Search Results (123)

With the wave of new campus construction gradually receding, the focus of green campus planning and design is shifting toward the low-carbon retrofitting of the existing built environment. University campuses often face challenges such as dispersed land use, inadequate spatial planning, disorganized road layouts, suboptimal landscape design, and low energy efficiency. Grounded in a review of current research on campus carbon emissions, this study integrates green technology indicators with planning and design approaches to establish a multi-scale, context-adaptive planning framework for carbon control, spanning five dimensions: intensive land use, spatial layout, transportation systems, landscape development, and facility integration. Employing a combined approach of bibliometric analysis and case studies, this research examines and compares typical university campuses both domestically and internationally to validate the effectiveness of the synergistic “technology-system-behavior” pathway in mitigating high-carbon lock-in. Through a systematic comparative analysis of representative low-carbon campuses, the synthesized results indicate that under optimal operational conditions, the clustered reorganization of functional zones demonstrates the potential to reduce transportation carbon emissions by approximately 25%; comprehensive retrofitting of building envelopes can decrease building energy consumption intensity by an estimated 30%; a multimodal coordinated transport system can increase the share of non-motorized travel to around 65%; establishing high carbon-sequestration plant communities can enhance carbon sink capacity by up to 30%; and smart facility integration can reduce overall campus carbon emissions by a projected range of 25–40%. It should be noted that these quantitative outcomes represent high-probability potential ranges, with actual performance subject to behavioral and operational fluctuations. This study provides theoretical support and practical pathways for achieving “near-zero carbon campuses” and underscores the important demonstrative role that higher education institutions can play in addressing climate change. Full article

Background: Ectopic pregnancy occurs in about 1–2% of all pregnancies, with 95% implanting in the fallopian tubes. Hepatic implantation is one of the rarest and most dangerous forms of abdominal ectopic pregnancy. Its diagnosis is often delayed because of nonspecific symptoms, and it is also often difficult for routine ultrasound imaging to visualize ectopic pregnancy sites that are not in the pelvis. Since this type of pregnancy carries a risk of severe hemorrhage, early identification is crucial. Case: A 30-year-old woman, gravida 3 para 2, presented with a serum β-hCG of 66,408 mIU/mL, but no intrauterine pregnancy was detected on ultrasound imaging. At an outside facility, a laparoscopy was performed, which also failed to show a pelvic ectopic pregnancy. The patient then received her first dose of methotrexate and was subsequently transferred to a tertiary care center for further evaluation. MRI and liver ultrasound showed a 2.3 cm subcapsular lesion in segment 5 of the liver that was suspicious for a hepatic ectopic pregnancy. However, these imaging studies could not exclude a gestational trophoblastic disease or hepatic neoplasm. A dilation and curettage revealed no trophoblastic tissue. The patient next received two additional doses of methotrexate on hospital days 4 and 7 due to an inadequate decline in interval β-hCG; β-hCG levels declined gradually but steadily over several months until they became undetectable and indicated a successful medical treatment of her hepatic ectopic pregnancy. Conclusions: This case highlights the complex diagnostic and treatment challenges presented by a hepatic ectopic pregnancy. Multimodal imaging, serial monitoring of β-hCG levels, and the engagement of a multidisciplinary team were essential factors in achieving a safe, nonsurgical, and successful resolution of this condition. When a pregnancy of unknown location is suspected, extended imaging studies are critical tools for patient evaluation after initial imaging studies and laparoscopy are inconclusive. Full article

Controlled-environment agriculture (CEA) and circular production systems require coordinated monitoring of biological and physicochemical processes across trophic levels. This project report presents the implementation of a multi-trophic controlled-environment agriculture demonstrator that integrates computer-vision-based monitoring with established sensor infrastructure for aquaculture, poultry, plants, microalgae, duckweed, and insect modules. Stereo imaging and RGB-D systems are deployed for non-invasive quantification of fish biomass and plant growth, while continuous water-quality and environmental measurements (e.g., pH, dissolved oxygen, nitrate, ammonium, temperature, CO₂) provide complementary process data. These data streams are synchronized within a shared database architecture to enable cross-module evaluation of nutrient dynamics, growth progression, and operational stability under real facility conditions. The implemented framework demonstrates how computer vision can extend conventional sensor-based monitoring by directly capturing biological performance indicators across aquatic, terrestrial, and microbial domains. While advanced predictive modeling and full digital twin simulation remain future development steps, the realized data-integration architecture establishes a structural foundation for the systematic evaluation of circular indoor food-production systems. The demonstrator illustrates how multimodal monitoring can support nutrient recirculation, transparency of biological variability, and data-driven assessment within controlled multi-trophic environments. Full article

The digital transformation of the tourism industry faces a dual challenge: the fragmentation of data across platforms and the lack of immersive “try-before-you-buy” experiences. While Large Language Models (LLMs) have revolutionized information synthesis, they typically lack real-time visual verification capabilities. This paper proposes a novel, multimodal AI Agent architecture that integrates advanced natural language planning with photorealistic 3D visualization. We present a system where a conversational agent, powered by Gemini 2.5 Flash, orchestrates a suite of dynamic tools to build structured travel itineraries (flights, hotels, activities) while simultaneously deploying a neural rendering engine. This engine utilizes a modular Structure-from-Motion (SfM) pipeline feeding into 3D Gaussian Splatting (3DGS) to render navigable, high-fidelity digital twins of hotel facilities directly within the chat interface. Positioned as a Technology Readiness Level 4 (TRL 4) proof of concept (PoC), this work demonstrates the technical feasibility of the multimodal integration between conversational logic and automated visual synthesis. The results demonstrate the technical feasibility of a pipeline that dynamically binds LLM inference to 3D spatial data, providing a foundation for high-fidelity, interactive travel consultancy. Full article

Developed by Marais and Rabinowitz, the A²/O process is a pivotal biotechnology for biological nitrogen and phosphorus removal, developed by optimizing the five-stage Phoredox protocol. Renowned for its efficient configuration and straightforward operation, it has been extensively adopted in municipal and industrial wastewater treatment projects globally, including numerous facilities in China. However, the conventional A²/O process faces inherent operational challenges: the conflicting SRT requirements between autotrophic nitrifying bacteria (needing long SRT for stable nitrification) and PAOs, intense competition for carbon sources among PAOs and denitrifying bacteria, and the inhibitory effects of residual nitrate and DO on phosphorus release and denitrification. To address these issues, a range of optimization strategies has been developed, including SRT adjustment, carbon source distribution optimization, the integration of biofilm carriers, the addition of external carbon sources, and innovative modified configurations such as the Reversed A²/O, JHB, UCT, and MUCT. These approaches synergistically mitigate nitrate interference and enhance nutrient removal efficiency by decoupling microbial SRT demands, supplementing readily biodegradable carbon sources, and optimizing hydraulic flow paths. Future research should focus on deepening the understanding of the metabolic mechanisms underlying nitrogen and phosphorus removal, developing sustainable and efficient external carbon source systems, refining multi-mode reactor design for engineering scalability, optimizing combined processes for ultra-low C/N ratio wastewater treatment, and advancing low-temperature adaptation technologies. These efforts aim to further improve the process’s efficacy, stability, and sustainability, enabling it to meet increasingly stringent environmental discharge standards. Full article

Membrane fusion is fundamental to intracellular transport and signal transduction, with its dysregulation implicated in various diseases. Deciphering its transient, microscale dynamics requires advanced sensing technologies. This review systematically evaluates optical and electrochemical sensing platforms for in vitro studies of membrane fusion. Optical sensing platforms provide greater intuitive readout of membrane fusion events, whereas electrochemical sensing platforms enable label-free, single-event resolution. We revisit classical fluorescence resonance energy transfer (FRET) strategies for lipid and content mixing, tracing their evolution from ensemble measurements to real-time, multiparameter, single-vesicle analysis. We further examine electrochemical platforms based on nanodisc-black lipid membranes (ND-BLMs) and solid-supported lipid bilayers (SLBs), highlighting their unique capabilities in characterizing fusion pore kinetics and virus–host membrane fusion. ND-BLM-based systems are irreplaceable for probing fusion pore kinetics, owing to their sub-millisecond temporal resolution and being essentially free from ion saturation and depletion effects. Meanwhile, SLB-based electrochemical sensing platforms excel at high-throughput detection of viral membrane fusion events by virtue of their excellent compatibility and facile integration. These sensors provide powerful tools for elucidating the molecular mechanisms underlying SNARE-mediated membrane fusion and viral fusion processes. Finally, this review outlines future directions centered on the integration of multimodal sensing and the construction of physiomimetic membranes, emphasizing the critical role of cross-scale, multiparameter sensing in bridging molecular mechanisms with biological functions and advancing the diagnosis and treatment of membrane fusion-related diseases. Full article

Piping systems in critical facilities, such as power plants, hospitals, and industrial sites, are essential nonstructural components determining operational continuity during seismic events. Past earthquake events, including those at Northridge, Kobe, and Chile, have repeatedly demonstrated the vulnerability of sprinklers and utility piping, wherein leakage and connection failures led to severe secondary hazards. However, existing conventional seismic evaluations based on equivalent static loading are limited in capturing the frequency-dependent dynamic characteristics and resonance potential of inherently multi-degree-of-freedom piping structures. This study proposes a modal-based dynamic screening approach to pre-emptively identify resonance-prone frequency bands by incorporating the frequency characteristics of representative earthquakes recorded in South Korea. Water supply, sprinkler, and cooling water piping systems were analyzed using three key indicators: effective modal mass participation, cumulative effective modal mass ratios, and directional translational components of mode shapes. The results demonstrate that the proposed dynamic screening approach effectively identifies resonance vulnerabilities across different piping configurations, proving its utility as a more precise seismic screening tool compared to conventional methods. This study underscores the practical necessity of modal analysis as a preliminary step for advanced dynamic evaluations and provides a rational framework for enhancing the seismic safety of nonstructural components in critical facilities. Full article

Microbial imbalance and the spread of pathogenic microorganisms pose severe threats to human health and ecological security. Traditional microbial detection methods suffer from several drawbacks such as long detection time, low sensitivity, and insufficient specificity. As an emerging fluorescent probe, carbon dots (CDs) offer an innovative direction for microbial labeling and detection due to their ultra-small particle size, unique optical properties, excellent biocompatibility, and facile surface modifiability. Herein, this article reviews the research progress of CDs on microbial labeling and detection. The content covers a brief introduction of CDs and explores the main recognition strategies including non-covalent interactions and biomolecule-mediated targeted binding. It also elaborates on the application status of multi-modal sensing technologies for microbial detection, such as CDs-based fluorescent sensing, electrochemical sensing, and surface-enhanced Raman scattering (SERS) sensing. Additionally, the challenges faced in current research, such as achieving simultaneous detection of multiple pathogens and in vivo dynamic tracking, are analyzed, and the development prospects of CDs in fields like clinical diagnosis and public health monitoring are prospected. This review aims to provide comprehensive references for further research and application of CDs in the field of microbial detection. Full article

Background: Functional Electrical Stimulation (FES) for post-stroke drop foot is commonly applied in acute and subacute stroke rehabilitation or as part of long-term home-based programs in chronic patients. Evidence supporting short facility-based rehabilitation programs incorporating FES in chronic populations remains limited. The aim of this study was to explore functional outcomes associated with such a program in a chronic population. Materials and methods: A 10-day facility-based rehabilitation program incorporating FES therapy followed by 3-month follow-up was delivered to 14 chronic post-stroke patients with foot drop (8 women; aged 62.6 ± 12.2 years). FES was applied during walking with stimulation synchronized to the swing phase of gait (35 Hz, 300 μs, 15 min per session). Activities of daily living and mobility were assessed using clinical outcome measures. Statistical significance (p < 0.05), effect sizes, and minimal clinically important difference (MCID) responder rates were evaluated. Results: Statistically significant improvements were observed across all outcome measures post-treatment and at follow-up, with MCID responder rates exceeding 50%. Conclusions: A short facility-based multimodal rehabilitation program incorporating FES was associated with functional improvements in chronic post-stroke patients. Given the multimodal design, these findings cannot be attributed to FES alone and should be interpreted as exploratory. Full article

Background: Physical inactivity remains a major public health challenge, particularly for underserved populations lacking exercise facility access. AI-powered smartphone applications with real-time human pose estimation offer scalable solutions, but they lack rigorous clinical validation. Objective: This study validates the clinical efficacy of a 16-week on-device AI-driven resistance training program using MediaPipe pose estimation technology in young adults with limited facility access. Primary outcomes included muscular strength (1RM squat), body composition, functional movement (FMS), and cardiorespiratory fitness (VO₂max). Methods: A single-group pre–post study enrolled 216 participants (mean age 23.77 ± 4.02 years; 69.2% male), with 146 (67.6%) completing the protocol. Participants performed three 30 min weekly sessions of seven compound exercises delivered via a smartphone app providing real-time pose analysis (97.2% key point accuracy, 28.6 ms inference), multimodal feedback, and personalized progression using self-selected equipment. Results: Significant improvements across all domains: muscular strength (+4.39 kg 1RM squat, p < 0.001, d = 1.148), body fat (−2.92%, p < 0.001, d = −1.373), skeletal muscle mass (+2.19 kg, p < 0.001, d = 1.433), FMS (+0.29 points, p = 0.001, d = 0.285), and VO₂max (+1.82 mL/kg/min, p < 0.001, d = 0.917). Pose classification accuracy reached 95.8% vs. physiotherapist assessment (ICC = 0.94). Conclusions: This study provides the first clinical evidence that on-device AI pose estimation enables facility-independent resistance training with outcomes comparable to traditional programs. Unlike cloud-based systems, our lightweight model (28.6 ms inference) supports real-time mobile deployment, advancing accessible precision exercise medicine. Limitations include a single-arm design and gender imbalance, warranting future RCTs with diverse cohorts. Full article

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

Efficient resource allocation is critical for transportation hub operations, yet current scheduling systems require substantial domain-specific customization when deployed across different facilities. This paper presents a domain-adaptive deep reinforcement learning (DADRL) framework that learns transferable optimization policies for dynamic resource allocation across structurally similar transportation scheduling problems. The framework integrates dual-level heterogeneous graph attention networks for separating constraint topology from domain-specific features, hypergraph-based constraint modeling for capturing high-order dependencies, and hierarchical policy decomposition that reduces computational complexity from $O\left(mnT\right)$ to $O(m+n+T)$. Evaluated on realistic simulators modeling airport gate assignment (Singapore Changi: 50 gates, 300–400 daily flights) and seaport berth allocation (Singapore Port: 40 berths, 80–120 daily vessels), DADRL achieves 87.3% resource utilization in airport operations and 86.3% in port operations, outperforming commercial solvers under strict real-time constraints (Gurobi-MIP with 300 s time limit: 85.1%) while operating 270 times faster (1.1 s versus 298 s per instance). Given unlimited time, Gurobi achieves provably optimal solutions, but DADRL reaches 98.7% of this optimum in 1.1 s, making it suitable for time-critical operational scenarios where exact solvers are computationally infeasible. Critically, policies trained exclusively on airport scenarios retain 92.4% performance when applied to ports without retraining, requiring only 800 adaptation steps compared to 13,200 for domain-specific training. The framework maintains 86.2% performance under operational disruptions and scales to problems three times larger than training instances with only 7% degradation. These results demonstrate that learned optimization principles can generalize across transportation scheduling problems sharing common constraint structures, enabling rapid deployment of AI-based scheduling systems across multi-modal transportation networks with minimal customization and reduced implementation costs. Full article

Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and simple multimodal fusion cannot fully exploit complementary cues. To address these gaps, we propose YOLO-MSRF, a lightweight RGB–NIR multimodal segmentation and refinement framework for robust tomato perception in facility agriculture. Firstly, we propose a dual-branch multimodal backbone, introduce Cross-Modality Difference Complement Fusion (C-MDCF) for difference-based complementary RGB–NIR fusion, and design C2f-DCB to reduce computation while strengthening feature extraction. Furthermore, we develop a cross-scale attention fusion network and introduce the proposed MS-CPAM to jointly model multi-scale channel and position cues, strengthening fine-grained detail representation and spatial context aggregation for small and occluded tomatoes. Finally, we design the Multi-Scale Fusion and Semantic Refinement Network, MSF-SRNet, which combines the Scale-Concatenate Fusion Module (Scale-Concat) fusion with SDI-based cross-layer detail injection to progressively align and refine multi-scale features, improving representation quality and segmentation accuracy. Extensive experiments show that YOLO-MSRF achieves substantial gains under weak and low-light conditions, where RGB-only models are most prone to boundary degradation and missed instances, and it still delivers consistent improvements on the mixed four-light validation set, increasing $mA{P}_{0.5}$ by 2.3 points, $mA{P}_{0.5–0.95}$ by 2.4 points, and mIoU by 3.60 points while maintaining real-time inference at 105.07 FPS. The proposed system further supports counting, size estimation, and maturity analysis of harvestable tomatoes, and can be integrated with depth sensing and yield estimation to enable real-time yield prediction in practical greenhouse operations. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

We present a new method for mapping global gas flaring using a multiyear spatio-temporal database of VIIRS Nightfire (VNF) nighttime infrared detections from the Suomi NPP, NOAA-20, and NOAA-21 satellites. The method is designed to resolve closely spaced industrial combustion sources and to produce a stable, physically meaningful flare catalog suitable for long-term monitoring and emissions analysis. The method combines adaptive spatial aggregation of high-temperature detections with a hierarchical clustering that super-resolves individual flare stacks within oil and gas fields. Post-processing yields physically consistent flare footprints and attraction regions, allowing separation of closely spaced sources. Flare clusters are assigned to operational categories (e.g., upstream, midstream, LNG) using prior catalogs combined with AI-assisted expert interpretation. In this step, a multimodal large language model (LLM) provides contextual classification suggestions based on geospatial information, high-resolution daytime imagery, and detection time-series summaries, while final attribution is performed and validated by domain experts. Compared with annual flare catalogs commonly used for national flaring estimates, the new catalog demonstrates substantially improved performance. It is more selective in the presence of intense atmospheric glow from large flares, identifies approximately twice as many active flares, and localizes individual stacks with ~50 m precision, resolving emitters separated by ~400–700 m. For the well-defined class of downstream flares at LNG export facilities, the catalog achieves complete detectability. These improvements support more accurate flare inventories, facility-level attribution, and policy-relevant assessments of gas flaring activity. Full article