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Staphylococcus aureus (SA) is a major bacterial pathogen and causes a wide range of clinical infections in humans leading to severe outcomes including meningitis, endocarditis, and sepsis. This literature review examines studies on host immune responses after infections with SA and methicillin-resistant Staphylococcus aureus (MRSA) and their immune evasion mechanisms. Furthermore, information about vaccines and immunotherapies against SA and MRSA is reviewed. We found promising toxoid vaccine approaches, which deserve further research. We also found support for antitoxin therapies and immunomodulating therapies as high-potential research areas. Although many promising vaccines and immunotherapy candidates have been studied in animal models, more human clinical studies are needed to confirm their long-term safety and efficacy. [ABSTRACT FROM AUTHOR]

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks. [ABSTRACT FROM AUTHOR]

Climate change is a significant and urgent threat, gaining traction in the scientific community around the globe and requiring immediate action across many sectors. In this context, the digital economy could provide a mutually beneficial solution by utilizing innovation and technical breakthroughs to establish a sustainable future that addresses environmental deterioration, promotes economic growth, and encourages energy conservation. Against this background, this study examined the diffusion of innovation modeling-based factors affecting small and medium-sized firms' (SMFs) adoption of the Internet of Things (IoT) technology and its impact on SMFs' sustainability performance related to environmental, economic, innovation, and energy conservation perspectives. The key findings revealed that (i) the relative advantage, trialability, and observability drive IoT adoption. However, compatibility and complexity hinder IoT adoption. (ii) When prioritizing the adoption factors, the relative benefit is the strongest driver, and compatibility is the most significant barrier to IoT adoption. (iii) IoT technology adopter SMFs spent less on natural resources and more on renewable energy and environmental monitoring systems than non-adopter firms, boosting their environmental sustainability. (iv) IoT technology adopter firms had greater revenue, profits, and credit access than non-adopters and lower input costs, improving their economic sustainability. (v) IoT adopter firms spent more on innovative products than non-adopter enterprises, demonstrating innovation performance. (vi) Compared to non-adopter firms, IoT technology adopter SMFs had lower utility expenses and spent more on energy-efficient technologies. (vii) To realize the full potential of the IoT for a more sustainable and inventive future, authorities may pursue a variety of policy actions involving the strengthening and implementation of IoT technology standards and regulations, securing the incentivization of financial resources to SMFs, diverting the allocation of resources to research and development avenues, prioritizing the capacity development and environmental awareness, and focusing on IoT infrastructure development. [ABSTRACT FROM AUTHOR]

The relation between fractional calculus and convexity significantly impacts the development of the theory of integral inequalities. In this paper, we explore the reverse of Minkowski and Hölder's inequality, unified Jensen's inequality, and Hermite–Hadamard (H - H)-like inequalities using fractional calculus and a generic class of interval-valued convexity. We introduce the concept of I. V - (⋏ , ℏ) generic class of convexity, which unifies several existing definitions of convexity. By utilizing Riemann–Liouville (R-L) fractional operators and I. V - (⋏ , ℏ) convexity to derive new improvements of the H - H - and Fejer and Pachpatte-like inequalities. Our results are quite unified; by substituting the different values of parameters, we obtain a blend of new and existing inequalities. These results are fruitful for establishing bounds for I. V R-L integral operators. Furthermore, we discuss various implications of our findings, along with numerical examples and simulations to enhance the reliability of our results. [ABSTRACT FROM AUTHOR]

The intent of the current study is to explore convex stochastic processes within a broader context. We introduce the concept of unified stochastic processes to analyze both convex and non-convex stochastic processes simultaneously. We employ weighted quasi-mean, non-negative mapping γ , and center-radius ordering relations to establish a class of extended c r -interval-valued convex stochastic processes. This class yields a combination of innovative convex and non-convex stochastic processes. We characterize our class by illustrating its relationships with other classes as well as certain key attributes and sufficient conditions for this class of processes. Additionally, leveraging Riemann–Liouville stochastic fractional operators and our proposed class, we prove parametric fractional variants of Jensen's inequality, Hermite–Hadamard's inequality, Fejer's inequality, and product Hermite–Hadamard's like inequality. We establish an interesting relation between means by means of Hermite–Hadamard's inequality. We utilize the numerical and graphical approaches to showcase the significance and effectiveness of primary findings. Also, the proposed results are powerful tools to evaluate the bounds for stochastic Riemann–Liouville fractional operators in different scenarios for a larger space of processes. [ABSTRACT FROM AUTHOR]

Distance learning has grown rapidly in recent years. E-learning can aid teachers of students with disabilities, particularly visually impaired students (VISs), by offering versatility, accessibility, enhanced communication, adaptability, and a wide range of multimedia and non-verbal teaching methods. However, the shift from traditional face-to-face instruction to online platforms, especially during the pandemic, introduced unique challenges for VISs, with respect to including instructional methodologies, accessibility, and the integration of suitable technology. Recent research has shown that the resources and facilities of educational institutions pose challenges for teachers of visually impaired students (TVISs). This study conducts a literature review of research studies from the years 2000 to 2024 to identify significant issues encountered by TVISs with online learning to show the effects of distance learning before, during, and after the pandemic. This systematic literature review examines 25 publications. The evaluation reveals technological problems affecting the educational experience of visually impaired educators through a methodical categorization and analysis of these papers. The results emphasize important problems and suggest solutions, providing valuable knowledge for experts in education and legislation. The study recommends technology solutions to support instructors in providing inclusive online learning environments for VISs. [ABSTRACT FROM AUTHOR]

Improving the efficiency and performance of control systems in food processing remains a significant challenge for engineers and researchers. In this paper, Proportional, Integral, and Derivative (PID) control; Model Predictive Control (MPC); and Adaptive Model Predictive Control (AMPC) were implemented on a Continuous Flow Ohmic Heater (CFOH) pilot plant to process tomato basil sauce. The sauce, composed of tomato puree, basil, spices, and other ingredients, was used to assess the effectiveness of these advanced control strategies. This research presents a case study on the pilot-scale heating of tomato basil sauce, with applications in the broader food industry. The performances and energy efficiencies of the different control techniques were compared, demonstrating significant improvements in controlling the CFOH process. The results highlight the industrial practicality of using CFOH technology with advanced process controls for food processing. [ABSTRACT FROM AUTHOR]

Background: A possible role of vitamin D epimers and metabolites in the measurement and response to treatment of vitamin D has been reported recently. Furthermore, the influence of underlying vitamin D receptor (VDR) genetic polymorphisms which have been linked to diseases such as obesity remains unclear. We therefore aimed to examine the influence of vitamin D3 and calcium supplements on vitamin D epimer and metabolite concentrations in subjects with and those without vitamin D receptor (VDR) gene polymorphisms. Methods: A total of 277 participants who were part of a randomized intervention trial of vitamin D3 and calcium or a placebo for 6 months had clinical and anthropometric assessments. Blood samples were taken for measurements of vitamin D, epimers and metabolites of vitamin D, four vitamin D receptor gene polymorphism SNPs, namely, BsmI, FokI, TaqI, and ApaI, metabolic and inflammatory markers, and related biochemical variables. Repeated-measures analysis of variance was used to assess the between-group difference in cumulative changes in vitamin D epimers and metabolites at 6 months after adjusting for the presence of the 4 VDR genotypes and allele gene polymorphisms. Results: Overall, 277 participants, with a mean (±SD) age of 41 ± 12 and 204 (74%) of whom were female, were included in the study. We found no statistically significant differences in vitamin D metabolites or (epimers) between male and females or younger subjects compared to those over 40 years of age except in 7C4 BL (p < 0.05). There was a statistically significant difference in 1,25(OH)2D3 concentrations between subjects with and those without genotypes AG and the allele G SNP2_Taql VDR gene polymorphism. Vitamin D3 concentrations were also significantly lower in subjects with the CC SNP3_Apal gene polymorphism compared to those without the CC SNP3 gene. No statistically significant effects were seen on vitamin D epimers and metabolites concentration in response to supplements before or after adjusting for the presence of the 4 VDR genotypes and allele gene polymorphisms. Conclusions: The CC SNP3 gene had statistically significant influence on vitamin D3 levels. Vitamin D and/or calcium supplements, however, had no effects on vitamin D epimer and metabolite concentration before or after adjusting for the presence of the 4 VDR genotypes and alleles. [ABSTRACT FROM AUTHOR]

Driven by their exceptional optical characteristics, robust chemical stability, and facile bioconjugation, gold nanoparticles (AuNPs) have emerged as a preferred material for detection and biosensing applications in scientific research. This study involves the development of a simple, rapid, and cost-effective colorimetric immuno-sensing probe to detect aflatoxin B1 and zearalenone using AuNP antibody (AuNP-mAb) conjugates. Anti-toxin antibodies were attached to the AuNPs by using the physical adsorption method. The colorimetric immunosensor developed operates on the principle that the optical properties of the AuNP are very sensitive to aggregation, which can be induced by a critical high salt concentration. Although the presence of antibodies on the AuNP surface inhibits the aggregation, these antibodies bind to the toxin with higher affinity, which leads to exposure of the surface of AuNPs and aggregation in a salt environment. The aggregation triggers a noticeable but variable alteration in color from red to purple and blueish gray, as a result of a red shift in the surface plasmon resonance band of the AuNPs. The extent of the shift is dependent on the toxin exposure dose and can be quantified using a calibration curve through UV–Visible–NIR spectroscopy. The limit of detection using this assay was determined to be as low as 0.15 ng/mL for both zearalenone and aflatoxin B1. The specificity of the prepared immunoprobe was analyzed for a particular mycotoxin in the presence of other mycotoxins. The developed immunoprobe was evaluated for real-world applicability using artificially spiked samples. This colorimetric immunoprobe based on localized surface plasmon resonance (LSPR) has a reduced detection limit compared to other immunoassays, a rapid readout, low cost, and facile fabrication. [ABSTRACT FROM AUTHOR]

In the current study, we compute some upper bounds for the remainder term of Boole's quadrature rule involving convex mappings. First, we build a new identity for first-order differentiable mapping, an auxiliary result to establish our required estimates. We provide several upper bounds by utilizing the identity, convexity property, and bounded property of mappings and some well-known inequalities. Moreover, based on our primary findings, we deliver applications to the means, quadrature rule, special mappings, and non-linear analysis by developing a novel iterative scheme with cubic order of convergence. To the best of our knowledge, the current study is the first attempt to derive upper bounds for Boole's scheme involving convex mappings. [ABSTRACT FROM AUTHOR]

Object detection in maritime environments is a challenging problem because of the continuously changing background and moving objects resulting in shearing, occlusion, noise, etc. Unluckily, this problem is of critical importance since such failure may result in significant loss of human lives and economic loss. The available object detection methods rely on radar and sonar sensors. Even with the advances in electro-optical sensors, their employment in maritime object detection is rarely considered. The proposed research aims to employ both electro-optical and near-infrared sensors for effective maritime object detection. For this, dedicated deep learning detection models are trained on electro-optical and near-infrared (NIR) sensor datasets. For this, (ResNet-50, ResNet-101, and SSD MobileNet) are utilized in both electro-optical and near-infrared space. Then, dedicated ensemble classifications are constructed on each collection of base learners from electro-optical and near-infrared spaces. After this, decisions about object detection from these spaces are combined using logical-disjunction-based final ensemble classification. This strategy is utilized to reduce false negatives effectively. To evaluate the performance of the proposed methodology, the publicly available standard Singapore Maritime Dataset is used and the results show that the proposed methodology outperforms the contemporary maritime object detection techniques with a significantly improved mean average precision. [ABSTRACT FROM AUTHOR]

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps. [ABSTRACT FROM AUTHOR]

Helicobacter pylori (H. pylori) infection is a widespread global health issue with a varying prevalence influenced by geography, socioeconomic status, and demographics. In the U.S., the prevalence is lower, though certain groups, such as older adults and immigrants from high-prevalence regions, show higher rates. The decrease in infection rates in developed countries is due to improved sanitation, antibiotics, and healthcare, whereas developing countries continue to experience high rates due to poor living conditions. H. pylori infection can be asymptomatic or cause symptoms like dyspepsia, abdominal pain, bloating, nausea, and loss of appetite. Pathophysiologically, H. pylori contribute to conditions such as gastritis, peptic ulcers, and gastric cancer through mechanisms including urease production and the release of virulence factors, leading to chronic inflammation and an increased cancer risk. Diagnostic methods for H. pylori have progressed significantly. Non-invasive techniques, such as serological assays, stool antigen tests, and urea breath tests, are practical and sensitive. Invasive methods, including endoscopic biopsy and molecular diagnostics, are more definitive but resource intensive. Recent advancements in diagnostic technology, including matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), biosensor technology, and next-generation sequencing (NGS), promise improved speed, accuracy, and accessibility. These innovations are expected to enhance the detection and management of H. pylori, potentially reducing the global disease burden. This review aims to discuss these diagnostic modalities with a focus on further advances under investigation. [ABSTRACT FROM AUTHOR]

In this paper, a heat generator with fluid agitation is developed and experimentally studied. This heat generator can convert kinetic energy from a wind turbine directly to thermal energy through the process of viscous dissipation—a process achieved through the agitation of the working fluid inside a container. In the experimental study, an electric motor (instead of a wind turbine) was used to provide the kinetic energy input to the heat generator. The torque, rotational speed, and temperature rise in the fluid were measured. Using the measured quantities, the efficiency of kinetic energy to sensible heat conversion was calculated. Experiments were conducted to investigate the effects of different impellers, rotational speeds, and working fluids, including distilled water, ethylene glycol (EG), and their respective nanofluids, with A l 2 O 3 nanoparticles at different concentrations. The study also found that the temperature rise in fluids due to viscous dissipation was influenced by the specific heat of the fluid, suggesting that the heat generator can be optimized for energy storage with high-specific-heat fluids, such as water, or for achieving a higher temperature rise with low-specific-heat fluids, such as ethylene glycol. The experimental results indicated that the heat generator was up to 90% efficient in converting kinetic energy to thermal energy. The study revealed that, for constant power input, the heat dissipation rate depends solely on the vessel's geometry, not the fluid properties. Optimizing the impeller design and baffles within the vessel is crucial for maximizing power input. For applications, a wind turbine can power this heat generator to provide heat to a house or a commercial building. [ABSTRACT FROM AUTHOR]

This study investigated the potential of blockchain technology to transform Electronic Health Record (EHR) administration, integrity, and security. EHRs store vital health information such as medical history, diagnosis, prescriptions, and imaging findings, which may be shared with healthcare professionals to improve patient care. The existing EHR systems have a centralized framework. These centralized systems have a single point of failure, data management, integrity, and security concerns. Blockchain technology provides a solution to these problems by delivering benefits such as safety, privacy, secrecy, and decentralization. This study presents a framework for adopting blockchain technology in EHR systems, providing a comprehensive, modular, and straightforward approach. Our proposed framework addresses the constraints of existing EHR systems by providing a platform for connected and interoperable EHRs. The proposed blockchain-based patient health records management framework demonstrates the potential to address the limitations of current centralized health records systems. It offers benefits such as data privacy and security, interoperability, audibility, decentralization, and automation through the use of smart contracts. The proposed framework is implemented in Ethereum. The evaluation, i.e., cost and performance results, show that this solution is reasonable and may be used on any blockchain network, whether it is permissioned or permissionless. [ABSTRACT FROM AUTHOR]

Background: Sleeve gastrectomy is the most common bariatric procedure worldwide, yet postoperative pain management remains a concern. This study aimed to compare pain medication usage and pain scores between laparoscopic sleeve gastrectomy (LSG) and robotic sleeve gastrectomy (RSG) patients, addressing the potential benefits of RSG in postoperative pain control. Methods: A retrospective review of our institutional bariatric surgery registry included 484 patients (435 LSG, 49 RSG) who underwent surgery between September 2015 and November 2020. Pain management medications, including opioid use converted to morphine milligram equivalents (MMEs), and Numerical Rating Scale (NRS) scores were analyzed postoperatively using mixed-effects models. Results: RSG patients reported lower pain scores in the initial 24 h post-surgery and received significantly lower doses of morphine equivalents compared to LSG patients (median 7.5 mg vs. 12.5 mg, p < 0.001). RSG procedures had longer operative times (122.5 ± 27.1 vs. 89.9 ± 30.5 min, p < 0.001) but a shorter average length of stay (2.24 ± 0.60 vs. 2.65 ± 1.49 days) compared to LSG. Conclusions: These findings suggest that RSG may lead to reduced immediate postoperative pain and lower opioid requirements compared to the laparoscopic approach, despite longer operative times. Further randomized controlled trials are needed to confirm these observations and evaluate long-term outcomes. [ABSTRACT FROM AUTHOR]

In this research, we developed undoped and aluminum-doped zinc oxide for antimicrobial and anticancer activities. This study focuses on the synthesis, characterization, and biological activities of zinc oxide nanoparticles (ZnO NPs) and aluminum-doped zinc oxide nanocomposites (Zn1−xAlxO NCs) at varying concentrations (x = 0, 0.25, 0.5, and 0.75 wt%) using the coprecipitation method. Various characterization techniques such as XRD, UV-Vis, FTIR, EDX, and SEM were performed to analyze the crystal structure, optical properties, functional group identification, elemental composition, and surface morphology. The antimicrobial activity test showed that Zn0.75Al0.25O NCs exhibited the strongest inhibition zone against Bacillus cereus compared to Staphylococcus aureus > Pasteurella multocida > Escherichia coli. Moreover, the cytotoxicity and cell viability of liver cancer (HepG-2), breast cancer (MCF-7), ovarian cancer (SKOV3), and normal liver cell lines) were evaluated using the MTT assay, demonstrating that Zn0.75Al0.25O NCs not only enhance cell destruction but also show low cytotoxicity and high biocompatibility at low concentrations. These results suggest that Zn0.75Al0.25O NCs could be a promising candidate for in vivo anticancer applications and should be further investigated. [ABSTRACT FROM AUTHOR]

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design. [ABSTRACT FROM AUTHOR]

Background and objectives: Hereditary spastic paraplegia (HSP) is characterized by unsteady gait, motor incoordination, speech impairment, abnormal eye movement, progressive spasticity and lower limb weakness. Spastic paraplegia 75 (SPG75) results from a mutation in the gene that encodes myelin associated glycoprotein (MAG). Only a limited number of MAG variants associated with SPG75 in families of European, Middle Eastern, North African, Turkish and Palestinian ancestry have been documented so far. This study aims to provide further insight into the clinical and molecular manifestations of HSP. Methods: Using whole-exome sequencing, we investigated a consanguineous Pakistani family where three individuals presented with clinical signs of HSP. Sanger sequencing was used to carry out segregation analysis on available family members, and a minigene splicing assay was utilized to evaluate the effect of the splicing variant. Results: We identified a novel homozygous pathogenic splice donor variant in MAG (c.46 + 1G > T) associated with SPG75. RNA analysis revealed exon skipping that resulted in the loss of a start codon for ENST00000361922.8 isoform. Affected individuals exhibited variable combinations of nystagmus, developmental delay, cognitive impairments, spasticity, dysarthria, delayed gait and ataxia. The proband displayed a quadrupedal stride, and his siblings experienced frequent falls and ataxic gait as one of the prominent features that have not been previously reported in SPG75. Conclusions: Thus, the present study presents an uncommon manifestation of SPG75, the first from the Pakistani population, and broadens the spectrum of MAG variants. [ABSTRACT FROM AUTHOR]

A novel tick-borne orthonairovirus called the Yezo virus (YEZV), primarily transmitted by the Ixodes persulcatus tick, has been recently discovered and poses significant threats to human health. The YEZV is considered endemic in Japan and China. Clinical symptoms associated with this virus include thrombocytopenia, fatigue, headache, leukopenia, fever, depression, and neurological complications ranging from mild febrile illness to severe outcomes like meningitis and encephalitis. At present, there is no treatment or vaccine readily accessible for this pathogenic virus. Therefore, this research employed an immunoinformatics approach to pinpoint potential vaccine targets within the YEZV through an extensive examination of its structural proteins. Three structural proteins were chosen using specific criteria to pinpoint T-cell and B-cell epitopes, which were subsequently validated through interferon-gamma induction. Six overlapping epitopes for cytotoxic T-lymphocytes (CTL), helper T-lymphocytes (HTL), and linear B-lymphocytes (LBL) were selected to construct a multi-epitope vaccine, achieving a 92.29% coverage of the global population. These epitopes were then fused with the 50S ribosomal protein L7/L12 adjuvant to improve protection against international strains. The three-dimensional structure of the designed vaccine construct underwent an extensive evaluation through structural analysis. Following molecular docking studies, the YEZV vaccine construct emerged as a candidate for further investigation, showing the lowest binding energy (−78.7 kcal/mol) along with favorable physiochemical and immunological properties. Immune simulation and molecular dynamics studies demonstrated its stability and potential to induce a strong immune response within the host cells. This comprehensive analysis indicates that the designed vaccine construct could offer protection against the YEZV. It is crucial to conduct additional in vitro and in vivo experiments to verify its safety and effectiveness. [ABSTRACT FROM AUTHOR]

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation ( R 2 = 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation. [ABSTRACT FROM AUTHOR]

Water resources, energy, and food are fundamental resources for high-quality regional development. In the process of rapid regional economic growth, integrating the utilization of these fundamental resources has become a critical challenge for achieving high-quality development in the Ili River Valley. To explore the coordinated development status of water, energy, and food (W-E-F) in the Ili River Valley, we constructed a comprehensive evaluation indicator system for the regional W-E-F system, and we calculated and analyzed the comprehensive development level, coupling coordination degree, and spatial equilibrium of the W-E-F system from 2008 to 2020. The results indicate that the comprehensive evaluation indicators of the W-E-F system in the Ili River Valley exhibited an overall upward trend, indicating that the system is moving in a positive direction. Among them, the water subsystem's comprehensive evaluation indicator showed an upward trend but fluctuated significantly during the study period, with the excessive proportion of agricultural water consumption being a key factor affecting its development. Furthermore, the comprehensive evaluation indicator of the energy subsystem showed a slight downward trend, indicating constraints on the development of energy subsystems. Agricultural surface pollution and industrial waste pollution are the primary factors limiting its development. Meanwhile, due to the significant attention from governments at all levels, the food subsystem has been developed rapidly, with its comprehensive evaluation indicator showing a significant upward trend, which shows that the region is actively promoting food production capacity enhancement initiatives. Additionally, the coupling degree of the W-E-F system remained in a state of coordinated coupling, with an average value between 0.7 and 1.0, indicating a high overall development level, and that the development of each resource affects and constrains that of the other two. The coupling coordination degree transitioned through phases of near coordination, primary coordination, good coordination, and moderate coordination, and all counties and cities showed a tendency to evolve towards high coupling, indicating significant potential for the further development of the regional W-E-F system coupling and coordination. Among the subsystems, the food subsystem exhibited the highest spatial equilibrium (0.78) and the smallest spatial disparities, while the energy subsystem demonstrated the lowest spatial equilibrium (0.40) and the largest spatial disparities. There were still significant issues with the utilization and equilibrium of the regional resource allocation, necessitating integrated planning for the coordinated development of the W-E-F system to achieve sustainable resource management and high-quality ecological and economic development. [ABSTRACT FROM AUTHOR]

Mucus plugging of the respiratory tract occurs in airway diseases, including asthma, chronic obstructive pulmonary disease, and cystic fibrosis. It can cause blockage of the airways, leading to breathlessness and lung failure. Here, we used a ventilatory setup to demonstrate the effect of BromAc® in dissolving mucus plugs in a novel ex vivo ovine obstructive lung model. Mucus simulant was filled into the trachea of freshly slaughtered ovine lungs and ventilated via an endotracheal tube (ETT) using Continuous Mandatory Ventilation. Predetermined single or repeated doses of Bromelain, Acetylcysteine (Ac), BromAc®, and saline control were administered via an Aerogen® vibrating nebulizer and ventilated for 30 or 60 min. Ventilatory recording of resistance, compliance, and tidal volume was conducted, and rheology pre- and post-treatment were measured. A significant decline in airway resistance (p < 0.0001) compared to the saline control was observed when treated with Bromelain, Ac, and BromAc®, with the latter showing a stronger mucolytic effect than single agents. The decline in resistance was also effective in shorter time points (p < 0.05) at lower doses of the drugs. Changes in compliance, peak pressure, and tidal volume were not observed after administration of the drugs. Rheology measurements revealed that BromAc®TM significantly reduced the viscosity of the mucin at the end of 30 min and 60 min time points (p < 0.001) compared to the saline control. BromAc® showed complete dissolution of the respiratory mucus simulant and improved ventilatory airflow parameters in the ex vivo ovine model. [ABSTRACT FROM AUTHOR]

Current medical treatments for diastasis recti often involve exercises to strengthen the core muscles, along with abdominal binders or supports. However, there is limited evidence comparing the effectiveness of surface electromyography (SEMG) biofeedback-assisted core strengthening exercises combined with kinesiotaping to other approaches. This study aimed to assess the impact of three interventions on core strength, inter-rectus distance, and quality of life in postpartum women with diastasis recti. The interventions included core strengthening exercises with kinesiotaping and SEMG biofeedback-assisted core strengthening with kinesiotaping. This randomized controlled trial (NCT05897255) included 24 postpartum women divided into three groups. We measured inter-rectus distance, quality of life using the Short Form Health Survey 36, and core strength using the McGill torso battery test. The SEMG biofeedback provided auditory and visual cues. We used one-way analysis of variance to compare outcomes between groups, while a t-test for within-group analysis. Both the SEMG biofeedback-assisted and non-assisted core strengthening exercises with kinesiotaping groups showed significantly greater improvements in energy, bodily pain, general health, physical functioning, and limitations due to physical problems than the core strengthening group. Additionally, the SEMG biofeedback-assisted group demonstrated a greater reduction in inter-rectus distance. There were no statistically significant differences in core strength improvement among the three groups. Core strengthening exercises with SEMG-assisted kinesiotaping were superior to core strengthening alone in reducing inter-rectus distance, enhancing physical function, energy levels, and general health, and decreasing bodily pain and limitations due to physical problems. Core strength improvements were similar across all groups. [ABSTRACT FROM AUTHOR]

This research aims to present a holistic view of sustainable investments on firm performance by exploring the dimensions of sustainable investments and firm performance concerning Indian private and publicly listed companies. Panel data regression has been used to explore the relationship between sustainable investment and firm performance. The data of 75 BSE-listed companies has been extracted from Prowess IQ from 2012–2022.Two panels based on different ownership structures were created to observe the impact of sustainable investment and firm performance. The findings of this study indicate that sustainable investments positively impact firm performance in private ownership but negatively impact the firm's performance in public ownership. The study provides an extensive and holistic view of sustainable investments in Indian companies listed in the BSE, which can be helpful for researchers, professionals, and policymakers in the field. The findings of this research contribute to the solutions to socio-economic challenges and support sustainable development goals through sustainable investments. This research is one of the first attempts to provide a holistic view of sustainable investments by including various aspects of sustainability and firm performance in Indian listed companies and ownership. [ABSTRACT FROM AUTHOR]

Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO2 emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the T m i n (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with R 2 = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions. [ABSTRACT FROM AUTHOR]

This study investigates the comparative analysis of the divergent pathways of sustainable energy development in Thailand and Japan. It offers a nuanced analysis of their policy frameworks, technological advancements, and socioeconomic contexts. This study elucidates the distinct strategies of the two nations by leveraging a robust dataset from sources including the Electricity Generating Authority of Thailand (EGAT) and Japan's Agency for Natural Resources and Energy (ANRE) toward renewable energy. The key findings indicate that Thailand has capitalized on policy instruments such as the Alternative Energy Development Plan 2018 (AEDP 2018) to augment its renewable energy capacity, particularly in the solar and biomass sectors. This policy-driven approach addresses the rural–urban energy divide and enhances energy access nationwide. Conversely, Japan's trajectory is characterized by integrating technological innovations like smart grids and the Feed-in Tariff (FiT) system, which have catalyzed significant increases in solar energy adoption and efficiency. Japan places great emphasis on technological solutions that underscore its strategy to mitigate the legacy constraints of energy infrastructure post-Fukushima. The implications of these findings are extended beyond national borders, offering critical insights into the complex interplay between policy, technology, and social engagement in the renewable energy transition. This study highlights the potential for community-based renewable energy projects in Thailand to drive economic growth and social equity. At the same time, Japan's experience illustrates the importance of regulatory reforms and technological leadership in overcoming structural barriers to energy innovation. These insights are particularly relevant for policymakers and stakeholders aiming to balance the imperatives of energy security, economic development, and environmental sustainability. Finally, this study emphasizes the need for tailored strategies that align renewable energy adoption with the unique contexts of each country, thereby enhancing global efforts against climate change. [ABSTRACT FROM AUTHOR]

In this paper, we derive a new generic equality for the first-order differentiable functions. Through the utilization of the general identity and convex functions, we produce a family of upper bounds for numerous integral inequalities like Ostrowski's inequality, trapezoidal inequality, midpoint inequality, Simpson's inequality, Newton-type inequalities, and several two-point open trapezoidal inequalities. Also, we provide the numerical and visual explanation of our principal findings. Later, we provide some novel applications to the theory of means, special functions, error bounds of composite quadrature schemes, and parametric iterative schemes to find the roots of linear functions. Also, we attain several already known and new bounds for different values of γ and parameter ξ. [ABSTRACT FROM AUTHOR]

The Tor browser is widely used for anonymity, providing layered encryption for enhanced privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities such as trafficking, smuggling, betting, and illicit trade. There is a need for Tor Browser forensics to identify its use in unlawful activities and explore its consequences. This research analyzes artifacts generated by Tor on Windows-based systems. The methodology integrates forensic techniques into incident responses per NIST SP (800-86), exploring areas such as registry, storage, network, and memory using tools like bulk-extractor, autopsy, and regshot. We propose an automated PowerShell script that detects Tor usage and retrieves artifacts with minimal user interaction. Finally, this research performs timeline analysis and artifact correlation for a contextual understanding of event sequences in memory and network domains, ultimately contributing to improved incident response and accountability. [ABSTRACT FROM AUTHOR]

The development of new technologies has significantly enhanced the monitoring and analysis of network traffic. Modern solutions like the Extended Berkeley Packet Filter (eBPF) demonstrate a clear advancement over traditional techniques, allowing for more customized and efficient filtering. These technologies are crucial for influencing system performance as they operate at the lowest layer of the operating system, such as the kernel. Network-based Intrusion Detection/Prevention Systems (IDPS), including Snort, Suricata, and Bro, passively monitor network traffic from terminal access points. However, most IDPS are signature-based and face challenges on large networks, where the drop rate increases due to limitations in capturing and processing packets. High throughput leads to overheads, causing IDPS buffers to drop packets, which can pose serious threats to network security. Typically, IDPS are targeted by volumetric and multi-vector attacks that overload the network beyond the reception and processing capacity of IDPS, resulting in packet loss due to buffer overflows. To address this issue, the proposed solution, iKern, utilizes eBPF and Virtual Network Functions (VNF) to examine and filter packets at the kernel level before forwarding them to user space. Packet stream inspection is performed within the iKern Engine at the kernel level to detect and mitigate volumetric floods and multi-vector attacks. The iKern detection engine, operating within the Linux kernel, is powered by eBPF bytecode injected from user space. This system effectively handles volumetric Distributed Denial of Service (DDoS) attacks. Real-time implementation of this scheme has been tested on a 1Gbps network and shows significant detection and reduction capabilities against volumetric and multi-vector floods. [ABSTRACT FROM AUTHOR]

In today's dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior's originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively. [ABSTRACT FROM AUTHOR]

Deepfake technology uses artificial intelligence to create realistic but false audio, images, and videos. Deepfake technology poses a significant threat to the authenticity of visual content, particularly in live-stream scenarios where the immediacy of detection is crucial. Existing Deepfake detection approaches have limitations and challenges, prompting the need for more robust and accurate solutions. This research proposes an innovative approach: combining eye movement analysis with a hybrid deep learning model to address the need for real-time Deepfake detection. The proposed hybrid deep learning model integrates two deep neural network architectures, MesoNet4 and ResNet101, to leverage their respective architectures' strengths for effective Deepfake classification. MesoNet4 is a lightweight CNN model designed explicitly to detect subtle manipulations in facial images. At the same time, ResNet101 handles complex visual data and robust feature extraction. Combining the localized feature learning of MesoNet4 with the deeper, more comprehensive feature representations of ResNet101, our robust hybrid model achieves enhanced performance in distinguishing between manipulated and authentic videos, which cannot be conducted with the naked eye or traditional methods. The model is evaluated on diverse datasets, including FaceForensics++, CelebV1, and CelebV2, demonstrating compelling accuracy results, with the hybrid model attaining an accuracy of 0.9873 on FaceForensics++, 0.9689 on CelebV1, and 0.9790 on CelebV2, showcasing its robustness and potential for real-world deployment in content integrity verification and video forensics applications. [ABSTRACT FROM AUTHOR]

Dengue fever, caused by the dengue virus (DENV), poses a significant global health threat, with a dramatic increase in cases driven by climate change, urbanization, and mosquito resistance. In Pakistan, a country with a population of 240 million, the world's fifth largest, dengue has emerged as an escalating public health crisis, with seasonal outbreaks severely straining the healthcare system. Despite decades of vector control efforts, there has not been much success, necessitating the introduction of dengue vaccination to boost population immunity. Recent advancements in vaccine development demonstrate promising efficacy and safety profiles, even in dengue-naive individuals. Implementing a dengue vaccination program in Pakistan could significantly reduce the disease burden, lower healthcare costs, and prevent future outbreaks. Integrating vaccination with existing public health initiatives can achieve high coverage and improve overall public health outcomes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic started in the last week of December, 2019. An emergency was declared throughout the globe as the virus spread over 208 counties within a short amount of time. This pandemic had significant negative impacts on common men living in densely populated countries, including Pakistan. Hence, this research aimed to record people's perception of the 7th largest settlement in Pakistan, namely Hyderabad. This study mainly focused on socioeconomic and psychological parameters. The close-ended questionnaire was designed concerning the Depression, Anxiety, and Stress Scale (DASS-21). The DASS-21 is an optimal tool for recording the emotional values of depression, anxiety, and stress. A total of 400 questionnaires were filled out; they used a 5-point Likert scale. Significant socioeconomic issues were noted, such as lower household income, economic recession, job cuts, inaccessible Internet services during online teaching or working from home, etc. The primary challenges in the lockdown situation were Internet access (4.06) and anxiety (4.86) in the female population. There were higher levels of depression (N = 200), anxiety (N = 341), and stress (N = 125) in the local inhabitants. Other notable problems were illuminated, such as restrictions on social gatherings, electricity load shedding, and lower incomes. This study concludes that the degree of psychological problems varied according to the typology of gender. Based on study findings, this study recommends the prompt execution of policies considering possible future pandemics to restrain anxiety and depression in the female population. The local government is also advised to revitalize infrastructure to provide uninterrupted power supplies and Internet facilities. The income-generating channels should be open for lower-income households concerning future lockdowns. [ABSTRACT FROM AUTHOR]

The current study investigates the effects of a digital growth mindset on the motivation and success of chemistry students. The approach involves the use of technological tools that encourage students to face challenges and keep trying even when things become difficult. Students can achieve milestones by following this fruitful methodology. This study utilized a mixed-method design of an ordered–explanatory type, as identified in one of the categories of mixed-method approaches. The quantitative aspects of the research project were conducted using a matching-only pre-test–post-test control-group design. This was conducted because the study was carried out on secondary school students in Lahore, Pakistan, and the population included students up to the tenth grade. Only the experimental group participated in digital growth mindset activities. The control group was taught using traditional methods. The qualitative aspect of the study involved conducting focus group discussions with students in the experimental group. The results showed a significant improvement in motivation and chemistry achievement among the students in the experimental group, as evidenced by the higher mean scores from the pre-tests and the post-tests compared to the control group. The present research findings reveal that digital growth mindset interventions, when appropriately incorporated into chemistry curricula, possess the capacity to not only improve student engagement and subsequent performance but also to provide educators with valuable insights into instructional practices that are worth implementing in the digital era. [ABSTRACT FROM AUTHOR]

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India's Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin's GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices. [ABSTRACT FROM AUTHOR]

Background and objectives: Pre-eclampsia (PE) is a pregnancy-specific condition characterized by significant health risks for pregnant women worldwide due to its status as a multi-organ disorder. High blood pressure (hypertension) with or without proteinuria is usually considered an initial clinical sign of PE. The pathogenesis of pre-eclampsia is highly complex and likely involves multiple factors, including poorly developed uterine spiral arterioles, immunological issues, placental ischemia or infarction, and genetic abnormalities. Inflammatory cytokine production, regulated by cytokine gene polymorphisms, is one of the factors likely contributing to the development of PE. The present study aimed to assess IL-6, IL-1β, and Apo B-100 gene polymorphism and to evaluate the association of these polymorphisms with PE. Materials and Methods: This cross-sectional observational study involved 99 participants aged 16 to 45 years from Bahawal Victoria Hospital Bahawalpur, Punjab, Pakistan. The participants were divided into three groups: Group 1 (PE with severe hypertension), Group 2 (PE with hypertension), and Group 3 (control), each comprising 33 individuals. Maternal blood samples were collected, DNA was extracted, and molecular genetic analysis of the IL-6, IL-1β, and Apo B-100 genes was performed using the PCR-RFLP method. Allelic frequencies were compared, and statistical analysis was conducted using SPSS 25, applying the Hardy–Weinberg equation and chi-square test to evaluate the results. Results: There are differences in the distribution of allelic frequencies for IL-6 -174G/C (CC, GC, GG), IL-1β-511C/T (CC, CT, TT), and Apo B-100 2488 C/T (CC, CT, TT) between pre-eclamptic patients and the control group. The analysis using the Hardy–Weinberg equilibrium and chi-square test showed an association between the IL-6-174 G/C polymorphism and the severity of pre-eclampsia. Conclusions: The polymorphisms of the IL-6, IL-1β, and Apo B-100 genes revealed different alleles. The IL-6 gene alone was found to be in disequilibrium according to the Hardy–Weinberg equation, indicating a potential link to the severity of pre-eclampsia in the population studied. [ABSTRACT FROM AUTHOR]

The main type of saponins occurring in the root of Platycodon grandiflorus (Jacq.) A. DC. are oleanolic acid glycosides. The CYP716 gene family plays a major role in catalyzing the conversion of β-amyrin into oleanolic acid. However, studies on the CYP716 genes in P. grandiflorus are limited, and its evolutionary history remains poorly understood. In this study, 22 PgCYP716 genes were identified, distributed among seven subfamilies. Cis-acting elements of the PgCYP716 promoters were mainly involved in plant hormone regulation and responses to abiotic stresses. PgCYP716A264, PgCYP716A391, PgCYP716A291, and PgCYP716BWv3 genes were upregulated in the root and during saponin accumulation, as shown by RNA-seq analysis, suggesting that these four genes play an important role in saponin synthesis. The results of subcellular localization indicated that these four genes encoded membrane proteins. Furthermore, the catalytic activity of these four genes was proved in the yeast, which catalyzed the conversion of β-amyrin into oleanolic acid. We found that the content of β-amyrin, platycodin D, platycoside E, platycodin D3, and total saponins increased significantly when either of the four genes was over expressed in the transgenic hair root. In addition, the expression of PgSS, PgGPPS2, PgHMGS, and PgSE was also upregulated while these four genes were overexpressed. These data support that these four PgCYP716 enzymes oxidize β-amyrin to produce oleanolic acid, ultimately promoting saponin accumulation by activating the expression of upstream pathway genes. Our results enhanced the understanding of the functional variation among the PgCYP716 gene family involved in triterpenoid biosynthesis and provided a theoretical foundation for improving saponin content and enriching the saponin biosynthetic pathway in P. grandiflorus. [ABSTRACT FROM AUTHOR]

This study investigates the influence of government effectiveness, health expenditure, and sustainable development goals (SDGs) on life expectancy in Pakistan. To accomplish this, a systematic analysis was conducted on time series data spanning from 2000 to 2020. Cointegration analysis was utilized to evaluate the long-term integration of all variables, while a comprehensive causality test was performed to investigate the short-term links among government effectiveness, health expenditure, SDGs, and life expectancy. The findings of the Johansen Cointegration test definitively confirmed the presence of long-term cointegration among all variables. In addition, the results of the Granger causality test show that there is a one-way causal relationship between government performance, health spending, and SDGs to life expectancy in the short term. The validation of both enduring and immediate connections among these factors emphasizes the crucial significance of healthcare services in Pakistan. Therefore, it is important to push for more healthcare investments and increased national budget allocations by the Pakistani government. Prioritizing the allocation of resources towards healthcare, bolstering the efficiency of the administration, and attaining SDG targets are all crucial for enhancing life expectancy in Pakistan. The study's results also carry significant policy implications, underscoring the necessity of strategically implementing health expenditure and SDG targets to enhance human capital and population well-being, as demonstrated by the increased life expectancy. [ABSTRACT FROM AUTHOR]

The sustainable development of an area is highly reliant on a reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller's performance is evaluated in a MATLAB environment, and simulation results show the proposed scheme's superiority under different operating conditions. The frequency is stable at 50 Hz after a 50 KW load is added. [ABSTRACT FROM AUTHOR]

A crucial component of digital integrated circuits with numerous power domains is the level shifter (LS). The traditional topologies are currently the mirror LS and cross-coupled LS. For full-swing level conversions from extremely low voltage to the nominal voltage of the supply, ahybrid LS is proposed in this paper, which is a combination of the current mirror LS and cross-coupled LS with a swing-aware output inverter. The proposed LS circuit is designed to ensure full swing, static current-free, and limited current-contention level conversions by preserving the benefits of the current mirror and cross-coupled LS, and using them to eliminate each other's shortcomings. The proposed hybrid LS is designed and implemented by using 45-nm technology in Cadence Virtuoso tool. Pass transistors and current limiters with multiple thresholds are included for the suggested LS. The proposed LS provides voltage conversion from 0.10 V to 1.20 V. At alevel-shifting voltage of 0.20 V, the proposed hybrid LS at 1 MHz input frequency demonstrates a delay of 8.38 ns, an average power consumption of 3.81 µW, and an energy per transition of 26.64 fJ. Additionally, the suggested LS has low delay, good supply voltage scaling, and delay scalability. [ABSTRACT FROM AUTHOR]

This study aims to enhance the prediction of COVID-19 vaccination trends using a novel integrated forecasting model, facilitating better public health decision-making and resource allocation during the pandemic. As the COVID-19 pandemic continues to impact global health, accurately forecasting vaccination trends is critical for effective public health response and strategy development. Traditional forecasting models often fail to capture the complex dynamics of pandemic-driven vaccination rates. The analysis utilizes a comprehensive dataset comprising over 68,487 entries, detailing daily vaccination statistics across various demographics and geographic locations. This dataset provides a robust foundation for modeling and forecasting efforts. It utilizes advanced time series analysis techniques and machine learning algorithms to accurately predict future vaccination patterns based on the Hybrid Harvest model, which combines the strengths of ARIMA and Prophet models. Hybrid Harvest exhibits superior performance, with mean-square errors (MSEs) of 0.1323, and root-mean-square errors (RMSEs) of 0.0305. Based on these results, the model is significantly more accurate than traditional forecasting methods when predicting vaccination trends. It offers significant advances in forecasting COVID-19 vaccination trends through integration of ARIMA and Prophet models. The model serves as a powerful tool for policymakers to plan vaccination campaigns efficiently and effectively. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM) is becoming a major global health concern, especially in developing nations. The high prevalence of obesity and related diabetes cases are attributed to rapid economic progress, physical inactivity, the consumption of high-calorie foods, and changing lifestyles. Objectives: We investigated the roles of pro-inflammatory chemokines CCL1, 2, 4, and 5 in T2DM with varying levels of obesity in the Asir region of Saudi Arabia. Materials and Methods: In total, 170 confirmed T2DM subjects and a normal control group were enrolled. Demographic data, serum levels of CCL-1, 2, 4, and 5, and biochemical indices were assessed in the subjects and control groups by standard procedures. Results: T2DM subjects were divided into four groups: A (normal body weight), B (overweight), C (obese), and D (highly obese). We observed that male and female control subjects had similar mean serum concentrations of pro-inflammatory chemokines CCL-1, 2, 4, and 5. T2DM subjects in all the four groups showed significantly higher levels of all the four chemokines compared to the controls, regardless of gender. In T2DM subjects with obesity and severe obesity, the rise was most significant. There was a progressive rise in the concentrations of CCL-1, 2, and 4 in T2DM subjects with increasing BMI. Serum CCL5 levels increased significantly in all T2DM subject groups. The increase in CCL5 was more predominant in normal-weight people, compared to overweight and obese T2DM subjects. Conclusions: Male and female control subjects had similar serum levels of pro-inflammatory chemokines CCL-1, 2, 4, and 5. The progressive rise in blood concentrations of three pro-inflammatory chemokines CCL-1, 2, and 4 in T2DM subjects with increasing BMI supports the idea that dyslipidemia and obesity contribute to chronic inflammation and insulin resistance. Serum CCL5 levels increased significantly in all T2DM subject groups. The selective and more pronounced increase in CCL5 in the T2DM group with normal BMI, compared to subjects with varying degrees of obesity, was rather surprising. Further research is needed to determine if CCL5 underexpression in overweight and obese T2DM subjects is due to some unexplained counterbalancing processes. [ABSTRACT FROM AUTHOR]

Silver oxide (Ag2O) particles are wonderful candidates due to their unique properties, and their use in a wide range of research, industrial and biomedical applications is rapidly increasing. This makes it fundamental to develop simple, environmentally friendly methods with possible scaling. Herein, sodium borohydride and Datura innoxia leaf extract were applied as chemical and biological stabilizing and reducing agents to develop Ag2O particles. The primary aim was to evaluate the anticancer and antiviral activity of Ag2O particles prepared via two methods. XRD, UV-visible and SEM analyses were used to examine the crystallite structure, optical properties and morphology, respectively. The resulting green-synthesized Ag2O particles exhibited small size, spherically agglomerated shape, and high anticancer and antiviral activities compared to chemically synthesized Ag2O particles. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium-bromide) assay of green-synthesized Ag2O particles showed high anticancer activity against MCF-7 cells with IC50 = 17.908 µg/mL compared to chemically synthesized Ag2O particles with IC50 = 23.856 µg/mL. The antiviral activity of green-synthesized Ag2O particles and chemically synthesized Ag2O particles was also evaluated by a plaque-forming assay, and green-synthesized Ag2O particles showed higher antiviral ability with IC50 = 0.618 µg/mL as compared to chemically synthesized Ag2O particles with IC50 = 6.129 µg/mL. We propose the use of green-synthesized Ag2O particles in cancer treatment and drug delivery. [ABSTRACT FROM AUTHOR]

We use the daily data from 1 October 2010 to 1 March 2021 on the stock prices of several global, regional, and country-specific ESG indices of the Dow Jones Sustainability Index family (DJSI) and MSCI family to analyse the relative performance of sustainable indices (ESG) and corresponding conventional benchmarks. In terms of classic risk and return characteristics and modern portfolio metrics, we report mixed evidence with some sustainability indices marginally outperforming the traditional benchmarks and others staying at parity. Regionally, the evidence is heterogeneous; the Pan Arab, the Middle East (including Israel), the United States, Emerging Markets, and Europe indices reported a slightly superior performance and those belonging to Asia Pacific, Emerging Africa, and Latin America marginally underperformed compared to their conventional counterparts. The findings of this study imply that sustainable investments appear to be an encouraging investment option, although their progress has not been substantial. For an appreciable outperformance of sustainable investments, a more conducive regulatory framework should be established, including robust incentivizing policies concerning tax rebates or low capital costs. [ABSTRACT FROM AUTHOR]

Learning and using technology in the workplace are essential for a company's commitment to the sustainable development of its resources. Finding competent engineers who can handle information communication technologies (ICTs) is a challenge for companies. Currently, however, the ability to use these technologies is limited to technicians with specialized training, and not everyone can engage in development. Therefore, it is safe to conclude that equity in the use of technology has not yet been realized. This study aims to analyze, based on actual cases, the necessary conditions and mechanisms for people with diverse experiences and circumstances, not limited to engineers, to participate in ICT development to address human resource diversity. The use of technology such as low-code platforms (LCPs) that have recently emerged on the market has shown that nonprofessional engineers without programming training can participate in development projects. This research will be useful to managers in advancing Diversity, Equity, and Inclusion (DEI) strategies in their workplaces and contribute to organizational research regarding new trends in technology use by individuals: low codability. The findings of this study are of significant relevance to the Sustainable Development Goals (SDGs) of decent work and economic growth, as well as gender equality. [ABSTRACT FROM AUTHOR]

Congestion persists despite various demand management techniques, particularly for handling recurrent congestion. However, non-recurrent congestion from events like VIP movements poses unique challenges, especially during peak hours. This study investigates the environmental and economic impacts of road blockages due to VIP movements in developing countries, focusing on Pakistan. Considering practiced standard operating procedures associated with VIP movements, this study finds significant delays and economic burdens in debt-ridden economies. It uses discrete choice modeling and microsimulation techniques to evaluate the value of travel time and quantifies road blockage effects on fuel consumption, travel time, and carbon emissions. Data from central blockage locations in Rawalpindi and Islamabad reveal a value of travel time estimated at 1.77 USD/h, with income and gender significantly influencing mode choices during VIP movements. Moreover, road blockages exceeding two minutes substantially negatively impact the environment and economy, particularly in developing nations. Urgent action is needed for effective mitigation strategies and sustainable transportation policies to address the detrimental effects and promote alternative transportation modes. Recommendations include limiting VIP blockages to a maximum of two minutes and implementing policies to discourage private car usage. Despite limitations, the study emphasizes the critical role of sustainable transportation policies in enhancing the well-being of road users in developing nations. [ABSTRACT FROM AUTHOR]