrate the practical application and success of such frameworks. By integrating predictive analytics and climate risk data, decision-makers can identify potential disruptions and make more informed decisions to mitigate these risks. The proposed solution not only improves resilience but also enables organizations to proactively adjust to changing environmental conditions, ensuring long-term operational stability. In this context, climate-aware decision intelligence becomes an essential tool for organizations seeking to future-proof their infrastructure and supply chain operations against the growing threat of climate change. This paper outlines the benefits and applications of the proposed framework and suggests future directions for research in this evolving field.

This study investigates the effect of environmental, social and governance (ESG) reporting on the market performance of listed firms operating in the oil and gas sector in Nigeria. An ex post facto research design was employed to select seven firms from the period of 2014 to 2023. Share price performance was used to measure market performance. The study conducted both descriptive statistics and inferential statistics to analyze the data sourced. The model is analyzed using Generalized method of moments (GMM). The results of this study disclosed that environmental disclosure (p<0.01) and social disclosure (p<0.05) have positive and significant effect on share price performance. Although, governance disclosure showed no statistically significant effect. These findings imply that disclosure of information on social and environmental practices in the Nigeria oil and gas industry favorably influenced the value of the share thus enhance market performance; whereas, governance disclosure is viewed as having less impact.

This study aims to analyze the influence of organizational structure and transactional leadership on teacher performance at SDI Sahabat Ilmu, with work motivation as a mediating variable. An explanatory quantitative method was used in this study, with a census sampling method involving all 30 teachers who were active in the school. Data analysis was carried out using Structural Equation Modeling (SEM) based on SmartPLS software. The research instruments were compiled based on organizational structure theory, transactional leadership concepts, work motivation, and teacher performance indicators, using a five-point Likert scale. The findings of the study show that organizational structure has a positive impact on work motivation, but the effect is indirect on teacher performance. Meanwhile, transactional leadership has been proven to have a positive and significant impact on teachers' performance and work motivation. However, the results of the analysis showed that work motivation did not mediate the relationship between organizational structure and transactional leadership and teacher performance. This study makes an important contribution to the development of human resource management and leadership strategies in educational institutions, especially in an effort to increase teacher productivity.

Background: The global incidence of partial restriction or completely frozen shoulder joints is on the rise. This study explored the Frozen Shoulder Syndrome (FSS), its relationship with Vitamin D Deficiency and insufficiency (VDD), and its impact on the spine owing to Repetitive Strain Injury (RSI), across a diverse Indian population. Methods: FSS was assessed using Shoulder Active Abduction Range (SAAR) through a standard Goniometer. Vitamin D levels were measured using the CLIA technology. The Windows version 26.0 of IBM SPSS was used to compute means, standard deviations, multivariate regression analysis utilised to adjust confounders, Chi-square tests, ANOVA, and post hoc testing were done through the Least Significant Difference (LSD) method. Risk estimates and odds ratios (95% CI) were calculated. The significance level was set at 0.05. This study retrospectively analysed 5265 approved patient records across various occupations and ages (4 to 90 years), over two decades. The gender distribution among individuals seeking treatment for back and neck pain was 2696 males and 2569 females. Results: Both left and right SAAR values were significantly lower and VDD was associated with both SAAR values. Upper back (UB) pain indicated a stronger correlation with the SAAR left than with the SAAR right. Lower back (LB) pain was moderately associated with the SAAR left and significantly with the SAAR right. A significant relationship between VDD and UB and LB pain was observed. Risk estimates were calculated and analyzed. Conclusion: This study proved the prevalence of repeated RSI on the spine owing to FSS and its relation to VDD. A simple non-invasive test like SAAR is highly beneficial for both clinician and health experts. There is an urgent need to identify FSS and VDD at an early age and across different ethnic groups and population.

The search for green and sustainable corrosion inhibitors has attracted increasing attention as alternatives to toxic synthetic chemicals. In this study, flavonoids extracted from Adansonia digitata (baobab) leaves were investigated as eco-friendly inhibitors for mild steel corrosion in hydrochloric acid medium through a combination of experimental and computational approaches. Weight loss measurements demonstrated a concentration-dependent inhibition effect, achieving a maximum efficiency of 86.4% at 500 ppm, with quercetin exhibiting the highest protection efficiency (90.2%), followed by luteolin (87.5%), kaempferol (83.1%), and apigenin (79.6%). FTIR spectra of the steel surface after exposure confirmed adsorption of flavonoids, showing characteristic shifts in the O-H stretching band (3420-3380 cm-1) and C=O stretching band (1662-1645 cm-1), indicative of coordination between hydroxyl/carbonyl groups and Fe atoms. Quantum chemical calculations using density functional theory (DFT) provided molecular-level insights into the inhibition mechanism: quercetin and luteolin displayed the highest HOMO energies (-5.81 eV and -5.94 eV) and lowest energy gaps ΔE (3.12 eV and 3.25 eV), consistent with their superior electron-donating capacity. Frontier molecular orbital (FMO) distributions and Fukui function mapping further identified hydroxyl and carbonyl sites as the dominant adsorption centers. The agreement between experimental and theoretical findings confirms that A. digitata flavonoids inhibit corrosion primarily via chemisorption through donor–acceptor interactions, offering a sustainable and highly effective alternative to toxic synthetic inhibitors.

Immunofluorescence is an immunological method used to detect the presence of immune deposits in tissue or serum. This has emerged as a valuable technique in diagnosing vesiculobullous disorders, especially when the histopathology is inconclusive. This review explores the diverse applications of immunofluorescence in oral disease, diagnosing autoimmune conditions like Pemphigus Vulgaris and Mucous Membrane Pemphigoid. This method utilises fluorescent-labelled antibodies to detect specific antigens or proteins in the tissues.

Background: Post-operative pain management following cesarean section remains a critical determinant of maternal recovery, psychological well-being, and early bonding with the newborn. Nurses, being the frontline caregivers, play an essential role in assessing, managing, and evaluating pain among post-cesarean women. Objective: This study explores the role of nurses in post-operative pain management after cesarean section at Aster Sanad Hospital, Riyadh, with a focus on practices, challenges, and patient-reported outcomes. Methods: A descriptive cross-sectional study was conducted between January and April 2025 among post-cesarean women admitted to Aster Sanad Hospital. A structured questionnaire, including validated patient-reported experience measures (PREMs) and nurse-reported practices, was administered to 450 participants (response rate: 92%). Quantitative data were analyzed using descriptive and inferential statistics, while qualitative narratives were thematically analyzed to capture patient perceptions. Results: Most patients (78%) reported moderate-to-severe pain within the first 24 hours post-surgery. Effective pain relief was strongly associated with timely nurse interventions (p < 0.05). Nurses employed multimodal strategies including pharmacologic management (NSAIDs, opioids, regional anesthesia follow-up) and non-pharmacologic techniques (positioning, breathing exercises, emotional reassurance). Patients rated nurse responsiveness and communication as critical factors in satisfaction with pain relief. However, barriers such as high workload, inconsistent protocols, and limited time for individualized care were identified. Conclusion: Nurses play a pivotal role in ensuring effective pain management after cesarean section. Strengthening nurse-led interventions, standardizing pain assessment protocols, and providing continuous training in pain management strategies can enhance patient outcomes and overall quality of care.

Sepsis is a life-threatening syndrome resulting from a dysregulated host response to infection, leading to organ dysfunction and high mortality. Early recognition and protocolized interventions are critical for improved outcomes. This narrative review synthesizes current evidence-based strategies relevant to medical–surgical settings, focusing on early detection, fluid resuscitation, antimicrobial stewardship, hemodynamic support, source control, and nursing-led monitoring. It emphasizes the importance of multidisciplinary collaboration among nurses, physicians, pharmacists, respiratory therapists, and dietitians, in alignment with the Surviving Sepsis Campaign guidelines. Persistent challenges such as antimicrobial resistance, delayed diagnosis, and resource limitations are discussed, alongside future directions including novel biomarkers, personalized care pathways, AI-driven early warning systems, and strengthened antimicrobial stewardship programs. Optimized nursing protocols, enhanced interprofessional communication, and advanced monitoring strategies can significantly reduce sepsis-related morbidity and mortality.

Tourism is a strategic sector for Indonesia’s economy but remains vulnerable to global and domestic crises. This study aims to analyse the implementation of sustainable tourism practices and their relationship with the financial resilience of tourism destinations in Jakarta and West Java. Using a mixed methods approach, data were collected through surveys of 150 respondents (tourism enterprises, destination managers, and local communities) and in-depth interviews with stakeholders. The research instruments measured three dimensions of sustainable tourism (economic, socio-cultural, environmental) and five dimensions of financial resilience (revenue stability, diversification, cost efficiency, adaptation and innovation, institutional support). Results from Structural Equation Modelling–Partial Least Squares (SEM-PLS) reveal that sustainable tourism practices significantly and positively influence financial resilience (β = 0.62; p < 0.01). The economic dimension contributed the most, followed by socio-cultural, while environmental was positive but not significant. These findings highlight that sustainability not only addresses socio-ecological concerns but also serves as a key strategy to strengthen the financial resilience of tourism destinations. Theoretically, this study reinforces the Resource-Based View and Stakeholder Theory within the Indonesian tourism context. Practically, it provides recommendations for policymakers and practitioners, including strengthening policy support, diversifying tourism products, enhancing digital innovation, and engaging local communities. Thus, sustainable tourism practices can form a foundation for developing resilient and competitive destinations in the future.

This study aims to investigate the flexural behavior of simply supported one-way reinforced concrete (RC) slabs constructed with high-strength concrete (HSC) and normal-strength concrete (NSC), reinforced with either glass fiber reinforced polymer (GFRP) bars or conventional steel reinforcement. Four large-scale reinforced concrete (RC) slabs were tested under four-point bending. The failure and cracking loads, deflection, crack patterns, and failure modes were considered as main parameters. Results showed that GFRP-reinforced slabs (both NSC and HSC) had higher deflections by 2.60 times and lower ultimate loads by 21.0% to 32.0% compared to steel-reinforced slabs. GFRP-reinforced specimens also experienced sudden failure due to bar rupture and exhibited wider cracks. The experimental results compared with those from analytical predictions based on ACI 440, CAN/CSA S806, and Eurocode 2, the results were in accordance. While CSA S806-12 slightly underestimated cracking loads for HSC-GFRP slabs, it provided accurate mid-span deflection estimates. Eurocode 2 predictions for crack widths were within 10% of the values observed.