Search Results (18,215)

The high energy consumption and spatiotemporal thermal asymmetry of data center cooling systems have become critical bottlenecks constraining their green and sustainable development. Traditional point-type temperature sensors suffer from insufficient spatial coverage, while conventional feedback control strategies exhibit delayed responses and limited adaptability under dynamic workloads. To address these challenges, this study proposes a real-time thermal symmetry management framework for data centers based on distributed fiber optic temperature sensing and model predictive control (MPC). The proposed system employs Brillouin scattering-based distributed sensing to continuously acquire high-density temperature measurements from thousands of points along a single optical fiber, enabling fine-grained perception of the three-dimensional thermal field. On this basis, a hybrid prediction model integrating thermodynamic physical equations with a Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (TCN–BiGRU) deep neural network is developed to achieve accurate and stable spatiotemporal temperature forecasting. Furthermore, a symmetry-aware MPC controller is designed with the dual objectives of minimizing cooling energy consumption and suppressing thermal field deviations, thereby restoring temperature uniformity through rolling-horizon optimization. Experimental validation in a production data center demonstrates that the distributed sensing system achieves a measurement deviation of 0.12 °C, while the hybrid prediction model attains a root mean square error of 0.41 °C, representing a 26.8% improvement over baseline methods. The MPC-based control strategy reduces daily cooling energy consumption by 14.4%, improves the power usage effectiveness (PUE) from 1.58 to 1.47, and significantly enhances both thermal symmetry and operational safety. The Thermal Symmetry Index (TSI) decreased from 0.060 to 0.035, indicating a 41.7% improvement in spatial temperature distribution uniformity. The TSI is defined as the ratio of spatial temperature standard deviation to mean temperature, where lower values indicate better thermal uniformity; TSI < 0.03 represents excellent symmetry, 0.03–0.05 indicates good symmetry, and TSI > 0.08 suggests significant asymmetry requiring intervention. These results provide an effective and practical solution for intelligent operation, energy-efficient control, and low-carbon transformation of next-generation green data centers. Full article

Instance segmentation of 3D point clouds is a fundamental task for scene understanding in applications such as autonomous driving, robotics, and augmented reality. The inherent irregularity and sparsity of point clouds, compounded by scale variations and instance adhesion, pose significant challenges to accurate segmentation. Existing grouping-based methods are often limited by the loss of geometric details in single-path backbones and by error propagation near complex boundaries. To address these issues, a Multi-grained Dual-aware Grouping algorithm (MDGroup) is proposed, which explicitly integrates multi-grained feature representation with dual awareness of class and boundary. The algorithm features a Dual-Resolution 3D U-Net (DRNet) that preserves local geometric details while aggregating global semantics through adaptive alignment. A four-branch prediction scheme enhances semantic and offset estimation with boundary and directional cues, enabling fine-grained boundary modeling. Furthermore, a Hierarchical Adaptive Multi-grained Feature fusion framework (HAMF) achieves efficient cross-scale alignment by combining Class-Aware Dynamic Voxelization and Class-Aware Pyramid Scaling. Finally, a Boundary-Aware Weighted Aggregation mechanism (BAWA) refines instance grouping by dynamically weighting semantic confidence, geometric distance, boundary probability, and directional consistency. To extend the model to dynamic scenes, a Temporal Adaptive Gating (TAG) module is introduced to leverage historical frame correlations. Extensive experiments on the ScanNet v2, S3DIS, STPLS3D, SemanticKITTI, LiDAR-Net, and OCID benchmarks demonstrate that MDGroup achieves state-of-the-art performance among grouping-based methods, particularly on small objects, complex boundaries, and dynamic environments. Full article

Precise position estimation is essential for mobile robots to operate autonomously. In industrial environments that require precision tasks such as docking—including structured indoor facilities such as hospitals, factories, and warehouses—highly accurate localization is often necessary, with accuracy demands ranging from the centimeter to millimeter level depending on the application. Various registration-based localization algorithms have been investigated in response to this requirement. However, fundamental limitations exist, such as a high dependency on initial position estimates, increased computational load, and difficulties in ensuring real-time performance in large-scale environments. The proposed method introduces a dynamic noise adaptation (DNA) technique applicable to the Monte Carlo localization (MCL) algorithm, a particle filter-based localization method, to overcome these limitations. The proposed algorithm improves real-time localization accuracy and estimation consistency by dynamically optimizing the motion noise of MCL using the non-penetration rate, which can serve as a reliability metric in light detection and ranging (LiDAR)-based localization. The proposed algorithm was evaluated in comparison with the expansion Monte Carlo localization 2 (EMCL2) algorithm in both simulation and real-world environments. In the simulated environment, the proposed method achieved lower localization error with respect to the ground truth compared to EMCL2 and the improved adaptive Monte Carlo localization (AMCL) method incorporating a virtual motion model. In real-world experiments, localization performance was evaluated through comparison with a reference trajectory, and the proposed algorithm consistently demonstrated reduced localization error. Full article

Short bowel syndrome (SBS) develops when the remaining intestine is unable to sustain adequate nutrient and electrolyte absorption following extensive bowel resection. The condition is characterized by malabsorption and significant fluid losses which lead to dehydration and progressive weight loss, thus promoting patient dependence on parenteral fluids or nutrition. After an initial acute phase marked by accelerated intestinal transit and gastric hypersecretion, long-term clinical outcomes are largely determined by the capacity of the remaining bowel for intestinal—a sustained process of structural, functional, and molecular remodeling that enhances absorptive efficiency and restores fluid and nutrient homeostasis. This review summarizes the key histological and cellular features of the adaptive response, including crypt and villus remodeling, mucosal hyperplasia, and smooth muscle hypertrophy, and integrates emerging concepts in crypt biology that define the dynamic cross-talk between intestinal stem cells and the mesenchymal niche, together with their upstream regulatory pathways. Full article

This paper extends the data-driven computational mechanics paradigm to nonlinear materials characterized by the Duncan–Chang Elastic-Bulk (E-B) constitutive model. Unlike in linear elastic systems, geotechnical media exhibit stress-dependent tangent moduli and non-convex constitutive manifolds. We propose a recursive nested data-driven solver that dynamically adapts the phase-space distance metric to account for pressure-dependent hardening. A rigorous mathematical analysis of convergence is provided, demonstrating that the solver’s performance is governed by the local transversality between the conservation law constraint set and the nonlinear material manifold. We derive explicit error bounds that couple spatial discretization resolution with material data density. Numerical experiments using triaxial test data from a high-altitude region validate the theoretical predictions, showing that the proposed scheme demonstrates convergence in single-element tests. Full article

Urban lighting systems contribute significantly to energy consumption and light pollution, raising environmental and societal concerns. This paper explores the potential of Artificial Intelligence (abbreviation: AI)-driven adaptive urban lighting as a sustainable solution, framed within a multi-level perspective on socio-technical transitions. At the landscape level, increasing urbanization and global sustainability targets exert pressure for energy-efficient practices, while traditional street lighting regimes remain largely rigid and resource-intensive. At the niche level, we propose a novel adaptive lighting system integrating real-time Internet of Things (abbreviation: IoT) sensor data and machine learning algorithms to dynamically adjust illumination based on traffic, pedestrian activity, weather conditions, and ambient light. Studies demonstrate that the proposed approach can significantly reduce energy use while minimizing light pollution, without compromising safety or visibility. The results indicate that such niche innovations, supported by AI and renewable energy integration, have the potential to influence broader regime change and contribute to sustainable urban development. This research highlights the importance of combining technological innovation with socio-technical frameworks to address pressing urban environmental challenges, offering insights for policymakers, urban planners, and energy managers seeking to balance efficiency, safety, and ecological impact. Full article

With the transition from Industry 4.0 to Industry 5.0, human–robot collaborative assembly (HRCA) has progressed from physical copresence to cognitive integration and knowledge sharing. Digital twins (DTs) serve as enabling technologies that connect physical and virtual spaces. Support is provided for dynamic, safe, and human-centered collaboration. This study presents a systematic review of the research progress and practical applications of DT-enabled HRCA. First, conceptual boundaries between HRCA and general human–robot collaboration (HRC) in manufacturing are defined. Core elements of DT-driven state perception, task planning, and constraint modeling are described. Second, four task-allocation paradigms are classified and summarized, including optimization-based, constraint satisfaction-based, data-driven intelligent, and large language model (LLM)-assisted approaches. Applicable scenarios are identified. Third, the effects of collaboration modes and interaction modalities on planning logic are analyzed. Collaboration modes are categorized as parallel, sequential, and tightly coupled. Interaction modalities are grouped into AR-based explicit interaction, implicit intention perception, and multimodal fusion. Fourth, cross-domain application characteristics and engineering bottlenecks are summarized. Target domains include precision assembly, disassembly and remanufacturing, and construction on-site operations. Finally, four core challenges are distilled, including dynamic uncertainty, multi-objective conflicts, human factor adaptation, and system integration. Four future directions are outlined: LLM-enabled adaptive planning, safety–efficiency co-optimization, personalized collaboration, and standardized integration. The proposed technology–application–challenge–outlook framework is intended to provide a theoretical reference and practical guidance for transitioning HRCA from laboratory prototypes to large-scale industrial deployment. Full article

Cancer immunotherapy has transformed modern oncology, yet durable responses remain limited for many patients due to immune exclusion, adaptive resistance, and tumor heterogeneity. Oncolytic viruses (OVs) have emerged as a novel class of immunotherapeutics that unify direct tumor cytolysis with stimulation of antitumor immunity. By inducing immunogenic cell death (ICD) and releasing tumor-associated antigens (TAAs), OVs remodel the tumor microenvironment (TME) into an inflamed and immune-permissive niche capable of enabling systemic immune activation. Rapid advances in viral engineering have strengthened the translational potential of OVs through tumor-selective gene deletions, tumor-specific promoters, microRNA-based detargeting, and receptor-retargeting strategies that collectively enhance safety, specificity, and intratumoral propagation. Next-generation OVs are increasingly “armed” with immunostimulatory payloads—including cytokines, chemokines, checkpoint inhibitors, bispecific T-cell engagers, and suicide gene systems—allowing localized immune modulation with reduced systemic toxicity. These innovations have propelled significant clinical progress, exemplified by the approvals of talimogene laherparepvec (T-VEC), G47Δ, and H101, and have driven a surge of combination trials integrating OVs with immune checkpoint blockade, adoptive cell therapies, radiotherapy, and targeted therapies to overcome multilayered tumor immune resistance. Despite this momentum, clinical implementation remains challenged by antiviral immunity, heterogeneous viral distribution, stromal barriers, and dynamic interferon (IFN) signaling in the TME. Emerging delivery approaches, including carrier cell systems, nanotechnology-enabled viral shielding, and synthetic virology platforms, offer promising solutions to these limitations. Oncolytic virotherapy is rapidly evolving into a multifunctional immunotherapeutic platform capable of reshaping antitumor responses at both local and systemic levels. By integrating advanced viral engineering with rational combination strategies and innovative delivery technologies, OVs hold substantial potential to overcome current barriers in cancer immunotherapy and advance precision oncology. Continued translational research will be essential to fully harness their therapeutic impact and broaden their clinical applicability. Full article

The efficient utilization of solar energy largely depends on the capability of a photovoltaic system to operate at its maximum power point under variable irradiance and temperature conditions. In this context, a control strategy that combines a sliding mode control scheme with a Perturb-and-Observe-based maximum power point tracking (MPPT) algorithm with adaptive step size is proposed and applied to a DC–DC boost–buck converter. The proposed approach aims to improve the dynamic stability of the system, ensure robustness against model uncertainties, and enhance conversion efficiency. The MPPT algorithm employs an adaptive perturbation step that reduces steady-state oscillations and accelerates convergence toward the optimal operating point, while the sliding mode controller guarantees accurate tracking of the converter voltage reference under external disturbances. Simulation and experimental results validate the effectiveness of the proposed strategy, achieving an overall efficiency of 99.42% and a startup time of 180 ms in the implemented version. These results confirm improved transient response, reduced steady-state error, and high efficiency compared to competing control strategies reported in the literature. Full article

Augmented reality (AR) is increasingly adopted in industrial environments as a tool for improving employee training and supporting complex assembly operations. The purpose of this study was to investigate the design, implementation, and strategic potential of AR-based work instructions using Microsoft HoloLens 2 in a real manufacturing environment. The study proposes and applies an integrated evaluation framework combining direct observation, performance evaluation, semi-structured interviews, quantitative SWOT analysis, PDCA-based process assessment, and economic cost analysis to assess AR-based training in a real manufacturing environment. AR training was implemented through Microsoft Dynamics 365 Guides for a standardized assembly procedure and evaluated with respect to training efficiency, user interaction, and feasibility of deployment. The results indicate improved task guidance consistency and descriptive performance indicators, suggesting enhanced training support under real production conditions. The SWOT analysis identified a favorable SO strategic position, highlighting strong internal capabilities and promising external opportunities for further deployment. The cost analysis shows that AR-based training becomes economically advantageous when applied to a larger number of trainees, despite high initial investment costs. Overall, the study demonstrates that AR-based training, when evaluated through a structured strategic and economic framework, represents a promising and strategically advantageous approach for industrial education, provided that ergonomic challenges, user adaptation, and financial constraints are systematically addressed. Full article

Objectives: Although behavioral studies suggest that syntactic comprehension is relatively preserved in healthy aging, the underlying neural mechanisms remain a subject of intense debate. This review aims to synthesize neuroimaging and electrophysiological evidence to clarify how the aging brain reorganizes to maintain language resilience. Methods: A systematic search was conducted across multiple databases such as PubMed and Web of Science. Twenty-three relevant empirical studies meeting our inclusion criteria were identified. The synthesis focused on regional activation patterns, functional connectivity, and temporal dynamics during syntactic processing in older adults compared to younger controls. Results: The review revealed four key findings. First, the core left-lateralized frontotemporal language network remains resilient during syntactic processing in older adults. Second, age-related changes in functional connectivity within the core network are heterogeneous, with evidence for both reduction and preservation. Third, right-hemisphere homologues are increasingly recruited, but its functional significance is condition-dependent, serving both compensatory and non-compensatory roles. Fourth, older adults increasingly engage domain-general cognitive control regions, such as the dorsolateral prefrontal cortex and pre-supplementary motor area, to support syntactic processing under high cognitive loads. Conclusions: On the basis of these findings, we propose the Graded Compensation and Cognitive Scaffolding (GCCS) model which posits that language resilience is maintained through a graded and condition-dependent adaptation of neural resources. This study critically evaluates the current literature and highlights the need for more methodologically rigorous studies to better understand the effects of aging on syntactic processing and its neural basis. Given the limited number of eligible studies, the findings of this review should be interpreted with caution. More well-powered, longitudinal research is needed to uncover the trajectory of neural reorganization during syntactic processing in older adults. Full article

Vision–language–action (VLA) models often suffer from limited robustness in long-horizon manipulation tasks—where robots must execute extended sequences of actions over multiple time steps to achieve complex goals—due to their inability to explicitly exploit structural symmetries and to react adaptively when such symmetries are violated by environmental uncertainty. To address this limitation, this paper proposes PI-VLA, a symmetry-aware predictive and interactive VLA framework for robust robotic manipulation. PI-VLA is built upon three key symmetry-driven principles. First, a Cognitive–Motor Synergy (CMS) module jointly generates discrete and continuous action chunks together with predictive world-model features in a single forward pass, enforcing cross-modal action consistency as an implicit symmetry constraint across heterogeneous action representations. Second, a unified training objective integrates imitation learning, reinforcement learning, and state prediction, encouraging invariance to task-relevant transformations while enabling adaptive symmetry breaking when long-horizon deviations emerge. Third, an Active Uncertainty-Resolving Decider (AURD) explicitly monitors action consensus discrepancies and state prediction errors as symmetry-breaking signals, dynamically adjusting the execution horizon through closed-loop replanning. Extensive experiments on long-horizon benchmarks demonstrate that PI-VLA achieves state-of-the-art performance, attaining a 73.2% average success rate on the LIBERO benchmark (with particularly strong gains on the Long-Horizon suite) and an 88.3% success rate in real-world manipulation tasks under visual distractions and unseen conditions. Ablation studies confirm that symmetry-aware action consensus and uncertainty-triggered replanning are critical to robust execution. These results establish PI-VLA as a principled framework that leverages symmetry preservation and controlled symmetry breaking to enable reliable and interactive robotic manipulation. Full article

Propose an enhanced LoRA (Low-Rank Adaptation) MoE (mixed expert) architecture, SLoRA (Enhanced LoRA MoE Architecture), aimed at addressing the key problem of efficient parameter fine-tuning in multitasking scenarios. Given the high cost of traditional full fine-tuning as the parameter size of visual language models increases, and the limitations of LoRA as a popular PEFT (parameter-efficient fine-tuning) method in multitasking, such as inadequate adaptability and difficulty in capturing complex task patterns, as well as the catastrophic forgetting and knowledge fragmentation challenges faced by existing research on integrating mixed expert (MoE) mechanisms into LoRA, SLoRA utilizes orthogonal constraint optimization to reduce disturbance to existing knowledge through constraint solution space initialization, alleviating catastrophic forgetting (old task accuracy retention rate reaches 92.4%, 16.1% higher than LoRA), and an optimized MoE structure that includes general experts (retaining pre-trained knowledge) and task-specific experts (dynamic routing adaptation tasks) to enhance multitask adaptability. Experimental results show that in commonsense reasoning tasks, SLoRA’s accuracy is 9.0% higher than LoRA and 3.7% higher than AdaLoRA on the WSC dataset, and its F1 score is 7.7% higher than LoRA and 2.9% higher than AdaLoRA on the CommonsenseQA dataset; in multimodal tasks, its average score is up to 15.3% higher than LoRA, demonstrating significant advantages over existing methods. Full article

Oncogenic kinase pathways, including PI3K/AKT, RAS/ERK/MAPK and JAK/STAT, are central drivers of cancer cell proliferation, survival and metastatic potential. However, excessive activation of these pathways imposes intrinsic cellular stresses, such as oncogene-induced senescence, DNA damage responses and apoptosis. Recent evidence reveals that cancer cells mimic immunoregulatory programs to mitigate these stresses by ectopically expressing inhibitory receptors traditionally found on hematopoietic cells. These receptors recruit phosphatases such as DUSPs, SHP1, SHIP1 and PP2A, which directly counteract hyperactivated kinases. Acting as dynamic homeostatic buffers, these phosphatases attenuate oncogenic signaling intensity, maintaining a balance that permits continued proliferation while preventing the activation of fail-safe tumor-suppressive mechanisms. This mechanism appears particularly relevant in metastasizing cancer populations, where elevated co-expression of inhibitory receptors and phosphatases correlates with survival advantage and adaptation under selective pressures. Understanding the dual roles of phosphatases, not only as classical tumor suppressors but also as modulators of signaling homeostasis, provides insight into cancer cell adaptation to oncogenic stress. Targeting the phosphatase–inhibitory receptor axis may selectively destabilize this balance, exposing vulnerabilities in aggressive, resistant or metastatic cancer cells. This review highlights emerging evidence for the phosphatase-mediated buffering of oncogenic kinase signaling, the molecular mechanisms underlying inhibitory receptor engagement and the clinical implications for tumor progression and therapy resistance. Full article

The development and deployment of robust technical solutions for sustainability improvement have become increasingly critical in response to growing environmental and social pressures while maintaining economic viability, particularly in industrial systems that require multi-stage technology implementation. Identifying such solutions requires the systematic treatment of significant uncertainties that affect sustainability-related decision-making. Among these, epistemic uncertainty, arising from incomplete or imperfect knowledge, is inherently subjective and, in principle, reducible. Fuzzy set theory provides an effective and well-established framework for representing and managing epistemic uncertainty in sustainability analysis. In this work, a fuzzy decision-making framework is proposed to support multi-stage technology development and deployment for dynamic sustainability performance improvement in industrial systems. The framework integrates comprehensive sustainability assessment with fuzzy representations of epistemic uncertainty to enable consistent comparison of alternative strategies at each implementation stage. It identifies the most appropriate strategy at each stage while ensuring alignment with long-term sustainability objectives. The proposed approach functions as a decision-support tool for guiding adaptive, stage-wise technology deployment under uncertainty. A case study of a nickel electroplating system is presented to demonstrate the applicability and effectiveness of the methodology. Full article