Search Results (11,985)

With the rapid advancement of digital technology, the self-service model has emerged, introducing a new work model known as digital shadow work (DSW). In this model, consumers perform tasks traditionally performed by employees, such as item scanning and self-checkout, without compensation. While this optimizes service processes and reduces business costs, it raises concerns about consumer rights, work value, and business sustainability. This study explores the psychological factors that affect consumer participation in DSW within self-service environments. Using a grounded theory approach and semi-structured interviews, the study reveals key psychological drivers under the dual-system framework. This study’s findings indicate that habitual behavior, impulsivity, time pressure, technological dependence, social identification, and delayed gratification significantly affect participation in DSW. Notably, the intuitive system (System 1) plays a dominant role in decision-making, leading consumers to make quick, automatic choices, often leaving them unaware of the work involved. By identifying these psychological factors, this research increases consumer awareness of DSW, promoting self-protection in self-service contexts. Additionally, understanding decision-making psychology provides essential insights for companies in non-face-to-face self-service technologies, supporting sustainable business practices. Full article

Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder characterized by hyperandrogenism, insulin resistance, oxidative stress, and chronic low-grade inflammation, while conventional therapies are often limited by adverse effects and suboptimal adherence. This narrative review aims to evaluate the chemical composition and mechanistic effects of bioactive compounds derived from Allium species in the context of PCOS. A comprehensive analysis of the literature was performed, focusing on organosulfur compounds and polyphenols, with emphasis on their structure, reactivity, transformation pathways, and biological activity, integrating findings from preclinical and clinical studies. The evidence indicates that key compounds, including allicin, ajoene, and diallyl sulfides, exert biological effects through modulation of redox balance, inhibition of inflammation-related signaling, and regulation of insulin signaling pathways, while also influencing steroidogenesis and androgen synthesis. Polyphenolic compounds contribute primarily through antioxidant mechanisms related to their structural features. However, the current evidence remains limited by the scarcity of large-scale, long-term human clinical trials, particularly in women with PCOS, which restricts definitive conclusions regarding clinical efficacy, optimal dosing, safety, and long-term therapeutic applicability. Overall, Allium species represent a promising source of multitarget bioactive compounds for PCOS management, and understanding the chemical basis of their activity is essential for optimizing their therapeutic potential and guiding future research. Full article

Silicone structural sealant is a key material in glass curtain wall systems, for which an appropriate hyperelastic constitutive model is essential for enhancing the accuracy of finite element analysis (FEA). Firstly, an improved comprehensive fixture of silicone structural sealants for tensile and shear tests was conducted. Secondly, the hyperelastic constitutive model was selected to fit data through experiments, which was comprehensively evaluated using three indicators, such as goodness of fit, root mean square error, and relative deviation. Thirdly, this constitutive model was utilized for FEA validation under uniaxial tensile and shear conditions, the Mooney–Rivlin 3 (M-R3) model demonstrated optimal fitting performance. Finally, the mechanical response of the glass curtain wall under wind load was systematically analyzed under various debonding scenarios. This study reveals the failure evolution law of silicone structural sealant under actual service conditions, providing practical guidance for enhancing the safety design of glass curtain wall structures. Full article

The determination of optimal geometric characteristics of a catamaran that minimize vessel motion responses under prescribed design and operational conditions remains insufficiently addressed in existing engineering practice. This study presents a systematic methodology for the evaluation of catamaran seakeeping performance through the structured parametric comparison of principal geometric parameters. The proposed methodology comprises the identification of relevant geometric variables, the specification of their admissible variation ranges in accordance with design constraints, the selection of appropriate numerical evaluation tools, and the quantitative analysis of resulting motion responses. The objective is to determine parameter combinations that yield minimum motion amplitudes. The methodology presented in this article is partly a complex methodology for evaluation of seakeeping and total resistance, and partly selection of the most favorable combinations of geometrical parameters satisfying the design task parameters across both above-mentioned hydrodynamic qualities. The resistance part of the methodology is presented in previous works with links and description provided in this article. A graphical system for presenting simulation results is developed, allowing arrangement of the calculation results on one horizontal axis, representing catamaran length variations, grouped by the speed and demihull separation values and including catamaran demihull symmetry considerations. Aligned under each other, the graphs provide an intuitive interpretation of total resistance trends and seakeeping across various geometric configurations and operational speeds. This method, the seakeeping part of which is illustrated in the results paragraph, enables a comprehensive comparison of multiple design variants within a clear visual framework. The methodology is applied to a representative catamaran configuration by parametrically varying key geometric characteristics, including vessel length, demihull separation, and hull symmetry. The corresponding seakeeping responses are evaluated using the Maxsurf Motions computational framework. The results demonstrate that systematic variation and analysis of geometric parameters enable the identification of configurations with significantly reduced motion amplitudes. Pitching RAO amplitudes for different catamaran lengths can vary 45–50%, for demihull separation—25–50% and for asymmetry 27–50%. Heaving RAO amplitudes for different catamaran lengths can vary 45–50%, for demihull separation—32–65% and for asymmetry 30–60%. The findings indicate that demihull separation, hull-form symmetry, and overall vessel length each play a significant role in determining catamaran seakeeping performance. The proposed approach provides a robust basis for the early-stage design structured parametric comparison of catamarans, facilitating the selection of geometric configurations that minimize projected vessel motions and improve overall seakeeping performance. Full article

Quantum computing presents a critical threat to the cryptographic basis of metaverse platforms, with Shor’s algorithm capable of breaking traditional public-key cryptography and Grover’s algorithm significantly weakening symmetric encryption. The present self-sovereign identity (SSI) ecosystems are built on classical cryptographic systems that are susceptible to quantum attacks; hence, there is an immediate need for quantum-secure identity management in persistent virtual environments. This article proposes a solution called Quantum-Resistant MetaSSI (QR-MetaSSI), which is a comprehensive model that integrates NIST-standardized post-quantum cryptography (PQC) with W3C-compliant SSI principles. We design lattice-based decentralized identifiers (PQ-DIDs), hash-based verifiable credentials (PQ-VCs), and a hybrid authentication protocol that meets the needs of the metaverse, such as latency, interoperability, and persistent identities. The framework is subjected to mathematical modeling and simulation studies. Our study indicates that QR-MetaSSI keeps the authentication delay below 150 ms, which is inside the VR comfort range with 128-bit quantum security. Besides that, a comparative evaluation reveals that the proposed solution drastically reduces the risk of a quantum attack compared with classical ECC-based SSI systems at a level of computational overhead that is completely reasonable. QR-MetaSSI is a major step forward in the security of the metaverse, providing not only theoretical bases but also practical implementation instructions for the migration to quantum-resistant identity management. This framework not only addresses the most important breaches in security but also keeps the performance standards that are necessary for the creation of virtual environments that are highly immersive. Full article

Fracture characterization is crucial to constrain a realistic subsurface reservoir model. They are key elements, affecting fluid flow, permeability and consequently recovery factor and productivity. Considering a proper assessment of fracture network from subsurface investigation is often difficult; in recent years, the application of Digital Photogrammetry (DP) has become popular for fracture network characterization. In this paper, we combined DP and Discrete Fracture Network modeling (DFN) to assess the fluid circulation analysis of the Monte Antola Formation (Northern Apennines, Italy). Thanks to the application of DP, it is possible to reconstruct Digital Outcrop Models (DOMs) and acquire high-precision fracture measurements such as size, location, and orientation. Utilizing quantitative measurements, we performed DFNs to simulate rock mass permeability. The primary findings from the DFNs indicate that fluid circulation is primarily influenced by (1) regions with a high density of fractures, which are associated with the primary structural features observed throughout the study area, and (2) locally, by the orientation of the dominant and persistent fracture set. The proposed approach highlights the importance of the use of DOMs for better reconstruction of the fracture network and defining an important number of relevant parameters; such quantitative information remarkably improves the reliability of DFNs. Full article

To support offline process-parameter screening for Czochralski (CZ) silicon single crystal growth, this paper proposes a surrogate-assisted optimization framework based on an improved Langevin evolutionary algorithm. First, a multi-variable constrained optimization model is established, with the LSA-Transformer-predicted solid–liquid interface deformation used as the objective evaluation and with process-smoothness and physical-feasibility constraints considered. Six key process parameters–heater power, pulling rate, argon flow rate, crystal rotation speed, crucible rotation speed, and magnetic field strength–are selected as decision variables. Second, building on the classical Langevin algorithm, an adaptive inertia weight mechanism, a diversity promoter (DP) operator, and a local escaping operator (LEO) are introduced to improve global exploration and local optima escape in complex search spaces. Verification on 23 classical benchmark functions indicates that the ILEE algorithm shows competitive overall performance and achieves better or comparable results on many functions when compared with particle swarm optimization (PSO), grey wolf optimization (GWO), the original Langevin evolutionary algorithm (LEE), and other baseline algorithms. The proposed framework is then used for offline candidate recipe generation during the crystal equal-diameter growth stage (200 mm, 400 mm, 600 mm, 800 mm, and 1000 mm). The optimized candidate parameter combinations yield lower surrogate-predicted interface deformation under the given LSA-Transformer model and physical constraints. Because these values are not independent CFD or experimental measurements, the results should be interpreted as process-parameter guidance for future physical validation. This work provides a feasible surrogate-assisted offline screening framework for CZ silicon single crystal growth. Full article

Rapid urbanization, uncoordinated land-use growth, and insufficient integration of public transport have led to severe traffic congestion and declining mobility in Peshawar, Pakistan, even after the implementation of a Bus Rapid Transit (BRT) system. The core research problem addressed in this study is the mismatch between growing travel demand and the limited capacity, coverage, and operational efficiency of the existing urban transport network. This research aims to evaluate the current performance of Peshawar’s transport system and to identify integrated, evidence-based strategies to alleviate congestion and enhance urban mobility. Specifically, the objectives are to assess roadway level of service on major corridors, examine public transport user satisfaction with the BRT system, and propose targeted infrastructure and operational improvements. A mixed-methods approach was employed, combining traffic volume and level-of-service (LOS) analysis, public transport user surveys, and field observations at critical intersections. The findings indicate that several key arterial roads operate at LOS E–F during peak hours, and future traffic projections indicate widespread capacity failures under existing road geometries. Survey results reveal significant dissatisfaction with the BRT system, particularly due to limited spatial coverage, inadequate feeder routes, overcrowding, and excessive travel times. Based on these results, the study proposes integrated interventions, including road widening and auxiliary lanes, geometric and signalized junction improvements, expansion of BRT feeder services, development of new arterial and ring roads, and enhanced pedestrian and parking infrastructure. This study links quantitative traffic performance measures with user-perceived service deficiencies. It provides practical, data-driven guidance for policymakers and planners to support a more efficient, accessible, and sustainable urban transport system in Peshawar. Full article

Microbiologically induced concrete corrosion (MICC) poses a severe challenge to the long-term durability of infrastructure, particularly in sewer networks and marine environments, which is driven by microbial metabolic activities that attack cement hydrates (Ca(OH)₂, C-S-H) mainly caused by biogenic sulfuric acid (from sulfur-oxidizing bacteria) or organic acids (from fungi), converting them into expansive gypsum and ettringite, and then cause cracking and spalling. This article reviews advances in mechanisms, key microorganisms, and protection strategies of MICC to enhance our understanding of MICC and provide a guideline for effective protection. The corrosion mechanisms differ by environment: sewers exhibit three-stage pH-driven succession, marine biofilms can either accelerate or inhibit corrosion, while fungi dominate in agricultural and historical settings. Core functional microorganisms involved in MICC include sulfur-oxidizing bacteria (SOB), sulfate-reducing bacteria (SRB), and acid-producing fungi (AF), following pH-dependent succession, while indicator microorganisms for protection efficacy include typical SOB, SRB, and AF that are involved in MICC, as well as general antimicrobial indicator strains (e.g., Escherichia coli and Staphylococcus aureus) which are used only to assess broad antimicrobial activity and do not represent MICC-specific resistance. Multi-scale deterioration proceeds from microstructural decalcification and pore coarsening to macroscopic mass loss and compressive strength reduction. Protection strategies are categorized into: (i) corrosion-resistant materials (e.g., calcium aluminate cement and alkali-activated materials), (ii) antimicrobial additives (e.g., nano-ZnO and Cu₂O), (iii) surface coatings (e.g., superhydrophobic coatings and electrodeposited Cu/Cu₂O layers), and (iv) ecological regulation. However, significant gaps remain between laboratory efficacy and field performance, highlighting the need for long-term validation, multi-scale characterization, intelligent responsive materials, eco-compatible protection systems, and standardized microbial exposure systems. Full article

With the rapid development of general artificial intelligence, large models have gradually become the key force driving the digital transformation of the field. Agriculture has distinct domain characteristics, and traditional deep learning models are difficult to meet its cross-regional and cross-task requirements. Large models specifically designed for the agricultural field can integrate multi-source data and prior knowledge to break through this bottleneck. Therefore, tracking the development trend of large agricultural models is an important prerequisite for building new, quality productive forces in smart agriculture and promoting the digital transformation of agriculture. This article conducts a literature search and review around the research on large agricultural models, following the PRISMA guidelines. It combines the keywords of large models, crops, livestock breeding, etc., and only includes journal papers from 2022 to 2026, totaling 713 articles. Then, it performs topic modeling to deeply clarify the current research and application status, and summarizes the challenges faced and makes future research prospects. Existing evidence indicates that current large agricultural models are gradually developing towards agents and embodied intelligence, and are widely applied in scenarios such as agricultural knowledge services, pest and disease diagnosis and prevention, livestock and fishery breeding, and smart agricultural machinery control. However, they still face many key challenges, and further exploration is needed in theoretical methods and practical applications. In the future, research can be further deepened and expanded in areas such as the construction of high-quality data sets, the construction of domain evaluation systems, strengthening model reliability, building multi-agent systems, and lightweight deployment of large models and embodied intelligence. Full article

Breast cancer is the most commonly diagnosed cancer in women, with early detection playing a critical role in clinical outcomes. Mammography remains the standard screening modality, producing X-ray images used to assess mammographic density, a key indicator of the proportion of fibroglandular tissue within the breast. The Breast Imaging-Reporting and Data System (BI-RADS) classification system is widely used to report density across four qualitative categories. High density can obscure malignancies and is independently associated with elevated breast cancer risk. Manual interpretation of mammographic density is prone to subjectivity and inter-observer variability, and supervised learning-based estimation methods trained on subjective labels may reflect this inherent subjectivity. This work proposes an unsupervised framework for quantitative breast density estimation that requires no labeled data in its core pipeline. Expert labels are used exclusively to calibrate post hoc discretization thresholds for binary classification, enabling comparison with supervised methods in the literature. The main contributions include: (i) an adaptive Region of Interest (ROI) extraction algorithm, (ii) a Convolutional Neural Network (CNN) based unsupervised segmentation pipeline tuned for mammographic density separation, (iii) a novel confidence metric for identifying unreliable segmentation outputs, (iv) a label correction mechanism for low-confidence cases, and (v) a confidence-filtered majority voting scheme for per-patient classification. The framework is evaluated on two public datasets, namely DDSM and INbreast, with segmentation performance yielding Silhouette scores exceeding 0.92. Agreement with expert labels reaches 71.43% and 79.28% for DDSM and INbreast, respectively. Image-level clustering quality assessment confirms effective unsupervised labeling, with Silhouette scores averaging 0.57 for DDSM and 0.50 for INbreast. The proposed framework provides a practical and non-subjective model for quantitative breast density estimation, with potential utility as a decision-support tool for radiologists that can be considered in clinical practice after further investigation. Full article

Carbonation treatment can effectively address defects in recycled aggregates (RA) while achieving CO₂ sequestration, thereby improving properties of recycled aggregate concrete (RAC). However, the compressive strength of carbonated recycled aggregate concrete (CRAC) is governed by complex interactions among multiple parameters, and existing machine learning (ML) studies often rely on heterogeneous literature data with limited parameter coverage, resulting in constrained predictive accuracy. To address this issue, this study established a robust ML framework for precise strength prediction. By integrating published literature with original experimental results, a dataset of 226 groups was constructed, incorporating 12 key parameters across RA properties, carbonation processes, mix proportions, and concrete age to systematically compare three ML models (GPR, SVM, EDT). To enhance model transparency, global sensitivity analysis used the SHapley Additive exPlanations (SHAP) method, while X-ray diffraction (XRD), scanning electron microscopy (SEM), and microhardness tests were employed to reveal reinforcement mechanisms at the phase, microstructural, and micromechanical levels, supporting the connection between intelligent prediction and mechanistic explanation. Results show that the GPR model exhibited the highest predictive performance and generalization capability (R² = 0.98 for training, R² = 0.94 for testing; RMSE = 1.08 MPa), outperforming comparative models in handling high-dimensional nonlinear relationships. SHAP analysis identified concrete age, water–cement (W/C) ratio, and the initial crush index of the RA as the primary factors, while carbonation process parameters, particularly relative humidity, carbonation pressure, and carbonation time, exerted significant regulatory effects on strength. XRD results qualitatively confirmed the formation of CaCO₃ after carbonation, while SEM and microhardness analyses indicated that carbonation products contributed to pore filling and interfacial transition zone (ITZ) strengthening, providing a physical basis for both macroscopic performance improvement and model reliability. This study provides a scientific, data-driven solution for the mix design optimization and performance prediction of CRAC, delivering substantial environmental and economic benefits. Full article

Human immunodeficiency virus and tuberculosis (HIV/TB) coinfection remains a major global health challenge. Immune dysregulation in HIV complicates TB diagnosis. The type of immune response mounted in tuberculosis is a key determinant in deciding the outcome of the infection. Hence, estimating immune markers is critical for developing diagnostic, monitoring and treatment approaches. A study was conducted to evaluate the diagnostic potential of host-based biomarkers in individuals with HIV/TB coinfection in comparison to HIV monoinfection receiving antiretroviral therapy. Host-based biomarkers were quantified using commercially available kits. Receiver operated curve (ROC) analysis was conducted to determine diagnostic performance. Routine investigations showed significantly raised ratios of neutrophils, monocytes, and platelets-to-lymphocytes and alkaline phosphatase levels in HIV/TB coinfection (AUC values > 0.76). Plasma galectin-9 and osteopontin levels had an AUC > 0.8. IFN-γ, TNF-α and IL-12 levels were also significantly raised (AUC values > 0.95) while levels of GM-CSF and IL-6 were significantly low in HIV TB coinfection. The ROC analysis revealed the highest diagnostic accuracy of IL-12, indicating its potential as an adjunct immunological biomarker in identifying TB among HIV-infected individuals. However, a large-scale prospective study is required to confirm the findings and to understand their role in predicting the development of tuberculosis disease in people living with HIV. Full article

This narrative review explores sauna exposure as an emerging adjunctive intervention with potential relevance to physiotherapy practice. The physiological effects of sauna exposure are presented as related to neuromusculoskeletal performance, cardiorespiratory function, exercise recovery, and systemic health domains relevant to advanced physiotherapy care. The key benefits, owing to hyperthermia and upregulation of heat shock proteins, include thermoregulatory, cardiovascular, neuroendocrine, and cytoprotective responses that support homeostasis and adaptive stress tolerance. From a clinical perspective, frequent sauna use is linked to improved aerobic capacity and blood pressure regulation, particularly when combined with exercise. Furthermore, sauna exposure may support post-exercise and post-intervention recovery through attenuation of muscle soreness and modulation of inflammatory and hormonal responses, contributing to tissue repair and a timely return of neuromuscular function. Despite these potential benefits, substantial variability in evidence-informed dosing parameters exists, underscoring the need for appropriate patient selection and safety considerations. Despite inconsistent dosing parameters, sauna exposure represents a physiologically plausible and increasingly evidence-informed intervention that may complement established physiotherapy interventions. Further clinical research is needed to define optimal dosing, safety guidelines, and its targeted role within rehabilitation populations. Moreover, studies comparing sauna exposure to other physiotherapy thermal modalities are needed to determine efficacy. This manuscript is classified as level 5 evidence based on the Oxford Centre for Evidence-Based Medicine guidelines. Thus, no indication of the superiority of sauna over other interventions is being established. Full article

The pouch cells are typically assembled into modules with mechanical preload to meet voltage/capacity requirements, and the stress state is a critical factor influencing the failure behavior of lithium-ion batteries during external short circuits. This study comparatively analyzes performance differences between mechanically preloaded and unconstrained batteries during external short circuits, quantitatively investigating dynamic trends and safety boundaries of electro-thermo-mechanical signals during short circuits in fully charged (100% SOC) batteries across preloads of 500~3500 N. Key findings indicate that under the 50C external short-circuit (ESC) condition, mechanical constraint significantly reduces the central peak temperature of the 100% SOC battery, with a measured reduction of 31.6 °C. Moreover, constrained cells exhibit well-defined lamellar graphite structures, unlike the surface cracking observed in unconstrained anodes, confirming enhanced safety. Rupture temperatures consistently ranged between 112.00 and 124.00 °C across all conditions, with stable temperature rise rates (~0.5 °C·s⁻¹) during short circuits indicating minimal preload impact on heat generation, though excessively high or low preloads accelerated physical damage. Further SOC investigations (10%~100%) demonstrate that lower SOC increases temperature rise rates due to polarization-induced resistance rise, resulting in shorter discharge durations with lower peak temperatures/swelling forces without leakage, while high-SOC cells exhibit prolonged discharge, yielding higher peak temperatures/swelling forces at rupture. This study provides critical insights for enhancing process safety in lithium battery energy storage systems. These findings collectively guide safer battery pack design, module constraint strategies and emergency response protocols to reduce cascading failure risks in stationary energy storage applications. Full article