Search Results (43,856)

The Changbai Mountain–Liaodong region is a crucial component of the global black soil belt in Northeast China and a significant national grain production base. However, like many high-latitude agricultural regions worldwide, it faces persistent challenges during the spring sowing period, including low soil temperatures and excessive moisture. Therefore, developing region-specific, effective methods of reducing soil moisture and increasing temperature while improving soil fertility is essential for improving agricultural productivity. To this aim, a field experiment was conducted with two factors: a main plot subjected to ridge tillage (RT) and flat tillage (FT) and subplots with biochar (BC) and straw (ST) amendments. A subplot with no amendment (CK) was used as a control. During maize growth, the daily soil temperature and moisture were monitored, and the soil water evaporation rates and physical structure, as well as the maize yield performance, were evaluated. The results showed that biochar and straw application significantly decreased the soil monthly water content by 1.69–2.22% (p < 0.05) in the surface soil layer (0–15 cm) from May to June, with a more pronounced effect under RT. In contrast, biochar application increased soil moisture and water storage from July to September, indicating that the influence of biochar on soil moisture depends on time and field aging processes. Biochar amendment raised the soil maximum temperature by 0.32–0.79 °C in the top 0–15 cm layer, while straw incorporation decreased the minimum soil temperature by 0.11–0.52 °C. The increase in soil temperature was primarily due to the biochar’s darker color, which facilitated solar radiation absorption, while the decrease in soil temperature was caused by the “Wind Leakage Effect” induced by the large particle size of the straw. Biochar and straw incorporation effectively enhanced maize dry matter accumulation by an average of 15.8% and 8.2%, respectively, and grain yield by 13.0% and 7.8%, respectively. Correlation analysis indicates that these increments are primarily due to enhanced soil moisture and available N content during the middle to late stages of maize growth. Therefore, the integration of straw and biochar with high-ridge cultivation is an effective strategy for excessive moisture reduction and warming in spring soil and it also contributes positively to maize yield. Full article

The effects of thermal and non-thermal pretreatments combined with different drying methods on the drying kinetics, physicochemical properties, and bioactive compounds of the Chilean wild mushroom Morchella conica were investigated. Fresh samples were subjected to hot-air drying (HAD, 60 °C), freeze-drying (FD), and a hybrid process (FD–HAD), applied directly or after pretreatments including thermal pre-drying (55 and 75 °C), ultrasound (US, 10 and 20 min), and high hydrostatic pressure (HHP, 600 MPa). Drying curves were successfully fitted using the Weibull model (R² > 0.987), showing that HAD combined with thermal and ultrasound pretreatments increased drying rates, while FD–HAD reduced total drying time. Freeze-drying better preserved color (ΔE < 2) and minimized shrinkage (<8%), whereas HAD produced darker samples and greater structural deformation. Water activity decreased below 0.30 in most treatments, ensuring microbiological stability. Thermal pretreatments enhanced total phenolic content, while FD preserved antioxidant capacity. Principal component analysis explained 62.2% of the total variance, revealing distinct quality profiles among drying methods. Overall, FD and hybrid FD–HAD combined with moderate pretreatments showed the best balance between drying efficiency and quality preservation, while HHP improved antioxidant properties under specific conditions. These findings highlight the potential of integrating innovative pretreatments with drying technologies to optimize processing of Morchella conica. Full article

Introduction: Asthma is one of the most common chronic diseases in childhood and represents a major global public health concern due to its high prevalence, healthcare burden, and impact on quality of life. Pediatric asthma is characterized by clinical and biological heterogeneity, reflected in variable airflow limitations and distinct inflammatory endotypes. Conventional diagnostic tools do not fully capture the metabolic mechanisms underlying lung function impairment and disease variability. Aim: This narrative review aims to synthesize evidence published linking metabolomic and breathomic signatures to lung function parameters in children with asthma. Methods: We searched PubMed, Scopus, and Google Scholar using predefined keywords including pediatric asthma, metabolomics, breathomics, volatile organic compounds, exhaled breath condensate, and lung function. The search covered publications from January 2015 to January 2026. Earlier studies were included when necessary for the conceptual or methodological context. We included human studies evaluating metabolomic or breathomic profiles in children (≤18 years) and reporting associations with lung function, severity, endotypes, or exacerbations. Duplicate records, adult-only studies, animal models, non-English publications, and conference abstracts without full data were excluded. Results: Alterations in lipid and sphingolipid metabolism, oxidative stress pathways, and purine metabolism were associated with airflow limitation and reduced FEV1. Breathomic analyses revealed associations between volatile profiles, small airway dysfunction, and inflammatory patterns. However, findings remain heterogeneous across biological matrices and analytical platforms. Conclusions: Metabolomic and breathomic profiling may complement conventional lung function assessment by providing additional mechanistic insight into pediatric asthma heterogeneity. Standardized methodologies, longitudinal validation, and integration within multi-omics approaches are required before routine clinical implementation. Full article

The low-carbon transition of industrial parks is driving an increasing demand for advanced energy systems. Integrated energy supply systems (IESSs), which couple multiple energy forms, offer a critical pathway to alleviate the high-carbon intensity of energy structures and supply–demand imbalances in industrial parks by enhancing energy efficiency and reducing carbon emissions. The rapid advancement of energy storage technologies, multi-energy system modeling, and advanced energy management strategies has further propelled the research and application of IESSs. This review comprehensively delineates the distinctions between IESSs and traditional energy systems, highlighting the architecture and operational characteristics of IESSs to elucidate the impacts of multi-energy coupling and source–grid–load–storage interactions. We examine existing equipment and system modeling approaches and load modeling methods, and discuss modeling techniques for variable operating conditions. We analyze operational optimization methods for IESSs under deterministic, multi-time-scale, and uncertain conditions, and investigate the utilization mechanisms of flexibility resources across source–grid–load–storage links to illustrate how system flexibility supports dynamic supply–demand coordination. The review also identifies emerging trends in AI-driven IESS operation, highlighting the integration of physics-informed modeling, large language models, and multi-agent systems. This review establishes a unified analytical perspective for flexible supply–demand matching within IESSs, offering theoretical support for the development of future low-carbon industrial energy systems. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM₂.₅ and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤ 10 µm, respectively, reported in micrograms per cubic metre (µg/m³). Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

Atmospheric temperature and humidity profiles are essential for numerical weather prediction and severe weather monitoring. To effectively utilize data from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4 satellite, this study proposes a retrieval method based on a radial basis function (RBF) neural network, which integrates numerical model background profiles with GIIRS simulated radiance errors to construct a mapping from these two inputs to background profile errors. A channel selection strategy is developed using correlations between background errors and radiance errors to identify channels sensitive to temperature and humidity variations at different pressure levels. Experiments are conducted using data from land stations in Zhejiang Province, China, from August to December 2024, including 829 clear-sky and 2109 cloudy profiles. Under clear-sky conditions, the method reduces temperature and humidity root mean square error (RMSE) by approximately 39% and 22.3% compared to background profiles. Under cloudy conditions, despite severe radiation interference, RMSE reductions of 38.5% for temperature and 15.3% for humidity are achieved, with notable improvements below 900 hPa and above 750 hPa for humidity. Compared with the multilayer perceptron (MLP) method, RBF shows superior performance under all test conditions, especially in cloudy-sky humidity retrieval. The proposed approach provides an effective, physically constrained framework for operational GIIRS data application in temperature and humidity retrieval. Full article

Near-infrared spectroscopy (NIRS) is increasingly studied as a non-invasive optical investigation tool for in vivo tissue characterization, including applications to skeletal muscle and brain regions. In this context, previous studies have demonstrated reliability in differentiating muscle sites, typically relying on dense acquisition schemes (≥50 spectra acquired per site) to ensure signal stability. However, this requirement may limit throughput and hinder real-world clinical translation. Optimizing the trade-off between acquisition burden and classification performance represents a key design problem for device scalability and feasibility of bedside deployment. In this study, we explored the impact of spectral sampling density on machine learning-based muscle discrimination. Thirty healthy adults provided 50 Vis–SWIR (Visible–Short-Wave Infrared; 350–2500 nm) reflectance spectra per biceps and triceps muscle sites (3000 spectra). Seven datasets were generated by random subsampling, progressively reducing the number of spectra (from 50 to 1 spectra/muscle/subject). All datasets underwent an identical preprocessing pipeline and were subjected to Partial Least-Squares Discriminant Analysis (PLS-DA) classification. PLS-DA achieved near-perfect discrimination from 50 to 5 spectra per muscle with a mean cross-validation (CV) accuracy ≥ 99.5%, whereas performance collapsed abruptly at three spectra (CV accuracy ~39%) and one spectrum (CV accuracy ~15%). Therefore, high machine learning classification performance is retained even when the number of acquired spectra is substantially reduced. These findings support the feasibility of acquisition-efficient protocols that may enhance device portability and reduce measurement time, thus enabling NIRS integration into clinical workflows. From a biomedical engineering standpoint, spectra number reduction without loss of predictive performance represents a key step toward scalable, real-time, and patient-centered Vis–SWIR diagnostic platforms. Full article

This paper investigates the anthropological and ethical roots of the global ecological and social crisis, centered on Pope Francis’s critique of the “throwaway culture” (Laudato Si’, LS). While LS identifies this crisis in the linear “take–make–dispose” model and the technocratic paradigm—which prioritizes efficiency over moral reflection—this research argues that these macro-societal failures originate in a foundational spiritual pathology: concupiscence. Drawing upon St. John Paul II’s Theology of the Body (TOB), we analyze concupiscence as “appropriation,” the direct antithesis to the human vocation of the “sincere gift of self.” This study aligns LS’s socio-economic critique with Karol Wojtyła’s personalist anthropology, asserting that the systemic exploitation of nature and the marginalization of the vulnerable are structural extensions of the human failure to reread the “language of the body” in truth. The throwaway culture is thus revealed as an axiological reduction—a societal manifestation of lust that reduces both the body and creation to mere objects of utility. Consequently, a genuine ecological conversion (LS) necessitates embracing the “ethos of redemption” (TOB). This transformation of desire is essential to restoring the harmony between humanity and nature, recognizing that the ‘cry of the earth’ and the ‘cry of the poor’ are inextricably linked within an integral ecology. Full article

Based on the stimulus–organism–response (S–O–R) framework, this study explored the psychological spillover mechanism through which tourism experiences in Museum Agglomeration Areas (MAAs) enhance city image and influence behavioral intentions. Structural equation modeling (SEM) based on survey data yielded several key findings. First, information visibility, content visibility, and the quality of amenities and the operational environment played critical roles in shaping tourists’ internal states, including perceived experiential value, affective response, immersion, and satisfaction. In addition, the social atmosphere emerged as an important factor in enriching these evaluations. Second, accessibility and connectivity were identified as factors that reduce friction along the visitor journey, thereby enhancing experiential continuity and immersion. Third, experiential value and immersion were found to be the primary mediators among the internal-state variables, transmitting the effects of environmental stimuli to city-level perceptions and behavioral intentions, such as revisit and recommendation intentions. These findings suggest that the competitiveness of MAAs lies not merely in spatial agglomeration itself but also in their ability to provide engaging and meaningful content, maintain safe and enjoyable operational environments, and develop integrated circulation and information systems. By conceptualizing MAAs as sites of district-scale tourism experiences, this study extends the application of the S–O–R framework to a multi-site urban cultural context and clarifies how differentiated internal states mediate the spillover from district experience to city-level perceptions and behavioral intentions. Rather than proposing a fundamentally new theoretical framework, the study offers a context-specific refinement of the organism layer and provides empirically grounded implications for design and operational strategies in culturally clustered urban districts. Full article

The solar-assisted air source heat pump (SAHP) is a key technology of low carbon heating. However, the SAHP is still inefficient and unstable at low temperatures. Cascaded latent heat storage (CLHS) can store multi-stage thermal energy, which provides the possibility for the multiple utilization of solar energy. Hence, this paper proposed the SAHP integrated with CLHS for building heating. The high-temperature and medium-temperature latent heat storage (LHS) units are used for direct heating, and the low-temperature LHS unit preheats the air for the air source heat pump (ASHP). The thermal performance of the CLHS device is evaluated through combined numerical simulations and experimental tests. Results show that the average heat storage rate of the cascaded system is 61.1% higher than that of a conventional single-stage LHS unit. The heat storage uniformity of CLHS gradually improves with increasing inlet flow rate, but shows a trend of first increasing and then decreasing with the increase in fluid inlet temperature. Among the three tested levels, 80 °C was found to be the most uniform heat storage of the CLHS device. The performance of the system was further analyzed using TRNSYS to assess seasonal building heating performance. The overall efficiencies of the high/middle/low temperature LHS units are 93.6%, 81.6% and 94.3%, respectively. And the solar heat supply accounts for 70.8% of the total heat supply of the system. Compared with the non-preheating system where the low-temperature LHS unit is removed, the COP of the graded heating system is increased by 18.3%, and the energy consumption is reduced by 16.6%. Further parametric optimization based on the Hooke–Jeeves method reduces total system energy consumption by 20.7% and associated pollutant emissions by 20.6% compared with the pre-optimization system. The findings provide practical insights into the application of CLHS in solar-assisted heat pump systems for building heating. Full article

Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) with a Bidirectional Gated Recurrent Unit (BiGRU) network to deliver robust and interpretable forecasts tailored to construction safety needs. STL first decomposes the original monthly accident series (January 2012–December 2024, OSHA) into trend, seasonal, and residual components, reducing structural complexity and mitigating non-stationarity. Independent BiGRU models are then trained on each component to capture bidirectional temporal dependencies, and final forecasts are reconstructed through component aggregation. Comparative experiments against Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and their STL-enhanced variants demonstrate that the proposed STL–BiGRU model achieves superior performance across both short-term and medium-term horizons. The model achieves the lowest error levels, with a short-term Root Mean Squared Error (RMSE) of 6.8522 and a medium-term RMSE of 7.0568, and shows consistent improvements in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results indicate that multi-scale decomposition combined with bidirectional deep learning provides a practical, forward-looking tool. It helps regulators and contractors anticipate high-risk periods, optimize resource allocation, and reduce fatal accidents through targeted preventive measures. Full article

To achieve accurate localization and tackle the inevitable dual challenges of ill-posedness and strong nonlinearity in limited-aperture translational target inversion, this paper proposes a novel integrated scheme that synergistically combines a domain contraction (DC) strategy with wavelength-dependent weighting (WW) of multi-frequency data. The DC strategy dynamically reduces the solution space to mitigate ill-posedness and enhance stability, while the WW strategy strategically prioritizes lower-frequency data to mitigate nonlinear effects. This organically integrated approach, termed the domain-contracted wavelength-dependent weighting RMC-CC-CSI (DC-WW-RMC-CC-CSI) algorithm, enables a more robust and efficient inversion process. Simulation results demonstrate that our DC-WW scheme delivers significant improvements over the baseline RMC-CC-CSI method in imaging accuracy, convergence speed, noise robustness, and computational efficiency. Full article

The installation of floating offshore wind turbines presents significant operational challenges due to coupled vessel platform dynamics and sensitivity to environmental conditions. This study proposes a response-based methodology for defining allowable operational limits and assessing operability for floating wind turbine generator (WTG) installation using the Nordic Wind concept. The approach integrates hydrodynamic modelling, time-domain simulations, and probabilistic weather-window analysis to evaluate installation feasibility under realistic offshore conditions. The methodology explicitly accounts for coupled vessel spar interactions, heading-dependent system response, and response-based failure criteria, including relative motion, gripper forces, and impact velocity. Allowable sea-state limits are derived for key installation phases and applied to multiple case studies representing different geographical locations and project scales. The results show that installation operability is governed primarily by system response rather than environmental parameters alone. Peak wave period and wave heading are identified as dominant factors, with longer wave periods leading to reduced operability due to response amplification. Across all case studies, the mating phase is found to be the most restrictive operation, controlling overall installation feasibility. Head sea conditions generally provide improved operability, while following seas lead to increased relative motion and reduced performance. The comparative analysis further demonstrates that environmental severity and project scale jointly influence installation duration. Milder environments result in higher operability, whereas harsher conditions, particularly those characterised by long-period swell, significantly reduce feasible weather windows. Larger installation campaigns increase cumulative exposure to weather downtime, even under favourable conditions. The proposed framework extends existing operability assessment methods by incorporating coupled multi-body dynamics and response-based criteria specific to floating wind installations. The results provide a quantitative basis for defining operational limits and support improved planning and decision making for offshore wind turbine installation. Full article