Search Results (4,478)

Background: Leakage is a major concern for individuals living with a stoma and may negatively impact quality of life (QoL). A digital leakage notification system (DLNS) recently launched in the UK provides timely notifications to users via their smartphone when faeces is detected underneath the baseplate. This provides predictability and enables users to take proactive measures to help avoid leakages outside the baseplate. Methods: A single-arm, observational, longitudinal study of the DLNS, including its associated support service, has been initiated to follow 300 users for a year in the UK to evaluate long-term health benefits of the DLNS and its implications for healthcare resource utilisation in a real-world setting. The DLNS is prescribed by healthcare professionals (HCPs), and all users were invited to participate in the study. Study participants complete questionnaires capturing data on QoL (using the Ostomy Leak Impact tool), number of leakages outside the baseplate, utilisation of ostomy products, interactions with HCPs, and work productivity (using the Work Productivity and Activity Impairment questionnaire) at baseline and then every third month for one year. Data from the planned interim analysis of the first 100 participants who had been in the study for 6 months is presented. Results: Use of the DLNS for 6 months together with the associated support service was associated with a 51% reduction in leakage episodes outside the baseplate (p < 0.001) and great improvements in QoL (p < 0.001). Use of the DLNS reduced the number of unplanned baseplate changes due to worry about leakage by 47% (p < 0.001) and overall was associated with a reduction in the number of baseplates used by 14% (p = 0.002). Total time spent with HCPs related to stoma care was reduced by 65% after 6 months compared with baseline (p < 0.001). Work absenteeism and presenteeism improved significantly with the use of the DLNS. Conclusions: The interim results of this prospective, longitudinal study provided first insights into the long-term benefits of the DLNS in a real-world setting. ClinicalTrials.gov ID: NCT06554015. Full article

This study addresses a pivotal strategic issue in hospitality e-commerce: how hotels can optimize cooperation with heterogeneous online travel agencies (OTAs). Moving beyond the conventional question of whether to cooperate, we investigate the interrelated decisions of which OTA type to partner with (quality-focused vs. price-focused) and which business model to adopt (merchant vs. agency). We develop a game-theoretic model that incorporates key e-commerce factors, including hotel capacity constraints, cross-channel spillover effects, and differential consumer acceptance of OTA types. Our analysis yields a contingent decision framework. We demonstrate that OTA cooperation becomes beneficial only when a hotel’s room capacity exceeds its direct-channel demand. The optimal strategy evolves with capacity: hotels with moderate capacity should partner with a single OTA type—predominantly the quality-focused one—while larger hotels should engage both types to maximize market coverage. In terms of business models, smaller hotels benefit from the risk-shifting merchant model, whereas larger hotels capture higher margins through the agency model. A key finding is the general superiority of a differentiated approach: applying the agency model to quality-focused OTAs and the merchant model to price-focused OTAs. This research provides a structured analytical framework to guide hotel managers in crafting e-commerce platform strategies and offers scholars a foundation for further inquiry into platform competition and contract design in digital marketplaces. Full article

High-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization. Full article

Legal judgment prediction (LJP) increasingly relies on large language models whose full fine-tuning is memory-intensive and susceptible to catastrophic forgetting. We present LawLLM-DS, a two-stage Low-Rank Adaptation (LoRA) framework that first performs legal knowledge pre-tuning with an aggressive learning rate and subsequently refines judgment relations with conservative updates, using dedicated LoRA adapters, 4-bit quantization, and targeted modification of seven Transformer projection matrices to keep only 0.21% of parameters trainable. From a structural perspective, the twenty annotated legal elements form a symmetric label co-occurrence graph that exhibits both cluster-level regularities and asymmetric sparsity patterns, and LawLLM-DS implicitly captures these graph-informed dependencies while remaining compatible with downstream GNN-based representations. Experiments on 5096 manually annotated divorce cases show that LawLLM-DS lifts macro F1 to 0.8893 and achieves an accuracy of 0.8786, outperforming single-stage LoRA and BERT baselines under the same data regime. Ablation studies further verify the contributions of stage-wise learning rates, adapter placement, and low-rank settings. These findings demonstrate that curriculum-style, parameter-efficient adaptation provides a practical path toward lightweight yet structure-aware LJP systems for judicial decision support. Full article

Reconstructing hand and object shapes from a single view during interaction remains challenging due to severe mutual occlusion and the need for high physical plausibility. To address this, we propose a novel framework for hand–object interaction reconstruction based on holistic, multi-stage collaborative optimization. Unlike methods that process hands and objects independently or apply constraints as late-stage post-processing, our model progressively enforces physical consistency and geometric accuracy throughout the entire reconstruction pipeline. Our network takes an RGB-D image as input. An adaptive feature fusion module first combines color and depth information to improve robustness against sensing uncertainties. We then introduce structural priors for 2D pose estimation and leverage texture cues to refine depth-based 3D pose initialization. Central to our approach is the iterative application of a dense mutual attention mechanism during sparse-to-dense mesh recovery, which dynamically captures interaction dependencies while refining geometry. Finally, we use a Signed Distance Function (SDF) representation explicitly designed for contact surfaces to prevent interpenetration and ensure physically plausible results. Through comprehensive experiments, our method demonstrates significant improvements on the challenging ObMan and DexYCB benchmarks, outperforming state-of-the-art techniques. Specifically, on the ObMan dataset, our approach achieves hand CD_h and object CD_o metrics of 0.077 cm² and 0.483 cm², respectively. Similarly, on the DexYCB dataset, it attains hand CD_h and object CD_o values of 0.251 cm² and 1.127 cm², respectively. Full article

Soft robotic grippers provide compliant and adaptive manipulation, but most existing designs address actuation speed, adaptability, modularity, or sensing individually rather than in combination. This paper presents an electro-actuated customizable stacked Fin Ray gripper that integrates these capabilities within a single design. The gripper employs a compact solenoid for fast grasping, multiple vertically stacked Fin Ray segments for improved 3D conformity, and interchangeable silicone or TPU fins that can be tuned for task-specific stiffness and geometry. In addition, a light-guided, vision-based sensing approach is introduced to capture deformation without embedded sensors. Experimental studies—including free-fall object capture and optical shape sensing—demonstrate rapid solenoid-driven actuation, adaptive grasping behavior, and clear visual detectability of fin deformation. Complementary simulations using Cosserat-rod modeling and bond-graph analysis characterize the deformation mechanics and force response. Overall, the proposed gripper provides a practical soft-robotic solution that combines speed, adaptability, modular construction, and straightforward sensing for diverse object-handling scenarios. Full article

This study investigates thermal stress and energy-related anxiety as lived, multidimensional manifestations of energy poverty in Western Macedonia, Greece, a coal phase-out region undergoing just transition. Using a 261-household survey, we construct a thermal stress index from four Likert-type items capturing seasonal thermal adequacy, energy anxiety, and restricted use of rooms. High thermal stress is defined as the upper quartile of the index. Descriptive results indicate that high thermal stress affects 27.2% of households, exceeding a 20% threshold, while energy-related anxiety and restricted room use are widespread. We then estimate logistic regression models to examine whether vulnerability characteristics (disability-related thermal/electric needs, single parenthood, dependent children, benefit receipt, elderly presence), financial stress indicators (arrears, energy debt, frequent forced reductions in consumption), and socio-economic controls (income, employment, tenure, age, gender) predict high thermal stress. Adjusted models show that vulnerability markers do not retain statistically independent associations once controls are included. In contrast, tenure and energy-related financial stress are significantly associated with the probability of high thermal stress. The findings highlight the importance of measurement choices and suggest that experiential indicators capture energy-poverty dynamics that are not reducible to income-based targeting, with implications for just-transition policy design and energy justice. Full article

Network intrusion behaviors exhibit high concealment and diversity, making intrusion detection methods based on single-behavior modeling unable to accurately characterize such activities. To overcome this limitation, we propose SGMT-IDS, a dual-branch semi-supervised intrusion detection model based on Graph Neural Networks (GNNs) and Transformers. By constructing two views of network attacks, namely structural and behavioral semantics, the model performs collaborative analysis of intrusion behaviors from both perspectives. The model adopts a dual-branch architecture. The SGT branch captures the structural embeddings of network intrusion behaviors, and the GML-Transformer branch extracts the semantic information of intrusion behaviors. In addition, we introduce a two-stage training strategy that optimizes the model through pseudo-labeling and contrastive learning, enabling accurate intrusion detection with only a small amount of labeled data. We conduct experiments on the NF-Bot-IoT-V2, NF-ToN-IoT-V2, and NF-CSE-CIC-IDS2018-V2 datasets. The experimental results demonstrate that SGMT-IDS achieves superior performance across multiple evaluation metrics. Full article

Automated sleep staging remains challenging due to the transitional nature of certain sleep stages, particularly N1. In this paper, we explore modulation spectrograms for automatic sleep staging to capture the transitional nature of sleep stages and compare them with conventional benchmark features, such as the Short-Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT). We utilized a single-channel EEG (C4–M1) from the DREAMT dataset with subject-independent validation. We stratify participants by the Apnea–Hypopnea Index (AHI) into Normal, Mild, Moderate, and Severe groups to assess clinical generalizability. Our modulation-based framework significantly outperforms STFT and CWT in the Mild and Severe cohorts, while maintaining comparable high performance in the Normal and Moderate AHI groups. Notably, the proposed framework maintained robust performance in severe apnea cohorts, effectively mitigating the degradation observed in standard time–frequency baselines. These findings demonstrate the effectiveness of modulation spectrograms for sleep staging while emphasizing the importance of medical stratification for reliable outcomes in clinical populations. Full article

In the pursuit of cleaner and more efficient internal combustion engines, dual-fuel strategies combining diesel and hydrogen are gaining increasing attention. This study employs detailed computational fluid dynamics (CFD) simulations to investigate the behaviour of pilot diesel injections in dual-fuel diesel–hydrogen engines. The study aims to characterize spray formation, ignition delay and early combustion phenomena under various energy input levels. Two combustion models were evaluated to determine their performance under these specific conditions: Tabulated Well Mixed (TWM) and Representative Interactive Flamelet (RIF). After an initial numerical validation using dual-fuel constant-volume vessel experiments, the models are further validated using in-cylinder pressure measurements and high-speed natural combustion luminosity imaging acquired from a large-bore optical engine. Particular attention was given to ignition location due to its influence on subsequent hydrogen ignition. Results show that both combustion models reproduce the experimental behavior reasonably well at high energy input levels (EILs). At low EILs, the RIF model better captures the ignition delay; however, due to its single-flamelet formulation, it predicts an abrupt ignition of all available premixed charge in the computational domain once ignition conditions are reached in the mixture fraction space. Full article

Attention can be automatically captured by the distractor that matches the representation of working memory (WM) in search tasks, impairing visual search efficiency and resulting in attentional capture effects. The resource hypothesis of visual search predicts that resource allocation affects attentional capture. However, previous studies have shown partly opposing results inconsistent with this prediction. The purpose of this study is to assess the connection between attentional capture and WM resource allocation. Two experiments were conducted to combine the attentional capture paradigm with continuous delayed-estimation tasks. In Experiment 1, we manipulated the number of memory items between one and two and measured the WM representation precision as well as the magnitude of attentional capture. In Experiment 2, we manipulated resource allocation using a retro-cue task with the presentation of two memory items. In Experiment 1, the results show that when remembering one item, a single-item representation had higher precision compared to the scenario for remembering two items, and it also involved a greater allocation of WM resources. However, there was no significant difference in the magnitude of attentional capture effects between the two conditions. In Experiment 2, the results show that memory precision was higher when the cue pointed to the item compared to when it did not, but there was no significant difference in the magnitude of attentional capture effects between the cued-match and non-cued-match conditions. The findings show that the size of attentional capture effects based on WM is unaffected by the distribution of WM resources. Attentional capture effects may reflect the attention bias of WM representation that occurs in preparation stage of memory-based attentional guidance. Full article

Analyzing the dynamics of muscle activation patterns and joint range of motion is essential to understanding human movement during complex tasks such as tooth brushing Activities of Daily Living (ADLs). In individuals with neuromotor impairments, accurate assessment of upper limb motor patterns plays a critical role in rehabilitation, supporting the identification of compensatory strategies and informing clinical interventions. This study presents the validation of a previously developed novel, low-cost, wearable, and portable multimodal prototype that integrates inertial measurement units (IMU) and surface electromyography (sEMG) sensors into a single device. The system enables bilateral monitoring of arm segment kinematics and muscle activation amplitudes from six major agonist muscles during ADLs. Eleven healthy participants performed a functional task, tooth brushing, while wearing the prototype. The recorded data were compared with two established gold-standard systems, Qualisys^® motion capture system and Biosignalsplux^®, for validation of kinematic and electrophysiological measurements, respectively. This study provides technical insights into the device’s architecture. The developed system demonstrates potential for clinical and research applications, particularly for monitoring upper limb function and evaluating rehabilitation outcomes in populations with neurological disorders. Full article

Early detection of small-scale fires is crucial for minimizing damage and enabling rapid emergency response. While recent deep learning-based fire detection systems have achieved high accuracy, they still face three key challenges: (1) limited deployability in resource-constrained edge environments due to high computational costs, (2) performance degradation caused by feature interference when jointly learning flame and smoke features in a single backbone, and (3) low sensitivity to small flames and thin smoke in the initial stages. To address these issues, we propose a lightweight dual-stream fire detection architecture based on YOLOv5n, which learns flame and smoke features separately to improve both accuracy and efficiency under strict edge constraints. The proposed method integrates two specialized attention modules: ESCFM++, which enhances spatial and channel discrimination for sharp boundaries and local flame structures (flame), and ESCFM-RS, which captures low-contrast, diffuse smoke patterns through depthwise convolutions and residual scaling (smoke). On the D-Fire dataset, the flame detector achieved 74.5% mAP@50 with only 1.89 M parameters, while the smoke detector achieved 89.2% mAP@50. When deployed on an NVIDIA Jetson Xavier NX(NVIDIA Corporation, Santa Clara, CA, USA)., the system achieved 59.7 FPS (single-stream) and 28.3 FPS (dual-tream) with GPU utilization below 90% and power consumption under 17 W. Under identical on-device conditions, it outperforms YOLOv9t and YOLOv12n by 36–62% in FPS and 0.7–2.0% in detection accuracy. We further validate deployment via outdoor day/night long-range live-stream tests on Jetson using our flame detector , showing reliable capture of small, distant flames that appear as tiny cues on the screen, particularly in challenging daytime scenes. These results demonstrate overall that modality-specific stream specialization and ESCFM attention reduce feature interference while improving detection accuracy and computational efficiency for real-time edge-device fire monitoring. Full article

Gene editing technologies have revolutionized therapeutic development, offering potentially curative and preventative strategies for cardiovascular disease (CVD), which remains a leading global cause of morbidity and mortality. This review provides an introduction to the state-of-the-art gene editing tools—including ZFNs, TALENs, CRISPR/Cas9 systems, base editors, and prime editors—and evaluates their application in lipid metabolic pathways central to CVD pathogenesis. Emphasis is placed on targets such as PCSK9, ANGPTL3, CETP, APOC3, ASGR1, LPA, and IDOL, supported by findings from human genetics, preclinical models, and recent first-in-human trials. Emerging delivery vehicles (AAVs, LNPs, lentivirus, virus-like particles) and their translational implications are discussed. The review highlights ongoing clinical trials employing liver-targeted in vivo editing modalities (LivGETx-CVD) and provides insights into challenges in delivery, off-target effects, genotoxicity, and immunogenicity. Collectively, this review captures the rapid progress of LivGETx-CVD from conceptual innovation to clinical application, and positions gene editing as a transformative, single-dose strategy with the potential to redefine prevention and long-term management of dyslipidemia and atherosclerotic cardiovascular disease. Full article

The objective of this study was to use Density Functional Theory (DFT) calculations to examine how boron doping modulates the electronic properties of graphene quantum dots (GQDs) and their interaction with the Hg²⁺ ion. Boron doping decreases the HOMO-LUMO gap and increases the GQD’s electrophilic character, facilitating charge transfer to the metal ion. The adsorption energy results were negative, indicating electronic stabilization of the combined systems, without implying thermodynamic favorability, with the GQD@3B_Hg²⁺ system being the strongest at −349.52 kcal/mol. The analysis of global parameters (chemical descriptors) and the study of non-covalent interactions (NCIs) supported the affinity of Hg²⁺ for doped surfaces, showing that the presence of a single boron atom contributes to clear attractive interactions. In general, configurations doped with 1 or 2 boron atoms exhibit satisfactory performance, demonstrating that boron doping effectively modulates the reactivity and adsorption properties of GQD for efficient Hg²⁺ capture. Full article