Search Results (11,712)

Smart retail adoption (SRA) is widely seen as a way to improve operations. But how it affects business performance (BP) is still unclear. This study builds a framework using information theory and the Technology–Organization–Environment (TOE) framework. We use data from 220 Chinese retail firms (2012–2023). Our analysis shows that SRA significantly improves BP. It does so by first reducing incomplete information (measured by analyst forecast dispersion, AFD) and then lowering uncertainty (UNC). These two factors work in sequence. Technological conditions (TECH), organizational conditions (ORG), and environmental conditions (ENV) all strengthen this effect. SRA also has strong long-term benefits. The effect is greater in non-state-owned firms, large firms, firms in central China, and those that rely mainly on offline channels. This study explains how SRA boosts BP and offers practical insights for retail transformation. Full article

Early detection of suicidal ideation and depressive risk remains a critical challenge, particularly when individuals express distress implicitly through metaphorical or obfuscated language. Existing approaches primarily rely on explicit linguistic signals, limiting their effectiveness in real-world settings. This paper proposes a unified multi-aspect transformer-based framework that integrates multi-source learning, multi-task optimization, affective feature fusion, and adversarial training to detect implicit psychological risk indicators in textual data. The model jointly learns suicidal ideation detection, depression severity classification, and perceived threat detection, while incorporating emotional representations derived from valence, arousal, and polarity signals. To improve robustness, an adversarial training strategy is employed to simulate obfuscated expressions, enhancing robustness and generalization under linguistic perturbations. Interpretability is ensured through a hybrid explainable AI approach combining attention mechanisms and SHAP-based feature attribution. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed approach achieves state-of-the-art performance (F1-score = 0.91), with statistically significant improvements over strong baselines. Additional analyses, including ablation studies, adversarial evaluation, and calibration assessment, confirm the effectiveness, robustness, and reliability of the proposed framework. These results highlight the potential of the model for deployment in high-stakes applications such as clinical triage and online risk monitoring, where early and interpretable detection of concealed psychological distress is essential. Full article

This study examines the human–nature relationship in the thoughts of Yunus Emre (d. ca. 1320) and addresses the Qur’anic positioning of humanity as laden with responsibility through the idea of amanah (entrustment), while focusing on Yunus Emre’s reflections on this concept as both a mystical stance and a moral state. His poems place humanity not as an absolute claim of ownership over the world and other beings, but rather within a relationship based on testimony, decency, and equality. He presents nature not as an object requiring protection or an area needing transformation but as a framework for contemplation and reflection in which the divine order is visible. In this context, humans’ established relationship with the world reflects a stance determined not by domination or interference but by a consciousness of limitation and a sense of moderation. By revealing the aspects of his understanding of humanity and nature that overlap with the concept of amanah in Islamic thought, this study argues that this overlap should be evaluated not as conceptual equivalence but rather in terms of mystical and moral affinity. This approach aims to demonstrate how Yunus Emre’s ideas, while not offering direct solutions to modern environmental debates, provide a historical mystical perspective that allows for a rethinking of the human–nature relationship. Full article

Withthe widespread deployment of intelligent terminals, mobile payment platforms, and Internet of Things devices, security systems are being progressively transformed from traditional transaction outcome analysis toward an intelligent perception paradigm centered on user behavior, device states, and environmental context. To address the challenges of multimodal data heterogeneity, non-independent and identically distributed data across nodes, and the difficulty of centralized modeling under privacy constraints in distributed scenarios, an artificial intelligence-driven federated multimodal security perception framework, namely FMS-LLM, is proposed. At its core, the framework introduces a Non-IID adaptive federated fusion mechanism that achieves dual-level alignment—structural alignment via parameter-level masks and semantic alignment via feature consistency constraints—to effectively mitigate cross-node distribution discrepancies. Additionally, an LLM-driven semantic enhancement module is developed, utilizing trend-guided token selection and inertia-suppression to map low-level sensing features into high-level risk semantic representations, thereby supporting logical reasoning and explainable decision-making. This framework takes user behavioral sensing data, device state information, environmental context data, and transaction behavior data as inputs, and constructs an integrated security analysis pipeline of “perception–collaboration–reasoning”. Experimental results on the distributed multimodal security perception task demonstrate that the proposed method achieves an Accuracy of 91.62%, a Precision of 91.04%, a Recall of 90.37%, an F1-score of 90.70%, and a ROC-AUC of 94.73%, consistently outperforming baseline methods including Logistic Regression, Random Forest, LSTM, the centralized multimodal deep model, FedAvg, FedProx, and MOON. Under strongly Non-IID conditions, when $\alpha =0.1$, the model still maintains an Accuracy of 88.47% and an F1-score of 87.11%, demonstrating stronger cross-node robustness. The ablation study further indicates that the complete model attains the best classification performance while reducing communication cost to 18.92 MB/Round. These results demonstrate that the proposed method can effectively fuse multi-source sensing information under privacy-preserving conditions and support intelligent security perception tasks with higher accuracy, stronger robustness, and improved interpretability. Full article

Modern enterprise breaches are no longer isolated events but coordinated, multi-stage campaigns whose success depends on the defender’s inability to translate detection into timely containment. While existing frameworks—such as attack-lifecycle models, Zero Trust architectures, and detection-driven systems—provide valuable capabilities, they lack a formal mechanism for coupling inferred adversarial state with coordinated, cross-layer enforcement. This paper presents AMNDA, an Adaptive Multi-layer, stage-aware Network Defense Architecture that operationalizes lifecycle-aware defense through explicit state-to-control mapping and executable orchestration. Adversarial progression is modeled as a probabilistic state-transition process, and inferred states are systematically mapped to synchronized controls across edge protection, identity governance, internal segmentation, and behavioral detection. A formally defined orchestration function transforms detection outputs into stage-conditioned policy updates, enforcing monotonic tightening of containment as adversarial capability escalates. AMNDA is implemented and validated in a reproducible Microsoft Azure environment. Empirical results show that stage-aligned enforcement actions execute within 1.0–3.1 s, while detection latency remains the dominant constraint, with a median of 1034 s across the validation corpus. This separation reveals a critical operational insight: in modern cloud environments, the limiting factor in lifecycle defense is not enforcement capability but detection timing. The contribution of AMNDA is therefore not a new detection technique but a formal, deployable architecture that converts attack-stage inference into coordinated, low-latency containment. By bridging lifecycle modeling, Zero Trust principles, and automated orchestration, the proposed approach establishes a practical foundation for state-aware, adaptive cyber defense. Full article

During flood disasters, on-site operations often face the “triple disruption” of network outages, power cuts and blocked roads. This renders terrestrial cellular infrastructure inoperable and disrupts communication links. Satellite internet can partially restore emergency communications thanks to its wide-area coverage and resistance to ground damage. However, limited computing power, memory and unstable bandwidth at the terminal prevent cloud-based flood segmentation from providing near-real-time situational awareness. This paper therefore proposes a lightweight semantic flood segmentation framework for emergency terminals that uses satellite internet. This comprises a parallel dual-branch design with a lightweight U-Net-style convolutional neural network (CNN) branch for local boundary details and a compact Transformer branch for global context. A dynamic gated fusion mechanism (DGFM) balances local texture and global information adaptively. Experiments on the public synthetic aperture radar (SAR) dataset Sen1Floods11 demonstrate that the hybrid architecture strikes a balance between accuracy and inference efficiency. The proposed method combines gated fusion with quality-aware training. Compared to a lightweight CNN baseline and state-of-the-art segmentation models using the same protocol, the proposed configuration (Hybrid-Gated with Quality-Aware Training) achieves the highest mean intersection over union and F1 score among the compared fusion variants, while maintaining competitive false alarm and risk-sensitive performance under deployment constraints. This aligns with the preferences of emergency decision makers. The framework provides a deployable perception module for emergency systems supported by low-orbit satellites and terrestrial networks under triple-disruption conditions. Full article

Psychosis presents significant treatment challenges, and standard Cognitive Behavioral Therapy for psychosis often faces limitations due to patient engagement issues and reliance on subjective self-reporting. The integration of Virtual Reality (VR), physiological biosensors, and artificial intelligence offers a transformative opportunity to address these challenges. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. A thorough literature search was performed across seven databases. Twelve randomized controlled trials involving 1504 participants were included to assess VR-assisted CBT, VR treatment, and AVATAR therapy. Meta-analyses showed that VR interventions significantly decreased auditory verbal hallucinations (pooled SMD = −0.24, p = 0.0011) and paranoid thoughts (SMD = −0.26, p < 0.0001) compared to control conditions. This review supports integrating multi-modal biosensors to collect real-time, objective physiological data. Such integration enables the development of AI-driven, closed-loop systems that dynamically adjust the virtual environment based on the patient’s physiological state. VR-assisted therapies effectively reduce positive symptoms of psychosis. Incorporating biosensors is a crucial step toward a data-driven approach for personalized, closed-loop psychiatric care. Future efforts should focus on large-scale clinical trials, biomarker validation, and robust ethical frameworks to ensure safe and effective implementation. Full article

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success). Full article

Accurate rice mapping is critical for grain yield estimation and food security, yet traditional methods often struggle with asynchronous data quality and the inherent statistical gap between SAR and optical signals. To bridge this gap, we propose MMDFRNet, a novel multi-modal deep learning framework that synergistically integrates Sentinel-1 SAR and Sentinel-2 optical imagery. Unlike conventional static fusion approaches, MMDFRNet features a dual-stream modality-specific encoder architecture designed to decouple structural backscattering signals from spectral reflectance. Central to this framework is the multi-modal feature fusion (MMF) module, which employs an adaptive attention mechanism to dynamically align and recalibrate features based on their reliability, effectively mitigating noise from compromised modalities. Additionally, a multi-scale feature fusion (MSF) module is incorporated to coordinate hierarchical semantic information, enhancing boundary delineation in fragmented landscapes. Extensive experiments conducted across multiple study areas in China demonstrate the superiority of MMDFRNet. The model achieves a Precision of 0.9234, an IoU of 0.8612, and an F1-score of 0.9252. Notably, it consistently outperforms state-of-the-art benchmarks (e.g., UNetFormer, STMA, and CCRNet) by margins of up to 11.72% (Precision) and 7.39% (IoU) compared to classic baselines. Furthermore, rigorous ablation studies and degradation analyses confirm the model’s robustness, verifying its ability to transform the degradation paradox into a performance booster through pixel-wise adaptive alignment. Consequently, MMDFRNet offers a promising solution for precise rice area statistics and long-term monitoring in complex agricultural landscapes. Full article

Pedestrian trajectory prediction is vital for crowd analysis and human–-robot interaction. Recent deep models enhance accuracy by modeling social interactions and scene context, but they often remain opaque and rarely address privacy risks associated with learning individualized motion patterns. We propose CAMP, a Context-Aware, Multimodal, and Privacy-preserving pedestrian trajectory prediction framework designed around a role-aligned multimodal architecture, in which trajectory representations, dynamic scene cues, and explicit spatial interaction constraints are modeled through complementary branches. In CAMP, the trajectory encoder separates shared motion regularities from individualized motion tendencies, the optical-flow encoder captures motion-centric transient scene dynamics, and the potential-field encoder provides an interpretable spatial cost prior for obstacle avoidance and social interaction modeling. A Transformer-based decoder fuses these modalities to predict future trajectory distributions. To reduce the exposure of personalized motion patterns, we apply targeted DP-SGD only to the individual branch during the private fine-tuning stage, while treating the remaining frozen components as post-processing under the stated threat model. Experiments on the ETH/UCY benchmark show that CAMP achieves competitive ADE/FDE performance under the reported setting, while its private variant DP-CAMP maintains a reasonable utility–privacy trade-off across several reported privacy budgets. Full article

Accurate classification of seal wear is essential for condition-based and predictive maintenance of hydraulic cylinders, where seal degradation can cause fluid leakage and impair normal system operation. This study investigates the adaptation of a Transformer-based audio model for classifying seal wear conditions using acoustic emission (AE) signals. Specifically, we adapt the Audio Spectrogram Transformer (AST), a convolution-free, purely attention-based model that operates directly on audio spectrograms. The Transformer architecture enables the modeling of long-range dependencies, while the model learns discriminative representations directly from AE data without relying on manually engineered features. A selective fine-tuning strategy was implemented by adding layer-freezing functionality to the AST training pipeline, enabling different freezing configurations during fine-tuning. This allowed earlier pretrained representations to be preserved while adapting the later layers to the target AE signals, thereby reducing the risk of overfitting in the small-data setting. In addition, validation-driven early stopping was implemented to further improve generalization during fine-tuning. The model was initialized with ImageNet and AudioSet pretrained weights to exploit general-purpose representations learned from large-scale datasets. The AE data were acquired under varying pressure conditions on a hydraulic test rig designed to simulate hydraulic cylinder leakage. The datasets were partitioned into fine-tuning, validation, and evaluation subsets and labeled into three wear states: unworn, semi-worn, and worn. In addition, data augmentation techniques were applied to the fine-tuning data to increase diversity and mitigate class imbalance. The adapted model achieved 97.92% classification accuracy across all wear conditions and pressure settings, demonstrating its ability to learn discriminative wear-related patterns directly from AE data. Furthermore, the framework’s versatility was further assessed on a bearing strip dataset acquired from the same hydraulic test rig. Using the same fine-tuning configuration, the model achieved 95.65% accuracy and 100% recall for the worn state. These findings highlight the potential of transformer-based architectures for data-efficient, end-to-end AE-based diagnostics across hydraulic system components. Full article

Over the last 20 years, Deep-Eutectic Solvents (DES) have been making a significant impact in the field of chemistry, with applications in nanotechnology, biomass transformation, electrochemistry pharmaceuticals and a host of other applications that includes catalysis. Considering the importance of chiral organocatalysis for the selective synthesis of drugs, pharmaceuticals and fragrances, etc. DESs were quickly harnessed as the media for carrying out organocatalytic transformations. In this review, we discuss some of the most important examples from the literature that have made an impact in the field over the last 5 years. A more recent development has been the incorporation of DESs in structured and self-organized gel-like assemblies that are known as EutectoGels. These soft structures offer a more defined and compact environment that can influence stereoselectivity by pre-organizing the reactants in three-dimensional space, and potential control the types of transition states that can be formed. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

Bone metastasis remains a major cause of morbidity in advanced cancer, driven not only by tumor–bone crosstalk but also by profound immune remodeling within the marrow. Myeloid-derived suppressor cells (MDSCs), including polymorphonuclear (PMN-MDSC) and monocytic (M-MDSC) subsets, are increasingly recognized as central effectors of this process, integrating inflammatory signals with metabolic and stromal cues to enforce immune suppression and support skeletal colonization. In this review, we synthesize current evidence that bone metastases transform the bone marrow into an “MDSC amplifier,” where vascular and endosteal niches, CXCL12-rich stromal compartments, hypoxia, and adipocyte-derived lipids collectively promote MDSC recruitment, persistence, and functional maturation. We discuss the dominant suppressive programs deployed by MDSCs in bone (e.g., arginase-1 activity, reactive oxygen/nitrogen species, and checkpoint ligand expression), and how these mechanisms converge to impair cytotoxic T-cell and NK-cell responses while fostering regulatory T-cell dominance. Importantly, because the marrow is a hematopoietic organ, bone lesions can also generate systemic consequences through myeloid spillover, providing a mechanistic basis for reduced responsiveness to immune checkpoint blockade in bone-dominant disease. We then evaluate pharmacologic strategies to target MDSCs in the context of bone metastasis, including approaches that block trafficking (e.g., CCR2/CXCR2 axes), deplete or reprogram suppressive myeloid states (e.g., STAT3-directed strategies, differentiation therapy), and disrupt bone-resorptive feedback loops (e.g., receptor activator of NF-κB ligand (RANKL) inhibition and bisphosphonates), emphasizing rational combinations and sequencing to limit marrow toxicity. Finally, we highlight emerging single-cell and spatial profiling tools that can resolve bone-specific heterogeneity in MDSCs and guide biomarker-driven, mechanism-informed therapeutic development. Full article

The rising incidence of skin cancer necessitates scalable and accurate diagnostic tools. While dermoscopy-based systems have achieved expert-level performance, clinical smartphone images pose challenges due to variability in lighting, resolution, and artifacts. Recent advances in multimodal deep learning have shown promise, yet most approaches rely on simple feature concatenation or single-model classifiers, limiting their ability to capture complex cross-modal interactions. This study aims to bridge the diagnostic gap in resource-limited settings by developing a robust multimodal framework that synergizes clinical smartphone images with structured patient metadata for automated skin cancer classification. We propose a novel hybrid architecture integrating a Swin Transformer V2 (SwinV2-Tiny) for hierarchical visual feature extraction and a Denoising Autoencoder (DAE) with PCA for robust metadata embedding. These heterogeneous modalities are fused via a Gated Attention Mechanism that dynamically weighs feature importance across streams. Classification is performed by a Heterogeneous Meta-Stack Ensemble comprising CatBoost, LightGBM, XGBoost, and Logistic Regression, designed to maximize calibration and generalization across imbalanced classes. Evaluated on the PAD-UFES-20 dataset (2298 clinical smartphone images, six diagnostic classes), the proposed framework achieves state-of-the-art performance with a macro-averaged F1-score of 0.977, accuracy of 0.978, and an AUC of 0.990. It significantly outperforms unimodal baselines and existing multimodal methods, demonstrating superior sensitivity (0.974) and precision (0.981), particularly for underrepresented malignant classes like Melanoma (F1: 0.995) and Squamous Cell Carcinoma (F1: 0.960). The integration of clinical metadata with advanced visual embeddings via gated attention significantly enhances diagnostic reliability. Comprehensive ablation studies confirm the contribution of each architectural component. This framework offers a viable pathway for deploying high-precision, AI-driven dermatological screening tools on standard smartphone devices. Full article