Search Results (731)

Background/Objectives: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic interstitial lung disease with high mortality and limited treatment options. Despite recent therapeutic advances, predicting survival remains challenging. Given the challenge of predicting disease progression in IPF, identifying reliable prognostic markers may support individualized treatment strategies, guide follow-up intensity, and improve clinical decision making. This study aimed to evaluate mortality rates and factors associated with poor prognosis in patients with IPF over a 10-year period at a tertiary care center. Methods: Medical records of 268 patients diagnosed with IPF between 2015 and 2024 were retrospectively reviewed. Demographic characteristics, comorbidities, radiological findings, pulmonary function test results, frequency of exacerbations and hospitalizations, treatment details, and survival outcomes were analyzed. Univariate and multivariate logistic regression analyses were performed to identify predictors of mortality. Results: This study included 268 patients (77.2% male; median age, 72 years). During a median follow-up of 24 months, 44% (n = 118) of patients died. Deceased patients were older (p < 0.001) and had higher rates of coronary artery disease, pulmonary embolism, pulmonary hypertension, and malignancy (all p < 0.05). A definite UIP pattern was more common among deceased patients (71.2% vs. 52.4%, p = 0.02). Acute exacerbations (23.3% vs. 8.1%) and hospitalizations (61.9% vs. 23.3%) were significantly more frequent in this group (p < 0.001). In multivariate analysis, GAP score (OR 11.68, p = 0.001), pulmonary hypertension (OR 15.39, p = 0.02), history of exacerbation (OR 56.2, p = 0.04), baseline FVC (OR 1.10, p = 0.02), mean platelet volume (OR 0.29, p = 0.01), and AST level (OR 1.12, p = 0.04) were independent predictors of mortality. Conclusions: Despite advances in management, IPF continues to carry a high mortality risk. This study represents one of the largest single-center IPF cohorts from our region with long-term real-life follow-up and additionally evaluates laboratory biomarkers such as MPV and AST, which have not been widely investigated as prognostic indicators in IPF. Advanced age, reduced pulmonary function, comorbidities, and acute exacerbations are major prognostic factors. Early recognition and proactive management of these parameters may help improve survival outcomes. Full article

Traditional diode-based rectifiers (TDRs) in railway traction substations (TSSs) are inefficient at handling bidirectional power flow and cannot recover regenerative braking energy (RBE). Replacing these conventional systems with reversible traction substations (RTSSs) requires detailed modeling, extensive simulations, and validation using real data. This paper presents a DT-oriented real-time modeling and Hardware-in-the-Loop (HIL) platform for the analysis and performance assessment of RTSSs in DC railway systems. The integration of interleaved PWM rectifiers enables bidirectional power flow, allowing efficient RBE recovery and its return to the main grid. Modeling railway networks with moving trains is complex due to nonlinear dynamics arising from continuously varying positions, speeds, and accelerations. The proposed approach introduces an innovative multi-train simulation method combined with low-level transient and power-quality analysis. The validated DT model, supported by HIL emulation using OPAL-RT, accurately reproduces real-world system behavior, enabling optimal component sizing and evaluation of key performance indicators such as voltage ripple, total harmonic distortion, passive-component stress, and current imbalance. The results demonstrate improved energy efficiency, enhanced system design, and reduced operational costs. Meanwhile, experimental validation on a small-scale RTSS prototype, based on data from the Italian 3 kV DC railway system, confirms the accuracy and applicability of the proposed DT-oriented framework. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

Continuous particle exchange thermal machines require no time-dependent driving, can be realised in solid-state electronic devices, and can be miniaturised to nanometre scale. Quantum dots, providing a narrow energy filter and allowing to manipulate particle flow between the hot and cold reservoirs are at the heart of such devices. It has been theoretically shown that through mitigating passive heat flow, Carnot efficiency can be approached arbitrarily closely in a quantum dot heat engine, and experimentally, values of 0.7${\eta }_C$ have been reached. However, for practical applications, other parameters of a thermal machine, such as maximum power, efficiency at maximum power, and noise—stability of the power output or heat extraction—take precedence over maximising efficiency. We explore the effect of the internal microscopic dynamics of a quantum dot on these quantities and demonstrate that its performance as a thermal machine depends on few parameters—the overall conductance and three inherent asymmetries of the dynamics: entropy difference between the charge states, tunnel coupling asymmetry, and the degree of detailed balance breaking. These parameters act as a guide to engineering the quantum states of the quantum dot, allowing to optimise its performance beyond that of the simplest case of a two-fold spin-degenerate transmission level. Full article

Controlled environment agriculture technologies are traditionally applied to higher plants to enhance growth and cultivation periods, but such a concept has seldom been applied to seaweed aquaculture. A new dimension has been opened, wherein preliminary investigations in Ulva ohnoi revealed that continuous exposure (24 h) of light modulates chlorophyll-a fluorescence, carbohydrate content, and biochemical composition affecting the daily growth rate. DGR (daily growth rate) increased 2.6 times under continuous illumination for 24 h compared to the 12 h L/D photoperiod. Mg and carbohydrate contents were raised by 1.1 and 1.2 times, respectively, under continuous illumination. DGR formed a strong positive correlation with carbohydrate, protein, carotenoid, chlorophyll-a fluorescence, C, H, and Mg levels. A short cultivation cycle (15 days) was proposed to enable a consistent, continuous high growth and to avoid the induction of reproduction. The feedstock demand for bio-products, aquaculture feed, biomaterials, functional food, and food additives is registering unprecedented feedstock demand for Ulva. However, further detailed studies are desired to understand the seasonality and economic viability of scaling up this technique for commercial implementation. Full article

Background/Objectives: Skin autofluorescence (SAF), a marker of advanced glycation end products (AGEs), reflects cumulative hyperglycemia and may predict vascular complications in diabetes. Continuous glucose monitoring (CGM) also provides detailed glycemic profiles, but their prognostic values in gestational diabetes mellitus (GDM) are unclear. The primary aim was to evaluate whether SAF predicts adverse maternal or neonatal outcomes, whereas secondary exploratory analyses assessed oxidative stress markers and CGM-derived metrics. Methods: We prospectively enrolled 115 Japanese pregnant women with plasma glucose ≥ 140 mg/dL at 60 min after 50-g GCT. At around 29 weeks’ gestation, SAF and diacron-reactive oxygen metabolites (d-ROMs) were measured, and a subset underwent 14-day CGM. Maternal and neonatal outcomes were obtained from medical records. Logistic regression and receiver operating characteristic (ROC) analyses were performed. Results: In the primary analysis of the overall cohort, SAF did not predict adverse outcomes. In the CGM subgroup, mean glucose level (MGL) was significantly higher in women with maternal complications. Multivariate analysis identified MGL as the only independent predictor of maternal adverse events (adjusted OR 10.45 per 10 mg/dL, 95% CI 1.93–56.5; AUC 0.818; cutoff 86.8 mg/dL). No marker predicted neonatal outcomes. Conclusions: The pre-specified primary endpoint was negative (SAF was not predictive), and oxidative stress markers were also not predictive, whereas CGM-derived MGL independently predicted maternal adverse outcomes, underscoring the utility of CGM for risk stratification in pregnant women with abnormal GCT results. Full article

Common bean (Phaseolus vulgaris, L.) is the most widely consumed grain legume globally. The seeds of common bean are a rich source of protein, but one of the seeds’ storage proteins is phytohemagglutinin (PHA), a lectin whose consumption in raw or inadequately cooked bean seed or products into which the seed is milled results in acute symptoms of food poisoning. Given that demand for incorporating common bean ingredients into foods is expanding, there has been a call for regulatory agencies to formulate more robust guidance on allowable levels of active PHA in beans and bean ingredients and for establishing standardized methodology for measuring active PHA. Herein, detailed protocols are provided for extraction of PHA from beans and for the use of digital image analysis in the traditional hemagglutination assay. Results are compared to an ELISA assay. Given reports that ingestion of four to five soaked raw dark red kidney bean (DRK) seeds can induce food poisoning, our focus was on this market class of bean. By ELISA assay, estimated concentration of active lectin in DRK was 223 ± 0.07 mg/g dry weight and the total amount of PHA contained in four seeds was 544 mg. Commercially cooked canned beans had >99% reduction in PHA (4.9 µg/g dry weight). Consumption of an entire can (1.5 cups, equivalent to 94 g dry matter) would equal 0.46 mg PHA which is approximately 1000-fold lower than the amount estimated to be associated with food poisoning. It is hoped that this report stimulates continued interest in standardizing methodology across laboratories and in setting standards of identity for active PHA in bean products. Full article

Ensuring navigational safety is one of the most critical challenges in autonomous maritime navigation research, requiring accurate real-time assessment of collision risks and prompt navigational decisions based on such assessments. Traditional rule-based systems utilizing radar and Automatic Identification Systems (AIS) exhibit fundamental limitations in simultaneously analyzing discrete objects such as vessels and buoys alongside continuous environmental boundaries like coastlines and bridges. To address these limitations, recent research has incorporated artificial intelligence approaches, though most recent studies have primarily focused on object detection methods. This study proposes a structured tag-based multimodal navigation safety framework that performs inference on maritime scenes by integrating YOLO-based object detection with the LLaVA vision–language model, generating outputs that include risk level assessment, navigation action recommendations, reasoning explanations, and object information. The proposed method achieved 86.1% accuracy in risk level assessment and 76.3% accuracy in navigation action recommendations. Through a hierarchical early stopping system using delimiter-based tags, the system reduced output token generation by 95.36% for essential inference results and 43.98% for detailed inference results compared to natural language outputs. Full article

In the energy industry, like industrial monitoring scenarios, using generative AI for video captioning technology is crucial in event understanding and safety analysis. Current approaches typically rely on a single language model to decode visual semantics from video frames. Lightweight pre-trained generative models often produce overly generic captions that omit domain-specific details like energy equipment states or procedural steps. Conversely, multimodal large generative AI models can capture fine-grained visual cues but are prone to distraction from complex backgrounds, resulting in hallucinated descriptions that reduce reliability in high-risk energy workflows. To bridge this gap, we propose a collaborative video captioning framework, EnerSafe-Cap (Energy-Safe Video Captioning), which introduces domain-aware prompt engineering to integrate the efficient summarization of lightweight models with the fine-grained analytical capability of large models, enabling multi-level semantic understanding, thereby improving the accuracy and completeness of video content expression. Furthermore, to fully exploit the strengths of both small and large models, we design a dual-path heterogeneous sampling module. The large model receives key frames selected according to inter-frame motion dynamics, while the lightweight model processes densely sampled frames at fixed intervals, thereby capturing complementary spatiotemporal cues global event semantics from salient moments and fine-grained procedural continuity from uniform sampling. Experimental results on commonly used benchmark datasets show that our model outperforms baseline models. Specifically, on the VATEX dataset, our model surpasses the lightweight pre-trained language model SwinBERT by 19.49 in the SentenceBERT metric, and outperforms the multimodal large language model Qwen2-vl-2b by 8.27, validating the effectiveness of the method. Full article

In hyperspectral image classification (HSIC), each pixel contains information across hundreds of contiguous spectral bands; therefore, the ability to perform long-distance modeling that stably captures and propagates these long-distance dependencies is critical. A selective structured state space model (SSM) named Mamba has shown strong capabilities for capturing cross-band long-distance dependencies and exhibits advantages in long-distance modeling. However, the inherently high spectral dimensionality, information redundancy, and spatial heterogeneity of hyperspectral images (HSI) pose challenges for Mamba in fully extracting spatial–spectral features and in maintaining computational efficiency. To address these issues, we propose S²GL-MambaResNet, a lightweight HSI classification network that tightly couples Mamba with progressive residuals to enable richer global, local, and multi-scale spatial–spectral feature extraction, thereby mitigating the negative effects of high dimensionality, redundancy, and spatial heterogeneity on long-distance modeling. To avoid fragmentation of spatial–spectral information caused by serialization and to enhance local discriminability, we design a preprocessing method applied to the features before they are input to Mamba, termed the Spatial–Spectral Gated Attention Aggregator (SS-GAA). SS-GAA uses spatial–spectral adaptive gated fusion to preserve and strengthen the continuity of the central pixel’s neighborhood and its local spatial–spectral representation. To compensate for a single global sequence network’s tendency to overlook local structures, we introduce a novel Mamba variant called the Global_Local Spatial_Spectral Mamba Encoder (GLS²ME). GLS²ME comprises a pixel-level global branch and a non-overlapping sliding-window local branch for modeling long-distance dependencies and patch-level spatial–spectral relations, respectively, jointly improving generalization stability under limited sample regimes. To ensure that spatial details and boundary integrity are maintained while capturing spectral patterns at multiple scales, we propose a multi-scale Mamba encoding scheme, the Hierarchical Spectral Mamba Encoder (HSME). HSME first extracts spectral responses via multi-scale 1D spectral convolutions, then groups spectral bands and feeds these groups into Mamba encoders to capture spectral pattern information at different scales. Finally, we design a Progressive Residual Fusion Block (PRFB) that integrates 3D residual recalibration units with Efficient Channel Attention (ECA) to fuse multi-kernel outputs within a global context. This enables ordered fusion of local multi-scale features under a global semantic context, improving information utilization efficiency while keeping computational overhead under control. Comparative experiments on four publicly available HSI datasets demonstrate that S²GL-MambaResNet achieves superior classification accuracy compared with several state-of-the-art methods, with particularly pronounced advantages under few-shot and class-imbalanced conditions. Full article

Digital battery passports are being adopted to provide traceable records of lithium-ion batteries across their lifecycle, credible performance, and durability. However, it requires continuous diagnostics rather than lab-based tests and conditions. This review establishes a materials-informed system that links (i) battery-passport frameworks, (ii) cell-level design, and (iii) online electrochemical impedance spectroscopy (EIS) observables. Therefore, a chemistry-aware indicator set is proposed for passport reporting that relies on capacity and impedance indices, each accompanied by explicit tests. A review of the common and commercial LIBs (LCO, NCA, NMC, LMO, LFP) explains differences and characteristics. In addition, online EIS is reviewed, and different techniques for battery online diagnostics and state estimation are described, with details on how this online analysis is incorporated into the battery passport framework. This review covers the battery passport framework, the materials used in commercial batteries that must be documented and traced, and how these materials evolve throughout the degradation process. It concludes with the state of the art in online battery cell inspection, which enables comparable health reporting, conformity assessment, and second-life grading. Finally, it outlines key implementation priorities related to the reliability and accuracy of battery passport deployment and online battery diagnostics. Full article

Based on the WRF-3DVar system, this study investigates the impacts of assimilating radar and automatic weather station (AWS) observations, both independently and jointly, for a squall line case that occurred over complex terrain in China on 30 May 2024. It is found that radar data assimilation with spatial truncation significantly enhances the representation of convective structures while reducing false echoes by about 40%. However, when the variance and correlation length scales are enlarged, reflectivity intensity is increased by 5–10 dBZ with false signals and positional errors also introduced, while a balanced scheme is observed to yield the highest skill scores. Assimilation of AWS alone provides limited improvements, whereas radar assimilation introduces localized structures that rapidly decay within 1–2 h due to the absence of boundary-layer constraints. The benefits of joint assimilation are clearly demonstrated in terms of spatial continuity and vertical consistency, with the assimilation order being identified as a decisive factor. When AWS is assimilated prior to radar, low-level thermodynamic and dynamic conditions are optimized, leading to strengthened cold pool structures by around 2 K, enhanced updrafts by over 20%, and improved wind distribution. The critical role of AWS-radar joint assimilation in capturing the dynamical characteristics of squall lines is thus highlighted. Detailed examination of the forecast and analysis indicates that assimilating AWS before radar not only optimizes boundary-layer conditions but also enhances the coupling between cold pools and updrafts, resulting in improved simulation accuracy in both horizontal and vertical structures. These findings provide valuable insights for advancing the prediction of severe convective systems. Full article

The present study aimed to document the diversity and seasonal dynamics of stored-product insects in an animal feed facility located in northern Greece. A total of 38 traps were installed across different operational areas of the facility and inspected over 51 consecutive sampling occasions. Captured insects were identified to the lowest possible taxonomic level, and their frequency and dominance were calculated. In total, 9047 insect species belonging to five orders, 14 families, and at least 18 insect species were recorded. The dominant species were Tribolium castaneum, T. confusum, Oryzaephilus surinamensis, Sitophilus granarius, Lasioderma serricorne, and Lepidoptera adults, which collectively accounted for more than 85% of all captures. The total number of insects exhibited marked seasonal fluctuations, with the highest captures during late summer and early autumn and minimal activity during winter. Positive and significant correlations were detected among several dominant species, notably between Lepidoptera and T. castaneum, suggesting overlapping environmental preferences within the facility. These findings provide a detailed overview of the insects associated with feed industries in Greece and underscore the importance of continuous monitoring for effective pest management. The results highlight the need for seasonally adjusted control measures and contribute to a better understanding of the dynamics of stored-product pests under industrial conditions. Full article

Cultural tourism has become an increasingly significant phenomenon in urban areas, especially in cities rich in heritage sites. However, when the number of visitors exceeds sustainable capacity thresholds, both the physical and psychological comfort and safety of individuals may be compromised. A higher number of visitors inside historic buildings leads to elevated concentrations of carbon dioxide (CO₂), particularly in poorly ventilated enclosed or confined spaces, primarily as a result of human respiration. Such conditions not only accelerate the deterioration processes affecting heritage materials but also introduce potential health risks for visitors. Parameters such as CO₂ concentration, indoor air temperature, and relative humidity represent key measurable parameters for assessing environmental Indoor Air Quality (IAQ) within heritage buildings. Digital real-time monitoring of these parameters plays a crucial role in preventive heritage conservation, sustainable site management, and in ensuring visitors’ comfort and well-being. This paper presents a procedure and methodology that use digital technological tools to efficiently estimate and monitor the Visitor Carrying Capacity (VCC) of enclosed/confined heritage spaces, especially Heritage Building Information Modelling (HBIM) and Sensor Technology. These kinds of spaces require particular attention due to their spatial characteristics. In order to do so, it is necessary to know the geometry of the site, and to consider IAQ conditions. This study also considers the number of People at One Time (PAOT) and Visitor Occupancy (VO). The results focus on the procedural development of the analysis and emphasise the role of digital tools not only due to their efficiency and accuracy in spatial analysis for estimating VCC, but especially for the real-time monitoring of visitors and surveying specific environmental parameters. The experimental phase of this study uses the Chapel of the Holy Chalice of the Valencia Cathedral (Spain) as a pilot case. Monitoring this space reveals how quickly high CO₂ levels are reached with continuous visitor presence, and how long it takes for them to decay in absence of people and under passive ventilation conditions. The outcome of this research is a detailed methodological framework designed to assess and monitor Visitor Carrying Capacity (VCC) in enclosed/confined heritage sites by integrating digital technologies, thereby enhancing sustainable management, planning and decision-making processes. Full article

Climate change, driven largely by anthropogenic greenhouse gas emissions, is a major global issue. Long-term high-frequency measurements of gas fluxes remain limited, especially outside the growing season. This study addresses two key gaps: the absence of continuous annual data capturing diurnal and seasonal variations, and the biases from suboptimal sampling timing. Using automated chambers, we monitored soil CO₂ and CH₄ fluxes throughout 2016 in a temperate forest on Changbai Mountain, China. Our results showed a strong negative correlation between annual CO₂ and CH₄ fluxes, with a slope of −0.21 and R² of 0.70. This relationship persisted from March to November but was absent during the winter and April. Both gases exhibited the largest diurnal variations in summer. Statistical analysis identified 16:00 as the optimal single sampling time for estimating daily mean fluxes in most months. CO₂ fluxes were primarily governed by temperature but modulated by VWC (soil volumetric water content). They were suppressed during summer drought and enhanced during winter freeze–thaw cycles. CH₄ uptake rates were strongly dependent on VWC throughout the growing season, while their temperature response underwent a reversal from positive in summer to negative in winter. Decision tree analysis revealed nonlinear threshold responses. CO₂ fluxes exhibited three temperature thresholds between 5.30 and 15.64 °C and two VWC thresholds between 0.30 and 0.42 m³ m⁻³. CH₄ fluxes showed five temperature thresholds ranging from 2.34 to 15.71 °C and seven VWC thresholds from 0.11 to 0.44 m³ m⁻³. The strongest anticorrelation between CH₄ flux and temperature occurred at intermediate VWC levels. This study provides detailed characteristics of greenhouse gas fluxes based on complete annual high-frequency data. It emphasizes the importance of year-round monitoring and offers improved sampling strategies and mechanistic insights for better flux monitoring and climate prediction. Full article