Search Results (40,814)

Sewerage systems are a main element of a city’s infrastructure. Roughness coefficients are fundamental parameters for sewage system operation. The intermittent nature of the flow leads to the appearance of deposits that become an integral part of the sewerage systems. Deposited material not only leads to the loss of hydraulic capacity and decreases the concentration of dissolved oxygen (which is found in direct relation to all quality parameters), but it also results in more transported particles being intercepted. In the design calculations, the roughness coefficient is estimated rather than calculated. It has been demonstrated that the estimation of stress within and above roughness elements improves the predictive capability for the concentration of suspended sediment. In this study, we focused on a local evaluation of the roughness coefficient when the flow velocity is below the minimum self-cleansing velocity. Some authors consider the selection of the most reliable method for estimating bed shear stress to be the main challenge. Other authors have suggested that all possible methods should be applied simultaneously to achieve a reliable bed shear stress estimation, knowing that the roughness coefficient can be determined through the shear boundary stress. We calculate the local roughness coefficient in Manning’s equation using a laboratory model, considering clear water flowing over a solid boundary with consolidated deposits, represented by artificial roughness elements (calibrated hemispheres). The European standard EN 752:2017 specifies a minimum average cross-sectional velocity of 0.7 m/s for pipe self-cleansing. This study established the range of possible roughness coefficient values when the minimum velocity design criterion is not met. The second criterion was to consider acceptable a sediment deposit occupying between 1% and 2% of the collector diameter. Velocity distributions around artificial roughness and statistical parameters of the turbulent flow were obtained using a PIV system. Five methods were implemented and the range of roughness coefficient values varied between 0.007 and 0.023. This variation is closely related to sewer performance. We selected the dissipation method as the primary reference for this study, as it is most closely aligned with the underlying physics of flow over roughness elements. This approach allows for robust validation by correlating multiple characteristic mechanisms of the turbulent cascade. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

Background/Objectives: Osteoporosis and osteoarthritis are age-related musculoskeletal disorders with a high socio-health burden, affecting both healthcare systems and individuals’ quality of life. Both conditions are generally accompanied by a concomitant decline in muscle mass and strength, referred to as sarcopenia. In this context, tensiomyography emerges as a novel, non-invasive potential diagnostic strategy for assessing muscle quality, as this parameter influences the progression of both conditions. Methods: Histomorphometric and immunohistochemical analyses were performed on vastus lateralis muscle tissue obtained from patients undergoing surgery for femoral fracture affected by osteoporosis or osteopenia, patients operated for hip osteoarthritis, and patients undergoing hip arthroplasty for osteoarthritis, concomitantly affected by osteoporosis or osteopenia. In addition, muscle function was assessed in these patients using tensiomyographic analysis. Results: In osteoarthritic, osteoporotic, and osteopenic patients, a reduction in muscle quality and function was observed compared with the other two experimental groups, indicating an unfavorable effect of the coexistence of the two conditions on the muscular component. Furthermore, contraction time (Tc) measured by tensiomyography was negatively correlated with lumbar spine bone mineral density values and positively correlated with the percentage of type II muscle fibers. Conclusions: This study highlights how tensiomyography may represent a valuable non-invasive diagnostic strategy for assessing muscle status in osteoporotic and osteoarthritic patients, as it is able to detect muscle alterations that parallel the worsening of bone status and that cannot be inferred from simple biopsy analysis. Thus, tensiomyography could be considered a practical adjunct tool in the clinical assessment of musculoskeletal frailty. Full article

This study addresses the low operational efficiency and high energy storage cost of wind–solar hybrid energy storage systems due to the strong volatility and intermittency of wind and photovoltaic power. Instead, the authors propose a dual-layer optimization model based on convolutional neural network–long short-term memory–attention mechanism (CNN-LSTM-AM) forecasting. First, a CNN-LSTM-AM forecasting model is constructed based on convolutional neural networks and long short-term memory networks. Then, the model is applied to wind and solar power forecasting to dynamically optimize the output power ratio of renewable sources and batteries based on predicted power, thereby reducing the start–stop frequency of compressed air energy storage (CAES) and improving operational efficiency. For lower-layer optimization, a weight evaluation model based on AHP is constructed and subsequently used to optimize the capacity configuration of the hybrid energy storage system to achieve overall system optimality. Case studies indicate that after upper-layer optimization, the number of CAES start–stop cycles decreases from 25 to 17, and further declines to 14 after optimization of the lower-layer capacity configuration, while the energy storage cost is reduced by 5.43% and the curtailment rate decreases by 0.15%. This validates the effectiveness of the proposed model in improving the economic performance and stability of renewable hybrid energy storage systems. Full article

Background/Objectives: Ginger has a long history as both a culinary and medicinal plant and is widely recognized in traditional medicine for its ability to promote health and well-being. The principal bioactive compounds of ginger are present in fresh and dried forms and have been largely studied for their therapeutic potential. These compounds exhibit a wide range of biological activities mediated through various mechanisms. Advances in nanotechnology have enabled the development of innovative delivery systems, thereby enhancing the bioavailability and therapeutic efficacy of ginger-derived compounds in modern medical applications. Methods: A comprehensive literature review was conducted to evaluate the characteristics of ginger and its potential role in disease prevention. Relevant studies were identified through the main research databases, publication screening, manual reference checks, and author consensus was conducted. Results: This narrative review provides an overview of the therapeutic potential of bioactive compounds in ginger for the management and prevention of cardiovascular, arthritis, neurodegenerative, and gastrointestinal diseases, with particular emphasis on the molecular mechanisms. In addition, their potential anti-aging properties are extensively discussed. The evidence reported is predominantly preclinical (in vitro and in vivo models), with more limited and heterogeneous clinical data. Recent studies have also highlighted the role of artificial intelligence (AI) in accelerating the discovery and evaluation of bioactive agents with therapeutic relevance across diverse biological systems. Conclusions: This review highlights the emerging applications of ginger extracts in human health and suggests their applications in both traditional medicine and contemporary drug discovery. Full article

With the acceleration of digital transformation in education, this paper examines how blockchain is being framed as a governance solution for trust, transparency, and decentralization. Using a hybrid bibliometric and thematic analysis of 93 Web of Science and Scopus publications, the study maps publication trends, leading outlets, author networks, and conceptual clusters. We analyze co-authorship networks, keyword co-occurrence patterns, and conceptual structures using VOSviewer version 1.6.19 and the R-based Bibliometrix package. Then, we apply qualitative coding to offer a more profound interpretation of governance stories. Findings show that blockchain in educational governance is predominantly positioned through techno-managerial lenses—focusing on secure credentials, tamper-proof records, and efficiency—while critical perspectives on power, equity, and participation remain limited. Global North institutions and computer science–oriented venues dominate the field, with little engagement from Global South contexts or educational leadership scholarship. The paper concludes by proposing a research agenda that reimagines blockchain not as a neutral tool, but as a socio-technical assemblage that must be interrogated through equity-, ethics-, and community-centered frameworks. Full article

The school garden has a long-standing pedagogical tradition linked to active, experiential, and community-based education, represented by authors such as Montessori, Freinet, and Dewey. Currently, its role has been consolidated as a relevant educational resource used to address the challenges of sustainability education, pedagogical innovation, and student holistic development. This research takes a qualitative approach based on a multiple case study conducted in four educational centers in Spain and Portugal. Semi-structured interviews, documentary analysis, and reflective memoranda were used. Content analysis was performed using a deductive–inductive coding approach in ATLAS.ti software v. 25th, combining literature-derived categories with those emerging from the data, following a thematic analysis (TA) approach. The results suggest that school gardens promote meaningful learning, the development of transversal competencies, improved school climate, and community involvement. Pedagogical, social, and emotional benefits were identified, as well as high levels of satisfaction among all participants. However, obstacles were found to persist, mainly related to a lack of time and teacher coordination. The study confirms that the school garden serves as a pedagogical resource with a high transformative potential. Its effectiveness depends on intentional curricular integration, teacher commitment, and the engagement of the educational community, aligning with the principles of an active, sustainable, and contextualized pedagogy. Full article

Background and objectives: Artificial intelligence (AI)-based adaptive learning has the potential to strengthen clinical decision-making and enhance quality of care at primary health centers. The present study assessed the perceptions, barriers, and learning needs involved in AI-based adaptive learning among primary care physicians in Northern Saudi Arabia. Methods: We used a cross-sectional study design to obtain data from 285 primary care physicians of different cadres working in various primary health centers. A validated data collection tool was used to measure three domains: perceptions, barriers, and learning needs. A multivariable analysis was carried out to identify the factors associated with these three domains. Results: Among the studied participants, low perceptions were observed in 55.1% of physicians; they were higher among those aged >40 years (p = 0.019) and non-Saudi nationals (p = 0.003). High barriers were reported by 42.5% of respondents, and this was higher among those aged >40 years (p = 0.031). Learning needs were higher among non-Saudi nationals (p = 0.017) and those with >10 years of experience (p = 0.007). The perception and learning need scores were positively correlated, and barrier scores were negatively correlated with the other two domains (p < 0.001). Conclusions: The authorities concerned may consider implementing targeted measures for AI-based adaptive learning. Moreover, efforts should be made to reduce the barriers to AI-based adaptive learning at all levels. These measures could strengthen primary care practice and enhance patient care. Full article

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration. Full article

Accurate assessment of light properties is essential and is measured with photometric and colorimetric standardized methods. However, the spatial characteristic of light—harshness—remains difficult to quantify. Building on the authors’ previous work, this study presented a fully automated method for determining light source harshness based on image analysis of cast shadows in a standardized environment. The improved method eliminated the need for manual shadow segmentation by introducing algorithmic noise removal and adaptive smoothing of shadow data. The method was applied to 180 test images comprising 30 combinations of photographic light-shaping attachments (e.g., softboxes, beauty dishes, and snoots) across two light sources (halogen and xenon) and three intensity levels. The results showed that the method was capable of detecting subtle differences in shadow properties and confirmed the influence of geometry, material, and orientation of the light modifiers on harshness. In addition, the results provided quantitative insight into the influence of photographic light modifiers on the original light. Full article

Access control systems rely increasingly on multimodal biometric and behavioral signals to enhance security and robustness against sophisticated attacks. However, when heterogeneous modalities provide conflicting evidence, such as valid biometric credentials accompanied by abnormal behavioral or acoustic patterns, traditional fusion strategies based on static thresholds or majority voting often fail, leading to false alarms or insecure authorization decisions. This paper addresses this critical limitation by proposing a contextual decision-making fusion framework designed to resolve conflicting multimodal evidence at the decision-making level. The proposed approach models access control as a decision-making problem in a context of uncertainty, where independent agents generate modality-specific evidence from authentication channels based on face, voice, and fingerprints. A centralized fusion mechanism integrates heterogeneous results using adaptive reliability weighting and contextual reasoning to resolve conflicts before operational decisions are made. Rather than treating each modality independently, the framework explicitly considers inconsistencies, uncertainties, and situational context when aggregating evidence. The framework is evaluated using public benchmarks, including VGGFace2, VoxCeleb2, and FVC2004, combined with controlled multimodal scenarios that induce conflicting evidence. Experimental results obtained under controlled contradiction scenarios show that the proposed fusion strategy reduces false alarms and improves decision consistency by approximately 18%. These results are interpreted within the scope of controlled multimodal simulations. Full article

The asphalt industry, essential for the global transport infrastructure, requires substantial investments to increase the durability of production facilities. The quality of asphalt depends, essentially, on the degree of drying of mineral aggregates. Therefore, the rotary dryer is of major importance for ensuring the quality of asphalt. The rotary dryer flights are subjected to an erosive-abrasive wear process during operation, generated by the impact of abrasive aggregates. These phenomena lead to severe degradation of the flights. Experimental research, carried out by the authors, on-site, aimed at identifying solutions to improve the wear behavior of the flights, by hardfacing with four wear-resistant materials (FLUXOFIL 51, FLUXOFIL 56, SAFER R 400, SAFER R 600), using the GMAW and SMAW processes. The results revealed a decrease in the wear rate and a flattening effect of the wear curve along the profile of the flight. The research targeted the upper rear surface of the flights, which is predominantly affected by erosive-abrasive wear phenomena. The resistance to abrasive wear of the flights was improved by hardfacing with FLUXOFIL 51 wear-resistant tubular wire, resulting in the lowest wear rate, especially between the areas marked 14–26, which are the areas most affected during operation. Full article

Generative AI is reshaping university teaching and creating tension around authority, evidence, and accountability when decisions are made using algorithms. From a student perspective, this study constructed a typology of distributed teacher–AI agency (TAI) and examined the discursive mechanisms that produce the illusion of teacher autonomy. A non-experimental, cross-sectional, explanatory study was conducted: a lexicometric analysis of the ALCESTE (IRAMUTEQ) questionnaire, using open-ended responses from 3120 students (Mexico, n = 2051; Ecuador, n = 1069), segmented into 1077 units, and analyzed using positioning theory. Co-agency was operationalized using Teacher Agency (A), Delegation to AI (D), Governance (G: disclosure, criteria, verification), and the Illusion Index (II = A/(D + G + 1)). Three configurations emerged: Immediate Customizer (28.8%) with very high A and minimal D/G (II = 25.4); Technological Literacy Facilitator (27.3%) with visible delegation and safeguards (II ≈ 2.0); and Operational Optimizer (43.9%) oriented toward accelerating tasks with moderate governance (II ≈ 2.7). The illusion was associated with the agentive erasure of AI and a rhetoric of immediacy/efficiency that replaced verifiable criteria. These findings transform the student voice into a criteria-based diagnostic tool for strengthening traceability, minimal verification, and responsible orchestration of AI in higher education. Full article