Search Results (8,244)

Dendrochemistry was applied to a small town, Taylorville, Illinois, which has a superfund site and apparently more cases of cancer than expected based on background rates. As an ecologic study, dendrochemistry is not intended to unequivocally associate particular elements to specific illnesses, but rather dendrochemistry serves more generally to characterize changes in element availability through time, which then might lead to follow-up epidemiological studies. In Taylorville, multiple elements measured in decadal chunks of tree rings of 12 trees showed no trend though time going back several decades. This non-result is important, demonstrating that element concentrations can remain constant across tree rings. By contrast, multiple other elements showed an uptick in concentration beginning by about 2000. Some of these elements are known to be harmful to human health, while others are not. More broadly, it could be of interest to consider increases through time in multiple metals as a combined burden in public health. Spatially, tree sampling for dendrochemistry is often not dense enough to isolate sources of element availability. Other techniques of environmental monitoring exist for elucidating spatial patterns of elements, and leaf surface chemistry is recommendable as a companion technique for dendrochemistry to discover spatial and temporal environmental patterns. Full article

The increasing power consumption reflects technological and industrial growth, but meeting this demand with conventional fossil-fuel-based plants is challenging. Microgrids address this issue by integrating renewable energy-based Distributed Energy Resources (DERs) and Energy Storage Systems (ESS). Efficient Microgrid operation requires a power management system to balance supply and demand, reduce costs, and ensure load prioritization. This paper presents a wide area measurement (WAMS)-based Centralized Power Management System (CPMS) for AC microgrids in both Islanded and Grid-Connected modes. The modified IEEE 13-bus system is utilized as a microgrid test system by integrating DERs and ESS. WAMS significantly enhances intra-microgrid communication by offering real-time, high-resolution monitoring of electrical parameters, surpassing the limitations of traditional SCADA-based monitoring systems. In grid-connected mode, the proposed CPMS effectively manages dynamic grid tariffs, generation variability in DERs, and state-of-charge (SoC) variations in the ESS while ensuring uninterrupted load supply. In islanded mode, a load prioritization scheme is employed to dynamically disconnect and restore loads to enhance the extent of load coverage across consumer categories. The inclusion of diverse load categories, such as domestic, industrial, commercial, etc., enhances the practical applicability of the CPMS in real-world power systems. The effectiveness of the proposed CPMS is validated through multiple case studies conducted in Simulink/MATLAB. Full article

In this connected world, communication in all kinds of complex environments is crucial. As a result, indoor satellite communication could enable many new applications and use cases. In this study, we explore the potential of Low Earth Orbit (LEO) satellites to provide indoor coverage. This is done by evaluating the signal strength of Orbcomm LEO satellite signals in multiple indoor environments within a suburban home. Starting from IQ samples, we developed an algorithm to calculate the Carrier-to-Noise Density Ratio (C/N0) as a key performance metric to compare environments when the Carrier-To-Noise Ratio (CNR) is above 0 dB. By utilizing a Software Defined Radio (SDR) in combination with this algorithm, we were able to evaluate the signal strength differences between environments. We found that the LEO satellite signals penetrated into every environment including the basement. The signals were even received with high signal strength in the attic, reaching values above 55 dB-Hz. Moreover, the signals were well received in every above-ground environment. Unsurprisingly, the satellite signals were received the weakest in the basement and only for a short duration of time. Full article

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article

Canine distemper virus (CDV) is the causative agent of a widespread infectious disease affecting both domestic and wild carnivores. Owing to its ability to cross species barriers and its high fatality rate in unvaccinated animals, CDV poses a significant conservation threat to endangered wildlife worldwide. To date, two distinct CDV lineages have been reported in Colombia, with cases documented separately in domestic dogs and wild peri-urban carnivores. This study aimed to detect and characterize the concurrent circulation of CDV in naturally infected domestic dogs and crab-eating foxes (Cerdocyon thous) from the same area in Colombia. Through molecular and phylogenetic analyses, we identified the South America/North America-4 lineage infecting both populations simultaneously. Our findings revealed high genetic variability, multiple virus reintroductions, and a close relationship with CDV strains previously detected in the United States. These results confirm the simultaneous circulation of CDV in the domestic and wildlife interface and underscore the urgent need for an integrated approach to CDV prevention and control involving both domestic and wildlife health interventions. Full article

Background/Objectives: This paper evaluates the incidence of thrombotic and/or hemorrhagic adverse events within 30 days after oral health procedures (OHPs) in patients taking antithrombotic agents. Secondary objectives were to determine proper antithrombotic management and its association with adverse events. Methods: As part of a multicenter multispecialty prospective observational study (ReQXAA), individuals with antithrombotic therapy and receiving at least one OHP were selected. Before OHP, participants were referred to their medical doctors to indicate the antithrombotic therapy management. Adverse events were evaluated thirty days after OHP by phone call. Proportions and odds ratios (ORs) were generated applying Fisher’s exact test, chi-square tests and multiple regression models. Results: A total of 138 patients underwent 144 OHPs. Fifteen adverse events (10.5%) were registered, among which the most frequent was slight bleeding (n = 13), which was followed by bleeding that required suspension of the antithrombotic agent (n = 1) and a myocardial infarction (n = 1). Antithrombotic management was appropriate in 122 (84.7%) cases. In 15.3% of the cases it was inappropriate, the main reason being the unnecessary interruption of the antithrombotic medication (n = 11; 50%). Inadequate management was associated with a higher incidence of adverse events (OR = 4.7; 95% confidence interval [1.3, 16.3]; p = 0.016) after adjusting for confounding factors. Conclusions: The incidence of adverse events 30 days after OHPs was low (10.5%). An inappropriate perioperative/periprocedural antithrombotic management occurred in 15.3% of the cases and was associated with a higher incidence of adverse events (OR = 4.7). Full article

Background: Chronic cough is a common symptom in patients with interstitial lung diseases (ILDs), which significantly affects health-related quality of life (HRQoL). The prevalence of chronic cough varies from 30% to almost 90% in different ILDs, with the highest rate in patients with idiopathic pulmonary fibrosis. However, the pathophysiology of cough in ILDs remains poorly understood, with multiple proposed mechanisms contributing to its development. This knowledge gap complicates both clinical assessment and treatment, as current therapeutic strategies target general cough mechanisms rather than ILD-specific pathways. This review synthesizes existing data to clarify distinct cough mechanisms across ILD subtypes and identify opportunities for more targeted therapeutic strategies in this challenging patient population. Moreover, cough can be a clinical marker of disease severity and a predictor of ILD progression and transplant-free survival. Effective cough-specific therapeutic options that consider potential mechanisms, comorbidities, and individual effects on HRQoL are needed for cough associated with ILD. Therefore, the aim of this review was to analyze the prevalence, the impact on HRQoL, the pathophysiology, and the management of chronic cough in ILDs. Methods: We performed a comprehensive search in PubMed, MEDLINE, Embase, and the Cochrane Library. This review included randomized clinical trials, observational studies, systematic reviews, and meta-analyses in adults with chronic cough comparing ILD types. The following were excluded: commentaries, letters, case reports and case series, conference abstracts, and studies and publications lacking cough-specific outcomes. Results: Several approaches to reduce cough frequency and severity were described: antifibrotic agents, neuromodulators, opiates, inhaled local anesthetics, oxygen, speech therapy, and anti-reflux therapy. Some therapeutic approaches, such as oral corticosteroids and thalidomide, can cause significant side effects. Novel agents, such as P2X3 receptor antagonists, which are in phase III trials (COUGH-1/2), show promising results for refractory cough and may benefit ILD-related cough. Conclusions: Thus, a comprehensive assessment of cough is required for effective cough treatment in patients with ILDs considering possible mechanisms and individual impact on QoL. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

In this study, the methods of tangential magnetic field (TMF), magnetic Barkhausen noise (MBN), and incremental permeability (IP) were employed for in the simultaneous, quantitative prediction of target properties (bidirectional residual stress, surface hardness, and case depth) in the 45 steel plate. The bidirectional magnetic signals and target properties were measured experimentally. The results of Pearson correlation analyses revealed that most parameters of the MBN and IP signals are strongly correlated with both residual stress and surface hardness under the influence of multiple target properties. The multiple linear regression (MLR) model demonstrated highly accurate quantitative prediction of residual stress and hardness in the y-direction. However, the simultaneous prediction of residual stress and case depth in the x-direction proved less effective than expected. To address this limitation, an inversion method was developed based on the regression model with the single parameter as the dependent variable and the target properties as the independent variable. By incorporating known magnetic parameters and target properties, the model effectively determined the unknown target properties. After applying the method, the coefficient of determination (R²) for x-direction residual stress increased from 0.89 to 0.96 and the absolute error (AE) of case depth decreased from 0.10 mm to 0.04 mm for case depths below 0.15. Full article

Trade credit is a crucial component of supply chain financing, enabling businesses to manage cash flow and optimize inventory levels. This study delves into the application and implications of multiple trade credit types with different repayment periods and financing costs in a supply chain, encompassing short-term trade credit concatenated with bank financing, long-term trade credit, and a trade credit portfolio. Using a two-stage newsvendor model, we analyze the impact of different trade credit types on supply chain profitability under various scenarios. When facing multiple trade credit types, the retailer prefers financing from the trade credit type that has a lower marginal cost, and the resulting form of financing ensures an equal expected cost of each financing type. The analysis shows that in the case of a monopoly supplier, a long-term credit supplier’s profit is higher than that of a short-term credit supplier. Meanwhile, when the bank interest rate is sufficiently high, the retailer’s profit is highest under the trade credit portfolio mode, whereas when the bank interest rate is sufficiently low, the retailer’s profit is highest under the single short-term credit model. Comparing the effects of different financing modes, we find that there is no optimal financing mode for the overall profit of the supply chain. Full article

Background: Neurofibromatosis type 1 (NF1) is a rare multisystem disorder characterized by variable expressivity and increased tumor risk. Café-au-lait macules (CALMs) are a hallmark of the disease, often representing one of the earliest clinical manifestations and allowing a clinical NF1 diagnosis if six or more are present. In this study, we aimed to investigate the prognostic value of CALMs at birth in NF1 patients. Methods: We conducted a retrospective study in patients aged ≥ 4 years presenting with CALMs at our Institution between 2020 and 2021, with a minimum follow-up of four years. We retrospectively collected data on CALMs at birth and other clinical manifestations associated with NF1. Results: Among 208 patients evaluated, including 147 with a confirmed diagnosis of NF1, 110 did not show CALMs at birth, and 98 had at least one. The absence of CALMs at birth did not correlate with a lower likelihood of NF1. In contrast, the CALM number at birth directly correlated with the likelihood of NF1, up to 95% in patients with ≥5 macules. Additionally, a higher number of CALMs correlated with a greater prevalence of plexiform neurofibromas (p < 0.001). Conclusions: Our findings suggest that a higher number of CALMs may indicate a more severe form of NF1, with an increased risk of plexiform neurofibromas. These results emphasize the importance of a comprehensive evaluation of patients with CALMs, especially in case of multiple lesions, aiming at implementing early NF1 diagnosis, follow-up strategies, and overall patient management. Full article

Positional accuracy in cadastral data is fundamental for secure land tenure and efficient land administration. However, many land administration systems (LASs) experience difficulties to meet accuracy standards, particularly when data come from various sources or historical maps, leading to disruptions in land transactions. This study investigates the use of unsupervised clustering algorithms to identify and characterize systematic spatial error patterns in cadastral maps. We compare Fuzzy c-means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMMs) in clustering error vectors using two different case studies from Greece, each with different error origins. The analysis revealed distinctly different error structures: a systematic rotational pattern surrounding a central random-error zone in the first, versus localized gross errors alongside regions of different discrepancies in the second. Algorithm performance was context-dependent: GMMs excelled, providing the most interpretable partitioning of multiple error levels, including gross errors; DBSCAN succeeded at isolating the dominant systematic error from noise. However, FCM struggled to capture the complex spatial nature of errors in both cases. Through the automated identification of problematic regions with different error characteristics, the proposed approach provides actionable insights for targeted, cost-effective cadastral renewal. This aligns with fit-for-purpose land administration principles, supporting progressive improvements towards more reliable cadastral data and offering a novel methodology applicable to other LASs facing similar challenges. Full article

In rolling bearing intelligent fault diagnosis (FD), lightweight models are constrained by issues such as noise interference and the scarcity of fault data, making it challenging to achieve real-time, high-accuracy diagnosis on resource-limited devices. To address these challenges, this study proposes a lightweight model that combines the hierarchical fine-grained decision fusion (HFDF) strategy with an improved EfficientNetV2 architecture (HFDF-EffNetV2). The model optimizes depth and width multiplicity factors to enhance parameter utilization efficiency. It uses pyramidal convolution (PyConv) combined with Fused-MBConv (Fused-MBPyConv) to obtain multi-scale time-frequency information. Additionally, an enhanced MBConv, termed BSMB-Conv-MLCA, integrates subspace blueprint separable convolution (BSConv-S) with mixed local channel attention (MLCA) extract deep-grained fault features. The HFDF strategy outputs confidence in stages and updates weights to prevent the model from falling into local overfitting when handling confusable samples. Experimental results on Case 1 and Case 2 show that HFDF-EffNetV2 achieved 100% accuracy with diagnostic times of 18.67 millisecond (ms) and 17.56 ms, respectively, and 1.85 million (M) parameters. Under noisy conditions, average accuracies reached 98.19% and 85.68%, respectively. Additionally, the model performed well with small samples, yielding accuracies of 98.69% and 97.51%. These results highlight its superior robustness to noise and lightweight performance compared with other advanced models. Full article

Within the framework of “dual carbon”, intending to enhance the use of green energies and minimize the emissions of carbon from energy systems, this study suggests a cost-effective low-carbon scheduling model that accounts for stepwise carbon trading for an integrated electricity, heat, gas, cooling, and hydrogen energy system. Firstly, given the clean and low-carbon attributes of hydrogen energy, a refined two-step operational framework for electricity-to-gas conversion is proposed. Building upon this foundation, a hydrogen fuel cell is integrated to formulate a multi-energy complementary coupling network. Second, a phased carbon trading approach is established to further explore the mechanism’s carbon footprint potential. And then, an environmentally conscious and economically viable power dispatch model is developed to minimize total operating costs while maintaining ecological sustainability. This objective optimization framework is effectively implemented and solved using the CPLEX solver. Through a comparative analysis involving multiple case studies, the findings demonstrate that integrating electric-hydrogen coupling with phased carbon trading effectively enhances wind and solar energy utilization rates. This approach concurrently reduces the system’s carbon emissions by 34.4% and lowers operating costs by 58.6%. Full article

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into machine learning (ML) models for credit delinquency prediction. The approach is grounded in a CRISP-DM framework, combining stationarity testing (Dickey–Fuller), causality analysis (Granger), and post hoc explainability (SHAP, LIME), along with performance evaluation via AUC, ACC, KS, and F1 metrics. The empirical analysis uses nearly 8.2 million records compiled from multiple sources, including 367,000 credit operations granted to individuals and microbusiness owners by a regulated Peruvian financial institution (FMOD) between January 2020 and September 2023. These data also include time series of delinquency by economic activity, external factor indicators (e.g., mortality, climate disruptions, and protest events), and their dynamic interactions assessed through Granger causality to evaluate both the intensity and propagation of external shocks. The results confirm that EF inclusion significantly enhances model performance and robustness. Time-lagged mortality (COVID MOV) emerges as the most powerful single predictor of delinquency, while compound crises (climate and unrest) further intensify default risk—particularly in portfolios without public support. Among the evaluated models, CNN and XGB consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. Post hoc analyses reveal that EF effects dynamically interact with borrower income, indebtedness, and behavioral traits. This study provides a scalable, explainable framework for integrating systemic shocks into credit risk modeling. The findings contribute to more informed, adaptive, and transparent lending decisions in volatile economic contexts, relevant to financial institutions, regulators, and risk practitioners in emerging markets. Full article