Search Results (2,963)

Rising global electricity demand and the expansion of distribution networks require a critical assessment of component-level greenhouse gas contributions. Distribution transformers, although indispensable, have significant life-cycle carbon impacts due to the use of materials, manufacturing, and in-service losses. This study conducts a life-cycle assessment of a single-phase, 75 kVA oil-immersed distribution transformer manufactured in Newfoundland, one of the provinces with the cleanest, hydro-dominated grids in Canada, and evaluates it over a 40-year lifespan. Using a cradle-to-use boundary, the analysis quantifies embodied emissions from raw material extraction, manufacturing, and transportation, alongside operational emissions derived from empirically measured no-load and load losses. All the data are collected directly during the manufacturing process, ensuring high analytical fidelity. The energy efficiency of the transformer is analyzed in MATLAB version R2023b using measured no-load and load losses to generate efficiency, load characteristics under various operating conditions. Under varying load factor scenarios and based on Newfoundland’s 2025 grid intensity of 18 g CO₂e/kWh, the lifetime operational emissions are estimated to range from 0.19 t CO₂e under no-load operation to 4.4 t CO₂e under full-load conditions. A linear regression-based decarbonization model using Microsoft Excel projects grid intensity to reach net-zero around 2037, two years beyond the provincial target, indicating that post-2037 transformer losses will remain energetically relevant but carbon-neutral. Sensitivity analysis reveals that temporary overloading can substantially elevate lifetime emissions, emphasizing the value of smart-grid-enabled load management and optimal transformer sizing. Comparative assessment with fossil fuel-intensive provinces across Canada demonstrates the dominant influence of grid generation mix on life-cycle emissions. Additionally, refurbishment scenarios indicate up to 50% reduction in cradle-to-gate emissions through material reuse and oil reclamation. The findings establish a scalable framework for integrating grid decarbonization trajectories, life-cycle carbon modelling, and circular-economy strategies into sustainable distribution network planning and transformer asset management. Full article

Global warming poses a threat to food security, particularly for essential crops like cowpea, which exhibits sensitivity to heat stress. This study aimed to evaluate the morpho-agronomic diversity of cowpea genotypes under different daily temperature regimes. The experiment was conducted in growth chambers, and biometric and productive traits were measured to quantify genetic divergence using Mahalanobis distance and UPGMA clustering. Temperature increases markedly altered trait expression. Under the 20–26–33 °C regime, 100-grain weight, leaf dry weight, pod weight, and stem dry weight accounted for 54.44% of the total variation. Under the higher temperature regime (24.8–30.8–37.8 °C), number of pods, plant height, stem fresh weight, and leaf dry weight explained 67.27% of the diversity, evidencing the impact of heat stress on vegetative and productive traits. Cluster analysis identified five distinct groups, confirming genetic variability and temperature-dependent dissimilarity patterns. Genotypes Bico de Ouro 17-53, Bico de Ouro 17-33 and BRS Tumucumaque maintained higher grain number and grain weight under elevated temperatures, whereas others showed yield reductions of up to 65%. These findings demonstrate exploitable genetic variability for heat tolerance in cowpea and support the use of morpho-agronomic traits as effective criteria for selecting genotypes adapted to warmer environments. Full article

Background: Depression remains a major global mental health concern, significantly intensified during the COVID-19 pandemic. As social media usage surged during this period, it emerged as a valuable source for identifying early signs of depression. Artificial intelligence (AI) offers powerful tools to analyze large volumes of user-generated content, enabling timely and effective detection of depressive symptoms. This review aims to preliminarily explore and compare evidence on the use of AI models for detecting depression in social content across the pre-, during, and post-pandemic phases, assessing their effectiveness and limitations. Methods: A narrative literature review was conducted using PubMed and Scopus, following the SANRA guidelines to ensure methodological quality and reproducibility. The study was pre-registered in the OSF database and employed the PICOS framework for the strategy. Inclusion criteria comprised studies in English from the past 10 years that analyzed depression detection via AI, machine learning (ML), and deep learning (DL) applied to textual data, images, and social metadata. This review addresses the following four research questions: (1) whether AI models improved effectiveness in detecting depression during/after the pandemic vs. pre-pandemic; (2) whether textual, visual, or multimodal data approaches became more effective during the pandemic; (3) whether AI models better addressed technical challenges (data quality/diversity) post-pandemic; and (4) whether strategies for responsible AI implementation improved during/after the pandemic. Results: Out of 349 identified records, nine primary studies were included, as most excluded articles had a predominantly technical focus and did not meet the clinical relevance criteria. AI models demonstrated strong potential in detecting depression, particularly through text-based classification and social content analysis. Several studies reported high predictive performance, with notable improvements in accuracy and sensitivity during and after the pandemic, although evidence remains limited. Conclusions: Our preliminary analysis suggests that AI-based depression detection on social media shows potential for clinical use, highlighting interdisciplinary collaboration, ethical considerations, and patient-centered approaches. These findings require confirmation and validation through larger, well-designed systematic reviews. Full article

As a critically important global food crop, wheat has been increasingly threatened by the frequent occurrence of extreme high-temperature events, which impairs its growth and development, resulting in reduced seed-setting rate, compromised grain quality and diminished yield. Therefore, identifying heat-tolerant genes and enhancing thermotolerance through molecular breeding are essential strategies for wheat improvement. In this study, we retrieved spatial transcriptomic data from the public database PRJNA427246, which captured gene expression profiles in flag leaves and grains of the heat-sensitive wheat cultivar Chinese Spring (CS) under 37 °C heat stress at time points of 0 min, 5 min, 10 min, 30 min, 1 h, and 4 h. Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression networks for flag leaf and grain transcriptomes. One highly significant module was identified in each tissue, along with 35 hub genes that showed a strong temporal association with heat stress progression. Notably, both modules contained the previously characterized thermotolerance gene TaMBF1c, suggesting that additional heat-responsive genes may be present within these modules. Simultaneous analysis of the expression data from four groups (encompassing different tissues and high-temperature treatments) for the 35 core genes revealed that genes from the TaHSP20 family, TaMBF1c family, and other related genes exhibit coordinated expression patterns in terms of the temporal dynamics and tissue distribution of stress responses. Additionally, 27 genes of the small heat shock protein (HSP20) family are predicted to be involved in the endoplasmic reticulum-associated degradation (ERAD) pathway. They assist in clearing misfolded proteins induced by stress, thereby helping to maintain endoplasmic reticulum homeostasis and cellular functions under stress conditions. Finally, the expression levels of three core genes, TaHSP20-1, TaPCDP4, and TaMBF1c-D, were validated by qRT-PCR in two wheat cultivars with distinct thermotolerance: S116 (Zhehuamai 2008) and S128 (Yangmai 33). These findings provide new insights into the molecular mechanisms underlying heat tolerance in wheat and offer valuable genetic resources for breeding thermotolerant varieties. Full article

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article

Forecasting stock market crashes is difficult because such events are rare, highly nonlinear, and shaped by latent structural and behavioral forces. This study introduces a calibrated and interpretable Random Forest framework for detecting pre-crash conditions through structural feature engineering, early-warning calibration, and model explainability. Using daily data on global equity indices and major large-cap stocks from the U.S., Europe, and Asia, we construct a feature set that captures volatility expansion, moving-average deterioration, Bollinger Band width, and short-horizon return dynamics. Probability-threshold optimization significantly improves sensitivity to rare events and yields an operating point at a crash-probability threshold of 0.33. Compared with econometric and machine learning benchmarks, the calibrated model attains higher precision while maintaining competitive F1 and MCC scores, and it delivers meaningful early-warning signals with an average lead-time of around 60 days. SHAP analysis indicates that predictions are anchored in theoretically consistent indicators, particularly volatility clustering and weakening trends, while robustness checks show resilience to noise, structural perturbations, and simulated flash crashes. Taken together, these results provide a transparent and reproducible blueprint for building operational early-warning systems in financial markets. Full article

Cable-driven robotic systems are widely used in applications requiring lightweight structures, large workspaces, and accurate force regulation. In such systems, the mechanical behavior of cable-driven actuators is strongly influenced by the elastic properties of the cable, transmission elements, and supporting structure, leading to an effective stiffness that varies with pretension, applied load, cable length, and operating conditions. These stiffness variations have a direct impact on force control performance but are often implicitly treated or assumed constant in control-oriented studies. This paper investigates the effects of operating-point-dependent (incremental) cable stiffness on actuator-level force control performance in cable-driven robotic systems. The analysis is conducted at the level of an individual cable-driven actuator to isolate local mechanical effects from global robot dynamics. Mechanical stiffness is characterized within a limited elastic domain through local linearization around stable operating points, avoiding the assumption of global linear behavior over the entire force range. Variations in effective stiffness induced by changes in pretension, load, and motion regime are analyzed through numerical simulations and experimental tests performed on a dedicated test bench. The results demonstrate that stiffness variations significantly affect force tracking accuracy, dynamic response, and disturbance sensitivity, even when controller structure and tuning parameters remain unchanged. Full article

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets. Full article

We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detection of rare extreme events, to support the reliable operation of renewable-rich power systems. The model combines a spatio-temporal transformer encoder with three convolutional neural network (CNN)/recurrent neural network (RNN) branches (CNN → long short-term memory (LSTM), LSTM → gated recurrent unit (GRU), CNN → GRU) and a dense pathway for tabular meteorological and calendar features. A multitask output head simultaneously performs the regression of PV power and binary classification of extremes defined above the 95th percentile. We evaluate HST–MB–CREH on the publicly available Renewable Power Generation and Weather Conditions dataset with hourly resolutions from 2017 to 2022, using a 5-fold TimeSeriesSplit protocol to avoid temporal leakage and to cover multiple seasons. Compared with tree ensembles (RandomForest, XGBoost), recurrent baselines (Stacked GRU, LSTM), and advanced hybrid/transformer models (Hybrid Multi-Branch CNN–LSTM/GRU with Dense Path and Extreme-Event Head (HMB–CLED) and Spatio-Temporal Multitask Transformer with Extreme-Event Head (STM–EEH)), the proposed architecture achieves the best overall trade-off between accuracy and rare-event sensitivity, with normalized performance of RMSE_z = 0.2159 ± 0.0167, MAE_z = 0.1100 ± 0.0085, mean absolute percentage error (MAPE) = 9.17 ± 0.45%, R² = 0.9534 ± 0.0072, and AUC_ext = 0.9851 ± 0.0051 across folds. Knowledge extraction is supported via attention-based analysis and permutation feature importance, which highlight the dominant role of global horizontal irradiance, diurnal harmonics, and solar geometry features. The results indicate that hybrid spatio-temporal multitask architectures can substantially improve both the forecast accuracy and robustness to extremes, making HST–MB–CREH a promising building block for intelligent decision-support tools in smart grids with a high share of PV generation. Full article

Tourism is increasingly acknowledged as a major driver of global greenhouse gas emissions. However, efforts to accurately assess its carbon footprint remain hindered by methodological inconsistencies and a reliance on fragmented case studies. This study undertakes a systematic review of 166 peer-reviewed research papers to critically evaluate prevailing approaches for quantifying tourism-related carbon emissions. Leveraging a structured framework encompassing four analytical dimensions and fourteen parameters, the analysis reveals that energy consumption and emission factors constitute the core elements of prevailing models. Nevertheless, only half of the papers account for indirect emissions, and the majority of studies are confined to national or subnational scales, offering limited insight into destination-specific impacts. This methodological heterogeneity undermines the comparability of results and constrains their utility in formulating coherent, evidence-based climate policies. By synthesising these diverse approaches, this review identifies critical methodological gaps, advocates for the harmonisation of best practices, and delineates a roadmap for more robust and context-sensitive carbon accounting within the tourism industry. The insights gained are practical for researchers and policymakers seeking to align tourism development with climate mitigation objectives, thereby fostering greater transparency and efficacy in carbon governance within the sector. Ultimately, such initiatives aim to fortify the sector’s contribution to global decarbonisation efforts. Full article

Background: Aspiration pneumonia (AP) remains a major cause of morbidity and mortality, yet non-invasive tools for monitoring lung injury in preclinical models are limited. Lung ultrasound (LUS) is widely used clinically, but existing murine scoring systems lack anatomical resolution and have not been validated for aspiration-related injury. Methods: We developed the Modified Lung Edema Ultrasound Score (MLEUS), a region-structured adaptation of the Mouse Lung Ultrasound Score (MoLUS), designed to accommodate the heterogeneous and gravity-dependent injury patterns characteristic of murine AP. Male C57BL/6 mice were assigned to sham, 6 h, 24 h, or 48 h groups. Regional LUS findings were compared with histological injury scores and wet-to-dry (W/D) ratios. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Results: Global LUS–histology correlation was weak (ρ = 0.33, p = 0.114). In contrast, regional performance varied markedly. The right upper (RU) zone showed the strongest correspondence with histological injury (r = 0.55, p = 0.005), whereas right and left diaphragmatic regions demonstrated minimal association. LUS abnormalities were detectable as early as 6 h, preceding clear histological progression. Inter-rater reliability was good (ICC = 0.87). Conclusions: MLEUS provides a reproducible, region-specific framework for evaluating aspiration-induced lung injury in mice. Although global correlations with histology were limited, region-dependent analysis identified that the RU zone as a reliable acoustic window for concurrent injury assessment. Early ultrasound changes highlight the sensitivity of LUS to dynamic aeration and interstitial alterations rather than cumulative tissue damage. These findings support the use of LUS as a complementary, non-invasive physiological monitoring tool in small-animal respiratory research and clarify its methodological scope relative to existing scoring frameworks. Full article

Background/Objectives: The underlying molecular mechanisms of deep vein thrombosis (DVT), which continues to be a major global public health concern, remain unclear. A key component of anticoagulant therapy, heparin (HP) interacts with heparin-binding growth factors including pleiotrophin (PTN) and midkine (MK), both of which have basic amino acid-rich domains that have a strong affinity for HP. The purpose of this study was to determine if changes in the levels of circulating HP, MK, and PTN are linked to the onset of acute DVT. Methods: Thirty patients diagnosed with acute DVT by venous Doppler ultrasonography (VDU) and 28 healthy controls with normal VDU findings were enrolled. Serum HP, MK, and PTN concentrations were measured using ELISA. In DVT patients, blood samples were obtained before and after routine subcutaneous low-molecular-weight heparin treatment; controls provided a single blood sample. ROC curve analysis was used to assess diagnostic performance. Results: Prior to treatment, patients with acute DVT exhibited significantly lower serum HP levels (p < 0.05) and significantly higher MK and PTN levels compared with healthy controls (both p < 0.05). Following heparin administration, serum HP levels increased significantly (p < 0.05), while MK and PTN levels showed a decreasing trend that did not reach statistical significance (p > 0.05). ROC curve analysis demonstrated limited diagnostic performance for HP (sensitivity 10.3%, specificity 68.8%), PTN (62.1%, 54.2%), and MK (82.8%, 35.4%). Conclusions: Decreased circulating HP and increased MK and PTN levels are characteristics of acute DVT that may indicate endogenous HP sequestration through binding to these growth factors. This imbalance could lead to less free HP being available, which would encourage the formation of thrombus. Therapeutic approaches that target MK- and PTN-mediated HP interactions may constitute a unique approach for the therapy of acute DVT, as evidenced by the partial normalization seen after exogenous heparin delivery. Full article

Against the backdrop of accelerating urbanisation in China, the urban-rural divide continues to widen, while cave dwellings along the Yellow River have been largely abandoned, facing the challenge of cultural erosion. This study breaks from conventional conservation approaches by empirically exploring the viability of living heritage in promoting sustainable rural revitalisation and integrated urban-rural development. Employing participatory action research, it engaged multiple stakeholders—including villagers, returning migrants, and urban designers—across 60 villages in the middle reaches of the Yellow River. This collaboration catalysed a “collective-centred” adaptive reuse model, generating multifaceted solutions. The case of Fangshan County’s transformation into a cultural ecosystem demonstrates how this model simultaneously fosters endogenous social cohesion, attracts tourism resources and investment, while disseminating traditional culture. Quantitative analysis using the Yao Dong Living Heritage Sensitivity Index (Y-LHSI) and Living Heritage Transmission Index (Y-LHI) indicates that the efficacy of collective action is a decisive factor, revealing an inverted U-shaped relationship between economic development and cultural preservation. The findings further propose that living heritage regeneration should be reconceptualised from a purely technical restoration task into a viable social design pathway fostering mutually beneficial urban-rural symbiosis. It presents a replicable “Yao Dong Solution” integrating cultural sustainability, community resilience, and inclusive economic development, offering insights for achieving sustainable development goals in similar contexts across China and globally. Full article

Cadmium (Cd) pollution is a global environmental issue that severely impacts crop growth and food safety. This study systematically investigates the accumulation characteristics and physiological responses of different varieties of non-heading Chinese cabbage under Cd stress. A Cd stress experiment was conducted using 79 non-heading Chinese cabbage varieties under nutrient film technique (NFT) cultivation, leading to the identification of 11 high-Cd accumulation varieties, 32 medium-Cd accumulation varieties, and 36 low-Cd accumulation varieties. The results showed that all varieties primarily accumulated Cd in the roots, with weak translocation of Cd to the aerial parts. To thoroughly analyze the physiological mechanisms of Cd accumulation, two extreme phenotypes, low accumulation (GX-61) and high accumulation (GX-05), were selected for subsequent comprehensive analysis. The low-accumulation variety (GX-61) exhibited higher sensitivity to Cd stress, with significant inhibition of leaf area, canopy area, and photosynthesis. In contrast, the high-accumulation variety (GX-05) maintained a more stable physiological state by enhancing photoprotective capacity and activating peroxidase (POD) to compensate for the functional loss of catalase (CAT). Cd stress inhibition of photosynthesis was initially limited by stomatal factors, later transitioning to non-stomatal limitations, and low concentrations of Cd induced a protective response that slightly promoted plant growth. This study, through high temporal resolution analysis at key growth stages, reveals the differential responses in growth, photosynthesis, and physiological metabolism between low- and high-Cd-accumulating non-heading Chinese cabbages, providing a theoretical basis for the selection of efficient phytoremediation materials and the safe production of non-heading Chinese cabbage. Full article

The current landscape of Environmental, Social, and Governance (ESG) ratings is fragmented by methodological inconsistencies, lack of standardization, and substantial divergences among rating providers. These discrepancies hinder comparability, reduce transparency, and undermine the reliability of ESG assessments, limiting their effectiveness for both investors and corporate decision-makers. To address these issues, this study introduces a standardized approach to ESG rating construction, aimed at enhancing the objectivity and interpretability of corporate sustainability evaluations. The methodology integrates the Global Reporting Initiative standards with the United Nations Sustainable Development Goals, thereby identifying a coherent set of key performance indicators across the ESG pillars. By relying solely on publicly available data and incorporating mechanisms for managing missing information, the model provides a transparent and reproducible framework for sustainability assessment. Its validity is demonstrated through an empirical application to firms in the financial and manufacturing sectors across Europe and the United States, with benchmarking against established ratings from providers. Rather than replicating existing ESG scores, the model offers a transparent and reproducible alternative built on disclosed performance data, without relying on forward-looking statements, corporate promises, or commercial data providers. By penalizing non-disclosure and enabling sector-specific sensitivity analysis, the framework supports more accountable and customizable sustainability assessments, helping align ESG evaluations with strategic and regulatory priorities. Full article