Search Results (5,769)

This study systematically investigates the velocity characteristics and coupling mechanisms of tunnel flow fields under the interactions of natural wind, traffic wind, mechanical ventilation, and structural factors (such as transverse passages and relative positions between vehicles and fans). Using CFD simulations combined with turbulence model analyses, the flow behaviors under different coupling scenarios are explored. The results show that: (1) Under natural wind conditions, transverse passages act as key pressure boundaries, reshaping the longitudinal wind speed distribution into a segmented structure of “disturbance zones (near passages) and stable zones (mid-regions)”, with disturbances near passages showing “amplitude enhancement and range contraction” as natural wind speed increases. (2) The coupling of natural wind and traffic wind (induced by moving vehicles) generates complex turbulent structures; vehicle motion forms typical flow patterns including stagnation zones, high-speed bypass flows, and wake vortices, while natural wind modulates the wake structure through momentum exchange, affecting pollutant dispersion. (3) When natural wind, traffic wind, and mechanical ventilation are coupled, the flow field is dominated by momentum superposition and competition; adjusting fan output can regulate coupling ranges and turbulence intensity, balancing energy efficiency and safety. (4) The relative positions of vehicles and fans significantly affect flow stability: forward positioning leads to synergistic momentum superposition with high stability, while reverse positioning induces strong turbulence, compressing jet effectiveness and increasing energy dissipation. This study reveals the intrinsic laws of tunnel flow field evolution under multi-factor coupling, providing theoretical support for optimizing tunnel ventilation system design and dynamic operation strategies. Full article

Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering. Full article

Objective: This pilot study assessed the psychological and physical impact of the COVID-19 pandemic on postpartum women that gave birth during the pandemic, and evaluated the feasibility of remote monitoring for maternal mental health. The study also proposes a conceptual framework to strengthen remote maternal care in future public health emergencies. Methods: Conducted between 2020 and 2021 in Reggio Emilia, one of Italy’s ten hardest-hit provinces during the early COVID-19 outbreak, this study enrolled 21 pregnant women (10 COVID-19-positive at delivery, 11 COVID-19-negative controls). Psychological and physical health were assessed using validated instruments: the Beck Depression Inventory (BDI) and Edinburgh Postnatal Depression Scale (EPDS) for depression, the State-Trait Anxiety Inventory (STAI) for anxiety, the Impact of Event Scale–Revised (IES-R) for trauma-related stress, and the SF-36 for physical functioning. Additional measures included breastfeeding experience and resilience. Remote assessments were conducted between 6 and 12 months postpartum to evaluate psychological recovery and satisfaction with perinatal care. C test was used to compare the two groups of women. Results: COVID-19-positive women reported significantly higher depressive symptoms (BDI: 13.50 ± 8.14 vs. 6.73 ± 4.73; U = 27, p = 0.048), and elevated state anxiety levels (STAI-S: 41.60 ± 10.23 vs. 33.64 ± 10.15; U = 27, p = 0.048) compared to controls. Post-traumatic stress symptoms were also higher among COVID-positive participants (IES-R total: 41.10 ± 19.33 vs. 30.64 ± 7.99; U = 24.5, p = 0.029). No significant differences emerged in EPDS or trait anxiety scores. Conclusions: Remote data collection proved feasible for postpartum women during the pandemic and highlighted elevated depressive, anxiety, and trauma-related symptoms in COVID-19-positive mothers. These findings support the development of flexible digital care frameworks for maternal well-being in crises. The introduction of the “10 Gold Rules for Remote Maternal Healthcare in Critical Situations” offers a forward-looking, expert-informed conceptual framework to guide the development of scalable, trust-based digital care models that go beyond monitoring to include proactive, patient-centred support. Full article

The global rise in diabetes has intensified the demand for advanced glucose monitoring technologies that provide continuous, accurate, and real-time detection. Traditional sensing approaches often face challenges related to sensitivity, long-term stability, and suitability for wearable or implantable systems. In this context, titanium dioxide (TiO₂) nanotube arrays (NTAs) have emerged as a versatile platform owing to their well-defined nanostructure, tunable surface properties, and semiconductor nature, which collectively enable enhanced performance across different sensing modes. These include enzymatic systems, non-enzymatic configurations, and photoelectrochemical (PEC) sensors. While each sensing strategy offers considerable potential, certain inherent limitations continue to be explored. Ongoing research is gradually uncovering various pathways to enhance performance and reliability through the introduction of novel materials and system designs. Looking forward, the broader integration of TiO₂-based sensing platforms with evolving technological frameworks may contribute to the advancement of more adaptive and user-friendly glucose monitoring solutions. Full article

Background/Objectives: The Parental Stressor Scale: Neonatal Intensive Care Unit (PSS:NICU) is one of the most widely used tools for assessing parental stress in neonatal intensive care settings. This study aimed to translate, culturally adapt and validate the PSS:NICU in a Greek cohort. Methods: A multicenter, cross-sectional study was conducted with 150 parents (89 mothers, 61 fathers; mean age = 34.1 years, SD = 7.2) of hospitalized neonates from three Greek NICUs. The translation followed forward–backward procedures, expert review, and pilot testing. Data were analyzed for internal consistency, factorial validity, and group differences. Results: Confirmatory factor analysis supported the adequacy of the original three-factor structure (Sights and Sounds, Infant Behavior and Appearance, and Parental Role Alteration). Cronbach’s alpha and McDonald’s omega coefficients indicated excellent reliability for the total scale and its subscales. Female participants reported higher stress levels than males in most dimensions. Conclusions: The Greek version of the PSS:NICU demonstrated strong psychometric properties and cultural relevance. This adaptation provides a valid and reliable tool for assessing parental stress in Greek NICUs and facilitates cross-cultural comparisons and the development of targeted psychosocial interventions. Full article

In his book The Sacred Mushroom and the Cross (1970), John Marco Allegro claimed that an obscure, 12th century CE fresco of the Fall of Adam and Eve in the Plaincourault Chapel in Mérigny, France, provided evidence of the persistence in Christian Europe of an underground sacred mushroom sect that had survived since New Testament times. At the heart of Allegro’s claim is the mushroom-like appearance of the Tree of the Knowledge of Good and Evil in the picture. Following up on Allegro’s claim, a small group of writers, led by Boston University’s Carl A. P. Ruck, spent decades seeking to validate Allegro’s theory by seeking out other examples of psychedelic mushrooms hidden in early Christian and Medieval art. The present article centers its discussion on the claims put forward by Allegro and his followers about the Plaincourault tree, but also about other images concerning which they have made similar claims. It concludes that the claims of Allegro and his followers concerning the Plaincourault tree fail due to their tendency to overpress similarities while ignoring differences. Full article

As an important part of the urban ecological environment, urban green space plays a crucial and irreplaceable role in improving air quality, promoting sustainable development, and enhancing residents’ quality of life. This study takes Beijing’s urban green space as the research object. Based on Landsat series satellite remote sensing images, the land use distribution of Beijing is obtained through supervised classification. Combined with data such as PM_2.5 concentration and wind speed, the dry deposition efficiency of PM_2.5 is quantitatively analyzed. The results show that: (1) Beijing’s urban green space has significant advantages in PM_2.5 dry deposition. In terms of dry deposition flux, the order of annual average deposition of different land types is: forest land > farm land > grassland > impervious surface > water body = unutilized land. Among them, forest land has the best dry deposition effect, with an annual average dry deposition of 1.13 g/m², which is 188.41 times that of impervious surface; cultivated land and grassland are 0.22 g/m² and 0.19 g/m² respectively, which are 37.13 times and 32.34 times that of impervious surface. (2) From 2000 to 2020, the PM_2.5 removal rate of green space continued to rise, but the reduction amount showed a trend of first increasing and then decreasing. There are significant seasonal differences. The reduction amount is the highest in autumn (reaching 449.90 tons in October), followed by summer, spring, and winter (the lowest in August, at 190.27 tons). (3) In terms of spatial distribution, the high-value areas of dry deposition are concentrated in the suburbs, showing a “southwest-northeast” axial distribution, while the low-value areas are mainly located in the outer suburbs, reflecting the imbalance of green space layout and the regional differences in PM_2.5 reduction. Combined with the current situation of green space in Beijing, the study puts forward targeted optimization suggestions, providing theoretical support and scientific basis for the construction of Beijing as a “garden city”. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

A prescribed-performance-based sliding mode control method with feed-forward inverse compensation is proposed in this study to improve the micropositioning accuracy and convergence speed of a piezoelectric actuator (PEA). Firstly, the piezo-actuated micropositioning system is described by a Hammerstein structure model, and an inverse Prandtl–Ishlinskii (PI) model was employed to compensate for its hysteresis characteristics. Then, considering modelling errors, inverse compensation errors, and external disturbances, a new prescribed performance function (PPF) with an exponential dynamic decay rate was developed to describe the constrained region of the errors. We then transformed the error into an unconstrained form by constructing a monotonic function, and the sliding variables were obtained by using the transformation error. Based on this, a sliding mode controller with a prescribed performance function (SMC-PPF) was designed to improve the control accuracy of PEAs. Furthermore, we demonstrated that the error can converge to the constrained region and the sliding variables are stable within the switching band. Finally, experiments were conducted to verify the speed and accuracy of the controller. The step-response experiment results indicated that the time taken for SMC-PPC to enter the error window was 8.1 and 2.2 ms faster than that of sliding mode control (SMC) and PID, respectively. The ability of SMC-PPF to improve accuracy was verified using four different reference inputs. These results showed that, for these different inputs, the root mean square error of the SMC-PPF was reduced by over $39.6\%$ and $52.5\%$, compared with the SMC and PID, respectively. Full article

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence (p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis. Full article

Ecological security barriers safeguard regional ecological security by blocking external risks and supplying internal services. However, existing research has primarily focused on optimizing the connectivity and protection of internal ecological patches within barriers. At a broader scale, there remains insufficient attention on coordinating the “blocking of external ecological risk corridors” and “connecting corridors that supply ecosystem services to internal urban areas”. To address this, this study develops a framework for constructing ecological corridors that integrates both reverse (resistance) and forward (provision) perspectives. Taking the Yanshan–Taihang Mountain Ecological Barrier as a case study, circuit theory is applied to identify risk corridors traversing the barrier area. Service supply corridors directed toward internal urban areas are also established, and key nodes along these corridors are identified. Furthermore, the XGBoost-SHAP method is employed to quantitatively analyze the influencing factors and mechanisms of these key nodes. Finally, strategies are proposed to block risk corridors and connect supply corridors. The main results are as follows: (1) A total of 29 risk corridors, 158 risk pinch points, and 210 risk barriers were identified, along with 250 supply corridors, 158 supply pinch points, and 118 supply barriers, revealing the distinct distribution patterns of both risk transmission and service supply corridors. (2) The dominant factors influencing different types of corridors exhibited significant differences: risk corridors were primarily regulated by natural factors such as mean annual evapotranspiration (EVA) and soil volumetric water content (VWC), whereas supply corridors were mainly influenced by human activities, including the human footprint index (HFP) and land surface temperature (TEM). (3) Even within the same type of corridor, the dominant factors and their operating mechanisms—such as threshold effects and nonlinear interactions—showed considerable heterogeneity across nodes of different characteristics. Based on these findings, differentiated policy recommendations were proposed. This study aims to synergistically enhance the bidirectional functionality of forest-mountain ecological barriers by disrupting external risk corridors and reconstructing internal supply networks. The framework and methodology presented here can provide theoretical and empirical references for the planning and management of other similar barrier regions. Full article

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

This study investigates the excavation stability of vertical shafts using the Raise Boring Machine (RBM) method in karst strata with multiple cavities, based on the ventilation shaft project of the Zimuyan Tunnel along the Wudao Expressway. A three-dimensional numerical model was established using ABAQUS (version 6.14) to simulate the RBM excavation process and to analyze the effects of cavity positions and depths on the stability of the surrounding rock during excavation. The results show that (1) when the cavities are located at the same position and depth, the radial displacement of the surrounding rock during the reverse reaming stage is reduced by approximately 60% on average compared to that during the forward reaming stage, and the radial stress is also significantly lower during the reverse reaming process; (2) when the cavities are at the same depth, symmetrically distributed cavities cause the surrounding rock displacement to increase by 15–20% compared to vertically aligned cavities, and the stress distribution becomes more complex; and (3) when the cavities are at the same horizontal position but located on different planes, the stability of the surrounding rock improves as the distance between the two cavities increases. Full article

In light of the global energy shortage, the development of renewable energy has become increasingly vital. With China’s commitment to achieving “carbon peak and carbon neutrality,” photovoltaic power generation has emerged as a focal point in new energy development. However, DC arc faults in photovoltaic systems pose significant safety hazards, potentially leading to electrical fires. While new detection technologies for DC arc faults in photovoltaic power generation systems have advanced rapidly, the diversity of international standards—such as UL 1699 B, GB/T 39750, IEC 63027, and CGC/GF 175—limits both the construction of experimental platforms and the universality of detection technologies. Current research often relies on a single standard to establish experimental platforms, resulting in detection methods with limited applicability and an inability to validate technological effectiveness fully. To address this issue, this paper conducts an in-depth study of four international and national standards (IEC 63027; UL 1699 B, GB/T 39750, and CGC/GF 175), focusing on the discrepancies in decoupling methods, impedance parameter settings, and experimental circuit topologies, including series and parallel arc scenarios. Through comprehensive comparative analysis of multiple standards, this study integrates major international and domestic specifications to develop a multi-standard compatible experimental platform. The platform is designed to accommodate diverse topologies and parameter requirements, enabling efficient collection of arc test data and performance evaluation of arc fault detection devices. It also provides a standardized foundation for the performance testing and classification of DC arc circuit breakers in photovoltaic power generation systems. Through a comprehensive multi-standard comparative analysis, we systematically analyze the technical differences in photovoltaic DC arc detection. We construct a multi-standard compatible experimental platform by integrating mainstream international and domestic standards. This platform is designed to accommodate various topological structures and parameter requirements, facilitating the collection of arcing experimental data and assessment of the performance of arc fault detection devices. The findings from this research provide both theoretical and experimental foundations for developing unified technical guidelines for photovoltaic DC arc protection. This will aid in standardizing the development of detection devices and enhancing the electrical safety of photovoltaic systems. Ultimately, this work is significant for promoting the safe utilization of new energy within the framework of the dual carbon goals. Moving forward, it is crucial to enhance the generalization abilities of detection algorithms further and foster the integration of standards and industrial applications. Full article

Background: Joint position sense (JPS) is a critical component of proprioception and postural control, especially in older adults, where deficits are associated with increased risk of falls and functional decline. Recent studies have explored smartphone-based digital inclinometers as accessible tools for clinical proprioceptive assessment, but their participant-level reproducibility in institutionalized elderly populations remains unexplored. Objective: We aimed to examine the reproducibility of joint position sense performance in institutionalized older adults, using a smartphone-based inclinometer that has been applied in other populations. Assessing joint position sense with accessible smartphone-based tools may provide practical insights for rehabilitation and functional assessment in older adults. Methods: Thirty-five-year-old or older adults (mean age 85.9 ± 8.19 years) participated in this test–retest study. JPS was assessed using the iPhone^® inclinometer. Participants memorized and attempted to reproduce a 30° forward trunk flexion while standing. The absolute angular error was measured across two sessions, one week apart. Relative and absolute reproducibility were analyzed using intraclass correlation coefficients (ICC 2,1), standard error of measurement (SEM), Smallest Real Difference (SRD), and Bland–Altman analysis. Results: The ICC for the whole sample was 0.839 (95% CI: 0.72–0.91), indicating good reproducibility of participants’ proprioceptive performance. SEM and SRD were 3.65° (33.3%) and 10.1° (92.3%), respectively. Bland–Altman plots showed minimal bias (0.23°) and only 5.71% of values fell outside the 95% limits of agreement. Conclusions: Participants demonstrated moderate-to-good reproducibility in replicating joint position sense, reflecting consistent proprioceptive performance across sessions. This approach demonstrates feasibility for repeated proprioceptive assessment in this population. These findings have potential applications for functional monitoring and fall-prevention programs in institutionalized older adults. Full article