Search Results (12,235)

Accurate prediction of groundwater level (GWL) is of great significance for refined groundwater management. This study establishes a multi-model framework for predicting groundwater level by integrating the three-dimensional transient MODFLOW model, artificial neural networks (ANN) and long short-term memory (LSTM). The results reveal that the numerical model satisfactorily reproduces groundwater level variations during calibration and validation periods, with relative errors within 1.5 m for over 91.67% of the monitoring wells. The performance of LSTM model is significantly outperformed by the ANN model with the NSEs greater than 0.92 and RMSEs smaller than 1.51 m during the training period and validation periods. Multiple scenarios were established to compare and verify the prediction accuracy of the LSTM and numerical models. RMSE values ranged from 0.054 to 0.187 and 0.012 to 0.121, respectively. In addition, the RMSE value increases with the extension of the prediction period. The uncertainty value of the LSTM model gradually decreased from 1.0 to 0.74, while that of the numerical model remained at 0.71. This indicates that the physical process constraints of the numerical model can enhance prediction stability and interpretability under different scenarios, while machine learning can efficiently satisfy high-frequency adjustment requirements and respond to abrupt disturbances. This study provides scientific references for accurately predicting GWL and comparative research between numerical models and machine learning models. Full article

This study designed, developed, and evaluated a combined egg incubator and hatchery system to enhance poultry breeding efficiency, reliability, and ergonomic operation. Utilizing a developmental research design, the project addressed challenges in traditional incubation and hatching processes, including inconsistent temperature and humidity control, inadequate ventilation, frequent power interruptions, limited access to affordable materials and technical expertise, insufficient safety mechanisms, and a lack of multifunctional capability. Data were collected from 30 experts in agricultural engineering and poultry technology to evaluate design, construction, material availability, functionality, usability, safety, modularity, and ergonomics. Findings revealed the system was highly efficient, safe, and user-centered, improving hatch rates and operator comfort. Full article

In deepwater oil and gas development, conventional well-by-well drilling and completion involve repeated equipment deployment, resulting in low efficiency. The Liza project in the Stabroek Block, Guyana, adopts a batch drilling and completion mode, significantly improving operational efficiency. However, inter-well towage of a suspended riser introduces challenges to riser integrity and safety. This study reviews key technologies in batch drilling and develops a mechanical model for riser hard hang-off using OrcaFlex 11.5 to assess the effects of wave height, wind speed and direction, and towing speed on riser stress and universal joint angular displacement. Simulations under representative Guyana conditions show that riser stress increases with wave height. The most critical scenario occurs when towing against the current with a following wind, where the maximum safe towing speed is 0.80 m/s, governed by angular displacement limits. Additionally, batch operations significantly reduce drilling and connection time and offer environmental benefits. These results provide guidance for optimizing deepwater batch drilling and ensuring towing safety. Full article

Fermented vegetables in Yunnan Province, China, harbor abundant microbial diversity. However, the development of indigenous starter cultures remains under-utilized. Genomic information regarding Leuconostoc (L.) mesenteroides isolates from this region is particularly scarce. To assess the genomic characteristics of eight L. mesenteroides isolates from traditional Yunnan fermented vegetables, we performed whole-genome sequencing and conducted a comparative analysis with 21 publicly available vegetable-derived genomes. Comparative genomic analysis revealed marked variation in genome size and plasmid content, and pangenome analysis indicated an open configuration. Core-genome multilocus sequence typing (cgMLST) of the eight indigenous isolates showed high allelic diversity, indicating a genetically heterogeneous and non-clonal population. Phylogenomic analysis revealed that the evolutionary relationships among the 29 strains were not strictly correlated with their vegetable sources, suggesting an influence from other factors, such as geographic origin and region-specific processing methods. Similar to the profiles of the 21 publicly available genomes, inactive prophages, intrinsic vancomycin resistance genes, and genomic island fragments were detected in eight isolates, whereas no known virulence genes were identified. Bacteriocin gene clusters varied among strains, while stress tolerance and probiotic-related genes were conserved. Overall, these results provide genomic indications relevant to the safety, adaptability, and fermentation potential of indigenous L. mesenteroides from Yunnan. However, because these functional traits are inferred solely from genomic predictions, subsequent experimental validation is essential to confirm their phenotypic properties and technological efficacy. Full article

Urban industrial heritage is increasingly embedded in urban regeneration, public space provision, and community governance, yet existing studies have insufficiently examined how heterogeneous publics perceive its value through everyday digital discourse. Taking the Guangzhou Iron and Steel Plant industrial heritage site (hereafter, the Guanggang industrial heritage site) as a case study, this study used user-generated content from Rednote posts and local WeChat public-account comments to identify platform-mediated expressions of public value perception. A corpus of 745 valid samples comprising 51,459 Chinese characters was constructed after data collection, screening, and text preprocessing. Word-frequency analysis, semantic network analysis, and sentiment analysis were conducted using ROST CM 6.0. The results show that the two retrieved platform-contextual corpora foregrounded different concerns. Rednote discourse foregrounded ruin landscapes, industrial aesthetics, photography-based check-ins, and exploratory experiences, whereas WeChat comments emphasized park construction, public facilities, governance responsiveness, safety, and the residential environment. At the corpus level, lexicon-based sentiment classification indicated that Rednote texts were dominated by positive and neutral categories, while WeChat comments contained a higher proportion of texts classified as negative. This study conceptualizes dual foregrounding as a bounded selection process through which platform affordances, user self-selection, and users’ relationships with the site influence which concerns become visible in each corpus; it does not treat the observed differences as a causal platform effect. It argues that industrial heritage regeneration must translate historical, technological, and aesthetic values into public values that are interpretable, accessible, usable, and trusted by local communities. Full article

The study introduces a portable sliding sand sieve, transforming traditional stationary systems into an innovative solution for sand separation in the construction industry. This innovative tool offers improved mobility, durability, and operational efficiency, particularly for construction workers, civil technology students, and educators in areas with limited access to advanced equipment. Utilizing a developmental research design, the study involved the conceptualization, fabrication, and evaluation of the prototype. The design incorporated locally available materials, including phenolic boards, mesh screens, steel tubing, and a sliding mechanism supported by bearings and brackets. The Input–Process–Output (IPO) model guided the development, ensuring focus on functionality, affordability, and user safety. To address this gap, the researchers aimed to design, develop, and evaluate a portable sliding sand sieve to enhance sand sieving in construction settings. Expert and student evaluators highly rated the portable sliding sand sieve for its design simplicity, functionality, durability, modularity, and ergonomics. It was praised for its ease of use, time-saving capability, and adaptability to various work environments. The sliding feature enabled continuous sand flow, enhancing productivity and reducing physical strain. Full article

South Africa’s public hospital system faces mounting pressure from ageing infrastructure, rising patient demand, and constrained maintenance budgets. While significant investment has been directed toward the construction of new healthcare facilities, the diffusion and adoption of advanced technologies within operations and maintenance (O&M) remain uneven and underdeveloped. This misalignment limits the long-term performance, safety, and sustainability of hospital assets. This study investigates technological diffusion within the O&M environment of a newly commissioned 500-bed regional hospital in Durban, KwaZulu-Natal. A qualitative single-case study approach was adopted, drawing on semi-structured interviews with 14 stakeholders across project delivery and facility management functions. Data were analysed thematically to identify systemic patterns and operational constraints. Findings reveal a persistent reliance on manual, reactive maintenance practices, with minimal integration of digital tools, including building management systems, predictive maintenance technologies, and real-time monitoring platforms. Key barriers include unclear institutional roles, inadequate handover processes, limited technical capacity, and the absence of strategic leadership to drive innovation. A critical disconnect was also identified between managerial expectations and operational realities. The study argues that technological adoption in hospital O&M is not merely a technical challenge but an institutional one. It recommends targeted capacity development, structured transition frameworks, and stronger governance mechanisms to enable sustainable digital integration. Full article

Artificial intelligence (AI) is increasingly integrated into emergency general surgery (EGS), where rapid diagnosis, accurate decision-making, and timely intervention are essential for improving patient outcomes. Recent advances in machine learning, deep learning, computer vision, and predictive analytics have enabled AI-assisted systems to support clinicians throughout the perioperative workflow. Current applications include radiologic image interpretation, diagnosis of acute abdominal conditions, surgical workflow recognition, intraoperative anatomical guidance, postoperative complication prediction, and intensive care monitoring. AI technologies may improve diagnostic accuracy, optimize operative planning, enhance surgical safety, and facilitate personalized perioperative management. In minimally invasive surgery, computer vision and real-time data analysis have shown promising results for intraoperative decision support and surgical education. However, important limitations remain, including concerns regarding data quality, algorithm transparency, ethical governance, regulatory approval, and implementation disparities between healthcare systems. In addition, much of the current evidence is derived from retrospective or highly specialized datasets, limiting broad clinical applicability. This narrative review summarizes the current clinical applications of AI in emergency general surgery and discusses emerging technologies, existing challenges, and future perspectives regarding the integration of AI into acute surgical care. Full article

To address safety hazards such as line damage and operational instability caused by icing on high-voltage overhead transmission lines, this study conducts numerical simulation research on wire vibration de-icing based on the ANSYS finite element platform. Using a 100 m span transmission line as the research model, 49.8 m ice-covered sections are set on both sides of the line, and the 0.4 m range in the middle is designated as the concentrated excitation force area of the vibration motor. By applying intermittent harmonic loads in the excitation stage, the process of mechanical vibration de-icing is accurately reproduced. At the same time, life and death element technology is introduced to remove ice-covered units with stress exceeding the critical failure threshold, accurately realizing the dynamic simulation of the entire process of ice-covering cracking and detachment. This study selects resonance frequency bands that are suitable for the structural characteristics of the transmission line through static analysis, modal analysis, and harmonic response analysis, and preliminarily locks in candidate excitation frequencies. Combined with transient dynamics simulation, the optimal excitation frequency for vibration de-icing of transmission lines is determined by comprehensively considering the efficiency of de-icing and the safety constraints of conductor dancing. A method for determining the optimal de-icing frequency based on multi-step finite element analysis has been developed, which can provide theoretical support and simulation reference for the structural design, frequency matching, and operational parameter optimization of mechanical vibration de-icing devices for high-voltage transmission lines and overhead cables. Full article

Improving tenderness in whole-muscle pork products remains a technological challenge, particularly when natural processing strategies are preferred over conventional additives, as texture is regarded as one of the most important quality attributes influencing consumer perception and acceptance of meat products. This study investigated whether two plant proteases, papain and bromelain, incorporated into a red algae-based brine containing Palmaria palmata could enhance the quality of injected pork loin without compromising microbiological safety or sensory acceptance. Seven batches were produced: a control sample and six enzyme-treated samples containing papain or bromelain at 0.015%, 0.030%, and 0.045%. Overall, the enzymatic treatments had a limited effect on proximate composition. However, a modest decrease in fat content was observed, from 3.09% in the control sample to 2.70–2.82% in the samples treated with the highest concentrations of papain and bromelain (0.045%). In contrast, instrumental color and texture were strongly affected. Enzyme-treated samples became lighter, less red, and less saturated, with redness decreasing from 13.07 in the control to 5.19–6.66 in the highest-dose treatments and total color differences reaching 8.66. The most relevant effect was observed in texture, where papain and bromelain markedly reduced shear force, shear work, hardness, gumminess, and chewiness; shear force decreased from 26.22 N/cm² in the control to 10.78 N/cm² and 9.38 N/cm² in the batches treated with the highest enzyme concentrations. During refrigerated storage, total viable counts increased gradually but remained low, with a maximum of 4.56 × 10² CFU/g, while Escherichia coli, Salmonella spp., and Listeria monocytogenes were not detected. Sensory analysis further showed that enzymatic treatment improved perceived tenderness and juiciness without reducing overall acceptability. These findings indicate that papain and bromelain can be used as natural tenderizing tools in injected pork loin, offering a promising route toward cleaner-label meat products with improved texture and preserved microbiological quality. Full article

Artificial intelligence (AI) is increasingly embedded in marketing work, yet employees who use AI tools may not always disclose AI’s role in producing their outputs. This study examines AI disclosure silence, defined as employees’ intentional withholding of information about the use, role, or contribution of AI tools in work-related outputs after AI has already been used. Unlike AI avoidance or resistance, this construct concerns post-adoption concealment; unlike general employee silence, it focuses on the hidden technological contribution behind visible work. Drawing on Conservation of Resources Theory and Psychological Safety Theory, the study investigates how threat-based conditions, safety and governance conditions, and AI-related capability are associated with AI disclosure silence. Data were collected through a two-wave survey of 635 marketing employees who actively used AI tools at work. The analysis combined measurement validation, Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and explainable machine learning. The findings show that no single condition operated as a strong necessary bottleneck. Instead, AI disclosure silence appeared through multiple pathways involving AI anxiety, fear of negative evaluation, perceived creativity threat, perceived job insecurity, low trust in management, weak psychological safety, and unclear AI policy. SHapley Additive exPlanations (SHAP)-based interpretation further indicated that fear of negative evaluation, AI anxiety, perceived creativity threat, and trust in management had the strongest model-based predictive relevance. The study contributes to workplace AI and employee silence research by positioning AI disclosure silence as an emerging post-adoption disclosure construct. It also highlights the need for clear AI disclosure norms, non-punitive managerial responses, AI-assisted authorship guidelines, and psychologically safe AI-governance practices. The findings should be interpreted as configurational and predictive evidence rather than causal effects, and further scale validation across sectors and cultures is encouraged. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Bionic sensors are emerging as powerful analytical platforms driving the development of next-generation detection technologies, particularly for small molecule sensing in complex environmental and biological systems. However, accurate and selective detection of small molecules remains fundamentally challenging due to their low molecular weight, limited structural specificity, and strong interference from complex matrices. This review provides a comprehensive overview of recent advances in bionic sensor technologies, focusing on how the integration of synthetic biology, nanomaterials, and artificial intelligence (AI) addresses these limitations. Key biorecognition elements, including enzymes, antibodies, aptamers, and molecularly imprinted polymers, are examined for their suitability in small molecule sensing applications. Advances in nanomaterials such as graphene, carbon nanotubes, quantum dots, and MXenes are discussed in relation to signal transduction enhancement, sensitivity improvement, and device miniaturization. In parallel, the roles of AI and machine learning in signal denoising, adaptive calibration, and molecular fingerprinting for complex datasets are highlighted. Applications in wearable and implantable biosensors, environmental monitoring, and food safety are analyzed, emphasizing real-time detection of metabolites, pollutants, and toxins. Key challenges associated with AI-driven systems, including scalability, cost, data reliability, and ethical concerns, are also discussed. Emerging trends such as hybrid sensing platforms, self-powered biosensors, and secure data integration frameworks are presented as future directions. This review aims to provide a problem-driven perspective on how next-generation bionic sensors can overcome current limitations and enable robust small molecule detection in real-world applications. Full article

Spent coffee grounds (SCG) are generated in millions of tonnes annually due to rising global coffee consumption, posing significant challenges, including greenhouse gas emissions, waste-disposal problems, and the loss of valuable compounds like caffeine, dietary fibre, phenolics, antioxidants, proteins, and lipids, offering prospects for potential valorization. Its composition is influenced by several factors. This review focuses on recent advancements in the valorization of SCG across sectors such as food, nutraceuticals, bioenergy, and packaging. The emphasis is on pretreatment, extraction, and bioconversion methods, as well as current research gaps, limitations, and future directions. SCG valorization is oriented toward integrated, multi-product biorefinery systems based on green extraction and bioconversion technologies to recover high-value compounds in both the food and non-food sectors. Nonetheless, industrial scalability is limited by composition variability, energy-intensive processing, techno-economic constraints, and safety and regulatory issues that remain unresolved. The shortcomings, such as inadequate standardized characterization, toxicological validation, and pilot-scale studies, are critical gaps. Scalable, energy-efficient processes, AI-assisted optimization, and regulatory alignment development should be a priority in future research, so that sustainable and commercial deployment is possible. Full article

Background: Digital health technologies have expanded access to home fetal heart rate (FHR) monitoring devices, enabling fetal surveillance outside clinical settings. However, evidence on women’s awareness, acceptance, and experiences with these devices remains limited. Objective: To assess awareness, adoption, user experience, perceived reassurance, and attitudes toward home FHR monitoring among pregnant and postpartum women. Methods: A cross-sectional online survey was conducted using a structured questionnaire distributed via Google Forms. Eligible participants were women aged ≥18 years who were currently pregnant or had been pregnant within the previous two years. The survey evaluated awareness and use of home FHR monitoring devices, usage patterns, sources of recommendation and instruction, emotional responses, perceived reassurance, mobile application integration, and overall attitudes. Descriptive statistics and exploratory subgroup analyses were performed. Results: A total of 225 women completed the survey; 166 (73.8%) reported using a home FHR monitoring device during pregnancy. Most users reported positive emotional experiences, with calmness as the most common response. Home monitoring was generally perceived as reassuring, and many participants felt calmer on days of device use. Gynecologists were the primary source of device recommendations and usage instructions. Participants highlighted the importance of professional guidance, clear instructions, and mobile application support. Primiparous women had significantly higher adoption rates than multiparous women (p < 0.001). Conclusions: Home FHR monitoring was widely accepted and commonly perceived as reassuring. These devices may support patient-centered prenatal care when accompanied by appropriate professional guidance. Further prospective studies are needed to assess their clinical utility, safety, and integration into prenatal care pathways. Full article