Search Results (58)

Securing thermal comfort while minimizing energy consumption in educational buildings is vital for achieving sustainable development goals. Drawing on the Environmental, Social, and Governance (ESG) framework, this systematic review synthesizes findings from 84 peer-reviewed studies published over the past decade, with a focus on how thermal comfort and energy use are assessed in educational contexts. The review identifies three primary research themes: climate resilience, multidimensional human-centric design, and energy decarbonization. However, it also reveals that existing studies have placed disproportionate emphasis on the environmental dimension, with insufficient exploration of issues related to social equity and governance structures. To address this gap, this study introduces an ESG-driven theoretical framework encompassing seven dimensions: thermal environment stability, multimodal thermal comfort assessment integration, sustainable energy use, heterogeneous thermal demand equality, passive–active design synergy, participatory thermal data governance, and educational thermal well-being inclusivity. By fostering interdisciplinary convergence and emphasizing inclusive stakeholder engagement, the proposed framework provides a resilient and adaptive foundation for enhancing indoor environmental quality in educational buildings while advancing equitable climate and energy strategies. Full article

In order to cope with the multimodal changes led by the digital era, enterprises urgently need to promote the construction of new-quality productive forces (NQPFs) through digital transformation. NQPFs take digital technology empowerment as the core driving force and emphasize the dynamic matching and synergy between the new-quality elements (digital infrastructure, digital talents, data resources, and diversified ecology) and the new-quality capabilities (digital dynamic capabilities) so as to unleash the innovation potentials of different production modes. Based on resource orchestration theory, this study constructs a “resource-capability-value creation” framework for digital empowerment (D-RCV) and employs fuzzy set qualitative comparative analysis (fsQCA) to examine 205 enterprise samples. Results reveal that enhanced innovation performance stems from digital empowerment at both resource and capability levels, generating three configurational paths: collaborative symbiosis, resource optimization, and data-driven approaches. These paths emerge through the interaction of resources and capabilities under different conditions. This study contributes by proposing a digital empowerment framework and exploring multiple pathway choices for new-quality productivity development. The findings provide theoretical insights for enterprise innovation research and practical guidance for innovation management strategies. Full article

Pancreatic cancer is a highly lethal malignant tumor characterized by challenges in early diagnosis and limited therapeutic options, leading to an exceptionally low clinical cure rate. With the advent of novel cancer treatment paradigms, ferroptosis—a form of iron-dependent regulated cell death driven by lipid peroxidation—has emerged as a promising therapeutic strategy, particularly for tumors harboring RAS mutations. However, the poor bioavailability and insufficient tumor-targeting capabilities of conventional drugs constrain the efficacy of ferroptosis-based therapies. Recent advancements in nanotechnology and imaging-guided treatments offer transformative solutions through targeted drug delivery, real-time monitoring of treatment efficacy, and multimodal synergistic strategies. This article aims to elucidate the mechanisms underlying ferroptosis in pancreatic cancer and to summarize the latest identified therapeutic targets for ferroptosis in this context. Furthermore, it reviews the recent progress in nanotechnology-based ferroptosis therapy for pancreatic cancer, encompassing ferroptosis monotherapy, synergistic ferroptosis therapy, and endogenous ferroptosis therapy. Subsequently, the integration of imaging-guided nanotechnology in ferroptosis therapy is summarized. Finally, this paper discusses innovative strategies, such as stroma-targeted ferroptosis therapy, immune-ferroptosis synergy, and AI-driven nanomedicine development, offering new insights and directions for future research in pancreatic cancer treatment. Full article

Background: This paper proposes a conceptual framework for a multifunctional mobility hub (MMH) that co-locates shared e-mobility services, urban logistics, and charging/storage infrastructure within a single site. Aimed at high-density European cities, the MMH model addresses current gaps in both research and practice, where multimodal mobility services, logistics, and energy are rarely planned in an integrated manner. Methods: A mixed-methods approach was applied, including a systematic literature review (PRISMA), expert interviews, case studies, and a stakeholder workshop, to identify synergies across fleet types and operational domains. Results: The analysis reveals key design principles for MMHs, such as interoperable charging, the functional separation of passenger and freight flows, and modular, scalable infrastructure adapted to urban constraints. Conclusions: The MMH serves as a preliminary concept for planning next-generation mobility stations. It offers qualitative insights for urban planners, operators, and policymakers into how multifunctional hubs may support lower emissions, more efficient operations, and shared infrastructure use. Full article

Predicting water inrush in coal mines faces significant challenges due to limited data, model generalization, and a lack of interpretability. Current approaches often neglect the inherent geometrical symmetries and structured patterns within the complex hydrological parameter space, rely on local parameter optimization, and struggle with interpretability, leading to insufficient predictive accuracy and engineering applicability under complex geological conditions. This study addresses these limitations by integrating Gaussian mixture modeling (GMM), manifold learning, and data augmentation to effectively capture multimodal hydrological data distributions and reveal their intrinsic symmetrical configurations and manifold structures, thereby reducing feature dimensionality. We then apply a whale optimization algorithm (WOA)-enhanced XGBoost model to forecast water inrush probabilities. Our model achieved an R² of 0.92, demonstrating a greater than 60% error reduction across various metrics. Validation at the Yangcheng Coal Mine confirmed that this balanced approach significantly enhances predictive accuracy, interpretability, and cross-scenario applicability. The synergy between high accuracy and transparency provides decision makers with reliable risk insights, enabling bidirectional validation with geological mechanisms and supporting the implementation of targeted, proactive safety measures. Full article

This eye-tracking investigation employed a 2 × 2 experimental design to examine multimodal lexical encoding processes. Eighty participants were systematically assigned to four conditions: Group A (text-only), Group B (text + image), Group C (text + sound), and Group D (text + image + sound). The results demonstrated significantly superior recall accuracy in Group D (92.00%) compared with unimodal conditions (Group B: 82.07%; Group C: 76.00%; Group A: 59.60%; p < 0.001), confirming robust audiovisual synergy. The novel Multimodal Coupling Strength Index (MCSI) dynamically quantified crossmodal integration efficacy through eye-tracking metrics (Attentional Synchronization Coefficient, ASC; Saccade Duration–Fixation Duration differential, SD-FD), revealing significantly stronger coupling in audiovisual conditions (C/D: 0.71; B/D: 0.54). Crucially, the established MCSI provides a transferable diagnostic framework for evaluating multimodal integration efficiency in learning environments. Full article

The scenario adaptability of agricultural machinery chassis in hilly and mountainous regions has become a key area of innovation in modern agricultural equipment development in China. Due to the fragmented nature of farmland, steep terrain (often exceeding 15°), complex topography, and limited suitability for mechanization, traditional agricultural machinery experiences significantly reduced operational efficiency—typically by 30% to 50%—along with poor mobility. These limitations impose serious constraints on grain yield stability and the advancement of agricultural modernization. Therefore, enhancing the scenario-adaptive performance of chassis systems (e.g., slope adaptability ≥ 25°, lateral tilt stability > 30°) is a major research priority for China’s agricultural equipment industry. This paper presents a systematic review of the global development status of agricultural machinery chassis tailored for hilly and mountainous environments. It focuses on three core subsystems—power systems, traveling systems, and leveling systems—and analyzes their technical characteristics, working principles, and scenario-specific adaptability. In alignment with China’s “Dual Carbon” strategy and the unique operational requirements of hilly–mountainous areas (such as high gradients, uneven terrain, and small field sizes), this study proposes three key technological directions for the development of intelligent agricultural machinery chassis: (1) Multi-mode traveling mechanism design: Aimed at improving terrain traversability (ground clearance ≥400 mm, obstacle-crossing height ≥ 250 mm) and traction stability (slip ratio < 15%) across diverse landscapes. (2) Coordinated control algorithm optimization: Designed to ensure stable torque output (fluctuation rate < ±10%) and maintain gradient operation efficiency (e.g., less than 15% efficiency loss on 25° slopes) through power–drive synergy while also optimizing energy management strategies. (3) Intelligent perception system integration: Facilitating high-precision adaptive leveling (accuracy ± 0.5°, response time < 3 s) and enabling terrain-adaptive mechanism optimization to enhance platform stability and operational safety. By establishing these performance benchmarks and focusing on critical technical priorities—including terrain-adaptive mechanism upgrades, energy-drive coordination, and precision leveling—this study provides a clear roadmap for the development of modular and intelligent chassis systems specifically designed for China’s hilly and mountainous regions, thereby addressing current bottlenecks in agricultural mechanization. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Nowadays, the development of the food industry and economic recovery have driven escalating consumer demands for high-quality, nutritious, and safe food products, and spectroscopic technologies are increasingly prominent as essential tools for food quality inspection. Concurrently, the rapid rise of artificial intelligence (AI) has created new opportunities for food quality detection. As a critical branch of AI, deep learning synergizes with spectroscopic technologies to enhance spectral data processing accuracy, enable real-time decision making, and address challenges from complex matrices and spectral noise. This review summarizes six cutting-edge nondestructive spectroscopic and imaging technologies, near-infrared/mid-infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy, hyperspectral imaging (spanning the UV, visible, and NIR regions, to simultaneously capture both spatial distribution and spectral signatures of sample constituents), terahertz spectroscopy, and nuclear magnetic resonance (NMR), along with their transformative applications. We systematically elucidate the fundamental principles and distinctive merits of each technological approach, with a particular focus on their deep learning-based integration with spectral fusion techniques and hybrid spectral-heterogeneous fusion methodologies. Our analysis reveals that the synergy between spectroscopic technologies and deep learning demonstrates unparalleled superiority in speed, precision, and non-invasiveness. Future research should prioritize three directions: multimodal integration of spectroscopic technologies, edge computing in portable devices, and AI-driven applications, ultimately establishing a high-precision and sustainable food quality inspection system spanning from production to consumption. Full article

Forest fire detection is vital for ecological conservation and disaster management. Existing visual detection methods exhibit instability in smoke-obscured or illumination-variable environments. Although multimodal fusion has demonstrated potential, effectively resolving inconsistencies in smoke features across diverse modalities remains a significant challenge. This issue stems from the inherent ambiguity between regions characterized by high temperatures in infrared imagery and those with elevated brightness levels in visible-light imaging systems. In this paper, we propose MCDet, an RGB-T forest fire detection framework incorporating target-aware fusion. To alleviate feature cross-modal ambiguity, we design a Multidimensional Representation Collaborative Fusion module (MRCF), which constructs global feature interactions via a state-space model and enhances local detail perception through deformable convolution. Then, a content-guided attention network (CGAN) is introduced to aggregate multidimensional features by dynamic gating mechanism. Building upon this foundation, the integration of WIoU further suppresses vegetation occlusion and illumination interference on a holistic level, thereby reducing the false detection rate. Evaluated on three forest fire datasets and one pedestrian dataset, MCDet achieves a mean detection accuracy of 77.5%, surpassing advanced methods. This performance makes MCDet a practical solution to enhance early warning system reliability. Full article

Electromyography (EMG) has emerged as a vital tool in the development of wearable robotic exoskeletons, enabling intuitive and responsive control by capturing neuromuscular signals. This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. Various EMG acquisition methods, including surface, intramuscular, and high-density surface EMG, are evaluated for their applicability in real-time control. The review addresses prevalent signal quality challenges, such as motion artifacts, power-line interference, and crosstalk. It also highlights both traditional filtering techniques and advanced methods, such as wavelet transforms, empirical mode decomposition, and adaptive filtering. Feature extraction techniques are explored to support pattern recognition and motion classification. Machine learning approaches are examined for their roles in pattern recognition-based and hybrid control architectures. This article emphasizes muscle synergy analysis and adaptive control algorithms to enhance personalization and fatigue compensation, followed by the benefits of multimodal sensing and edge computing in addressing the limitations of EMG-only systems. By focusing on EMG-driven strategies through signal processing, machine learning, and sensor fusion innovations, this review bridges gaps in human–machine interaction, offering insights into improving the precision, adaptability, and robustness of next generation exoskeletons. Full article

Remote sensing tasks, such as land cover classification, are increasingly becoming multimodal problems, where information from multiple imaging devices, complementing each other, can be fused. In particular, synergies between optical and synthetic aperture radar (SAR) are widely recognized to be beneficial in a variety of tasks. At the same time, archival of multimodal imagery for global coverage poses significant storage requirements due to the multitude of available sensors, and their increasingly higher resolutions. In this paper, we exploit redundancies between SAR and optical imaging modalities to create a joint encoding that improves storage efficiency. A novel neural network design with progressive attentive fusion modules is proposed for joint compression. The model is also promptable at test time with a desired tradeoff between the input modalities, to enable flexibility in the fidelity of the joint representation to each of them. Moreover, we show how end-to-end optimization of the joint compression model, including its modality tradeoff prompt, allows for better accuracy on downstream tasks leveraging multimodal inference when a constraint on the rate is to be met. Full article

When a disaster occurs, a large number of social media posts on platforms like Weibo attract public attention with their combination of text and images. However, the consistency between textual descriptions and visual representations varies significantly. Consistent multi-modal data are crucial for helping the public understand the disaster situation and support rescue efforts. This study aims to develop a systematic framework for assessing the consistency of multi-modal disaster-related data on social media. This study explored how the congruence between text and image content affects public engagement and informs strategies for efficient emergency responses. Firstly, the Clip (Contrastive Language-Image Pre-Training) model was used to mine the disaster correlation, loss category, and severity of the images and text. Then, the consistency of image–text pairs was qualitatively analyzed and quantitatively calculated. Finally, the influence of graphic consistency on social concern was discussed. The experimental findings reveal that the consistency of text and image data significantly influences the degree of public concern. When the consistency increases by 1%, the social attention index will increase by about 0.8%. This shows that consistency is a key factor for attracting public attention and promoting the dissemination of information related to important disasters. The proposed framework offers a robust, systematic approach to analyzing disaster loss information consistency. It allows for the efficient extraction of high-consistency data from vast social media data sets, providing governments and emergency response agencies with timely, accurate insights into disaster situations. Full article

Pine nuts hold significant economic value due to their rich plant protein and healthy fats, yet precise variety classification has long been hindered by limitations of traditional techniques such as chemical analysis and machine vision. This study proposes a novel near-infrared (NIR) spectral feature selection algorithm, termed the improved binary equilibrium optimizer with selection probability (IBiEO-SP), which incorporates a dynamic probability adjustment mechanism to achieve efficient feature dimensionality reduction. Experimental validation on a dataset comprising seven pine nut varieties demonstrated that, compared to particle swarm optimization (PSO) and the genetic algorithm (GA), the IBiEO-SP algorithm improved average classification accuracy by 5.7% (p < 0.01, Student’s t-test) under four spectral preprocessing methods (MSC, SNV, SG1, and SG2). Remarkably, only 2–3 features were required to achieve optimal performance (MSC + random forest: 99.05% accuracy, 100% F1/precision; SNV + KNN: 97.14% accuracy, 100% F1/precision). Furthermore, a multimodal data synergy strategy integrating NIR spectroscopy with morphological features was proposed, and a classification model was constructed using a soft voting ensemble. The final classification accuracy reached 99.95%, representing a 2.9% improvement over single-spectral-mode analysis. The results indicate that the IBiEO-SP algorithm effectively balances feature discriminative power and model generalization needs, overcoming the contradiction between high-dimensional data redundancy and low-dimensional information loss. This work provides a high-precision, low-complexity solution for rapid quality detection of pine nuts, with broad implications for agricultural product inspection and food safety. Full article

Antibiotics are frequently used in the poultry industry, which has led to the emergence of bacterial strains that are resistant to antimicrobial treatments. The main objectives of this research were to conduct a multimodal risk assessment, to determine the extent of contamination of chicken meat with Escherichia coli, assess the prevalence of strains resistant to extended-spectrum cephalosporins (ESC), and characterise the genes associated with resistance and virulence. A standardised procedure involving enrichment in buffered peptone water and isolation of E. coli on MacConkey agar was carried out on 100 chicken carcasses. Subsequently, the sensitivity of the strains was tested against 21 antibiotic discs. Additionally, ESBL production was detected using a double synergy test. Specific PCRs were employed to identify resistance to critical antibiotics in human medicine (such as cephalosporins, carbapenems, fluoroquinolones, and colistin), as well as the presence of virulence genes. The contamination rate of chicken meat with E. coli was 82%. The prevalence of ESC-resistant isolates was 91.2%. Furthermore, 76.5% of the isolates exhibited ESBL production, with the different beta-lactamase genes (bla_CTXM, bla_TEM, and bla_SHV). The mcr-1 gene, associated with colistin resistance, was detected in four strains (5.9%). Some isolates also carried resistance genes such as sul1, sul2, sul3, tetA, tetB, qnrB, and qnrS. In addition, several virulence genes were detected. In our study, we were able to link the expression of AMR to the iron metabolic regulatory elements using a multimodal machine learning approach; this mechanism could be targeted to mitigate the bacteria virulence and resistance. The high prevalence of ESBL-producing and multi-resistant E. coli strains in poultry presents significant human health risks, with the focus on antibiotic-resistant uropathogenic strains since poultry meat could be an important source of uropathogenic strains, underscoring the danger of hard-to-treat urinary tract infections, stressing the need for controlled antibiotic use and thorough monitoring. Full article