Search Results (113,890)

Background: Recent studies have shown that catheter ablation of atrial fibrillation leads to an improvement in mortality and a reduction in hospitalization in patients with end-stage heart failure. It is therefore hypothesized that in an end-stage heart failure population, atrial fibrillation is of great relevance and that interventional therapy is crucial to preventing further progression, especially with the aim of avoiding a heart transplant. In this paper, we describe the clinical presentation of atrial fibrillation and its management in a real end-stage heart failure cohort of patients actively waiting on a heart transplant through EUROTRANSPLANT. Methods: A total of 577 patients have been actively listed for heart transplant in our clinic. Of these, we examined all patients who were actively listed by the key date of 31.12.2024. Patients already treated by assist devices such as the left-ventricular assist device and high-urgency listed patients were excluded, as were minors and patients in need of simultaneous transplantation of other organs in addition to the heart. Results: Thirty-one patients were included in our analysis. In this cohort, 18 patients (58%) had no diagnosis of atrial fibrillation or atrial flutter. A total of 13 patients (42%) presented with atrial fibrillation or flutter: 3/13 (23%) paroxysmal, 8/13 (62%) persistent, 1/13 (8%) permanent atrial fibrillation, and 1/13 (8%) atrial flutter. Moreover, 9/13 (69%) patients with atrial fibrillation had been diagnosed during evaluation and before the active listing period for heart transplant. Only three patients developed atrial fibrillation during the active listing period (two with atypical atrial flutter, one with atrial fibrillation). In those three patients, rhythm control could be achieved: the patient with new-onset atrial fibrillation was treated by pulmonary vein ablation, and in the two patients with newly diagnosed atypical atrial flutter, electrical cardioversion was performed. Conclusions: In our real end-stage heart failure cohort, more than half of the patients do not have atrial fibrillation. Patients diagnosed with atrial fibrillation often receive their diagnosis before they are listed for heart transplant. However, atrial fibrillation is not a common cause of clinical worsening while actively waiting on a heart transplant. Full article

The degradation of forest landscapes in West Africa, particularly in Togo, threatens ecological and socio-economic sustainability. This study analyzes the spatio-temporal dynamics of land use in the central plains of Togo between 1991 and 2022, and projects its evolution for 2030 and 2050 to guide restoration strategies. The methodology integrates the interpretation of Landsat images (1991, 2005, 2022) and the analysis of indicators, including conversion rates and the anthropization index. Prospective modeling (Markov chains and neural networks) follows a trend scenario. The results reveal a sharp decline in natural forest formations: dense semi-deciduous and dense dry forests (−50.55%) and woodlands (−62.06%), converted mainly to cropland, plantations, and built-up areas. Shrub/tree savannas, the dominant class, represent a transitional stage resulting from forest degradation. The average annual deforestation rate is 0.75%. The ecological disturbance index increased from 0.24 (1991) to 0.45 (2005), and then to 0.56 (2022), reflecting increased human impact and fragmentation. Projections indicate that these trends will continue, highlighting the growing vulnerability of ecosystems and the need to integrate this dynamic into sustainable management and restoration policies. Full article

Thaumasite sulfate attack (TSA) under elevated water pressure has important implications for the durability of deep underground concrete structures, yet the deterioration process and the coupled effect of water pressure and carbonate supply remain insufficiently understood. In this study, laboratory pressurized sulfate exposure tests were conducted to investigate the evolution of macroscopic performance and microstructure of cement mortars with different limestone powder contents (0%, 15%, and 30%) under water pressures of 0, 2.5, and 5.0 MPa. The results show that elevated water pressure promotes sulfate ingress into the mortar and accelerates later-stage strength loss; this interpretation is supported by the depth-dependent distribution of soluble SO₄²⁻ measured in mortars without limestone powder. Two-way ANOVA indicates that both water pressure and limestone powder content have significant effects on compressive strength, and their interaction becomes statistically significant at 120 d. XRD, FT-IR, and SEM/EDS results show that, under elevated water pressure and high limestone powder content, the corrosion products gradually evolve from gypsum-related products to ettringite- and thaumasite-related products, with a certain spatial differentiation. Specifically, the gray–white, mud-like surface products are consistent with thaumasite-rich assemblages, whereas the needle- and column-like crystals in the interior are consistent with ettringite-rich assemblages. Overall, elevated water pressure mainly promotes sulfate transport, while limestone powder mainly increases carbonate availability. These two factors may jointly intensify TSA deterioration in mortar through a pathway involving transport enhancement, carbonate supply, corrosion product evolution, and aggravated macroscopic damage. This study provides a reference for understanding the sulfate deterioration mechanism of limestone powder-containing cement-based materials in deep underground environments under elevated water pressure. Full article

This study proposes a three-stage chronological production simulation method to enhance inter-provincial resource coordination. The core innovation lies in combining the “multi-time-scale decomposition” strategy with the “intra-provincial balancing and inter-provincial mutual assistance” mechanism. A three-stage optimization model for annual chronological production simulation is constructed. Specifically, the inter-provincial monthly medium-term mutual assistance stage takes into account the constraints of inter-provincial monthly transaction electricity volume, so as to adapt to the current situation in China where inter-provincial medium-term power transactions are mostly carried out on a monthly cycle. Simulation analysis was conducted based on a case study built with actual data from the Chongqing and Sichuan power grids in the southwestern region of China, which verifies the effectiveness of the proposed method. Full article

Primary succession following glacier retreat provides a natural system for testing whether soil development simply shifts fine roots along a single acquisitive–conservative axis orinstead changes the nutrient-acquisition pathway that dominates at the community level. We hypothesized a stage-dependent sequence, from substrate-limited exploration, to transient morphological capture, and finally to rhizosphere-mediated biochemical mobilization. To test this idea, we quantified fine-root morphology, absorptive-transport partitioning, anatomy, phosphatase activity, exudation, community-scale belowground structure, and soil and rhizosphere properties across woody communities representing approximately 20, 40, and 90 years since deglaciation in the Hailuogou Glacier foreland. Across succession stages, bulk density and pH declined, whereas field capacity, soil carbon, and soil nitrogen increased, indicating rapid development of the belowground resource environment. Fine-root strategies did not fall along a single acquisitive–conservative continuum. Instead, morphological nutrient capture peaked at intermediate succession: the 40-year stage had the highest specific root length, specific root area, absorptive-to-transport root length ratio, and root nitrogen concentration. In contrast, the 90-year stage showed lower specific root length but higher dry matter content, thicker cortex, greater standing fine-root biomass, larger rhizosphere volume, higher phosphatase activity, and greater area-based carbon exudation. This late-successional syndrome coincided with stronger extracellular enzyme activity, larger dissolved organic carbon and nitrogen pools, and higher microbial biomass, despite negative net nitrogen mineralization. Species-level analyses showed that biochemical-input traits were jointly shaped by successional stage, species identity, and their interaction. Together, these results show that primary succession did not simply increase or decrease root acquisitiveness. Instead, as soils developed, it changed the nutrient-acquisition pathway that dominated, with direct implications for nutrient cycling and vegetation dynamics in rapidly developing glacier-foreland ecosystems. Full article

The Duolong porphyry copper–gold district, located in the northwestern segment of the Bangongco–Nujiang metallogenic belt on the southern margin of the South Qiangtang terrane (Tibet), hosts typical porphyry-style Cu-Au mineralization with well-defined alteration zoning from potassic through chlorite–sericite to propylitic assemblages. Based on integrated in situ major/trace element and sulfur isotope analyses of pyrite and chalcopyrite from different alteration zones, we identify two discrete episodes of magmatic-hydrothermal activity that exerted distinct controls on metal endowment. Sulfur isotope signatures define a systematic evolution from the earliest, high-temperature potassic stage (δ³⁴S: Py-Ⅰ –3.70 to –1.16‰, mean –2.14‰; Cp-Ⅰ –4.92 to –0.90‰, mean –2.54‰) through propylitic alteration (Py-Ⅱ: 1.20–5.16‰, mean 3.06‰) to the later chlorite–sericite stage (Py-Ⅲ: –2.00 to 1.86‰, mean 0.06‰; Cp-Ⅱ: –2.50 to 0.58‰, mean –0.77‰), tracking progressive fluid cooling and changing fluid sources. Trace element systematics further discriminate these episodes: sulfides from potassic and chlorite–sericite zones are enriched in trace elements, whereas propylitic pyrite is depleted, with potassic pyrite recording the highest Cu concentrations (559–7256 ppm, mean 2302 ppm) and chlorite–sericite pyrite containing the lowest Au contents (0.01–0.59 ppm, mean 0.10 ppm). Gold mineralization occurs as native gold exsolved from chalcopyrite, and the markedly low Au concentrations in chlorite–sericite pyrite (0.01–0.59 ppm, mean 0.10 ppm) demonstrate that gold exsolution was largely completed during the first, high-temperature magmatic-hydrothermal stage. Collectively, these results establish a detailed geochemical framework linking sulfide composition to specific hydrothermal stages, providing new constraints on the processes of porphyry copper–gold mineralization in a collisional setting. Full article

Background/Objectives: Artificial intelligence (AI) models are increasingly applied across the assisted reproductive technology (ART) workflow, including male-factor assessment, ovarian stimulation, endometrial receptivity evaluation, embryo selection and prediction of pregnancy outcomes. However, many systems remain difficult to interpret, raising concerns regarding transparency, clinical integration and patient communication. Explainable artificial intelligence (XAI) aims to address these limitations by making model behavior more accessible to clinicians and embryologists. This review aimed to provide a narrative, concept-driven synthesis of how XAI has been implemented in ART, to critically examine methodological quality and clinical relevance and to outline priorities for responsible translation into practice. Methods: A structured narrative review was conducted using PubMed/MEDLINE as the primary database, supplemented by targeted reference-list screening of key primary studies and recent cross-disciplinary reviews relevant to AI in ART. Studies were curated and classified according to stage of the ART workflow, data modality, model family, explanation technique and validation strategy. Methodological features, performance reporting and implementation considerations were qualitatively appraised. Results: Most XAI applications in ART fall into two dominant categories: (i) feature-attribution methods such as SHAP and LIME applied to tabular clinical and laboratory data and (ii) saliency-based approaches, including Grad-CAM and related techniques, applied to embryo and ultrasound imaging. These methods can improve transparency and support counselling by clarifying which variables or image regions influence predictions. However, the majority of studies are retrospective and single centre, with limited external validation and heterogeneous outcome definitions, often prioritising clinical pregnancy over live birth. Calibration, decision-analytic evaluation and prospective assessment remain uncommon. XAI outputs are frequently interpreted as biologically causal despite being derived from observational data, highlighting the need for cautious clinical framing. Conclusions: XAI in ART has progressed from proof-of-concept demonstrations to early clinically oriented tools, but robust validation, standardised reporting and thoughtful workflow integration are still needed. Explanations can enhance auditability and communication, yet they do not compensate for methodological weakness. Future progress will depend on higher-quality multi-centre data, evaluation beyond discrimination metrics and governance frameworks that ensure transparency, fairness and sustained performance in real-world practice. Full article

Hydrochar has emerged as a promising carbonaceous amendment to enhance soil quality, yet its short-term effects on soil carbon (C) and nitrogen (N) dynamics and microbial functioning remain poorly understood. Here, a 77-day greenhouse pot experiment was conducted using a Cambisol cultivated with sunflower (Helianthus annuus L.) under two irrigation regimes simulating well-irrigated (WI) and water-deficit (WD) scenarios. Two doses of chicken-manure-derived hydrochar (3.25 and 6.5 t ha⁻¹, corresponding to 2.35 and 4.69 g kg⁻¹ of dry soil, respectively) and mineral fertilizer (MF) treatments providing equivalent N inputs were evaluated. Hydrochar promoted microbial growth and enhanced enzymatic and respiratory activities despite its low apparent C and nutrient input. After 77 days under WI, the addition of 6.5 t ha⁻¹ hydrochar enhanced the activity of phenol oxidase (POA) and acid phosphomonesterase (AcPA). Concomitantly, the availability of soluble C and N increased, whereas total organic C (TOC) and N decreased relative to the initial values. These responses may suggest enhanced mineralization potentially related to early-stage priming processes. The increase in POA relative to β-glucosidase is in line with a functional shift from a predominant degradation of labile compounds towards an increased oxidation of more complex structures. This interpretation is supported by solid-state ¹³C NMR data, revealing a higher degradation index of the soil organic matter. Under WD, the overall effects of hydrochar were attenuated or suppressed, particularly those related to C and N dynamics, emphasizing the interactive influence of moisture and amendment dose. Overall, our results show that hydrochar can modulate short-term soil biochemical processes, partly through enhanced microbial responses. Full article

Background/Objectives: Disease-related malnutrition is highly prevalent in oncology and is associated with poor clinical outcomes. Early detection through nutritional screening is essential; however, the optimal screening tool for ambulatory cancer patients remains uncertain. This study aimed to evaluate the agreement and diagnostic performance of the malnutrition screening tool (MST) and NUTRISCORE compared with the Global Leadership Initiative on Malnutrition (GLIM) criteria in a large nationwide cohort of ambulatory cancer patients. Methods: In this multicenter, observational, cross-sectional nationwide study, adult patients attending oncology day hospitals for intravenous antineoplastic treatment between April and November 2021 were included. Nutritional risk was assessed using MST (cut-off ≥ 2) and NUTRISCORE (cut-off ≥ 5). Malnutrition was diagnosed according to GLIM criteria. Agreement between tools was assessed with Cohen’s kappa, and diagnostic performance was evaluated by sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Analyses were stratified by tumor nutritional risk and cancer stage. Results: Among 4440 patients from 86 hospitals, 50.7% met the GLIM criteria for malnutrition; 72.5% had moderate and 27.5% severe malnutrition. MST identified 37.5% of patients as being at nutritional risk, compared with 17.3% identified by NUTRISCORE. Agreement between MST and NUTRISCORE was moderate overall (κ = 0.48; 95% CI, 0.45–0.51), but varied markedly according to tumor nutritional risk, ranging from high agreement in high-risk tumors (κ = 0.82) to low agreement in low-risk tumors (κ = 0.28). Relative to GLIM, MST was more sensitive than NUTRISCORE (0.51 vs. 0.27), whereas NUTRISCORE was more specific (0.92 vs. 0.76) and had a higher positive predictive value (0.77 vs. 0.68). Negative predictive value was low for both tools. Conclusions: GLIM-defined malnutrition was highly prevalent in this large cohort of ambulatory patients with cancer. MST provided greater case detection, whereas NUTRISCORE showed a more conservative profile with higher specificity but substantially lower sensitivity. These findings suggest that the choice of screening tool should consider clinical context- and tumor-related nutritional risk, and that neither instrument alone reliably excludes malnutrition in outpatient oncology settings. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article

Life cycle assessment (LCA) is an important analytical method used to evaluate the environmental impacts of products, services, or processes throughout their entire life cycles—from the extraction of raw materials and production to use and end-of-life treatment. LCA enables the identification of stages with the highest environmental impact burden (hotspots) and supports strategic environmental initiatives, the circular economy, standards, and policies aimed at improving sustainability. This paper analyses the application of LCA in metallurgy, with a focus on primary aluminium production. It outlines the principles of life cycle thinking and explores decarbonisation opportunities within the aluminium industry. This study includes a life cycle impact assessment case study comparing the most significant environmental impacts of primary aluminium production in different regions of the world, including Europe and Asia. The analysis was performed using openLCA software 2.5 with the OzLCI2019 database. Environmental impacts were calculated using the ReCiPe 2016 Midpoint (H) method. The results indicate that primary aluminium production mainly affects impact categories related to high energy consumption, the use of carbon anodes, and associated emissions. The highest impacts were identified in ecotoxicity, followed by global warming, land use, ozone formation, and fossil resource scarcity. No significant regional differences were observed. Full article

Comminution is the most energy-intensive stage in mineral processing and a major source of indirect greenhouse gas (GHG) emissions in mining. This study evaluates the impact of Ultra-High-Intensity Blasting (UHIB) on downstream comminution energy demand and associated GHG emissions under conditions representative of large-scale Chilean mining. Fragmentation from conventional blasting and UHIB was simulated using JKSimBlast, and the resulting particle size distributions were used as input for four comminution circuit configurations modeled in JKSimMet. Two ore hardness scenarios were analyzed: hard ore (Bond Work Index, BWI = 19 kWh/t) and soft ore (BWI = 11 kWh/t). Power draw of crushers and mills was used to estimate specific energy consumption and GHG emissions based on the Chilean electrical system emission factor. Results show that UHIB enables significant reductions in comminution energy demand, reaching approximately 18% for hard ore and over 30% for soft ore. These reductions are primarily associated with circuit simplification, including the removal of energy-intensive stages such as primary crushing and SAG milling. The results demonstrate that improved fragmentation can reduce downstream energy demand and carbon intensity, highlighting UHIB as an effective mine-to-mill strategy for energy efficiency and emission reduction. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article

Background/Objectives: Portal vein thrombosis (PVT) is a clinically significant condition in which early risk stratification remains challenging, particularly in emergency settings where rapid decision-making is required. This study aimed to evaluate the prognostic value of the Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and the Model for End-Stage Liver Disease (MELD) score in predicting the need for intensive care in patients with PVT. Methods: A retrospective analysis was conducted on adult patients (>18 years) diagnosed with PVT in the emergency department between January 2018 and December 2024. A total of 29 patients meeting the inclusion and exclusion criteria were included. Demographic characteristics, laboratory parameters, Intensive Care Unit (ICU) admission status, and 90-day mortality were analyzed. The sensitivity and specificity of MELD, SII, and SIRI for predicting ICU admission were calculated. Non-normally distributed variables were expressed as median (interquartile range, IQR) and compared using the Mann–Whitney U test. Results: The mean age of patients was 60.5 ± 16.2 years, and 18/29 (62.1%) were male. ICU admission was required in 9/29 (31.0%) of cases. MELD score (median 18.7 [11.0–21.9] vs. 7.9 [6.7–13.5], p = 0.003), bilirubin (median 2.4 [1.0–4.2] vs. 0.7 [0.4–1.1], p = 0.016), and SIRI (median 6.4 [2.3–21.3] vs. 1.4 [0.6–9.3], p = 0.038) were significantly higher in ICU-admitted patients. MELD score showed 66.7% sensitivity and 95% specificity, while SIRI had 88.9% sensitivity and 55% specificity for ICU prediction. Conclusions: MELD score, bilirubin, and SIRI are significantly associated with ICU admission in PVT patients. Their integration into emergency department protocols may assist in early risk stratification and resource allocation. Full article

Mountain railway engineering is constrained by fragile ecology, complex terrain, and intensive construction interfaces, making the project-level selection and implementation of green technologies a critical management issue. Existing studies mainly focus on single-technology assessment or static weighting and provide limited support for linking planning-stage prioritization with implementation-stage demonstration. This study proposes a multi-stage importance–performance evaluation framework for green technologies in mountain railway engineering. The framework integrates rough set theory for factor screening, the CRITIC–grey relational method for planning-stage importance assessment, Extremely Randomized Trees for implementation-stage importance identification, and the Logical Framework Approach for deviation diagnosis and management feedback. In the example application, four of seven candidate evaluation factors were retained after rough set reduction, and the corresponding weights for environmental impact, lifecycle, technical efficiency, and compatibility were 0.247, 0.263, 0.264, and 0.238, respectively. Under the simulated construction scenario, stage-to-stage deviations ranged from −0.24 to 0.11. These results show that the proposed framework can identify key factors, compare prescribed and implementation-stage importance patterns, and support adjustment-oriented management decisions. Because the implementation-stage demonstration is based on simulated rather than real project data, this study is positioned as a methodological and conceptual demonstration rather than an empirically validated case study. Full article