Search Results (8,681)

Human Activity Recognition (HAR) from inertial sensors must run continuously on battery-powered wearables under tight latency, memory, and energy budgets. While tiny Transformers can be effective on inertial time series, end-to-end co-design across quantized inference and heterogeneous low-power platforms remains underexplored. We present STORM (Small Transformer for On-node Recognition of Motion), a deployment-oriented [round-mode=places, round-precision=1]19.7k-parameter 1D Transformer co-designed with X-HEEP, an open-source low-power single-core RISC-V SoC, and a tightly coupled streaming CGRA for nonlinear primitives (e.g., softmax). We build a cross-source 8-class benchmark by harmonizing 3 public datasets under a stringent, deployment-aligned protocol that exposes both cross-subject and cross-source shift. Using 1.280 s windows with 0.640 s stride, the protocol models continuous on-node HAR under cross-dataset generalization. After quantization-aware training and INT8 C inference export, STORM achieves [round-mode=places, round-precision=3]0.799/[round-mode=places, round-precision=3]0.801 accuracy/macro-F1 on this benchmark. Deployed on an FPGA prototype of X-HEEP with the streaming CGRA backend, STORM requires round(6739790/ (100* 1000000)* 1000, 1) ms per inference at 100 MHz, while activity-based power analysis estimates a total inference energy of 632.4 $\mathsf{\mu }$J, satisfying the stride-driven real-time constraint. These results support the practical viability of compact attention-based HAR on low-power wearable-class embedded platforms. Full article

Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry. Full article

Visual prostheses aim to approximate biomimetic visual function by electrically simulating surviving retinal neurons. Improving the spatial resolution of electrically elicited artificial vision remains a critical challenge for retinal prostheses. We investigate how dynamic virtual channel (DVC) parameters shape retinal ganglion cell (RGC) population responses to improve spatial precision and activation efficiency in epiretinal stimulation. We developed a computational modeling framework to quantify DVC performance using a hierarchical optimization strategy. First, static virtual channels (SVCs) were used to map how current ratio (α) and stimulus intensity govern RGC activation, defining an optimal SVC parameter space. Building on this baseline, DVC protocols were refined by evaluating the combined effects of inter-virtual–channel interval (ΔT), α, and intensity. This strategy significantly reduces the complexity of DVC parameter optimization. Under SVC stimulation, increasing intensity improved the linearity of receptive field (RF) centroid displacement with α, while α and intensity jointly set RF centroid location and activated area. Under DVC stimulation, ΔT strongly modulated RGC activation, especially at short intervals. Initializing from SVC-optimized parameters, tuning ΔT and intensity produced more confined activation at lower stimulus intensities than SVC, indicating that DVC can serve as a novel stimulation strategy to enhance spatial precision and activation efficiency in retinal stimulation. This study provides the first systematic analysis of retinal DVC stimulation and a practical optimization framework for next-generation prostheses. Full article

Mitochondrial dysfunction is a relevant hallmark of Alzheimer’s disease (AD), contributing to the impaired metabolic homeostasis involved in neuronal loss and cognitive decline. In this study, we target the metabolic dysfunction occurring in AD through a novel pharmacological approach involving the modulation of glutamate dehydrogenase (GDH), which converts glutamate to α-ketoglutarate and supports the tricarboxylic acid (TCA) cycle. In our experimental models (i.e., differentiated SH-SY5Y cells and primary rat cortical neurons exposed to glyceraldehyde and amyloid-beta peptide 1-42, respectively), the allosteric GDH activator 2-Aminobicyclo-(2,2,1)-heptane-2-carboxylic acid (BCH) increased mitochondrial ATP production, improved cellular bioenergetics, and reduced oxidative stress, ultimately promoting neuronal survival. Ionic dysfunctions in AD are linked to disrupted calcium homeostasis and organelle storing properties. In this context, GDH activation potentiated mitochondrial and endoplasmic reticulum calcium buffering capacity by enhancing store-operated calcium entry. Oxidative stress, largely driven by mitochondrial ROS overproduction, represents another major contributor to AD pathology. In our AD models BCH-mediated GDH activation reduced ROS formation and restored mitochondrial membrane potential (ΔΨ_m). Importantly, these metabolic and ionic improvements were associated with decreased accumulation of amyloid-β (Aβ_1-42) and phosphorylated tau (pTau), two key AD biomarkers. Overall, modulation of the GDH/TCA pathway represents a promising approach for restoring metabolic dysfunctions and counteracting oxidative stress and ionic dysregulation and therefore AD neurodegeneration. Full article

Draining peatlands for peat extraction converts them into significant sources of greenhouse gas (GHG) emissions. Quantifying GHG emissions at the regional scale remains challenging because direct field measurements are spatially limited, while GHG accounting for land-use planning requires spatially explicit information. Building on the advances in remote sensing (RS) as a scalable low-cost emission accounting tool for large areas, this study presents a proof-of-concept workflow that integrates satellite-based land-cover classification with an emission-factor (EF) approach to support spatial upscaling of peatland GHG estimates. Using Sentinel-2 imagery and a supervised Random Forest classifier, peatland-related land-cover classes were mapped for selected sites in Latvia. The classification results show higher accuracy for spectrally distinct classes such as raised bogs and active peat-extraction areas, while more heterogeneous classes exhibited lower performance. The study provides an overview of how to utilize the RS approach to generate accurate land-cover maps, which can be used to upscale GHG estimation in Latvia when field data is limited. The study does not include calibration against site-level flux measurements, uncertainty propagation, or temporal variability analysis; therefore, the emission results are illustrative and consistent with current EF-based inventory practice rather than validated site-specific fluxes. Full article

As China’s first World Heritage Mixed Property site, Mount Tai enjoys international renown, with its historic buildings serving both as the central carriers of its cultural heritage and as significant tourism resources. Existing studies have predominantly emphasized the form, scale, and construction techniques of individual buildings or architectural complexes, while less attention has been given to the overall spatial pattern shaped by the interplay of natural and social environments and to the mechanisms underlying its formation. Taking the administrative area of Tai’an City as the study extent, this research selects 451 officially protected historic buildings, classified by period and type, and employs GIS-based spatial analysis and statistical methods to examine their spatiotemporal distribution patterns and influencing factors. The results indicate the following. (1) The temporal distribution exhibits an И-shaped fluctuation pattern, with ancient architecture and ancient sites together accounting for nearly 60% of the total and constituting the core resource categories. This distribution curve is shaped jointly by preservation conditions, social stability, and heritage designation preferences. (2) The spatial distribution displays a pronounced clustering pattern, with the kernel density core shifting over forty kilometers from southwest to northeast, generating an evolutionary trajectory from Dawen River basin agglomeration to Mount Tai mountain belt agglomeration. (3) The overall pattern is associated with both natural and anthropogenic factors. During the early stages, natural conditions such as hydrology and topography provided foundational constraints, whereas in later periods, human factors, including fengshan ritual culture, religious activities, economic development, and institutional governance, exhibit increasingly apparent associations with the distribution pattern. Based on these findings, this study proposes a strategic spatial framework comprising one cultural pilgrimage ring and four thematic corridors, which translates the spatial analytical results into planning implications for the regional integration of historic building resources, and discusses differentiated conservation strategies, thereby providing an analytical foundation and a reference pathway for the dissemination of Mount Tai culture and the sustainable development of heritage tourism. Full article

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization. Full article

Shadow banking poses a significant challenge to China’s financial sustainability. This study examines how city-level regional financial structure influences shadow banking activities among non-financial firms, with implications for building a more sustainable financial system. Exploiting data of Chinese listed firms from 2012 to 2023 and employing fixed-effects regressions with instrumental variable (IV) and dynamic GMM approaches to address endogeneity, the study finds that bank-dominated financial structures significantly reduce corporate shadow banking financing. This effect weakens among financially constrained firms, revealing shadow banking’s role as a gap-filling mechanism, but strengthens when firms exhibit higher digitalization or market attention through enhanced information transparency. These findings suggest that achieving long-term financial sustainability requires regionally nuanced policy interventions rather than uniform regulatory tightening. Instead, policy interventions should be regionally nuanced: expanding formal credit in inland provinces can mitigate financial exclusion, while fostering corporate digitalization helps bridge the information gap between lenders and firms. Furthermore, enhancing market-based oversight is essential to redirecting capital into more transparent and regulated frameworks. Full article

With growing health awareness and an increasing preference for indoor exercise among the elderly, the demand for community indoor activity spaces is rising in the northern regions of China with cold winters and hot summers. While previous community studies have primarily focused on residential buildings, limited attention has been given to indoor activity spaces for the elderly. Moreover, field measurements expose critical thermal deficiencies in these spaces, where indoor temperatures remain substandard in both winter and summer, particularly falling substantially below the WHO health-based threshold (≥18 °C) in winter. Recognizing that transitional spaces are effective for improving indoor thermal conditions, this study explored their potential to enhance the indoor thermal environment, leading to targeted retrofitting schemes. The results showed that although additional transitional spaces effectively enhance the thermal performance, the strategies for winter and summer often conflict. Specifically, enclosed transitional spaces are effective for winter insulation but are prone to overheating in summer, whereas semi-outdoor configurations on the south and west facades are beneficial for summer heat prevention. Based on these findings, optimal retrofitting schemes were identified: for Site A, the existing interior corridor is transformed into a semi-outdoor transitional space; for Site B, an Adaptive Façade system is proposed for the south façade. Furthermore, despite the passive benefits, auxiliary HVAC systems remain necessary to maintain temperatures strictly within the comfort range during extreme weather. This study provides a scientific basis for research on transition spaces and offers a reference for retrofitting buildings in similar climatic regions. Full article

The use of bio-based and biodegradable plastic products (BBpPs) ensures the mitigation of environmental effects of fossil-based plastics, especially in humanitarian crises where waste management is challenging. Polyhydroxyalkanoates (PHAs) are promising biodegradable biopolymers that are biocompatible and do not cause microplastic pollution. However, experimental assessment of PHA biodegradation is challenged by its time- and resource-intensiveness. In this study, a comprehensive computational Quantitative Structure–Activity Relationship (QSAR)-based approach was developed to predict biodegradability of short chain length (scl)-PHA-based formulations consisting of various additives and building blocks. A novel curated dataset for the (scl)-PHA poly(-3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), with literature-reported environmental and biodegradation parameters from lab-scale experiments in soil, marine, freshwater and compost systems, was constructed and used to develop and validate the introduced approach. Random forest (RF) and Extreme Gradient Boosting (XGBoost) machine learning (ML) models were optimized and validated with cross-validation and test set predictions. The optimal models reported high accuracy values of the coefficient of determination R², indicating excellent relationships between structure and biodegradation metrics. Further analysis of descriptor variable importance confirmed that biopolymer biodegradability was favorably affected by biodegradation time, while mechanisms, environmental conditions, and additives contributed secondary yet physically consistent effects. The proposed QSAR framework demonstrated a robust and interpretable web-based tool for predicting the environmental fate of PHBV in natural environments and supported the sustainable safe-by-design (SSbD) approach of next-generation biodegradable polymers. Full article

Background: Gaucher disease (GD) arises from pathogenic variants in the GBA1 gene and is known for its wide range of clinical presentations—a variability that genotype alone cannot adequately account for. Objective: This study aimed to explore transcriptomic factors that might help explain why two genetically identical twins with type 1 GD developed noticeably different clinical outcomes. Methods: We isolated peripheral blood mononuclear cells from both twins and two age-matched controls, then differentiated them into macrophages in vitro before conducting RNA sequencing. Gene expression differences were analyzed using established bioinformatics pipelines, and a subset of genes were subsequently assessed by quantitative real-time PCR (qRT-PCR) to confirm the sequencing findings. Results: Both twins shared a GD-associated transcriptional signature broadly reflecting immune activation and lysosomal stress. Interestingly, the twin who experienced systemic complications had a relative enrichment of interferon-responsive transcripts, while the less severely affected twin showed more pronounced suppression of small nucleolar RNA clusters. That said, neither difference held up after correcting for multiple comparisons, so these patterns are best viewed as exploratory trends rather than definitive findings. The qRT-PCR results lend partial support to this picture: stress- and immune-related genes (DDIT4, RPH3A, SAMSN1) trended toward higher expression in patients versus controls, and interferon-stimulated genes (ISG15, RSAD2, IFI44L) were more elevated in M2 than in M1. Conclusions: Taken together, these findings suggest that factors beyond genetics—whether epigenetic, environmental, or otherwise—may play a meaningful role in shaping how GD manifests differently even between individuals with identical DNA. Although the data are preliminary, they point to transcriptomic profiling, paired with targeted validation, as a useful starting point for building hypotheses about why this disease looks so different from one patient to the next, even when the underlying mutation is the same. Full article

To respond to the national “double carbon” strategic goal, promote the green and low-carbon transformation of the building materials industry, and develop low-carbon and environmentally friendly grouting materials, this study prepared an alkaline-activated coal gangue-based geopolymer grouting material (AACGM). The effects of CG content, alkali activator modulus, and alkali activator content on material fluidity, setting time, compressive strength, and impermeability were systematically studied using orthogonal tests. The optimal mix ratio was determined and the internal mechanism was revealed by microscopic analysis. The results show that the comprehensive performance is the best when the content of CG is 50%, the modulus of alkali activator is 1.6, and the content of alkali activator is 14%. The primary and secondary order of influence of various factors on the performance is as follows: CG content > alkali activator content > alkali activator modulus. Microscopic analysis revealed that the hydrolysis polymerization products of the material are mainly C-S-H, C-(N)-A-S-H gel, and zeolite-like phase, forming a dense three-dimensional network structure, which is the internal mechanism of its good mechanical and impermeability properties. This study provides a new concept for the utilization of CG, and the prepared materials are of great significance in the field of grouting reinforcement in underground engineering. Full article

In the Industry 4.0 era, the Architecture, Engineering, Construction, and Operation (AECO) sector faces a strategic challenge in integrating Mechanical, Electrical, and Plumbing (MEP) systems during early design stages, where a lack of “Design for Maintainability” contributes to building defect rates of up to 28%. These failures not only incur significant resource waste but also undermine long-term building sustainability. This study evaluates a digital innovation framework synthesizing Serious Games and Cooperative Problem-Based Learning (CPBL) via Minecraft to foster systemic thinking and spatial reservation logic at Level of Development (LOD) 100–150 as a catalyst for digital transformation. Utilizing a mixed-methods design (n = 25), the curriculum employed a “Mirror Mapping” mechanism, translating game physics into real-world electrical and plumbing logic. While results showed 93% management competency, a significant 13% “Symbolic Transformation Gap (STG)” (80% in system analogy) persisted, indicating that symbolic fluency does not automatically yield professional engineering reasoning. These findings validate the framework’s potential for spatial externalization and emphasize the necessity of “bridging activities” and Digital Twin linkages to optimize building lifecycle performance and reduce carbon footprints, ultimately achieving sustainable building goals. Full article

Green building façades are increasingly recognized as a key strategy for decarbonizing the built environment, addressing climate change, urbanization, and the urban heat island effect. This paper investigates two main façade approaches: nature-based solutions (NBS), such as green façades and living walls, and Building-Integrated Solar Energy Systems (BI-SES), including photovoltaic, solar thermal, and hybrid BIPV/T systems. The building envelope is framed as an active interface for both energy efficiency and on-site renewable energy generation. Through a structured state-of-the-art review, the study compares these systems in terms of energy performance, environmental benefits, costs, maintenance, lifecycle implications, and adaptability across climatic contexts. Results show that NBS provide consistent benefits in thermal regulation and cooling-load reduction, while solar façades are strongly influenced by orientation, geometry, and urban shading. To complement the qualitative analysis, a preliminary energy–environmental assessment is conducted for three façade configurations (conventional wall, green façade, and combined green–PV façade) across three Italian climates (Milan, Rome, and Palermo). Results indicate that vegetation reduces heat losses and CO₂ emissions, with further improvements in integrated systems. Overall, NBS and solar façades emerge as complementary strategies whose integration can enhance building performance and support the transition towards net-zero carbon environments. Full article

Peptide therapeutics occupy a unique chemical space between small molecules and biologics, combining high target specificity with structural programmability and favorable safety profiles. Recent regulatory approvals and expanding clinical pipelines underscore the growing therapeutic and commercial relevance of peptide-based drugs. This review outlines chemical modification approaches and contemporary design strategies, and evaluates their impact on proteolytic stability, pharmacokinetics, membrane permeability, and target engagement. We then highlight recent advances in artificial intelligence (AI)-guided peptide drug design, including machine learning models, protein language models, and generative architectures that enable high-throughput activity prediction, property optimization, and de novo sequence generation. These approaches collectively accelerate the traditional discovery–design–validation cycle while reducing experimental attrition through data-driven, structure-informed modeling frameworks. Among these applications, AI also enables the rational design of cell-penetrating peptides (CPPs) to enhance intracellular delivery and biological activity. Building on these methodological advances, we further examine their application to peptide therapeutics, with particular emphasis on AI-based predictive models for CPPs as well as on therapeutic applications within the central nervous and pulmonary systems. We conclude by outlining future perspectives and emphasize that the systematic integration of AI-enabled sequence design with rational chemical engineering and advanced delivery technologies, supported by rigorous experimental validation, will be critical for developing robust and clinically durable peptide-based medicines. Full article