Search Results (206)

Ensuring equitable and inclusive access to children’s extracurricular activity facilities represents a profound manifestation of educational equity and is crucial for promoting children’s holistic development and societal sustainability. However, the underlying spatial mechanisms shaping their equity remain insufficiently explored. Using Tianjin’s central urban area as a case study, this study examines the spatial accessibility and equity of such facilities under dual government–market provision systems. The multi-mode Huff two-step floating catchment area model (MM-Huff-2SFCA) was employed to assess accessibility across walking, e-bike, public transport, and private car modes, integrating facility quality, household preference, and time-based distance decay. Equity was further evaluated using Lorenz curves and Gini coefficients across multiple spatial scales, while geographically weighted regression (GWR) identified spatial heterogeneity in factors such as child population density, transport infrastructure, household economic status, and basic education coverage. Results indicate that macro-level spatial balance masks substantial micro-scale inequities, particularly among transport-disadvantaged groups. Government and market systems exhibit contrasting spatial logics, forming a compensation–complementarity pattern across urban space. These findings underscore the need for refined and differentiated governance in extracurricular activity facilities planning, integrating spatial planning, transport accessibility, and social equity to advance child-friendly urban development and equitable public service provision. Full article

Residential energy renovation remains a central pillar of climate mitigation and social sustainability strategies, yet renovation rates persistently lag behind policy targets, particularly in older urban neighborhoods. This study investigates the underlying causes of renovation inertia using a neighborhood-scale mixed-methods approach that combines door-to-door household surveys, façade infrared thermography, and expert focus groups. Using a post-industrial residential district in Liège, Belgium, as an exploratory case, the study jointly analyzes building conditions, household characteristics, and renovation contexts. The results reveal that renovation failure cannot be explained solely by technical deficiencies. Instead, three interacting socio-technical mechanisms emerge: adaptive occupant behaviors that mask poor building performance, a constrained renovation agency shaped by tenure and income asymmetries, and the stratification of energy awareness along social lines. Together, these mechanisms reinforce a form of renovation lock-in in which technical degradation, behavioral adaptation, and institutional fragmentation mutually sustain inaction. By integrating physical diagnostics with social and experiential data, the study explains why conventional incentive-based renovation policies systematically underperform in comparable urban contexts. Rather than treating energy renovation as a purely technical or economic decision, the findings highlight the need for policy instruments that explicitly address agency constraints, behavioral compensation, and unequal exposure to energy-related risks. The proposed mixed-method framework is transferable to other urban neighborhoods and offers a replicable approach for diagnosing renovation barriers, supporting more socially sustainable energy transition strategies. Full article

Floor plans are a central representational component of architectural design, operating in close relation to sections, elevations, and three-dimensional reasoning to support the production and understanding of architectural space. In this context, we address the bounded computational task of completing incomplete floor plan representations as a form of early-stage design assistance, rather than treating the floor plan as an isolated architectural object. Within this workflow, being able to automatically complete a floor plan from an unfinished draft is highly valuable because it allows architects to generate preliminary schemes more quickly, streamline early discussions, and reduce the repetitive workload involved in revisions. To meet this need, we present FP-MAE, a self-supervised learning framework designed for floor plan completion. This study proposes three core contributions: (1) We developed FloorplanNet, a dedicated dataset that includes 8000 floorplans consisting of both schematic line drawings and color-coded plans, providing diverse yet consistent examples of residential layouts. (2) On top of this dataset, FP-MAE applies the Masked Autoencoder (MAE) strategy. By deliberately masking sections of a plan and using a lightweight Vision Transformer (ViT) to reconstruct the missing regions, the model learns to capture the global structural patterns of floor plans from limited local information. (3) We evaluated FP-MAE across multiple masking scenarios and compared its performance with state-of-the-art baselines. Beyond controlled experiments, we also tested the model on real sketches produced during the early stages of design projects, which demonstrated its robustness under practical conditions. The results show that FP-MAE can produce complete plans that are both accurate and functionally coherent, even when starting from highly incomplete inputs. FP-MAE is a practical and scalable solution for automated floor plan generation. It can be integrated into design software as a supportive tool to speed up concept development and option exploration, and it also points toward broader opportunities for applying AI in architectural automation. While the current framework operates on two-dimensional plan representations, future extensions may integrate multi-view information such as sections or three-dimensional models to better reflect the relational nature of architectural design representations. Full article

In the event of a fault in a shipboard medium-voltage direct-current (MVDC) power system, a fault reconfiguration method issues control commands to the switchgear to execute switching actions, thereby redistributing power flow, isolating the faulted zone, and restoring power to the de-energized loads. Existing fault reconfiguration strategies mainly use classical optimization methods. These methods are usually centralized, and as the system scale increases, they suffer from the curse of dimensionality, which degrades real-time performance and reduces computational efficiency. This paper proposes a MADRL-based fault reconfiguration method for shipboard MVDC power systems. The proposed method considers load priority levels, maximizes total restored load, and improves load balancing. To this end, a QMIX-based method, Dependency-Corrected QMIX with Action Masking (Dep-QMIX-Mask), was developed, introducing a dependency correction mechanism to handle action dependencies during decentralized execution and applying action masking to rule out invalid switching actions under operational constraints. A shipboard MVDC power system model was established and used for validation. Across three representative fault cases, Dep-QMIX-Mask achieves served load rates of 0.88, 0.67, and 0.43, with SLR improvements of up to 19.6% over baseline methods. It consistently produces feasible switching sequences in all 20 independent runs per case, improving feasibility by 10 to 30 percentage points. In addition, Dep-QMIX-Mask improves zonal load balancing by reducing the PUR variance by 40.5% to 99.2% compared with baseline methods. These results indicate that Dep-QMIX-Mask can generate feasible sequential reconfiguration strategies while improving both load restoration and load balancing. Full article

Cardiogenic pulmonary edema (CPE) is a life-threatening manifestation of acute heart failure characterized by rapid accumulation of fluid in the interstitial and alveolar spaces, leading to severe dyspnea, hypoxemia, and respiratory failure. The condition arises from elevated left-sided filling pressures that increase pulmonary capillary hydrostatic pressure, disrupt alveolo-capillary barrier integrity, and impair gas exchange. Neurohormonal activation further perpetuates congestion and increases myocardial workload, creating a vicious cycle of hemodynamic overload and respiratory compromise. Respiratory support is a cornerstone of management in CPE, aimed at stabilizing oxygenation, reducing the work of breathing, and facilitating ventricular unloading while definitive therapies, such as diuretics, vasodilators, inotropes, or mechanical circulatory support (MCS), address the underlying cause. Among available modalities, non-invasive ventilation (NIV) with continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) has the strongest evidence base in moderate-to-severe CPE, consistently reducing the need for intubation and providing rapid relief of dyspnea. High-flow nasal cannula (HFNC) represents an emerging alternative in patients with moderate hypoxemia or intolerance to mask ventilation, and should be considered an adjunctive option in selected patients with less severe disease or NIV intolerance, although its efficacy in severe presentations remains uncertain. Invasive mechanical ventilation is reserved for refractory cases, while extracorporeal membrane oxygenation (ECMO) and other advanced circulatory support modalities may be necessary in cardiogenic shock. Integration of respiratory strategies with hemodynamic optimization is essential, as positive pressure ventilation favorably modulates preload and afterload, synergizing with pharmacological unloading. Future directions include personalization of ventilatory strategies using advanced monitoring, novel interfaces to improve tolerability, and earlier integration of MCS. In summary, respiratory support in CPE is both a bridge and a decisive therapeutic intervention, interrupting the cycle of hypoxemia and hemodynamic deterioration. A multidisciplinary, individualized approach remains central to improving outcomes in this high-risk population. Full article

Background/Objectives: Diffusion-weighted imaging (DWI) is a magnetic resonance technique used to map the apparent diffusion coefficient ($ADC$) of water in human tissue. $ADC$ assessment plays a central role in clinical diagnostics, as malignant tissues typically exhibit reduced water mobility and, thus, lower $ADC$ values. Accurately measuring the $ADC$ requires effective fat suppression to prevent contamination from the residual fat signal, which is commonly believed to cause $ADC$ underestimation. This study aimed to demonstrate that $ADC$ overestimation may occur as well. Methods: Our theoretical analysis shows that out-of-phase conditions between fat and water signals lead to $ADC$ overestimations. We performed demonstration experiments on fat–water phantoms and the breasts of 10 healthy female volunteers. In particular, we considered three out-of-phase conditions: First and second, short-time inversion recovery (STIR) fat suppression with incorrect inversion time and incorrect flip angle, respectively. Third, phase differences due to spectral fat saturation. The $ADC$ values were assessed in regions of interest (ROIs) that included both water and residual fat signals. Results: In the phantoms and the volunteer data, ROIs containing both fat and water signals consistently exhibited lower $ADC$ values under in-phase conditions and higher $ADC$ values under out-of-phase conditions. Conclusions: We demonstrated that out-of-phase conditions can result in $ADC$ overestimation in the presence of residual fat signals, potentially resulting in false-negative classifications where malignant lesions are misinterpreted as benign due to an elevated $ADC$. Out-of-phase fat and water signals might also reduce lesion conspicuity in high b-value images, potentially masking clinically relevant findings. Full article

The Czochralski method is the dominant technique for producing power-electronics-grade silicon crystals. At the beginning of the seeding stage, an excessively high (or low) temperature at the solid–liquid interface can cause the time required for the seed to reach the specified length to be too long (or too short). However, the time taken for the seed to reach a specified length is strictly controlled in semiconductor crystal growth to ensure that the initial temperature is appropriate. An inappropriate initial temperature can adversely affect crystal quality and production yield. Accurately evaluating whether the current temperature is appropriate for seeding is therefore essential. However, the temperature at the solid–liquid interface cannot be directly measured, and the current manual evaluation method mainly relies on a visual inspection of the meniscus. Previous methods for detecting this temperature classified image features, lacking a quantitative assessment of the temperature. To address this challenge, this study proposed using the duration of the seeding stage as the target variable for evaluating the temperature and developed an improved multimodal fusion regression network. Temperature signals collected from a central pyrometer and an auxiliary pyrometer were transformed into time–frequency representations via wavelet transform. Features extracted from the time–frequency diagrams, together with meniscus features, were fused through a two-level mechanism with multimodal feature fusion (MFF) and channel attention (CA), followed by masking using spatial attention (SA). The fused features were then input into a random vector functional link network (RVFLN) to predict the seeding duration, thereby establishing an indirect relationship between multi-sensor data and the seeding temperature achieving a quantification of the temperature that could not be directly measured. Transfer comparison experiments conducted on our dataset verified the effectiveness of the feature extraction strategy and demonstrated the superior detection performance of the proposed model. Full article

Millions of healthcare workers experience percutaneous exposure to bloodborne communicable infectious disease pathogens annually, with the risk of contracting occupationally acquired infections. In this study, we aimed to assess the status of occupational safety and outbreak preparedness in Congolese nurses and laboratory technicians in Kongo central and the Katanga area, amidst multiple ongoing public health emergencies in the Democratic Republic of the Congo (DRC). This was a multicenter analytical cross-sectional study conducted in five referral hospitals located in Kongo central province and the Katanga area between 2019 and 2020 amidst Ebola, Yellow fever, Cholera and Chikungunya outbreaks. Participants were adult A0 grade nurses, A1 nurses, A2 nurses and medical laboratory technicians (N = 493). They answered a structured, self-administered questionnaire related to hospital hygiene and standard precautions for occupational infection prevention. The majority of the respondents were females (53.6%), and 30.1% of them have never participated in a training session on hospital infection prevention during their career. The proportions of those who have been immunized against hepatitis B virus (HBV) was markedly low, at 16.5%. Of the respondents, 75.3% have been using safety-engineered medical devices (SEDs), whereas 93.5% consistently disinfected medical devices after use. Moreover, 78% of the respondents used gloves during medical procedures and 92.2% wore masks consistently. A large majority of the respondents, 82.9%, have been recapping the needles after use. Regarding participation in outbreak response, 24.5% and 12.2% of the respondents were Chikungunya and Cholera epidemic responders, respectively; 1.8% have served in Ebola outbreak sites. The proportion of the respondents who sustained at least one percutaneous injury by needlestick or sharp device, blood/body fluid splash or both in the previous 12-month period was high, 89.3% (41.8% for injury, 59.2% for BBF event), and most of them (73%) reported over 11 events. Compared to laboratory technicians, nurses had higher odds for sustaining percutaneous injury and BBF events [OR = 1.38 (0.16); p < 0.01], whereas respondents with longer working experience were less likely to sustain those events [OR = 0.47 (0.11); p < 0.001]. Findings from this study suggest that Congolese nurses and laboratory technicians experience a high frequency of injury and BBF events at work, and remain at high risk for occupationally acquired infection. There is a need for periodic capacity-building training for the healthcare workforce to improve infection prevention in health settings, the provision of sufficient and appropriate PPE and SEDs, post-exposure follow-up and keeping records of occupational injuries in hospitals in Congolese healthcare settings. Full article

This article analyzes the effectiveness of Poland’s central government administration in implementing the 2030 Agenda for Sustainable Development, addressing the context of high-level strategic declarations versus actual policy outcomes. The study employs a qualitative critical document analysis, conducted as comprehensive desk research. This method involves a comparative analysis of official strategic and policy documents (e.g., “Strategy for Responsible Development”) against the empirical findings of external audits from the Supreme Audit Office (NIK), supplemented by national (GUS) and international statistical data. The analysis reveals a fundamental “implementation gap.” While Poland has successfully created a robust strategic and institutional framework, reflected in high international SDG rankings, this success masks deep deficits and stagnation in key areas, particularly in the environmental dimension. Audits consistently confirm systemic problems with inter-ministerial coordination, ensuring adequate financing, and the lack of reliable evaluation for key programs, such as “Clean Air” or the circular economy roadmap. Considering these findings, the study concludes that operational effectiveness does not match strategic declarations. The analysis identifies systemic weaknesses and recommends urgent, targeted strategic actions to bridge the gap between policy and practice, particularly by strengthening coordination and evaluation mechanisms. Full article

Identifying instruments in polyphonic audio is challenging due to overlapping spectra and variations in timbre and playing styles. This task is central to music information retrieval, with applications in transcription, recommendation, and indexing. We propose a dual-branch Convolutional Neural Network (CNN) that processes Mel-spectrograms and binary pitch masks, fused through a cross-attention mechanism to emphasize pitch-salient regions. On the IRMAS dataset, the model achieves competitive performance with state-of-the-art methods, reaching a micro F1 of 0.64 and a macro F1 of 0.57 with only 0.878M parameters. Ablation studies and t-SNE analyses further highlight the benefits of cross-modal attention for robust predominant instrument recognition. Full article

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

The autonomous coordination of multi-robot systems in complex, environments remains a fundamental challenge. Current Multi-Agent Reinforcement Learning (MARL) methods often struggle to reason effectively about the dynamic, causal relationships between agents and their surroundings. To address this, we introduce the Graph-Gated Transformer (GGT), a novel neural architecture designed to inject explicit relational priors directly into the self-attention mechanism for multi-robot coordination. The core mechanism of the GGT involves dynamically constructing a Tactical Relational Graph that encodes high-priority relationships like collision risk and cooperative intent. This graph is then used to generate an explicit attention mask, compelling the Transformer to focus its reasoning exclusively on entities rather than engaging in brute-force pattern matching across all perceived objects. Integrated into a Centralized Training with Decentralized Execution (CTDE) framework with QMIX, our approach demonstrates substantial improvements in high-fidelity simulations. In complex scenarios with dynamic obstacles and sensor noise, our GGT-based system achieves 95.3% coverage area efficiency with only 0.4 collisions per episode, a stark contrast to the 60.3% coverage and 20.7 collisions of standard QMIX. Ablation studies confirm that this structured, gated attention mechanism—not merely the presence of attention—is the key to unlocking robust collective autonomy. This work establishes that explicitly constraining the Transformer’s attention space with dynamic, domain-aware relational graphs is a powerful and effective architectural solution for engineering safe and intelligent multi-robot systems. Full article

Background/Objectives: Physical activity (PA) and energy intake (EI) are central targets of community initiatives to reduce the prevalence of childhood obesity. The general effects of PA and EI in influencing energy balance and body composition are clear. However, the independent impacts of PA and EI on the adiposity of children growing up amidst westernized lifestyles are inconclusive, as few studies have employed sufficiently robust methodology to provide solid independent associative data. Methods: We carried out a systematic review of the research addressing the independent associations of adiposity with each of PA and EI in free-living town or city-dwelling children and adolescents. Acceptable publications included objective measures of fat mass and PA, best standard practice EI assessments, and appropriate statistical modeling. Results: Of approximately 700 publications explored, only four satisfied all the pre-set methodological standards. All four studies involved predominantly White participants from westernized cities and had the same outcomes. Adiposity was strongly independently and negatively related to PA, but there was no evidence of any independent relationship between adiposity and EI. Potential misreporting was considered, especially under-reporting by participants with greater adiposity, butpost-hoc assessments were unable to find any evidence that this influenced the outcomes. Conclusions: In general, children with higher adiposity consumed no more food and beverage energy than their leaner counterparts, but they were less active. However, despite some support for the validity of the commonly used and validated EI assessments, their subjective nature raises the possibility that inaccuracy masked relationships. Additional well-designed research is needed, and notwithstanding the vital role that sound nutrition plays in the healthy development of our youth, the consistency of outcomes of the well-executed studies in this review suggests that campaigns targeting youth obesity would benefit from strategies focusing strongly on increasing PA. Full article

Background: Central nervous system (CNS) tumors during pregnancy are rare but present significant diagnostic, therapeutic, and ethical challenges. These include both primary and metastatic lesions, which share overlapping clinical features and management complexities. Their clinical course is influenced by gestational physiological changes, which can mask symptoms and delay diagnosis, thereby increasing maternal and fetal risks. Objective: This review aims to synthesize current evidence on the epidemiology, pathophysiology, clinical presentation, diagnostic strategies, treatment options, prognosis, and ethical considerations related to CNS tumors in pregnant patients. Methods: A comprehensive literature review was conducted, including retrospective and prospective studies, clinical guidelines, and systematic reviews focusing on brain and spinal tumors diagnosed during pregnancy. Particular attention was given to the impact of gestational age, tumor histology, and maternal condition on treatment outcomes. Results: Hormone-sensitive tumors such as meningiomas and prolactinomas may exhibit accelerated growth during pregnancy due to elevated progesterone and prolactin levels. Diagnosis is often delayed due to symptom overlap with normal gestational changes. MRI without contrast remains the imaging modality of choice. Glucocorticoids and selected chemotherapy agents can be cautiously used depending on gestational age. Surgical resection, particularly in the second trimester, has been shown to be safe and effective in appropriate clinical scenarios. Multidisciplinary coordination is essential. Prognosis varies based on tumor type and timing of intervention, with maternal survival prioritized in high-risk situations. Ethical management hinges on patient autonomy, informed consent, and proportionality of medical interventions. Conclusions: CNS tumors during pregnancy require early recognition, individualized treatment planning, and ethical vigilance. Multidisciplinary collaboration is vital to optimizing outcomes for both mother and fetus. Future efforts should focus on developing standardized protocols and expanding evidence through multicenter studies. Full article

Ocean fronts are central to upper-ocean dynamics and ecosystem processes, yet recognizing their evolutionary trends from satellite data remains challenging. We present a 3D U-Net-based multi-task framework that jointly performs ocean-front detection (OFD) and ocean-front evolutionary trend recognition (OFETR) from sea surface temperature gradient heatmaps. Instead of cascading OFD and OFETR in separate stages that pass OFD outputs downstream and can amplify upstream errors, the proposed model shares 3D spatiotemporal features and is trained end-to-end. We construct the Zhejiang–Fujian Coastal Front Mask (ZFCFM) and Evolutionary Trend (ZFCFET) datasets from ESA SST CCI L4 products for 2002–2021 and use them to evaluate the framework against 2D CNN baselines and traditional methods. Multi-task learning improves OFETR compared with single-task training while keeping OFD performance comparable, and the unified design reduces parameter count and daily computational cost. The model outputs daily point-level trend labels aligned with the dataset’s temporal resolution, indicating that end-to-end multi-task learning can mitigate error propagation and provide temporally resolved estimates. Full article