Search Results (13,274)

Background/Objectives: Stress urinary incontinence (SUI) remains a common and functionally relevant complication after radical prostatectomy (RP) and substantially impairs quality of life (QoL). Although pelvic floor muscle training (PFMT) is guideline-recommended, its real-world effectiveness is often limited by accessibility, standardization, and adherence. Digital health interventions may improve adherence to PFMT, potentially influencing continence recovery. We conducted a multicenter randomized controlled trial to evaluate whether a structured, modular app-based PFMT program improves early continence recovery compared with conventional physiotherapist-guided training. Methods: Between September 2022 and September 2024, 62 preoperatively continent men undergoing radical prostatectomy were enrolled in this multicenter randomized controlled trial (Pelvintense). Both groups received perioperative PFMT: Patients were randomized 1:1 to either a modular app-based PFMT program (intervention group) or a standard physiotherapist-guided PFMT (control group). Both app-based PFMT and standard physiotherapist-guided PFMT started three weeks before surgery and continued for 90 days postoperatively. The primary endpoint was continence at 90 days, defined as ICIQ-SF Q1 = 0 (absence of involuntary SUI). Secondary endpoints included continence sub-scores, QoL, erectile function, adherence, and decision regret. Analyses were performed using a modified intention-to-treat approach applying logistic regression and non-parametric tests with sensitivity analyses. Results: A total of 62 patients were included in the study and randomized, with 31 allocated to the app-based PFMT arm and 31 to the standard physiotherapist-guided arm. Three patients in the control arm withdrew consent for data usage after randomization, resulting in a modified intention-to-treat population of 59 patients. At 90 days, continence rates were higher in the app-based group compared with the control group (74.2% versus 21.4%; p < 0.001), corresponding to an absolute risk reduction of 52.8% and a number needed to treat of two. In multivariable analysis, participation in the app-based program was independently associated with higher odds of continence recovery (odds ratio 13.80, 95%-confidence interval 3.22–59.12; p < 0.001). Continence at 30 days and continence-related QoL favored the intervention, whereas no significant differences were observed in erectile function at 90 days. Adherence to the PFMT was higher in the intervention group. Sensitivity analyses confirmed the robustness of the primary outcome. Conclusions: In this randomized controlled trial, a modular app-based PFMT program was associated with early continence recovery after prostatectomy compared with the standard-of-care physiotherapist-guided PFMT. Improved adherence, modular progression of exercises, and a more structured training delivery may have contributed to the effect. App-based PFMT might represent a scalable strategy to implement guideline-recommended supportive care. These findings warrant confirmation in studies with a longer follow-up. Full article

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for prognostics and health management. However, standard Gaussian processes (GPs) face challenges in scalability and capturing complex global degradation trends, while deep learning models often lack principled uncertainty quantification. To bridge this gap, this study proposes a novel deep mixed-effect Gaussian process (DME-GP) model, which decomposes the predictive function into a global multi-layer perceptron (MLP)-based feature mapping component and a sample-specific local GP component under the mixed-effect paradigm. This hybrid architecture synergistically captures intricate global patterns and provides probabilistic uncertainty estimates. The model’s performance was rigorously validated on a real-world battery RUL dataset. Quantitative results demonstrate its superior accuracy, achieving a reduction in root mean square error (RMSE) by up to 63.41% and in mean absolute error (MAE) by up to 62.63% compared to a standard GP baseline. The proposed DME-GP framework provides a robust and reliable data-driven solution for advancing battery health monitoring systems. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

This study proposes a novel data-driven machine learning (ML) framework for multi-criteria environmental, social, and governance (ESG) evaluation. The framework aims to address the transparency, consistency, and subjectivity limitations of existing ESG evaluation systems by employing a fully data-driven, modular, and ML-supported architecture. It comprises three main modules: (i) ESG data preprocessing with missing-data imputation by the MissForest algorithm; (ii) a three-plane ESG feature selection workflow that integrates clustering, feature importance, and classification algorithms to identify representative ESG indicators; and (iii) a hybrid weighting and ranking procedure that combines unsupervised principal component analysis (PCA), criteria importance through inter-criteria correlation (CRITIC), and technique for order preference by similarity to ideal solution (TOPSIS) methods. A recent 2024 real-world application involving 57 listed Chinese pharmaceutical and biotechnology companies and 70 ESG indicators demonstrates the framework’s practical utility in producing transparent and objective ESG rankings. The main contributions of this work are fourfold: (1) the development of an end-to-end, entirely data-driven ML framework for ESG evaluation; (2) the introduction of an innovative three-plane ESG feature selection workflow within the framework; (3) the first study for designing a hybrid PCA-CRITIC-TOPSIS approach in ESG weighting and ranking; (4) the validation of the framework through a real-world industry application using recent and authentic ESG data. Full article

Plant disease diagnosis in real-world agricultural environments is challenged by data scarcity, domain shift, privacy constraints, and limited edge-device resources. This paper proposes FMEL-FSDA, a Federated Multimodal Edge Learning framework with Few-Shot Domain Adaptation for robust field-based plant disease recognition. The framework integrates attention-based RGB–text feature fusion, privacy-preserving federated learning, rapid few-shot personalization, and uncertainty-aware inference within an edge-efficient architecture. Federated training enables collaborative learning across distributed farms without sharing raw data, while few-shot adaptation allows fast deployment to new regions using only 1–10 labeled samples per class. Experiments on the PlantWild in-the-wild dataset show that FMEL-FSDA outperforms centralized, federated, and few-shot baselines, achieving 93.78% accuracy, 93.33% F1-score, and 0.97 AUC. The model maintains strong performance under privacy mechanisms such as gradient perturbation and secure aggregation, reduces communication overhead by up to 4×, and supports low-latency edge inference. Uncertainty estimation and Grad-CAM-based explainability further enhance reliability by identifying low-confidence cases and highlighting disease-relevant regions. Overall, FMEL-FSDA offers a scalable, privacy-aware, and field-ready solution for intelligent plant disease diagnosis in precision agriculture. Full article

The TWC Sigma model, part of the Topological Weighted Centroid (TWC) family, is introduced as a spatial framework for source localization in systems where network information is incomplete or unavailable. Its architecture relies on two alternative approaches: one based on nonlinear correlation, capable of capturing complex spatial dependencies among observed signals, and another based on supervised neural networks, which use adaptive learning on a discretized spatial grid to estimate the probability of hidden source localization. In both cases, TWC Sigma provides a robust and consistent mechanism to estimate the probable positions of hidden sources using only spatial coordinates and signal intensity. Applications on both synthetic and real-world datasets—such as those collected by Minna-no Data Site on post-Fukushima radiocesium contamination—confirm the model’s ability to identify both primary and secondary emission zones with strong spatial coherence. These results highlight TWC Sigma as an efficient and interpretable model that can be used both independently and as a complementary tool to more complex network-based frameworks, offering rapid and reliable localization even in the presence of sparse, noisy, or heterogeneous data. Full article

Background: Ceftolozane/tazobactam (C/T) has demonstrated activity against ESBL-producing Enterobacterales (ESBL-E) and provides a carbapenem-sparing option. Broader use of C/T alongside other carbapenem/inhibitor combinations may expand therapeutic choices and reduce selection pressure for carbapenem resistance, supporting antimicrobial stewardship and limiting the spread of carbapenem-resistant Enterobacterales. Methods: SPECTRA was a multicenter, retrospective real-world study of hospitalized adults (≥18 years) who received ≥48 h of C/T across seven countries (Australia, Austria, Germany, Italy, Mexico, Spain, and the UK) from January 2016 to November 2020. Medical-record data were collected for up to 6 months before treatment and 30 days after the final C/T dose (or until death). This sub-analysis describes clinical outcomes and healthcare utilization in patients with laboratory-confirmed ESBL-E (n = 39). Results: Thirty-nine ESBL-E patients were included (mean age 59.3 years; 56.4% male); 79.5% had ≥1 comorbidity (mean 2.2 per patient). Common pathogens were Escherichia coli (n = 23) and Klebsiella spp. (n = 12). Investigator-assessed clinical success was 64.9%, microbial eradication was 27.0%, in-hospital mortality was 20.5%, and 30-day readmission was 5.1%. ICU admission during the index hospitalization occurred for 38.5% of patients (mean ICU stay: 16.0 days). The median treatment duration was 11 days while the mean hospital stay after C/T initiation was 13.5 days. Conclusions: In this real-world multi-country cohort, C/T showed clinical effectiveness in ESBL-E infections, with outcomes consistent with the overall SPECTRA population. C/T offers a carbapenem-sparing strategy that broadens treatment options and may help reduce reliance on carbapenems, supporting efforts to limit carbapenem-resistant Enterobacterales. The findings warrant evaluation in larger and comparative studies. Full article

Background/Objectives: Acute carotid tandem lesions (TLs), defined by concurrent cervical internal carotid artery (ICA) stenosis or occlusion and intracranial large vessel occlusion, occur in 10–20% of patients undergoing mechanical thrombectomy (MT) for acute ischemic stroke (AIS). Optimal periprocedural antiplatelet management during emergent carotid artery stenting (eCAS) remains uncertain, particularly regarding the balance between preventing stent thrombosis and avoiding hemorrhagic complications. Methods: A narrative review was conducted using PubMed and Scopus (until 6 March 2026) to identify English-language studies evaluating antiplatelet therapies during eCAS for TLs. We included seven real-world studies and registry analyses. Data on study design, patient characteristics, procedural strategies, angiographic results, functional outcomes, and safety metrics were extracted. Results: No randomized controlled trials (RCTs) were identified. The available evidence is derived exclusively from observational studies. Across these cohorts, glycoprotein IIb/IIIa inhibitors (GPIs), particularly tirofiban, were generally associated with lower rates of in-stent thrombosis and higher reperfusion success, with symptomatic intracranial hemorrhage (sICH) rates that appeared comparable to or lower than those reported with acetylsalicylic acid (ASA). Cangrelor, an intravenous (IV) P2Y₁₂ inhibitor, was associated with improved stent patency and increased likelihood of complete reperfusion, although reported effects on clinical outcomes were inconsistent when compared with GPIs or ASA. Aside from abciximab, potent IV antiplatelet agents did not consistently show an increased sICH signal. Oral dual antiplatelet therapy was also associated with improved technical outcomes without a clear excess in bleeding complications. Conclusions: Current real-world observational data suggest that rapid-acting IV antiplatelet agents—particularly GPIs and, increasingly, cangrelor—may represent feasible periprocedural options during eCAS for TLs, with potential benefits for technical success and no consistent evidence of increased hemorrhagic risk. However, interpretation is limited by study heterogeneity and non-randomized designs. The absence of RCTs highlights the need for prospective comparative studies and standardized periprocedural antiplatelet protocols. Full article

Existing studies of street-related emotional perception mainly rely on static scene evaluations, which cannot capture the cumulative effects of environmental exposure during continuous walking. To address this limitation, this study proposes a method for predicting pedestrian physiological responses in sequential audiovisual street environments. Four real-world walking routes were selected, with outbound and return directions treated as independent paths, yielding eight paths and 32 valid samples. EEG, ECG, sound pressure level, first-person video, and GPS data were synchronously collected to construct a 1 s multimodal time-series dataset. Pearson correlation, Kendall correlation, and mutual information analyses were used to examine linear, monotonic, and nonlinear relationships between environmental variables and physiological indicators, and the resulting weights were incorporated into a Long Short-Term Memory (LSTM) model for multi-step prediction. Visual elements and noise exposure were the main factors influencing physiological responses. Among the models, the mutual-information-weighted LSTM performed best, achieving an R² of 0.77 for heart rate variability (RMSSD), whereas prediction of the EEG ratio (β/α and θ/β) remained limited. An additional independent street sample outside the training set was then used to generate a dual-dimensional EEG-ECG physiological response map, demonstrating the model’s potential for identifying emotional risk segments and supporting street-level micro-renewal. Full article

The lack of scientific methods for selecting monitoring indicators and equipment undermines the efficiency of forest fire emergency response. To address this gap, we developed a novel evaluation model for emergency monitoring indicators based on the spatiotemporal evolution of forest fires. The model, comprising four primary and eight secondary factors, leverages a hybrid TriFAHP and DBN approach to objectively determine factor weights based on survey data from 20 domain experts. The results indicate that the primary factor weights rank as follows: Monitorability (0.3807) > Timeliness (0.3353) > Sensitivity (0.1874) > Feasibility (0.0966). Four indicators (wind speed, temperature, flame, and gas) were identified as the most suitable for core monitoring. Furthermore, stage-specific monitoring strategies were proposed, prioritizing different core indicators across the ignition, spread, and fully developed fire stages. An indicator and equipment association was established, recommending optimal configurations such as UAV-mounted thermal imagers and lidar anemometers. The practical applicability of the proposed framework was successfully validated through real-world case studies, including the 2019 to 2020 Australia bushfires. This study provides a standardized framework aligning indicators, equipment, and scenarios, offering theoretical and practical guidance for optimizing emergency monitoring systems. Full article

Mycelium-based composites (MBCs) have already been applied in various fields, like construction, architecture, packaging, waste management and many others, as sustainable replacement materials. The composites created from such materials are lightweight, biodegradable and can take many different geometrical shapes. As there are many different combinations of fungal mycelium and organic substrates, it is not only important to investigate and determine which of these combinations perform best from an acoustic perspective but also from an environmental point of view. The sound absorption qualities of these biocomposites have been investigated. It was found that the sound absorption coefficients range from 0.33 to 0.49 in the mid-high frequency range for the four different mixtures of substrate and oyster mushroom (Pleurotus ostreatus). The results from the acoustic testing are promising, but the environmental impact of these mycelium-based composites also needs to be determined. The impacts from water and especially from energy, used during the growth and preparation cycles, are the main contributors to the environmental impact of MBCs, which is also confirmed by the relevant literature. A cradle-to-grave life cycle assessment (LCA) was conducted, utilizing the ReCiPe method, with selected environmental impact categories, based on real-world production data and the scientific literature. The results obtained were also compared with a commercially produced acoustical stone wool panel. The influence on environmental impact of the different substrates is also analyzed, determining which MBC is the most environmentally friendly and has the best acoustical properties. Full article

Animal-vehicle collisions (AVCs) pose a significant threat to road safety, wildlife conservation, and transportation systems worldwide. Advances in artificial intelligence (AI) and computer vision have enabled intelligent detection and mitigation systems aimed at reducing such collisions. This review synthesizes the current state of AI-powered AVC prevention systems, examining deep learning architectures, multimodal sensor technologies, real-time processing frameworks, and system-level integration strategies. We analyze the transition from traditional computer vision methods to modern deep neural networks, evaluate sensor fusion approaches, and assess existing wildlife detection datasets and benchmarking practices. Key technical challenges are identified, including environmental variability, long-range detection constraints, dataset scarcity, cross-species generalization limitations, and real-time safety requirements. Rather than framing AVC prevention solely as an object detection task, this review conceptualizes it as a safety-critical perception and risk assessment pipeline operating under strict latency and deployment constraints. Persistent gaps in wildlife-specific detection, standardized evaluation protocols, and scalable edge deployment are discussed. To organize these insights, we present WildSafe-Edge as a conceptual reference architecture derived from the literature, synthesizing system-level design considerations and highlighting open research directions. Future research directions include transfer learning, synthetic data augmentation, vehicle-to-everything (V2X) integration, and edge-centric architectures to enable robust, real-world collision mitigation systems. Full article

Accurate demand forecasting with uncertainty quantification is critical for materials management in power grid enterprises, yet existing methods struggle to capture multi-scale temporal dynamics across heterogeneous material categories while providing reliable confidence estimates. This paper proposes an Adaptive Decomposition–Ensemble Modeling (ADEM) method that integrates adaptive CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) with category-specific depth selection, a heterogeneous ensemble of a GBM (Gradient Boosting Machine), ELM (Extreme Learning Machine), and SVR (Support Vector Regression) with per-component optimized weights, and Bayesian uncertainty quantification with conformal calibration for distribution-free coverage guarantees. Experiments on real-world data spanning 18 material categories over 60 months demonstrate that ADEM consistently outperforms 14 baselines spanning statistical, machine learning, deep learning, and decomposition-based methods in both point prediction accuracy and prediction interval quality. Rolling-origin evaluation across six temporal windows further exhibits the robustness and statistical significance of these improvements. Full article

Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework designed to support multi-source air quality forecasting by jointly leveraging regulatory monitoring stations of the Central Pollution Control Board (CPCB) as reference-grade anchors and community-driven Internet of Things (IoT) sensing platforms for spatial densification. The proposed end-to-end workflow addresses key challenges associated with heterogeneity, data quality, and interoperability through systematic schema harmonization, multi-stage data cleaning, and robust missing data imputation using a Robocentric Iterated Extended Kalman Filter (RIEKF). The processed data are temporally aligned to a uniform sampling grid and enriched with spatial descriptors, including geospatial coordinates, administrative boundaries, and proximity-based emission features. These enriched observations are subsequently fused into a unified spatio-temporal representation that captures both spatial dependencies and temporal dynamics across the sensor network. Dynamic graphs constructed from this representation are processed using a Mobility-Aware Peripheral-Enhanced Graph Neural Network to forecast pollutant concentrations and generate categorical air quality indices. The framework is evaluated using regression metrics reported as RMSE/MAE in µg/m³ and MAPE in %, together with standard AQI classification metrics, demonstrating its effectiveness in improving predictive accuracy and robustness for real-world air quality forecasting applications. Full article

Background: Population-level evidence on delivery of neuro-oncology care is essential for evaluating access, equity, and quality of treatment pathways. However, real-world data describing how patients with central nervous system (CNS) tumors, especially with glioblastoma, are managed across healthcare systems remain limited. This study aimed to characterize treatment pathways using linked registry and administrative data within a regional care network. Methods: All adult CNS tumors diagnosed between 2016 and 2020 were identified in the Veneto Cancer Registry. Tumor grading was derived using a validated text-mining algorithm, and surgical, radiotherapy, and systemic treatments were captured through linkage with regional healthcare utilization databases. Patterns of care were evaluated by tumor subtype, grade, and diagnostic pathway. Results: Among 1634 histologically confirmed tumors, glioblastoma represented the largest group. Surgical intervention was widely implemented, with high resection rates in glioblastoma and meningioma. Combined chemoradiotherapy constituted the primary adjuvant approach for glioblastoma and high-grade diffuse gliomas, whereas management of lower-grade tumors showed greater variability. Approximately one-third of patients received no oncologic therapy, primarily associated with older age or diagnostic uncertainty. Analysis of recurrent glioblastoma showed heterogeneous systemic treatment use, reflecting evolving therapeutic practice. Conclusions: Linking population-based registry and administrative data provides actionable insight into real-world delivery of neuro-oncology care, in particular for glioblastoma patients. This approach enables monitoring of treatment variability, identification of potential access gaps, and evaluation of system-level performance, supporting data-driven planning of multidisciplinary services and future quality improvement initiatives. Full article