Search Results (2,294)

Although the long-term behavior of ground anchors depends fundamentally on interfacial behavior, the independent effect of the strand–grout interface on load loss has not been comprehensively investigated. This study establishes a physical model testing method that isolates the strand–grout interface and systematically investigates both short-term and long-term load loss behavior. Pull-out tests and long-term monitoring tests were conducted using grout uniaxial compressive strength ($q_u$ = 18–30 MPa) and bond length ($L_b$ = 900–1500 mm) as primary design variables. Long-term monitoring confirmed that prestress loss at the strand–grout interface is induced by the progressive pull-out displacement of the strand over time, following a logarithmic decay pattern. The load reduction coefficient n was significantly more sensitive to $L_b$ than to $q_u$; n increased sharply from 0.015 to 0.069 as $L_b$ decreased. Anchors with insufficient bond length exhibited secondary load reduction behavior that disrupted the stable log-linear decay, posing significant risk to long-term performance. Based on RMSE analysis of the fitted logarithmic model, a minimum monitoring period of approximately 50 days is recommended for reliable long-term prediction when bond length is adequate. These findings identify $q_u$ and $L_b$ as the governing parameters, providing a quantitative basis for optimizing prestress design and enhancing the long-term reliability of anchor systems. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

In this work, a lightweight framework enabled by the modified YOLOv11s-seg model for tea shoot detection and plucking point localization is proposed. Detecting tea shoots and localizing plucking points with higher accuracy generally require larger model size and more model parameters, making it difficult to balance accuracy and lightweighting. To overcome this limitation, a modified lightweight YOLOv11s-seg model is developed. First, the multi-scale edge information enhancement is introduced into the conventional YOLOv11s-seg to extract edge feature better and improve the detection accuracy of tea shoots. Meanwhile, context anchor attention is utilized to modify the cross stage partial spatial attention module in a backbone network to improve the detection capability for small objects. Moreover, the detail calibration reconstruction feature pyramid network is proposed. It utilizes spatial and contextual semantic information to reconstruct and calibrate features in key regions, enhancing the capability for object fusion and recognition at various scales. Furthermore, with the modified model performing instance segmentation to acquire the contour of each tea shoot, the coordinates of the three lowest pixel points in the contour are captured to localize the plucking point based on the average coordinates. In addition, the layer-adaptive magnitude-based pruning (LAMP) method is used to lighten the model. The experimental results show that the LAMP-pruned modified YOLOv11s-seg model with a speedup ratio of 1.5 achieves a mAP@0.5 of 86.5% for tea shoot detection, exhibiting a 4.7 percentage point improvement over the conventional YOLOv11s-seg model. Moreover, it exhibits an accuracy of 81.9% for plucking point localization on the validation and test subsets with 232 images in total, and its number of parameters, model size and floating point operations (FLOPs) separately achieve reductions of 67.3%, 66.2%, and 24.9% over the conventional model as well. Therefore, the proposed LAMP-pruned modified model shows good balance between lightweighting and detection accuracy. Finally, the modified LAMP-pruned YOLOv11s-seg model is deployed on a Jetson Orin NX edge module and measured in a tea plantation, with the measured results exhibiting a detection speed of 34.1 FPS and verifying its availability in practical applications. Full article

The biosynthesis of sex pheromones in lepidopteran pheromone glands is tightly regulated by pheromone biosynthesis-activating neuropeptide (PBAN) signaling; yet the contribution of non-coding RNA-mediated post-transcriptional regulation remains largely unclear. This study aimed to characterize temporal transcriptomic changes, candidate non-coding RNA-mediated regulatory associations, and temporal molecular dynamics underlying transcriptional remodeling after PBAN treatment in Ostrinia furnacalis. First, we performed comprehensive whole-transcriptome sequencing (WTS) on 18 biologically independent samples collected at six time points (0, 20, 40, 60, 90, and 120 min) after PBAN injection. Then, we systematically identified and quantified the dynamic expression patterns of differentially expressed (DE) mRNAs, miRNAs, lncRNAs, and circRNAs in response to PBAN stimulation. By integratively analyzing these multidimensional omics datasets and inferring sequence-based interaction relationships, we inferred a dynamic candidate competing endogenous RNA (ceRNA) like regulatory network. The candidate ceRNA network anchored four core node genes: the PBAN receptor (PBANR), the rate-limiting enzyme acetyl-CoA carboxylase (ACC), and the terminal biosynthetic enzymes desaturase (DES) and fatty acyl-CoA reductase (FAR). The qRT-PCR results further support the temporal expression pattern of key genes during the PBAN response, suggesting that this network can provide a valuable resource for further functional studies. Full article

This paper investigates the feasibility of 3D self-localization and tracking using chipless radio frequency identification (RFID) tags operating in the terahertz (THz) frequency band. The primary objective is to achieve sub-millimeter (sub-mm) localization and tracking accuracy while minimizing reliance on external infrastructure. To this end, a hybrid localization framework is proposed that jointly exploits round-trip time-of-flight (RToF) and angle-of-arrival (AoA) measurements to enhance localization performance. Although near-field propagation effects are inherently significant in the considered THz operating regime, a simplified far-field approximation is adopted to facilitate tractable system modeling and analytical development. The proposed framework is further extended to dynamic scenarios through an extended Kalman filter (EKF)-based tracking algorithm, which incorporates temporal state evolution to improve estimation robustness under noisy measurements. Furthermore, the Cramér–Rao lower bound (CRLB) for the hybrid RToF-AoA system is derived to establish the fundamental limits of localization accuracy under varying system configurations and measurement conditions. Simulation results demonstrate that the proposed approach is capable of achieving sub-mm localization and tracking accuracy with a highly constrained anchor infrastructure, including operation with a single anchor in the considered scenario. These findings highlight the potential of THz chipless RFID technology as a promising enabling solution for next-generation high-accuracy localization and tracking applications. Full article

As generative AI expands the technical frontiers of prediction, measurement, and design, a growing tension has emerged between algorithmic fluency and institutional trust. This conceptual article offers a narrative synthesis of recent work in learning analytics, educational data science, human–AI interaction, and AI governance to propose stewardship as a necessary fourth paradigm of educational data science. Stewardship represents the professional, epistemic, and institutional work of governing judgment in an environment where analytic systems are increasingly generative and persuasive. Rather than treating stewardship as a general ethics checklist, the article positions it as the governance of epistemic and pedagogical authority: who determines what counts as evidence, interpretation, and educational action when AI systems help produce those judgments. The synthesis suggests that while GenAI can support bounded analytic tasks, evidence for systemic educational transformation remains limited and uneven. The field’s primary challenge is therefore not technical performance alone, but the governance of interpretation, validation, delegation, and action. By centering provenance, uncertainty, accountable oversight, learner agency, and institutional learning, stewardship provides an actionable framework for anchoring analytic innovation in responsible educational improvement. Full article

Trustworthy digital credential management is increasingly important in LMS-connected higher-education information systems, yet institutions still lack controlled implementation-oriented evidence on how cloud-native service decomposition and blockchain-backed trust services influence deployment performance. This study develops and evaluates a cloud-native architecture that combines containerized microservices with Hyperledger Fabric-based permissioned ledger services and a Polygon-linked public-chain anchoring path for credential issuance, learning-record verification, and record validation. Unlike largely conceptual prior work, it benchmarks four functionally aligned deployment paths in a unified Kubernetes-managed testbed: a monolithic baseline, a microservices-only baseline, a Hyperledger Fabric-integrated variant, and a Polygon-linked anchoring path. The credential-service paths were evaluated under stepped workloads from 1000 to 20,000 scheduled virtual users. Evaluation focused on service-path latency, throughput, tamper-detection accuracy, and resource utilization. The microservices-only architecture achieved the lowest baseline latency (182 ms), Hyperledger Fabric maintained stable response times for trusted institutional workflows (352 ms at baseline and 485 ms at 20,000 virtual users), and the Polygon-linked anchoring path reached the highest observed service-path throughput (228 TPS) in the tested prototype. Both blockchain-integrated variants detected tampered credentials in all successfully processed tamper cases. Overall, the results show that cloud-native decomposition and ledger-backed trust and anchoring can support scalable and trustworthy credential services when platform choice aligns with institutional governance scope, verification audience, and deployment constraints. Full article

Achieving long-term urban sustainability requires energy subsidy frameworks that evolve with changing technological conditions and system needs. Renewable energy subsidy regimes have played a decisive role in accelerating building-integrated solar photovoltaic deployment, but many were designed for an earlier expansion phase focused mainly on increasing generation capacity and reducing technology costs. As electricity systems move toward an integration phase characterized by higher renewable penetration, flexibility constraints, storage needs, and cross-sectoral coordination, generation-centric subsidy architectures may become increasingly misaligned with system-level requirements. This study conducts a structured comparative analysis of subsidy design in Hong Kong, Chinese Mainland, and Australia, examining legal foundations, target scope, incentive structures, and technology orientation across expansion and integration phases. Despite major differences in governance systems and market organization, the findings show a common pattern: Principal subsidy instruments remain anchored in output-based performance metrics, while storage, hydrogen, and hybrid technologies are generally supported through supplementary rather than core mechanisms. The study argues that this policy layering may limit technological inclusiveness and reduce alignment between subsidy design and evolving system needs. It therefore proposes a system-value-oriented comparative framework for subsidy redesign that recognizes flexibility, reliability, and integrated clean energy performance in the built environment. Full article

Synthetic aperture radar (SAR) imagery provides all-weather, day-and-night observation capabilities that complement electro-optical (EO) imaging; however, the limited number of operational SAR satellites and the difficulty of acquiring expert-annotated SAR datasets constrain deep-learning-based SAR image analysis. In this paper, we propose EO2SAR-Diff, a conditional latent diffusion framework that translates EO aerial images into realistic synthetic SAR images. The framework comprises three core components: (1) domain-adaptive LoRA pre-training that anchors the Stable Diffusion backbone in the remote sensing domain, (2) a style extraction and injection network that captures SAR-specific visual characteristics via multi-scale feature encoding and parallel cross-attention, and (3) a multi-branch ControlNet with three parallel branches for complementary structural guidance. These components are coordinated by a dual-axis feature injection strategy that modulates conditioning strength along both spatial (per-block) and temporal (per-timestep) dimensions. Experiments on the DOTA 1.0 and SARDet-100K datasets demonstrate that EO2SAR-Diff ranks in the top tier among all compared methods in distributional alignment with real SAR imagery, in terms of FID and KID computed with two SAR-domain-adapted feature extractors. Augmenting the SAR training set with our synthetic images yields consistent improvements in downstream object detection performance, confirming the practical utility of the proposed framework. Full article

Ship detection in synthetic aperture radar (SAR) imagery, an indispensable all-weather technology for marine engineering and coastal safety, remains challenging in complex nearshore scenes due to coupled speckle noise, sea–land clutter, large scale variation, and extreme class imbalance. Existing decoupled pipelines fail to jointly mitigate these degradations, leading to high false alarm rates and poor generalization. We propose DN-AnchorNet, an end-to-end unified framework integrating a detection-oriented structure-preserving enhancement branch, a scale-adaptive anchor mechanism, and an adaptive weighted Smooth L1 loss. The detection-guided enhancement branch operates without paired clean data to preserve critical ship structures. The scale-adaptive anchor design enhances matching for small, elongated, and arbitrarily oriented ships, while the tailored loss improves regression robustness through dynamic threshold adjustment and valid positive-sample regression masking under class imbalance. Extensive experiments under the adopted fixed nearshore stress-test protocol of RSDD-SAR and SSDD+ show that DN-AnchorNet achieves the best overall performance among the compared representative oriented object detectors in this evaluation setting, with AP₅₀ values of 0.699 and 0.610, and F1-scores of 0.757 and 0.689, respectively. A strict zero-shot cross-dataset evaluation on HRSID provides supplementary evidence of DN-AnchorNet’s transferability to unseen marine SAR conditions. These results suggest that joint optimization can achieve a favorable accuracy–false-detection balance under challenging nearshore SAR detection conditions. Full article

The sluggish kinetics of alkaline hydrogen oxidation reaction (HOR) and high cost of Pt–based catalysts have long hindered large–scale deployment of alkaline membrane fuel cells. Via first–principles calculations, we designed a series of 3d transition metal single atoms anchored on PdN₂ monolayer (TM–PdN₂, TM = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) and evaluated their alkaline HOR performance. Ti-, Cr-, Fe-, Co-, Ni-modified systems exhibit excellent thermodynamic and electrochemical stability under operating conditions. Single-atom doping tunes the p-band center of N and d-band center of metal sites, enabling precise modulation of H and OH adsorption strengths. Mechanistic analysis reveals HOR follows H₂ + 2OH* → H* + OH* + H₂O → 2H₂O, with the final step as rate-determining step. H adsorption contributes 3.45 times more to HOR activity than OH adsorption. Fe–PdN₂ delivers the best performance, with an ultra–low barrier of 0.11 eV and a rate constant of 2.82 × 10¹⁰ s^–1·site⁻¹, values that significantly outperform those of Pt(111) (0.22 eV, 4.5 × 10⁹ s⁻¹·site⁻¹). This work provides theoretical guidance for rational design of high–performance alkaline HOR electrocatalysts. Full article

Maintaining structural stability and reliable mooring performance remains a key challenge for offshore floating photovoltaic (FPV) systems. This study investigates the coupled hydrodynamic and mooring behavior of a novel large-scale hexagonal rigid FPV platform through 1:25-scale physical model tests. A near-zero-pre-tension slack mooring arrangement was adopted to isolate the effects of mooring type, including anchor chain (M1), steel cable (M2), and elastic cable (M3). The results show that the influence of mooring configuration is strongly degree-of-freedom dependent. Surge motion is highly sensitive to mooring type, whereas heave and pitch remain largely consistent among the three cases. In regular waves, the maximum surge-acceleration RAO of M2 is 1.82 and 2.27 times those of M1 and M3, respectively. Peak mooring tension shows a strong correlation with maximum surge acceleration in both regular and irregular waves, indicating that surge motion can serve as a useful indicator of extreme mooring loads under similar slack-mooring conditions. Among the three configurations, M1 exhibits the strongest short-term peak-load buffering. Under extreme irregular waves, its peak mooring tension is 82.4% and 24.7% lower than those of M2 and M3, respectively. These results provide experimental guidance for the mooring design of large-scale rigid FPV systems. Full article

This paper asks whether the development of national artificial intelligence ecosystems contributes to greener economic performance, and whether public governance shapes that relationship. The analysis covers a balanced panel of 36 advanced and emerging economies from 2017 to 2023. We capture general national artificial intelligence ecosystem development through a multidimensional index built on five pillars (innovation, economic diffusion, skills, policy, computing infrastructure) aggregated by within-pillar principal component analysis, and estimate the model by two-step System-GMM, with instrumentation anchored in Wooldridge endogeneity tests robust to heteroscedasticity. Green growth is highly path-dependent, with an autoregressive coefficient close to 0.96 that corresponds to an annual convergence speed of 4.5 percent. Government effectiveness contributes positively and significantly. The artificial intelligence ecosystem index displays no detectable independent effect once persistence and endogeneity are addressed, and its interaction with government effectiveness is similarly indistinguishable from zero, a result that calls for caution in narratives expecting artificial intelligence to deliver sustainability gains on its own. Full article

Background/Objectives: Recent advances in automated mitosis detection in H&E histopathology have expanded AI applications in digital pathology for tumor grading and proliferation assessment. However, reported performance remains difficult to interpret because it is strongly influenced by benchmark selection and heterogeneous evaluation regimes. This review examined how recent methodological advances, dataset context, and evaluation-regime stratification shape performance interpretation. Methods: We conducted a systematic review of peer-reviewed English-language studies published between January 2018 and December 2025. PubMed, Scopus, and IEEE Xplore were searched for mitosis detection, localization, or counting in H&E histopathology. After screening and full-text assessment, 66 studies met the inclusion criteria. We synthesized 60 method papers and considered 6 dataset/challenge descriptor papers separately. Extracted data included task formulation, datasets, evaluation regime, and outcomes. Results: The 60 method papers showed a methodological shift from patch/cell-level classification toward one-stage and two-stage detectors, dense segmentation/heatmap approaches, hybrid pipelines, and emerging robustness-oriented methods. F1 was reported in 59/60 studies, but evaluation practice was heterogeneous: custom hold-out testing predominated, whereas external validation and explicit domain-generalization protocols were uncommon. Evidence remained concentrated in legacy breast benchmarks, while MIDOG-family datasets anchored most robustness-oriented studies. Importantly, dataset names alone were insufficient to determine comparability; for example, “testing on ICPR2014” could refer to organizer-governed hidden-test scoring, post-challenge labels, or author-defined splits of public data. Conclusions: Automated mitosis detection research has diversified rapidly, but cross-study comparability remains limited by inconsistent evaluation and scarce cross-domain testing. Clearer reporting of dataset partitions, evaluation governance, and metrics, with more routine external or domain-held-out evaluation, would strengthen evidence for AI-driven digital pathology and precision oncology. Full article

This study examines the association between digitalization-enabled green human resource management (GHRM) practices and employee green behavior (EGB) within a South African metropolitan municipality. Anchored in an extended Ability–Motivation–Opportunity (AMO) framework, a convergent mixed-methods design was employed. Quantitative data were collected from 66 HR employees (from a target population of 80) and analyzed using Spearman’s correlation and hierarchical regression, while qualitative data from seven HR managers were analyzed thematically. Results indicate statistically significant positive associations between digital green training (ρ = 0.524, p < 0.01) and EGB, and between digital performance management (ρ = 0.463, p < 0.01) and EGB. However, regression estimates suggest moderate explanatory power within this context-specific public-sector setting. Qualitative findings identify automation, paperless systems, and e-HRM tools as key digital enablers, alongside infrastructural constraints, skills deficits, and institutional barriers that limit implementation. By integrating quantitative associations with qualitative evidence of implementation gaps, the study proposes a Digitalization-Integrated GHRM–EGB framework and demonstrates that digital HR systems are associated with pro-environmental workplace behaviors, contingent on organizational readiness in resource-constrained municipal environments. Full article