Search Results (19,796)

Cancer-associated thrombosis (CAT) is a major cause of morbidity and mortality in patients with cancer. The management of special situations—including recurrent venous thromboembolism (VTE), thrombosis at unusual sites, and central venous catheter-associated thrombosis (CVC-AT)—remains particularly challenging because of the limited availability of high-quality evidence. This narrative review synthesizes recommendations from major international and Spanish clinical practice guidelines and expert consensus documents, including those from SEOM, ESMO, ASCO, NCCN, ITAC and SEMI, to provide a structured framework for the management of these complex scenarios. Our analysis identified substantial heterogeneity across guidelines, particularly regarding anticoagulant selection, dosing strategies, and treatment duration. Although some convergence exists in the management of CVC-AT, important discrepancies and evidence gaps persist in areas such as splanchnic vein thrombosis, hepatic impairment, central nervous system involvement, and recurrent VTE despite treatment. In many cases, recommendations are based primarily on expert opinion rather than robust trial data, and several clinical scenarios are addressed by only a limited number of guidelines. These findings underscore the need for more standardized management strategies and prospective clinical studies to better inform decision-making in daily practice. Overall, this review highlights the growing importance of individualized anticoagulant management aimed at balancing thrombotic and bleeding risks in high-risk oncology patients, thereby helping to bridge the gap between expert consensus and evidence-based precision anticoagulation. Full article

Background: Accurate identification and positioning of thoracic devices on chest radiographs is critical for patient safety in intensive care. Multimodal large language models (LLMs) offer potentially generalizable automated evaluation, but their performance in this domain is underexplored. Methods: Three multimodal LLMs (GPT-4o, gpt-4o-2024-08-06; Gemini 3.1 Flash Lite Preview; Claude Sonnet 4.6) were evaluated on 4813 chest radiographs from the RANZCR CLiP dataset for device presence and positioning of ETT, NGT, CVC, and Swan–Ganz catheters. Performance was quantified with 95% Wilson confidence intervals, balanced accuracy, MCC, Cochran’s Q, Bonferroni-corrected McNemar, and Cohen’s/Fleiss’ kappa. Six additional analyses were performed: a blinded paired reader study (n = 377; two board-certified radiologists, blinded to ground truth and to all LLM outputs), external validation on PadChest (n = 200, device-presence detection only—PadChest lacks granular position labels), three-variant prompt-sensitivity analysis (n = 103), repeat-inference stability across three runs (n = 50), systematic error taxonomy, and a failure-case analysis. Results: Device-presence performance varied widely across models; abnormal-position sensitivity was uniformly poor (MCC ≤ 0.028; balanced accuracy 0.41–0.53). Inter-model agreement was poor to slight (Fleiss’ κ: 0.005–0.383 for presence; −0.280 to −0.025 for classification). Radiologists numerically outperformed all three LLMs in 42/42 paired comparisons; the superiority was statistically significant after Bonferroni correction in 33/42 (32/42 at p < 0.001). PadChest replicated the negative finding for device-presence detection (malposition not externally validated). Prompts and inference stochasticity introduced 2–3× sensitivity swings and run-to-run κ from 0.20 to 0.85. Case failures concentrated systematically in multi-device cases (p < 0.0001) but not in abnormal-position cases (p = 0.14). Conclusions: Current general-purpose multimodal LLMs are not yet reliable for autonomous thoracic-device assessment; their failure patterns are structurally characterizable across models, prompts, and case types and support, at most a circumscribed role, as adjunct device-presence screening tools. The findings do not generalize to purpose-built, regulator-approved clinical AI systems. Full article

This study develops a Fuzzy C-Means-based mathematical framework for the storage-oriented evaluation and classification of hybrid energy system alternatives. The analysis considers fifteen hybrid configurations generated through pairwise combinations of solar, wind, biomass, geothermal, hydropower, and fossil-based energy sources. These alternatives are evaluated with respect to fourteen storage-related criteria, namely energy efficiency, exergy efficiency, entropy, lifetime, cost, CO₂ emissions, recyclability, decarbonization potential, discharge duration, charge duration, power capacity, energy capacity, sustainability, and environmental impact. After constructing and normalizing the decision matrix, the Fuzzy C-Means algorithm is employed to identify latent similarity structures and to determine the degree of membership of each hybrid alternative to multiple clusters. To support the selection of an analytically meaningful partition, alternative cluster structures are compared in terms of partition quality and interpretability. The results indicate that the considered hybrid configurations can be grouped into distinct yet partially overlapping storage-oriented profiles, reflecting differences in technical performance, environmental burden, and sustainability characteristics. In particular, hydropower-supported systems are associated with more stable and infrastructure-compatible profiles, while biomass- and geothermal-related combinations occupy more balanced transitional positions. By extending fuzzy clustering to the storage-oriented analysis of hybrid energy systems, the study provides a mathematically transparent basis for comparative assessment, exploratory classification, and preliminary decision support. Full article

The growing demand for renewable energy, together with the need to mitigate climate change and promote more sustainable agriculture systems, has stimulated interest in energy crops. In this context, invasive alien plant species (IAPS), which have progressively colonized abandoned farmland, degraded ecosystems, and marginal areas, represent a key bioresource. IAPS have a dual nature combining high ecological invasiveness and fast growing rate with notable energetic potential. These aspects have generated a still ongoing debate among farm managers, ecologists, and policymakers regarding their role within the future bioeconomy. The present study provides a review of the IAPS black locust (Robinia pseudoacacia L.) on its real benefits as a source of bioenergy, ecological impact, and the management strategies adopted. We examine the trade-offs between containment efforts and use for renewable bioenergy production, particularly in marginal areas where few alternatives exist. This review highlights the need for stratified site-specific approaches that balance biodiversity conservation with bioresource exploitation. Finally, this study also contributes to the ongoing discussion on whether IAPS should be regarded primarily as a management challenge or a multifunctional bioresource, as in the production of bioenergy. Full article

Distributed economic dispatch in AC distribution systems relies heavily on communication networks and is therefore vulnerable to denial-of-service (DoS) attacks. To address this issue, this paper proposes a resilient event-triggered distributed economic dispatch control strategy. Two typical DoS attack scenarios, namely communication-link blocking and node isolation, are first modeled, and an event-triggered distributed economic dispatch controller is then developed to maintain incremental cost consensus and system power balance while reducing communication overhead. Based on Lyapunov stability theory and a linear matrix inequality approach, sufficient conditions for the asymptotic stability of the closed-loop system are derived, tolerable bounds on the frequency and duration of DoS attacks are established, and the absence of Zeno behavior is proved. Simulations on the IEEE 33-bus AC distribution system show that, under load disturbances, dispatch-command variations, and DoS attacks, the proposed strategy can maintain stable system operation, restore dispatch performance after attacks, and reduce communication overhead by 91.86% compared with a fixed-step periodic updating baseline. These results demonstrate the effectiveness and resilience of the proposed method for distributed economic dispatch in AC distribution systems under DoS attacks. Full article

Poor indoor air quality (IAQ) in naturally ventilated school buildings remains a widespread problem, particularly during the heating season, when limited ventilation leads to elevated CO₂ concentrations. At the same time, increasing ventilation rates may significantly increase energy demand, creating a conflict between IAQ and energy efficiency. This study aims to evaluate whether CO₂-based demand-controlled mechanical ventilation, particularly with heat recovery (HRV), can improve IAQ while maintaining acceptable energy performance in existing school buildings. A previously validated CONTAM model of a Polish primary school classroom was used to simulate natural ventilation, mechanical exhaust ventilation, and balanced ventilation with heat recovery. In mechanical systems, CO₂-based demand-controlled ventilation (DCV) was applied. The resulting airflow rates were then used in EnergyPlus simulations to assess seasonal heating and primary energy demand under Kraków climatic conditions. Increasing the outdoor air supply rate significantly reduced indoor CO₂ concentration but led to higher heating demand in exhaust ventilation systems. In contrast, HRV reduced heating energy demand by more than 80% compared with exhaust ventilation while maintaining comparable indoor air quality. Although HRV required additional electricity for fan operation, the total primary energy consumption remained low. The results demonstrate that CO₂-based DCV systems with heat recovery provide an effective balance between indoor air quality and energy performance. These findings support the application of HRV as a practical retrofit solution for improving ventilation in existing school buildings. Full article

Background: Vision is a key sensory system for postural regulation; however, the effects of degraded visual input and complete visual occlusion on static balance are not fully understood. The aim of the present study was to compare postural control during single-leg stance under two reduced-vision conditions (eyes open in darkness vs. complete visual occlusion) in healthy young adults and examine the potential influence of sex and mild visual deficits. Materials and Methods: This within-subject laboratory study included 42 healthy young adults (21 males, 21 females; mean age 20.67 ± 0.48 years). Participants performed three valid 20 s single-leg stance trials on a force platform under two visual conditions: eyes open in darkness and complete visual occlusion using an opaque mask. The order of conditions was randomized and counterbalanced, and the mean value of the three valid trials under each condition was used for analysis. Postural sway outcome variables included CoP Area, Oscillation Width, Oscillation Height, Total Displacement, and Mean Velocity. A two-way mixed-design ANOVA examined the effects of visual condition and sex. Additional mixed ANCOVA analyses were performed using body weight as a covariate to verify whether the sex-related findings remained after adjustment for body weight. Exploratory subgroup analyses based on mild visual deficits were performed using independent-samples t-tests. Results: No significant overall main effect of visual condition was observed for any postural sway variable (all p > 0.05). However, a significant condition × sex interaction was found for CoP Area (F(1,40) = 9.910, p = 0.003, η²p = 0.199), indicating different response patterns between males and females across conditions. Significant main effects of sex were also found for Total Displacement (F(1,40) = 9.212, p = 0.004, η²p = 0.187) and Mean Velocity (F(1,40) = 9.090, p = 0.004, η²p = 0.185), with males showing higher values overall. The sex-related findings for CoP Area, Total Displacement, and Mean Velocity remained significant after adjustment for body weight. No significant sex effects were found for Oscillation Width or Oscillation Height, and no significant differences were observed between participants with and without mild visual deficits in either condition (all p > 0.05). Conclusions: Altered visual input did not produce a uniform overall effect on postural sway during single-leg stance in healthy young adults. Instead, the findings indicate a more differentiated pattern, with a sex-specific response for CoP Area and overall sex-related differences in Total Displacement and Mean Velocity that were not explained by body weight. Mild visual deficits were not associated with significant balance alterations under the present experimental conditions. These findings support a more nuanced interpretation of postural regulation under reduced visual input and highlight the importance of considering individual characteristics, particularly sex, in balance assessment. Full article

The third United Nations Sustainable Development Goal promotes health and well-being. Despite the existence of academic literature examining the relationship between health and income inequality, evidence on the role of healthcare systems in this inequality remains limited. This article aims to analyse the extent to which healthcare systems are associated with differences in economic inequality. To this end, a balanced panel of 27 European Union countries for the period 2005–2022 is used, applying t-tests for differences in means and linear regression models using S80/S20, Gini and Palma inequality measures. The main results show that countries with a Social Health Insurance System (SHIS) exhibit, on average, lower levels of income inequality, despite not being the highest spenders on healthcare. On the other hand, healthcare expenditure has a negative and statistically significant relationship with inequality, whereas in countries with a Mixed Healthcare System (MHS), this association is not statistically significant. A disaggregated analysis of public and private spending indicates that public expenditure is particularly relevant in SHIS countries being negatively associated with income inequality, whereas this relationship differs in countries with a National Health System (NHS). Thus, it is concluded that healthcare systems display significant differences in the relationship under study. Full article

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article

Accurate forecasting of typhoon evolution from satellite cloud imagery is critical for disaster preparedness and mitigation, yet remains challenging due to the complex spatiotemporal dynamics of typhoon systems. While deep learning models have shown promise in spatiotemporal sequence prediction, existing approaches often struggle to balance the modeling of large-scale structural evolution with fine-grained local dynamics. In this paper, we propose FusionTyphoonPredictor, a novel dual-branch encoder–decoder framework designed for typhoon cloud image prediction. The model integrates a Global Fusion Module to capture multi-scale spatial interactions using large-kernel attention and multi-scale convolution, and an ST Recurrent Refiner to enhance temporal consistency and local detail through recurrent processing with ConvGRU and residual blocks. Extensive experiments on the Digital Typhoon dataset demonstrate that our approach achieves improved performance compared to existing methods (including PredFormer and PhyDNet) across most metrics and forecasting horizons. Specifically, FusionTyphoonPredictor shows consistent advantages in SSIM, MAE, and MSE, with particular strength in short-term forecasting. Comprehensive ablation studies validate the complementary design of the two branches and confirm the effectiveness of each proposed component. Our work advances typhoon forecasting and has potential for real-time operational deployment. Full article

With the rapid deployment of deep neural networks (DNNs) on edge devices, traditional hardware accelerators face significant challenges in terms of data security, computational redundancy caused by sparsity, and uneven utilization of on-chip resources. This paper proposes SaE-FPGA, a secure and efficient DNN accelerator designed specifically for edge FPGA platforms. The architecture introduces three core innovations: (1) Hash-Bypass Processing Unit (HBPU): Integrating a high-speed SHA-256 hardware engine with a hash-sparse bitmap mechanism, it enables real-time data integrity verification within a single clock cycle while skipping computations for redundant zero-value data. (2) Flexible Mixed-Precision Processing Element (FMP): By reconfiguring idle BRAM and LUT resources into an active lookup table multiplication engine, it overcomes the physical bit-width limitations of DSP blocks and supports INT8/INT6/INT4 mixed-precision multiplication. (3) Multi-mode Reconfigurable Streaming Frame (MRSF): A sparse-aware, elastic load balancing and data routing mechanism designed to mask long memory access latencies and ensure high hardware resource utilization. Experimental results on the Zynq 7045 platform demonstrate that SaE-FPGA reduces redundant computations by 23.2% while maintaining high precision and minimizing precision loss. The system effectively mitigates the risk of physical tampering. When tested on ResNet-50, it achieved a 27.2% improvement in energy efficiency and a 2.97× speedup compared to DSP-based FPGA solutions. Furthermore, by fully exploiting the hybrid BRAM-LUT and DSP configuration, the proposed accelerator achieves a remarkable peak throughput of 782.4 GOPS. Full article

Bird feathers possess functions such as water resistance, thermal insulation, and air permeability, providing inspiration for the design of functional fabrics. Based on the functional differentiation of different feather regions and the structural constraints associated with these functions, this study selected down feathers, feather vanes, hooklets, and fluffy feather filament node structures as biomimetic prototypes. Four biomimetic knitted structures were designed for outdoor environments with significant temperature fluctuations and for the thermo-moisture comfort needs of older adults. Through macro- and micro-structural feature extraction, three-dimensional modeling, and experimental testing, a multi-parameter evaluation system covering water resistance, thermal resistance, thermal insulation rate, air permeability, moisture vapor transmission, and moisture management was established to systematically evaluate the thermo-moisture regulation performance of the fabrics. The results showed that each structure exhibited distinct performance advantages: Structure 1 demonstrated the best thermal insulation performance; Structure 2 showed relatively superior water resistance and outstanding air permeability; Structure 4 exhibited relatively superior moisture vapor transmission and moisture management performance; and Structure 3 achieved the highest gray relational optimality value, indicating a relatively balanced thermo-moisture regulation capability. Among all performance indicators, air permeability showed the highest correlation with the knitted structures. Based on these results, and considering regional differences in heat generation and sweating across different body parts of older adults, this study further explored zonal application strategies for elderly outdoor clothing to improve wearing comfort and functionality under environments with fluctuating thermal conditions. Full article

The performance of a sequencing batch biofilter granular reactor (SBBGR), followed by a dual media granular activated carbon (GAC) column, was evaluated in terms of its ability to remove selected per- and polyfluoroalkyl substances (PFAS) from landfill leachate. The results show that the SBBGR achieved an overall reduction of 51%, with the preferential removal of long-chain PFAS, while short-chain PFAS were only partially removed. Subsequent GAC treatment exhibited compound-specific breakthrough behavior, which was governed by chain length. Short-chain PFAS (e.g., perfluorobutanoic acid) exhibited rapid bed volumes at 50% breakthrough (BV₅₀ ≈ 88), whereas long-chain PFAS (e.g., perfluorooctanoic acid and perfluorooctanesulfonic acid) were substantially more retained (BV₅₀ ≈ 446 and 361, respectively), with perfluorohexanesulfonic acid and perfluorodecanoic acid failing to reach BV₅₀ within the monitored period. Mass balance analysis showed that the hybrid GAC column captured ~73% of the influent PFAS mass. This resulted in >80–95% retention of long-chain PFAS and <40% retention of short-chain PFAS. Although long-chain PFAS were preferentially adsorbed, mobile short-chain species dominated residual effluent loads. These findings highlight the need for optimized contact times or dual-media strategies to control the breakthrough of short-chain PFAS. Full article

Elicitation is a powerful strategy for increasing bioactive metabolites in plant systems. This study is among the first to integrate growth responses, antioxidant enzyme activities, and metabolite profiling in G. paniculata adventitious roots (ARs). The study aims to evaluate the effects of yeast extract (YE) and salicylic acid (SA) on biomass traits, antioxidant enzymes (peroxidase, polyphenol oxidase, and phenylalanine ammonia-lyase), and phenolic metabolite profiles. ARs were exposed to YE (0.25–2 g L⁻¹) and SA (50–400 µM) for 28 days. Yeast extract significantly enhanced antioxidant capacity by promoting enzyme activities, phenolics, and flavonoids. In contrast, SA exhibited concentration-dependent effects. Moderate concentrations improved antioxidant activity, while higher concentrations promoted the accumulation of specific flavonoids. Maximum biomass production was achieved with 1 g L⁻¹ YE, which also resulted in the highest metabolite productivity. Conversely, SA treatments caused a progressive reduction in biomass with increasing concentration, although they enhanced the accumulation of selected bioactive compounds. Notably, 100 µM SA resulted in the highest phenolic content and antioxidant activity, whereas 400 µM SA markedly increased flavonoids such as rutin and quercetin. HPLC analysis identified seventeen phenolic compounds, demonstrating that YE acts as a broad-spectrum elicitor, whereas SA functions as a selective metabolic modulator. The differential enzymatic responses further highlight elicitor-specific regulatory patterns in antioxidant defense and secondary metabolism. Overall, these findings demonstrate that elicitor type and concentration differentially influence the balance between growth and secondary metabolism, providing a framework for optimizing metabolite production in controlled in-vitro systems. Full article

Thyroid diseases are very common endocrine diseases that afflict millions of people around the world and need proper and timely diagnosis to ensure proper treatment. Although machine learning and hybrid metaheuristic methods have advanced, current models have high computation costs, low interpretability, and low probability calibration, which limit their use in clinical settings. In this research, a new DFSel-FT (Differentiable Feature Selection and an FT-Transformer) system is suggested, which combines DFSel-FT to allow one to diagnose thyroid disease effectively and interpretably. It employs Concrete (Gumbel-Softmax) gates to select the features end-to-end to make sure that only the most relevant clinical attributes are carried through the training. A Transformer-based architecture is then used to process the chosen features to learn intricate interdependencies. The model is trained with class-balanced focal loss and temperature scaling to better enhance calibration. Experimental evaluation on the UCI Thyroid Disease Dataset (22,632 samples) showed that the proposed model achieved 97.85% accuracy, 97.65% Macro-F1, and 98.10% AUC-OVR, showing competitive performance compared with traditional machine learning models, modern tabular deep learning baselines, and hybrid metaheuristic methods. Other indicators of robustness and reliability include MCC (0.955), Cohen Kappa (0.951), and small calibration error (ECE = 0.021). SHAP and LIME explainability analysis reveals clinically relevant features that include TSH, TT4, and T3. The proposed framework provides a balanced integration of predictive performance, interpretability, and probability calibration, making it a promising benchmark-level framework for interpretable and calibrated thyroid disease classification, requiring external clinical validation before real-world deployment. Full article