Search Results (3,345)

The optimisation design of agricultural machinery is shifting from offline, experience-driven engineering towards adaptive, data-driven, and closed-loop intelligent optimisation. Conventional approaches based on computer-aided engineering (CAE), empirical testing, mathematical modelling, and static multi-objective optimisation have provided an important engineering foundation, but they remain limited under unstructured field conditions involving soil heterogeneity, crop variability, climatic disturbance, and nonlinear machinery–environment interactions. This review systematically examines the evolution of intelligent optimisation design for agricultural machinery from conventional simulation-based methods to artificial intelligence (AI)- and digital twin (DT)-enabled paradigms. First, mathematical modelling, response surface methodology, discrete element method (DEM), computational fluid dynamics (CFD), multi-body dynamics (MBD), heuristic algorithms, and early AI-assisted surrogate optimisation are reviewed to clarify their contributions and limitations. Second, frontier enabling technologies are analysed, including agriculture-specific large models, generative AI, lightweight edge intelligence, deep reinforcement learning (DRL), embodied AI, federated learning (FL), and privacy-preserving computing. Third, system-level applications integrating DT and AI are discussed, with emphasis on full-lifecycle machinery optimisation, device–edge–cloud collaborative control, multi-agent fleet coordination, predictive maintenance, and Agriculture 5.0-oriented intelligent equipment systems. Key deployment bottlenecks are further identified, including sim-to-real inconsistency, virtual–physical mismatch in DTs, edge-side trade-offs among accuracy, latency, energy consumption, and cost, insufficient validation standards, and economic adoption barriers. Finally, a 2025–2030 roadmap is proposed, highlighting large-model–DT closed loops, control biomimetics, green low-carbon optimisation, and trustworthy human–machine symbiosis for sustainable Agriculture 5.0. Full article

Motor imagery (MI)-based brain–computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human–computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal–spectral–spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time–frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency. Full article

Objectives: The rise in the frequency of mental health concerns (depression and suicide) expressed on social media calls for reliable, explainable, and efficient computational methods for mental health surveillance. In this paper, we propose an interpretable framework for text-based detection of post- and community-level mental health risk on social media. Methods: The framework combines (i) Secretary Bird Optimization (SBO) for feature selection of informative linguistic and psychological features, (ii) a BERT (Bidirectional Encoder Representations from Transformers)—CNN (Convolutional Neural Network) model for post-level reasoning, and (iii) a Graph Neural Network (GraphSAGE) for community-level reasoning. The graph is estimated based on semantic similarity between posts and author relations, instead of social interactions (e.g., mentions, replies) between authors. We use SHAP and LIME for model interpretability, uncertainty, and calibration analysis to evaluate the trustworthiness of predictions. Results: The model delivers 93.1% accuracy, 0.91 F1-score, and 0.944 ROC-AUC on the eRisk and CLPsych datasets using a strict user-disjoint validation strategy. SBO lowers the number of features by about 38%, leading to better generalization. The graph-based model enables improved learning of post and user representations by capturing relational dependencies. Conclusions: Our approach offers an explainable and robust means of detecting mental health risk from text. Graph-based representations of semantic and authorship interactions enable community-level analyses, while interpretability and uncertainty estimation facilitate possible human-in-the-loop decision-making. This research does not explicitly consider a human-in-the-loop experiment. Full article

With the rapid development of Artificial Intelligence (AI) technology, human-like AI service agents have been increasingly applied in service marketing. Online travel agency (OTA) platforms provide an important application context for such service agents in consumer-facing service interactions, such as travel planning and related services. Drawing on social cognitive theory and control theory, this study examines the psychological mechanisms underlying consumers’ intentions to adopt AI service agents. One pretest and two experiments involving 521 participants were conducted to investigate the effects of the AI service agent role on consumers’ willingness to use substitute vs. assist AI. The results show that consumers are more willing to use assist AI service agents than substitute AI service agents. This effect is mediated by human identity threat and sense of control. Moreover, higher consumer technology readiness moderates these effects, mitigating the preference for assist over substitute AI service agents. This study extends the conceptual framework of AI service agents in human–computer interaction research and offers practical implications for the effective design and deployment of AI service agents in OTA applications. Full article

Accurate recognition of human emotions is crucial for human–computer interaction, and speech, as an important external manifestation of emotion, has attracted significant attention. Existing speech emotion recognition (SER) methods are predominantly based on single-task learning, which inadequately model speaker variability and other latent factors in speech, thereby limiting recognition performance. In this paper, a multi-task learning-based SER method leveraging a pre-trained acoustic model is proposed. Speech emotion recognition is treated as the primary task, while speaker recognition, gender recognition, and automatic speech recognition are introduced as auxiliary tasks. A multi-task learning framework based on hard parameter sharing is constructed to guide the model to learn shared acoustic representations that simultaneously encode emotional category characteristics, speaker identity, and other relevant information. Experiments conducted on the IEMOCAP dataset demonstrate that the proposed model achieves weighted accuracy (WA) and unweighted accuracy (UA) of 83.24% and 83.36%, respectively, under five-fold cross-validation, and 83.86% and 84.23%, respectively, under ten-fold cross-validation. In both settings, the proposed method consistently outperforms the baseline models, confirming its effectiveness in improving speech emotion recognition performance. Full article

In the human reproductive system, extracellular vesicles (EVs) have been recognized as playing a vital role in mediating cell–cell communication. They are considered critical for embryo development, implantation, gamete interaction, and fertilization. The various cargoes carried by EVs, depending on the physiological and pathological state of the cell, include proteins, lipids, nucleic acids, and mitochondrial components. EVs are recognized as critical carriers of redox-related signals and mitochondrial components, linking oxidative stress (OS) to reproductive failure and influencing gamete quality and embryo competence. Although considerable progress has been made, research remains poorly integrated, despite individual omics technologies providing valuable molecular insights. The use of multi-omics technologies, including transcriptomics, proteomics, metabolomics, and microbiome analysis, has been proposed as a global approach to understanding the complexities associated with EVs and discovering new biomarkers associated with infertility. ML and AI have been proposed to identify predictive signatures linked to ART effectiveness and reproductive outcomes, with a strong capacity to handle high-dimensional data. The review aims to provide an overview of current knowledge on EV-mediated redox–mitochondrial signaling in human reproduction, while highlighting the importance of emerging multi-omics and AI technologies for EV-mediated biomarker development. The review discusses the promise of EVs in the development of minimally invasive diagnostic approaches and therapeutic interventions, as well as the challenges in the standardization, integration, and clinical translation of EV-mediated research. In addition, the review proposes integrating computational approaches to better understand molecular pathways involved in the development of next-generation precision medicine in human reproduction. Full article

Plant leaf starch content is a critical indicator of metabolic status, yet traditional enzymatic methods are destructive, labor-intensive, and costly. This study proposes a novel non-destructive detection method using watermelon–pumpkin grafted seedlings. To optimize hardware design, 12 characteristic wavelengths were identified via competitive adaptive reweighted sampling (CARS). A portable multispectral imaging system was developed, featuring narrowband LEDs and integrated human–computer interaction software for real-time visualization. We constructed a multimodal deep learning architecture that integrates a convolutional neural network (CNN) for spatial feature extraction from RGB images, a fully connected neural network (FCNN) for spectral data, and a Transformer network for high-level feature fusion. Experimental results showed that the ShuffleNet v2-Transformer model achieved an R² of 0.956 (RMSE = 0.036) for watermelon leaves, while the EfficientNet b1-Transformer model reached an R² of 0.967 (RMSE = 0.052) for pumpkin leaves. This multimodal approach significantly outperformed conventional PLSR and single-modal CNN models, demonstrating superior ability in processing long-range dependencies within spectral–spatial data. The system enables accurate detection with a throughput of 120 samples per hour at a hardware cost approximately 90% lower than commercial multispectral cameras. This provides an efficient, low-cost solution for large-scale monitoring of plant physiological indicators in precision breeding. Full article

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning systems. This paper proposes a dynamic warning distance model that integrates mixed-traffic flow composition—comprising human-driven vehicles (HDVs), Level 2 advanced driver-assistance system vehicles (ADASVs), and automated vehicles (AVs) of Level 3 and above—within a geospatial risk propagation framework. The model introduces vehicle-type weighting coefficients to quantify response differences, incorporates interaction delays calibrated through SUMO microsimulations, and accounts for cascading reaction delays caused by abrupt HDV braking. The methodology is illustrated using a counterfactual reconstruction of the 2024 Meizhou–Dapu Expressway collapse in China (52 fatalities). Based on reconstructed traffic conditions (80% HDVs, 15% ADASVs, 5% AVs; average speed 27.5 m/s; flow 1800 veh/h), the calculated dynamic warning distance is 153 m, which is 12% shorter than the speed-matched conventional stopping sight distance of 174 m (computed under consistent wet-pavement assumptions). Sensitivity analyses reveal that warning distance decreases substantially with increasing AV penetration (to 42 m in AV-dominated scenarios, a potential reduction of up to 74% compared with the HDV-dominated baseline, provided that residual HDVs are supported by V2X-based alerting) and varies monotonically with traffic flow, demonstrating the model’s adaptive capability. The proposed framework provides a theoretical foundation for adaptive geospatial disaster warning strategies and offers practical guidance for infrastructure development in the era of mixed-traffic automation. Full article

With the rapid development of flexible electronics technology, flexible wearable sensors based on Lanthanum Strontium Manganese Oxide (La_1-xSr_xMnO₃) have garnered extensive attention in recent years due to their excellent multi-functional integration, environmental stability and biocompatibility. This review systematically analyzes the preparation methods, process optimization strategies, multi-performance integration technologies, and the expansion of the application field of La_1-xSr_xMnO₃-based flexible sensors. Firstly, the basic characteristics and sensing mechanism of the La_1-xSr_xMnO₃ material were presented, including its temperature sensitivity, strain response characteristics, and magnetoresistance effect. Secondly, the fabrication process of flexible sensors was elaborately discussed, with a focus on analyzing crucial technologies, such as laser induction and transfer printing technology. Subsequently, the strategies for regulating the electrical, thermal, and mechanical properties of materials through element doping, along with the multimodal sensing integration and signal decoupling methods, were expounded. Furthermore, the actual performance of this type of sensor in fields such as health monitoring, human–computer interaction, and extreme environment applications was summarized. Finally, the challenges and future development directions of La_1-xSr_xMnO₃-based flexible sensors are outlined, providing theoretical references for the design and optimization of next-generation flexible electronic devices. Full article

Human pose estimation requires accurate localization of body keypoints under complex backgrounds, occlusion, and diverse human postures. Existing high-resolution pose-estimation networks preserve spatial details effectively, but their static information flow limits their adaptability to different image contexts. To address this limitation, this paper proposes a context-aware hierarchical information arbitration method that dynamically regulates feature interaction at both multi-resolution fusion and residual feature refinement levels. The proposed method achieves superior performance on COCO, reaching 77.0 average precision and improving the High-Resolution Network baseline by 3.6 percentage points, with only a minor increase in model parameters. These results demonstrate that adaptive information arbitration improves pose-estimation accuracy and robustness while maintaining computational efficiency. Full article

Collaborative aircraft design environments face significant challenges in intuitive geometry manipulation and tool integration. This research develops an AI-assisted interface using the Model Context Protocol (MCP) to bridge natural language commands with established aerospace tools. The approach integrates large language models with three specialized applications for optimization, visualization, and aircraft geometry modification. Results demonstrate successful implementation, enabling designers to accomplish complex tasks such as multi-objective optimization and empennage reconfiguration through conversational prompts. While occasional AI misinterpretations required prompt refinement, the system proved effective at translating intent into precise tool operations. The study concludes that MCP provides a viable framework for creating intuitive design interfaces while maintaining accuracy via integration with domain-specific computational methods. Full article

This paper addresses the important issue of the proper management and protection of subterranean monuments. It concerns the analysis and decoding of the microclimate that is created in heritage structures, which are structures located beneath the soil or carved into rock. The aim of this study is to understand the hygrothermal processes occurring in the mass of underground structural elements, such as evaporation, condensation, water content, and heat fluxes, based on the principles of building physics. The methodology used is the following: a systematic literature review on the topic, an overview of the factors affecting the microclimate, the assessment methodology, and the simulation tools used to decode and evaluate microclimate in subterranean heritage structures; a discussion of the current gaps; and finally, a proposal for future directions for research. A review of the literature reveals that researchers worldwide have employed similar methodologies to approach this complex issue. Recordings and analyses of the microclimate inside underground monuments lead to decision-making and the formulation of actions for optimal preservation. Due to the large number of parameters involved in microclimate analysis, computer software for numerical simulation has been used in many cases. Following the review of the relevant literature in the field of study, a critical discussion concludes by proposing directions for future research on this important topic. Basic results of this research identify current gaps, problems, and limitations. These include technical and practical issues or gaps concerning lack of data for material properties and weather conditions. Another significant limitation arises from the complexity of physical interactions, as well as from the human factor, which involves the proper use of the simulation program and the correct interpretation of the calculation results. This study demonstrates that the microclimate of subterranean heritage structures is the result of complex interactions between climate, geology, architectural design, material properties, and human use. Across different geographical and cultural contexts, subterranean monuments exhibit distinct microclimatic behaviors. The comparative analysis of case studies highlights that while subterranean environments generally benefit from thermal stability, they remain highly vulnerable to moisture dynamics, ventilation changes, and external climatic coupling. Hence, there is a necessity for context-specific approaches rather than generalized conservation solutions. Decoding subterranean microclimates requires a multidisciplinary framework that combines environmental monitoring, material indicators, architectural analysis, and numerical modeling. Full article

Digital-avatar systems still provide limited control over emotionally expressive behavior in human–computer interaction, especially in Large Language Model (LLM)-based chatbots and virtual assistants with personalized visual embodiments. To address this problem, we propose Multimodal Avatar Generation (MAVAGEN), a multimodal avatar generation framework for synthesizing upper-body digital avatars with personalized appearance and controllable emotional expression. The user specifies the desired gender and age, as well as provides a short text input from which the target emotional state is inferred. MAVAGEN then retrieves an identity image from the HaGRIDv2-1M corpus and generates an avatar clip with synchronized facial expressions, hand gestures, and expressive speech. The framework uses the following six feature streams: textual features, emotion-distribution features, landmark-based pose features, depth-geometry features, RGB-appearance features, and acoustic features. In a quantitative evaluation against recent human animation methods, MAVAGEN achieves the best overall avatar quality, with FID 48.20, FVD 592.00, SSIM 0.741, Sync-C 7.40, HKC 0.929, HKV 25.30, CSIM 0.563, and EmoAcc 0.88. Ablation results show that emotion and acoustic features contribute most to emotional agreement, while landmark-based pose and depth features improve geometric and motion stability. These results support the practical use of MAVAGEN in personalized LLM-based assistants and other emotion-sensitive interactive systems. Full article

Background: Neglected diseases caused by protozoan parasites remain a major public health burden, particularly in low- and middle-income countries. Among the chemical motifs explored in antiparasitic drug discovery, guanidine-containing compounds have attracted considerable attention due to their strong cationic character, high capacity for hydrogen bonding, and versatility in interacting with biological targets. Methodology: This review summarizes advances reported in the last decade regarding guanidine derivatives with activity against pathogens associated with Chagas disease, human African trypanosomiasis, Leishmaniasis, tuberculosis, toxoplasmosis, dengue and schistosomiasis. Results: Evidence gathered from synthetic, natural, and drug-repurposing studies indicates that the guanidine, guanidine-containing and guanidine-related compounds contribute to modulating biological activity by changing electrostatic interactions, hydrogen-bonding networks, and physicochemical properties, with enzymes, nucleic acids, and membrane-associated targets essential for parasite survival. Across the analyzed studies, several emerging structure–activity relationship trends were identified, including the contribution of polycationic or dicationic architectures, the influence of halogenated or lipophilic substituents, and the dependence of biological activity on the complete molecular framework, including heterocyclic systems, macrocycles, peptide conjugates, hybrid scaffolds, and repurposed drugs. In addition to direct antiparasitic effects, certain guanidine-containing and guanidine-related compounds demonstrate immunomodulatory or host-protective properties, expanding the therapeutic relevance of this class. Despite promising in vitro results, protonation trapping, efflux pump susceptibility, and pharmacokinetic limitations such as poor oral absorption, high polarity, plasma protein binding and limited membrane permeability remain significant challenges for clinical translation. Nonetheless, the integration of medicinal chemistry, computational modeling, and biological screening continues to accelerate the identification of optimized scaffolds. Conclusions: Overall, guanidine-based compounds constitute a promising scaffold for the development of new therapeutic strategies targeting neglected parasitic diseases, and further structural optimization may enable the emergence of candidates with improved efficacy, selectivity, and drug-like properties. Full article