Search Results (27,259)

IgG Fc binding protein (FcGBP) is a mucin-like protein that binds strongly to IgG and IgG–antigen complexes in intestinal mucus. FcGBP presence and its altered expression levels in meconium accumulating in the fetal intestine and amniotic fluid flowing in the intestine may provide new knowledge of the mechanisms responsible for the immune adaptation of the fetus to extrauterine life. FcGBP concentrations were measured by ELISA in the first-pass meconium and amniotic fluid samples collected from 120 healthy neonates delivered by either vaginal birth (n = 35) or cesarean section (n = 85) at 36 to 41 weeks gestation. The meconium FcGBP concentrations (405.78 ± 145.22 ng/g) decreased (r = −0.241, p = 0.007) over the course of 36 to 41 weeks gestation, but there were no significant changes (p > 0.05) in the amniotic fluid FcGBP (135.70 ± 35.83 ng/mL) in the same period. Both meconium and amniotic fluid FcGBP concentrations were higher (p < 0.05) in neonates delivered by cesarean section. Decreases in the meconium FcGBP concentrations correlated (r = −0.37, p = 0.027) with the gestational age in neonates delivered by vaginal birth but not in those delivered by cesarean section (p > 0.05). No association was found between the FcGBP concentrations in meconium and amniotic fluid and the birth weight (p > 0.05). With the development of the mucosal immune system in the fetal intestine over the course of the third trimester of gestation, the meconium FcGBP concentrations decrease. Increased FcGBP concentrations measured in the meconium and amniotic fluid of neonates delivered by cesarean section may possibly indicate altered intestinal mucosal function. Intrauterine growth is not associated with the intestinal mucosal barrier maturation involving FcGBP. Full article

Despite the widespread accessibility of vaccines and antivirals, seasonal influenza virus epidemics continue to pose a threat to public health. In this study, we constructed a recombinant replication-deficient simian adenovirus type 25 vector carrying the full-length hemagglutinin (HA) of the H1N1 influenza virus, named rSAd25-H1. Both systemic and mucosal humoral immune responses, as well as the protective efficacy, were assessed in mice immunized via the intramuscular (IM) or intranasal (IN) route. A single-dose IM or IN administration of rSAd25-H1 elicited a robust systemic IgG antibody response, including hemagglutination inhibition antibodies. As expected, only IN immunization was able to induce IgA production in serum and respiratory mucosa. Notably, a single dose of rSAd25-H1 at the highest dose (10¹⁰ viral particles) conferred complete protection against lethal homologous H1N1 challenge in mice despite the route of administration. These findings demonstrate the potential of simian adenovirus type 25-based vectors as a promising candidate for intranasal vaccine development targeting respiratory pathogens. Full article

Background: While several test-automation maturity models (e.g., CMMI, TMMi, TAIM) exist, none explicitly integrate ISO 9001-based quality management systems (QMS), leaving a gap for organizations that must align E2E test automation with formal quality assurance. Objective: This study proposes a test-automation maturity model (TAMM) that bridges E2E automation capability with ISO 9001/ISO 9004 self-assessment principles, and evaluates its reliability and practical impact in industry. Methods: TAMM comprises eight maturity dimensions, 39 requirements, and 429 checklist items. Three independent assessors applied the checklist to three software teams; inter-rater reliability was ensured via consensus review (Cohen’s κ = 0.75). Short-term remediation actions based on the checklist were implemented over six months and re-assessed. Synergy with the organization’s ISO 9001 QMS was analyzed using ISO 9004 self-check scores. Results: Within 6 months of remediation, mean TAMM score rose from 2.75 → 2.85. Inter-rater reliability is filled with Cohen’s κ = 0.75. Conclusions: The proposed TAMM delivers measurable, short-term maturity gains and complements ISO 9001-based QMS without introducing conflicting processes. Practitioners can use the checklist to identify actionable gaps, prioritize remediation, and quantify progress, while researchers may extend TAMM to other domains or automate scoring via repository mining. Full article

This study explores the transformative potential of blockchain technology in overhauling conventional housing rental systems. It specifically addresses persistent issues, such as information asymmetry, fraudulent listings, weak Rental Agreements, and data breaches. A comprehensive review of ten academic publications highlights the architectural frameworks, underlying technologies, and myriad benefits of decentralized rental platforms. The intrinsic characteristics of blockchain—immutability, transparency, and decentralization—are pivotal in enhancing the credibility of rental information and proactively preventing fraudulent activities. Smart contracts emerge as a key innovation, enabling the automated execution of Rental Agreements, thereby significantly boosting efficiency and minimizing reliance on intermediaries. Furthermore, Decentralized Identity (DID) solutions offer a robust mechanism for securely managing identities, effectively mitigating risks associated with data leakage, and fostering a more trustworthy environment. The suitability of platforms such as Hyperledger Fabric for developing such sophisticated rental systems is also critically evaluated. Blockchain-based systems promise to dramatically increase market transparency, bolster transaction security, and enhance fraud prevention. They also offer streamlined processes for dispute resolution. Despite these significant advantages, the widespread adoption of blockchain in the rental sector faces several challenges. These include inherent technological complexity, adoption barriers, the need for extensive legal and regulatory adaptation, and critical privacy concerns (e.g., ensuring compliance with GDPR). Furthermore, blockchain scalability limitations and the intricate balance between data immutability and the necessity for occasional data corrections present considerable hurdles. Future research should focus on developing user-friendly DID solutions, enhancing blockchain performance and cost-efficiency, strengthening smart contract security, optimizing the overall user experience, and exploring seamless integration with emerging technologies. While current challenges are undeniable, blockchain technology offers a powerful suite of tools for fundamentally improving the rental market’s efficiency, transparency, and security, exhibiting significant potential to reshape the entire rental ecosystem. Full article

This review outlines a comprehensive methodology for the research and development of heterogeneous catalytic technologies (R&D_HeCaTe). Emphasis is placed on the fundamental interactions between reactants, solvents, and heterogeneous catalysts—specifically the roles of catalytic centers and support materials (e.g., functional groups) in modulating activation energies and stabilizing catalytic functionality. Particular attention is given to catalyst deactivation mechanisms and potential regeneration strategies. The application of molecular modeling and chemical engineering analyses, including reaction kinetics, thermal effects, and mass and heat transport phenomena, is identified as essential for R&D_HeCaTe. Reactor configuration is discussed in relation to key physicochemical parameters such as molecular diffusivity, reaction exothermicity, operating temperature and pressure, and the phase and “aggressiveness” of the reaction system. Suitable reactor types—such as suspension reactors, fixed-bed reactors, and flow microreactors—are evaluated accordingly. Economic and environmental considerations are also addressed, with a focus on the complexity of reactions, selectivity versus conversion trade-offs, catalyst disposal, and separation challenges. To illustrate the breadth and applicability of the proposed framework, representative industrial processes are discussed, including ammonia synthesis, fluid catalytic cracking, methanol production, alkyl tert-butyl ethers, and aniline. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security. Full article

In the face of growing demand for food and climate change, ensuring the stability and height of crop yields is becoming a key challenge for modern agriculture. One of the solutions supporting the sustainable development of crop production is the use of certified seed. The aim of this study was to assess the impact of using certified seed on the level and stability of yields of three cereal species: winter wheat, winter triticale and spring barley, in temperate climate conditions. Data came from surveys conducted on over 8000 farms in six agroecoregions of Poland in 2021–2023. The analysis showed significantly higher yields on farms using certified seed for all species studied. Additionally, greater yield stability (lower values of Shukla variance and Wricke ecovalence) was noted in the case of using certified seeds, especially in region IV. This indicates the positive impact of certified seeds (e.g., genetic purity, health, and vigor) on the efficiency and resilience of agricultural systems. This phenomenon is of particular importance in the context of climate change and may be an important element of risk management strategies in agriculture. Full article

Background: this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method: the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result: the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions: the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning. Full article

To improve the postharvest preservation of cherry tomatoes and combat pathogenic, both bacterial and fungal contamination (particularly Alternaria alternata), a novel biodegradable coating was developed based on a water-in-water (W/W) Pickering emulsion system. The emulsion was stabilized by L. plantarum (Lactobacillus plantarum), with maltodextrin (MD) as the dispersed phase and hydroxypropyl methylcellulose (HPMC) as the continuous phase. Characterization of emulsions at varying concentrations revealed that the optimized W/W-PL^8 film exhibited superior stability, smooth morphology, and low water vapor permeability (WVP = 220.437 g/(m²·24 h)), making it a promising candidate for fruit and vegetable preservation. Furthermore, the coating demonstrated strong antioxidant activity, with scavenging rates of 58.99% (ABTS) and 94.23% (DPPH), along with potent antimicrobial effects, showing inhibition rates of 12.8% against Escherichia coli and 23.7% against Staphylococcus aureus. Applied to cherry tomatoes, the W/W-PL^8 coating significantly reduced respiration rates, minimized decay incidence, and maintained nutritional quality during storage. Remarkably, the coating successfully controlled Alternaria alternata contamination, enhancing the storage duration of cherry tomatoes. These findings highlight the potential of W/W-PL^8 as an eco-friendly and functional packaging material for fresh produce preservation. Full article

Autism Spectrum Disorder (ASD) is frequently accompanied by gastrointestinal disturbances, dietary selectivity, and altered stress responses, with growing evidence pointing to gut–brain axis involvement. While intestinal microbiota has been extensively studied, the role of the oral microbiota remains underexplored. This study investigates the associations between oral microbiota composition and behavioral, gastrointestinal, dietary, and neuroendocrine parameters in children with ASD. A total of 45 children aged 2–18 years comprised the study group. Data collection included oral swabs for 16S rRNA gene sequencing, salivary cortisol sampling, dietary records, and standardized behavioral assessments using the Vineland Adaptive Behavior Scale. A total of 363 microbial species across 11 phyla were identified. Significant correlations were observed between specific bacterial taxa and functional gastrointestinal disorders (FGIDs), dietary patterns, salivary cortisol rhythms, and functioning. Children with FGIDs, food selectivity, or macronutrient imbalances exhibited enriched pro-inflammatory taxa (e.g., Selenomonas, Megasphaera), whereas those with typical cortisol secretion or higher adaptive functioning showed greater microbial diversity and abundance of health-associated genera (e.g., Bifidobacterium dentium). These findings suggest that oral microbiota profiles may reflect systemic physiological and neurobehavioral traits in children with ASD. Further longitudinal studies are needed to clarify causal relationships and support the development of microbiota-targeted interventions. Full article

Maternal undernutrition (MUN) causes severe metabolic disruption in the offspring of mammals. Here we determined the role of histone modification in hepatic gene expression in late-gestation fetuses of nutritionally restricted cows, an established model using low-nutrition (LN) and high-nutrition (HN) conditions. The chromatin immunoprecipitation sequencing results show that genes with an altered trimethylation of histone 3 lysine 4 (H3K4me3) are associated with cortisol synthesis and secretion, the PPAR signaling pathway, and aldosterone synthesis and secretion. Genes with the H3K27me3 alteration were associated with glutamatergic synapse and gastric acid secretion. Compared to HN fetuses, promoter H3K4me3 levels in LN fetuses were higher in GDF15, IRF2BP2, PPP1R3B, and QRFPR but lower in ANGPTL4 and APOA5. Intriguingly, genes with the greatest expression changes (>1.5-fold) exhibited the anticipated up-/downregulation from elevated or reduced H3K4me3 levels; however, a significant relationship was not observed between promoter CpG methylation or H3K27me3 and the gene set with the greatest expression changes. Furthermore, the stress response genes EIF2A, ATF4, DDIT3, and TRIB3 were upregulated in the MUN fetal liver, suggesting activation by upregulated GDF15. Thus, H3K4me3 likely plays a crucial role in MUN-induced physiological adaptation, altering the hepatic gene expression responsible for the integrated stress response and systemic energy metabolism, especially circulating lipoprotein lipase regulation. Full article

The large intelligent surface (LIS) concept represents an architectural advance for enhancing the performance of 6G wireless communication systems. In this work, we address the problem of jointly selecting active panels and associating terminals to outputs of such active panels in a panel-based LIS framework to maximise the minimum signal-to-interference-and-noise ratio (SINR) across all terminals. Due to the nature of the mixed-integer linear programming (MILP) formulation, we propose an alternative approach based on a genetic algorithm (GA) that efficiently explores the solution space through tailored crossover via column swapping and adaptive mutation. We compare the GA’s performance against the CPLEX solver under various configurations and time constraints. The performance results show that the GA provides competitive solutions with reduced computational complexity, showcasing its potential for scalable LIS implementations with complex resource allocation. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

We recently reported that phospholipase A2 (PLA2)-mediated production of prostaglandins within the ventromedial hypothalamus (VMH) plays a critical role in systemic glucose homeostasis. However, the role of PLA2 in the VMH in regulating food intake is still unclear. Here, we attempted to investigate the role of PLA2 in regulating food intake and body weight in male mice. We injected an adeno-associated virus encoding short hairpin RNA (AAV-shRNA) targeting cytosolic phospholipase A2 (shPla2g4a) into the VMH. We assessed food intake, body weight, oxygen consumption, glucose tolerance, and insulin sensitivity. Three weeks after the AAV injection, the shPla2g4a group exhibited increased food intake and body weight gain compared to controls (shSCRM). Energy expenditure, oxygen consumption, and respiratory quotient (RQ) were comparable between groups. Our findings suggest that the cPLA2-mediated pathway in the VMH is critical for feeding behavior and maintaining energy homeostasis. Further investigation is needed to elucidate the underlying mechanisms. Full article