Search Results (759)

Space energy supply is critical for human space exploration, serving as the foundation to support long-term space missions and future permanent settlement beyond Earth. To date, humanity has developed a variety of technologies for space energy supply. However, due to the constraints of the space environment and the diversity of energy sources, the energy supply technologies adopted by space exploration missions mainly depend on the feasibility of energy acquisition. This review presents a systematic review of the technical principles, power supply devices, and practical applications of space energy supply systems. First, this review summarizes the technologies for space-based solar power generation and energy storage, as well as strategies for improving the efficiency of solar power generation in space. Next, an overview of dynamic power generation technologies and static power systems for space thermal energy is investigated, along with a performance evaluation comparing these two types of systems. Subsequently, the work reviews space nuclear power systems based on thermoelectric generation technology, discusses recent advancements in nuclear fusion research, and analyzes the feasibility of utilizing helium-3 (³He) fusion technology on the Moon. Finally, to address the challenges associated with the storage and transportation of space energy, the review also introduces the applications of battery and fuel cell technologies in space. This review also discusses the technical challenges faced by space energy supply systems and explores future development prospects, aiming to provide a reference for the comprehensive development and utilization of space energy in the future. Full article

Recognizing human actions from static images in complex industrial environments remains challenging due to insufficient feature representation and high computational complexity. This issue is particularly critical in power-grid safety monitoring, where improper worker postures (e.g., bending, climbing, falling) can lead to severe accidents and personal injuries, necessitating automated monitoring systems that operate reliably on resource-constrained edge devices. This study proposes a bio-inspired lightweight recognition framework that integrates an improved YOLO-Pose model with a gated recurrent unit (GRU) network. The scientific motivation is grounded in the observation that the human musculoskeletal system achieves highly efficient motion perception through three key mechanisms: hierarchical muscle coordination providing intrinsic rotation invariance, proprioceptive feedback enabling real-time error correction, and selective neural gating reducing redundant information transmission. These biological principles directly inspire our technical contributions: polar-coordinate encoding provides rotation invariance, three-stage filtering mimics proprioceptive feedback, and GRU gating mirrors selective information propagation. Unlike prior approaches that treat pose-based action recognition as a generic computer vision problem, this work explicitly incorporates anatomical structural constraints into the computational pipeline. The framework addresses three research gaps: (1) existing methods lack biomechanically derived invariance properties; (2) GCN-based approaches use fixed topologies that fail to adapt to occlusion patterns; (3) the trade-off between model complexity and accuracy remains unsatisfactory for edge deployment. Experiments on the self-constructed SKPose dataset demonstrate that the proposed method achieves 95.04% accuracy, outperforming ST-GCN by 3.67 percentage points and 2s-AGCN by 1.94 percentage points, with an inference speed of 48 FPS on 8.7 M parameters in underground power-grid environments and provides practical support for biomimetic perception systems and industrial safety monitoring. Full article

Background: Extremely low-frequency electromagnetic fields (ELF-EMFs), generated mainly by power infrastructure and household devices, have raised scientific interest due to their potential impact on the endocrine system. Animal research consistently shows effects on melatonin secretion, stress hormone levels, thyroid activity, and reproductive function—largely mediated by oxidative stress and calcium ion imbalance. In contrast, human studies remain inconsistent, often hindered by methodological limitations and insufficient exposure characterization. Objective: This review synthesizes experimental and epidemiological studies examining low-frequency electromagnetic field exposure (≤100 kHz) and its influence on hormonal regulation. Methods: A bibliometric analysis highlights focused interest on specific endocrine targets, particularly the pineal gland. Importantly, many experimental studies use field strengths above those found near high-voltage power lines, limiting direct applicability. Conclusions: While a definitive causal link has not been established, the widespread exposure to low-frequency electromagnetic fields justifies precautionary considerations. Several important research gaps remain, many of which are identified in this review. The topic of low-frequency electromagnetic field effects on the endocrine system requires more rigorous, long-term human studies with accurate exposure assessment. Full article

This is a conceptual paper on next-generation digital trade that proposes a multi-layered taxonomic integration of Web 3.0, decentralized e-commerce, and the emerging paradigm of Agentic Commerce. While current literature often conflates technological infrastructure with institutional governance, this paper utilizes a bibliometric diagnostics and Natural Language Processing (NLP) BERT clustering of 25 core empirical studies to delineate these boundaries. We introduce the “Semantic Web of Retail” as a foundational data layer, arguing that it is a structural necessity for the Machine-to-Machine (M2M) economy, where autonomous AI agents, or “synthetic shoppers,” execute transactions on behalf of human principals. Our results indicate that while Web 3.0 provides the technological toolkit for programmable ownership, decentralized e-commerce dictates the institutional logic required for trustless verification. Furthermore, we identify a “Shopper Schism” in consumer behavior, where the delegation of economic power to algorithms introduces novel financial risks, including oracle vulnerabilities and principal–agent moral hazards. The study concludes that integrating semantic interoperability with decentralized transaction rails is essential for mitigating systemic risks and enabling secure, autonomous digital markets, and it formalizes the ‘Shopper Schism’ as a novel principal–agent configuration unique to agentic markets. Full article

Human mesenchymal stem/stromal cells (MSCs) have attracted significant interest in regenerative medicine due to their self-renewal capacity, immunomodulatory functions, multipotency, and relative ease of isolation and expansion. However, several limitations restrict their clinical application, including cellular heterogeneity, challenges in large-scale expansion, and poor in vivo persistence after transplantation. Systemically administered MSCs are rapidly cleared because of limited adhesion, short survival time, and inefficient extravasation, resulting in suboptimal therapeutic efficacy. To overcome these challenges, various strategies have been developed, such as hypoxic preconditioning, biomaterial-based approaches, and genetic modification. Among these, genetic modification represents a particularly powerful and versatile strategy, as it enables targeted enhancement of specific functional properties of MSCs and even the introduction of novel therapeutic capabilities. In this review, we summarize recent advances in genetically engineered MSCs and categorize these approaches into four functional domains: migration, adhesion, secretion, and survival. We further discuss their therapeutic outcomes across diverse disease models in vivo. Collectively, genetic modification substantially enhances the intrinsic therapeutic potential of MSCs and represents a promising direction for the development of next-generation cell-based therapies. Full article

Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis, and acute respiratory infections remain a major global health challenge due to their complex pathophysiology and limited therapeutic options. Conventional 2D cultures and animal models have provided foundational insights; however, they often fail to accurately replicate the human lung’s intricate architecture, immune interactions, and patient-specific variability. Recent advances in vitro technologies have transformed pulmonary research, enabling the generation of physiologically relevant and translational disease models. The review highlights the progression of lung research platforms from traditional monolayer cultures to advanced systems such as air–liquid interface models and 3D lung organoids. These cutting-edge models more effectively mimic the biochemical, mechanical, and spatial microenvironment of the respiratory system, enhancing the fidelity of disease modelling and drug screening. In parallel, the integration of computational modelling and artificial intelligence (AI) has emerged as a powerful synergistic approach. AI-driven analytics facilitate high-throughput imaging, biomarker discovery, and patient-stratified therapeutic prediction, while computational tools simulate disease networks, mechanobiological interactions, and pharmacological responses. The convergence of these technologies supports a deeper understanding of pulmonary disease progression and accelerates the development of precision therapeutics. Collectively, this review underscores the transformative potential of combining in vitro lung models with advanced computational and AI methodologies. This synergy not only improves translational relevance and reduces reliance on animal testing but also paves the way for personalised interventions that better address the complexity of human pulmonary disease. Full article

Background: Corneal organoids derived from pluripotent stem cells have emerged as powerful tools for studying corneal development, disease modeling, and regenerative medicine applications. While previous protocols have successfully generated corneal tissue structures, there remains a need for three-dimensional models that recapitulate the complex cellular architecture and diversity of native human cornea. Methods: We developed a modified spontaneous three-dimensional corneal organoid model using human embryonic stem cells (hESCs) through an adapted Self-formed Ectoderm Autonomous Multi-zone (SEAM) protocol. hESCs were cultured as spheroids in ultra-low-binding plates under normoxic conditions and differentiated over 7–8 weeks. Organoids were characterized using immunofluorescence staining for corneal-specific markers and single-cell RNA sequencing to assess cellular composition and gene expression patterns. Results: Approximately 20% of organoids developed transparent regions characteristic of corneal tissue by day 30 of differentiation. Immunofluorescence analysis revealed spatially organized expression of corneal markers, including ZO-1 and E-cadherin in the outermost epithelial layers, P63α-positive putative limbal stem cells at the epithelial–stromal interface, vimentin-positive stromal cells in the interior, and laminin-1 deposition that suggests Bowman’s membrane formation. The organoids expressed cornea-specific keratins (K3, K12, and K15) and the master regulator PAX6 in appropriate cellular compartments. Single-cell RNA sequencing identified 18 distinct cell clusters, including three corneal epithelium subclusters with differential expression of MUC16, KRT12, and ΔNp63α, two stromal populations with distinct inflammatory profiles, and a corneal endothelium cluster. Transcriptomic analysis confirmed expression of key corneal genes, including AQP3, CDH1, multiple keratins, mucins, and extracellular matrix components (HAS2, CD34, CD44, COL8A1, and KERA). Conclusions: This three-dimensional spheroid-based putative corneal organoid model successfully recapitulates the multilayered architecture and cellular diversity of human cornea, including stratified epithelium, putative limbal stem cells, stroma, and endothelium in spatially appropriate arrangements. The model demonstrates molecular signatures consistent with native corneal tissue and provides a valuable platform for studying corneal development, disease mechanisms, and potential therapeutic applications. Future optimization to improve organoid formation efficiency and functional maturation will enhance the utility of this system for both basic research and translational medicine. Full article

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

I recently introduced the concept of eco-cognitive openness and situatedness to explain how cognitive systems—human or artificial—dynamically interact with their environments to generate information and creative outputs through abductive cognition. Humans display high eco-cognitive openness, integrating tools and cultural contexts through “unlocked strategies” that also enable exceptional creativity. By contrast, generative AI like LLMs operates via “locked strategies” based on pre-existing datasets with limited real-time interaction, which constrains higher creativity. Although LLMs surpass humans in many cognitive tasks, they lack the openness required for truly advanced abductive performance. Notably, most human cognition is repetitive and imitative—humans themselves often resemble “stochastic parrots.” In this sense, LLMs reveal human intellectual poverty more than they expose flaws in artificial intelligence. I will illustrate how LLMs can act as powerful enhancers of human performance while simultaneously threatening our most distinctive prerogative: creativity. Future human–AI collaboration could expand our eco-cognitive openness, but demands vigilant oversight to counter bias and so-called overcomputationalization. GenAI can serve as an epistemic mediator toward unlocked creativity only if humans maintain agency and embed its outputs in broader socio-cultural frameworks. My greatest concern is that ethical and legal safeguards will remain ineffective in practice, resulting in mere “ethics washing” and “law washing” without genuine enforcement. Full article

Background/Objectives: Population pharmacokinetic (PopPK) modeling in NONMEM requires iterative, expertise-dependent workflows. Naïve zero-shot prompting of general-purpose large language models (LLMs) typically produces NONMEM code that fails to execute. This study introduces PKGPT, a recursive agentic LLM system designed to automate NONMEM-based PopPK model development and benchmarks its performance against human expert models. Methods: PKGPT, powered by Google’s Gemini 3.0 Flash, embeds pharmacometrics expertise into phase-specific expert-agent prompts orchestrated across five sequential phases: base model establishment, structural diagnostics, overfitting reduction, random-effects optimization, and covariate analysis. The system recursively executes NONMEM, parses outputs, and iteratively refines control streams. PKGPT was evaluated on three public datasets (warfarin, theophylline, and tobramycin) and benchmarked against independently developed human expert models. Results: PKGPT consistently produced executable, converging NONMEM models across all three datasets. In warfarin, both PKGPT and the human expert selected a one-compartment oral structure (ADVAN2), but the expert achieved a lower OFV (294.41 vs. 484.43) via covariate scaling. In theophylline, PKGPT produced parameter estimates close to the expert solution (Ka = 1.59 vs. 1.46 h⁻¹; CL = 0.0399 vs. 0.0404 L/h/kg). In tobramycin, PKGPT correctly identified a two-compartment structure but produced physiologically implausible peripheral volume estimates (V2 = 149 L vs. expert’s 13.2 L). Across datasets, PKGPT did not identify clinically established covariates, and run-to-run reproducibility was variable. Conclusions: PKGPT substantially improves the robustness and usability of LLM-generated NONMEM code compared with naïve zero-shot prompting, accelerating model drafting and iterative refinement, but physiological plausibility and clinical interpretability still require a human-in-the-loop oversight. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

This paper presents a comprehensive experimental assessment of electromagnetic field (EMF) exposure dynamics during the transition from IEEE 802.11ax (Wi-Fi 6) to IEEE 802.11be (Wi-Fi 7). Using a human-centric experimental setup, we evaluate the impact of Wi-Fi 7’s core innovations—4096-QAM modulation, 320 MHz bandwidth, and Multi-Link Operation—under iPerf3-controlled high-traffic conditions. A key contribution of this study is the analysis of multi-client influence, comparing EMF emission profiles when one versus two devices are active. Our results reveal a significant paradigm shift: while Wi-Fi 7 generates higher near-field peaks (up to 955.92 mV/m in MLO mode at 20 cm) to sustain high-order modulation, it exhibits an aggressive spatial decay, with E-field intensity collapsing by up to 76.6% at one meter. We demonstrate that the transition from a single-client to a dual-client configuration significantly alters the stochastic nature of the field, increasing the probability of transient high-power events, as characterized by our Complementary Cumulative Distribution Function (CCDF) framework. The findings confirm that Wi-Fi 7’s performance gains are decoupled from long-range exposure; the high-intensity field remains strictly localized, providing a natural safety buffer. This study provides new experimental vista into how next-generation WLAN systems trade near-field strength for far-field safety, maintaining compliance with international limits while supporting multi-device gigabit connectivity. Full article

The aim of this study is to evaluate the biomechanical response of a seated operator subjected to whole-body vibrations generated by two agricultural tractors with different propulsion systems: a diesel model (TD80D) and an electric prototype (TE-0). An integrated experimental–numerical approach was employed, combining triaxial accelerometer measurements under real operating conditions (constant speed of 5 km/h on unprepared terrain) with random vibration response simulations performed in Altair SimSolid. The excitation input for the numerical model was defined using frequency-dependent power spectral density (PSD) functions derived from experimentally measured acceleration signals and scaled to a representative global RMS value. The analysis focused on the distribution of mechanical stress in key anatomical regions of a virtual human dummy in a seated posture, including the foot sole, knee, lumbar region, and head. The results indicate that, under the analysed conditions, the electric tractor (TE-0) exhibits improved vibration attenuation, leading to significant reductions in mechanical stress across all analysed regions, with decreases of up to 56.3% at the foot sole, 50.0% at the knee, 53.3% in the lumbar region, and 91.1% at the head compared to the diesel tractor (TD80D). These findings highlight the relevance of integrating experimental measurements with numerical simulation for assessing operator exposure to vibrations and suggest that electric tractor configurations may provide improved biomechanical comfort under the analysed operating conditions. Full article

The evolution toward sixth-generation (6G) networks envisions humans as active nodes within a fully interconnected digital ecosystem, supported by data collected from in-body and on-body sensors. Since many of these devices are not equipped to connect directly to 6G networks, Wireless Body Area Networks (WBANs) serve as an essential intermediate layer. However, conventional radio-frequency technologies face limitations in terms of energy efficiency, security, and data integrity, motivating the adoption of lightweight security mechanisms. Physical Layer Security (PLS), and in particular Physical Key Extraction (PKE), offers a promising solution by enabling legitimate devices to derive shared cryptographic keys from the reciprocal properties of the communication channel. Galvanic coupling (GC) communication has recently emerged as an on-body transmission technology alternative to radio-frequency (RF), which exploits low-power electrical signals propagating through biological tissue. Building on prior feasibility studies, this work proposes a PKE framework tailored to GC channels, integrating a lightweight key reconciliation method, based on Hamming (7,4) error-correction codes, and evaluating system performance through dedicated reliability and security Key Performance Indicators (KPIs). Results reveal a trade-off shaped by electrode placement and channel quantization parameters. Among the ones tested, the optimal configuration is achieved with a 3 cm transverse inter-electrode spacing at both transmitter and receiver, and a 3 cm longitudinal separation between transmitter and receiver, by quantizing the channel impulse response with two quantization bits. While this work focuses on validating the method in controlled conditions in order to establish a reliable study framework, future developments will focus on enhanced reconciliation, privacy amplification, and analysis of the GC channel considering physiological and environmental variations. Full article

Surface electromyography (sEMG) signals are important for assessing muscle activity, neuromuscular behavior, and movement stability. sEMG signals are widely used in athlete performance monitoring and human–machine interface applications. However, existing methods have limitations in classification, accuracy and generalization across users. In this study, a real-world dataset was generated from 30 professional wrestlers using an 8-channel system based on 10 physical movements and technical elements. Nine time-domain and energy features, mean absolute value (MAV), integrated EMG (IEMG), root mean square (RMS), simple square integral (SSI), fourth power (4POW), wavelength (WL), difference absolute standard deviation (DASDV), variance (VAR), and average amplitude change (AAC), were systematically evaluated separately and in combination. Five classifiers were compared: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Neural Networks (NNs). The models were evaluated for accuracy, sensitivity, specificity, positive predictive value, and F1-score. The generalization ability was analyzed through cross-subject (24/6) and cross-session validation protocols. The nine feature combinations achieved the highest classification accuracy of 97.8% with the RF algorithm. The proposed approach can serve as a practical basis for real-time muscle activity monitoring, movement classification, and rehabilitation systems. Full article