Search Results (12,643)

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We developed the framework using a targeted narrative synthesis of contemporary governance guidance and documented early implementation experiences, purposively selected to represent major strands of current practice and debate. A structured expert panel consultation (n = 9) was subsequently conducted to assess the face validity and content validity of the proposed framework domains. Results: We propose the Public Health Responsible AI Capability (PH-RAIC) framework, which adapts principles of transparency, accountability, fairness, ethics, and safety to institutional realities faced by public health agencies. PH-RAIC identifies four interdependent capability domains: (1) strategic governance and alignment; (2) data and infrastructure stewardship; (3) participatory design, equity, and public engagement; and (4) lifecycle oversight, learning, and decommissioning. All four domains achieved Content Validity Index (CVI) values ≥ 0.85 in the expert panel consultation. The framework is presented as a conceptual, meso-level model that has undergone preliminary expert validation but requires further empirical testing in real-world agency settings. Conclusions: PH-RAIC links these domains to example practices, diagnostic questions, and illustrative measurement indicators to help agencies navigate efficiency–equity trade-offs and strengthen legitimacy and accountability in AI-enabled public health systems. It offers a validated conceptual basis for future empirical testing and operational readiness tools. Full article

Large-scale hydrogen storage is expected to play a critical role in balancing the variability of renewable energy systems, particularly those driven by wind power. However, the combined influence of storage capacity and deliverability on supply reliability remains insufficiently characterized. This study investigates the reliability limits of hydrogen storage systems operating under variable hydrogen production and time-varying demand. A dimensionless modeling framework is developed to map system performance across a wide range of storage capacities and deliverability levels. The results reveal a clear transition between reliable and unreliable operating regimes. Reliable operation requires a minimum deliverability level approximately equal to the mean hydrogen production rate, corresponding to a value of about 1.05–1.10 times the average production across the range of intermittency conditions considered in this study (from moderate to highly variable production). Below this threshold, increasing storage capacity alone cannot prevent supply shortfalls. Once this threshold is exceeded, further increases in deliverability provide diminishing returns and storage capacity becomes the dominant factor governing reliability. In this regime, the required storage capacity approaches a plateau on the order of 10–30 days of average hydrogen throughput, depending on the level of production variability. The proposed regime-based framework provides a practical tool for evaluating storage feasibility and guiding preliminary capacity design in renewable hydrogen systems. Full article

In plant factories with artificial lighting (PFALs), spectral regulation serves as the predominant factor governing plant growth and development. The implementation of red-enriched spectral regimens during cultivation promotes biomass accumulation, whereas blue-dominant spectra enhance the biosynthesis of phytochemicals and nutritional compounds in plants. Nevertheless, systematic investigations into the effects of staged spectral regimens on both plant development and secondary metabolite biosynthesis remain limited. This study evaluated four distinct stage-specific dynamic lighting regimens (T1–T4) under a constant total photosynthetic photon flux density (PPFD) of 200 μmol·m⁻²·s⁻¹. The treatments utilized three distinct red-to-white photon flux ratios (R:W = 3:1, 1:1, and 1:3) administered sequentially during critical developmental phases of Pak choi: the seedling stage, the early growth stage (15 days after transplanting, DAT), and the late growth stage (16–30 DAT). The effects of these treatments on biomass production, morphological development, photosynthetic pigments, nutritional metabolites, antioxidant levels and radical quenching capacity were evaluated. The results demonstrated that the T4 treatment significantly enhanced biomass production, increasing shoot fresh weight by 51.3% compared to the T1 treatment at the late growth stage. The application of a higher red-light proportion (HR, R:W = 3:1) during the seedling stage significantly increased leaf area by 70% compared to the low red-light treatment (LR, R:W = 1:3). Regarding nutritional quality, while carotenoid content showed no significant differences among treatments, higher blue-light proportions selectively stimulated the biosynthesis of chlorophyll, vitamin C, and soluble proteins. Specifically, the T3 treatment enhanced certain traits during the early growth stage, whereas the T2 treatment best maintained specific antioxidant capacities (FRAP and flavonoids) at the late growth stage prior to harvest. Notably, nitrate levels were not significantly affected by the spectral shifts. This study establishes that the temporal modulation of red-to-white spectral ratios enables the targeted optimization of either crop yield (T4) or specific harvest-stage nutritional attributes (T2) in Pak choi. Full article

Offshore oil and gas exploitation is one of the riskiest businesses to invest in and is dominated by various uncertainties: high deepwater pressure, low temperatures, remote operation, long-distance tiebacks and transportation, as well as environmental factors such as wind, waves and ocean currents. Serving as a profitability threshold, the minimum economic field size is defined as the economic recoverable reserve level that an oilfield must exceed to achieve economic returns. This paper develops an approach for determining the minimum economic field size of offshore oil and gas reservoirs. It categorizes the capital expenditure into four major components: drilling and completion costs, platform costs, pipeline costs, and subsea production system costs. The regression models of drilling costs and subsea production costs are developed respectively, with water depth and recoverable reserves as key influencing factors. The pipeline costs are estimated using the unit pipeline cost per mile and pipeline length. A profit model for the offshore field is established under the constraints of the contract, which allocates the oilfield’s production profits between the contractor and the government according to the contractual fiscal terms. Finally, taking the Lucius oilfield in the Gulf of Mexico as a case study, the paper simulates its investment, operating costs, and oilfield revenues. The minimum economic field size is calculated, accompanied by the derivation of the sensitivity boundaries for the primary parameters. Full article

The V-Model is widely used in safety-critical engineering; however, its application to rapid prototyping remains challenging due to limited integration between lifecycle governance and executable validation workflows. This paper addresses end-to-end mechatronic ECU development through the general objective of designing an integrated, prototyping-oriented software platform that operationalizes a V-Model-based rapid prototyping lifecycle with explicit, process-level traceability. Four specific objectives structure the contribution. First, bibliometric analyses of the keywords “V-Model” and “Rapid Prototyping Platforms,” conducted using the Web of Science database and VOSviewer, highlight the limited integration of structured lifecycle approaches with practical prototyping workflows. Second, the V-Model is adapted for mechatronic ECU development by introducing domain-specific decomposition and a dependency-driven testing sequence. Third, the BIOComProP (Basic Input Output Communication Protocol Platform)—comprising reusable ECU firmware, PC based test software, and a dedicated request–response communication protocol—is developed, and its testing capabilities are mapped to V-Model phases. Finally, a logic-based workflow is defined to translate patent derived requirements into executable development and validation steps. The results demonstrate staged verification aligned with technical dependencies, structured traceability across development activities, and firmware reuse across multiple prototypes, offering a coherent and reproducible approach for rapid prototyping of mechatronic ECU systems without relying on heavyweight MBSE or ALM toolchains. Full article

Precise assessment of commercial service facilities (CSFs) is a vital pillar for megacity governance. However, existing evaluations rely on static population and 2D metrics, overlooking behavioral heterogeneity and 3D spatial supply at the micro scale. This study constructs a “3D Supply–Group Demand–Matching” framework at the community level. On the supply side, a Building Coupling Entropy (BCE) model integrates 3D volume and morphology to characterize service capacity. On the demand side, a dynamic behavioral model measures multi-group needs. Mismatch patterns are identified using the Entropy-modified Spatial Disparity Ratio (ESDR). Using Guangzhou as a case, the results reveal three paradigms: (1) Core districts exhibit rigid path dependency, where first-tier sub-districts rose from 48 to 51, and elderly service shortages in old areas plummeted by nearly 80% via micro-regeneration; (2) Growth poles show spatial fragmentation, with core labor demand spilling over but infrastructure lagging, creating a fast production–slow urbanism mismatch; (3) Far-suburban areas reduced extreme-shortage sub-districts from 38 to 34, identifying resource islands besieged by residential demand. Overall, the framework elucidates the shape–flow mismatch mechanism and provides a transferable basis for precision zonation governance, supporting a shift from static quantity-based allocation to dynamic quality-oriented provision in high-density megacities. Full article

The widespread adoption of Generative Artificial Intelligence (GenAI) in regulated enterprises is currently hindered by the “Static Alignment Trap”: the inability of traditional safety methods, such as Reinforcement Learning from Human Feedback (RLHF), to adapt to rapidly shifting compliance landscapes without costly retraining. This paper introduces and evaluates the Agreement Validation Interface (AVI), a modular governance architecture that functions as a deterministic middleware layer. By decoupling governance from the core inference engine, AVI implements Dynamic Bilateral Alignment (DBA), enforcing policy constraints at both the input and output stages through vector-based semantic retrieval. Adopting a Design Science Research (DSR) methodology, we validated the system against the FinanceBench financial benchmark ($N=150$ queries, three repeated runs, 450 total observations) and a proprietary Russian-language provocative content dataset developed internally at MWS AI ($N=201$ queries; not publicly available). The empirical results demonstrate that the architecture achieves an 83.2% Large Language Model (LLM)-judge compliance rate (95% confidence interval, CI: 79.4–87.1%), statistically significantly exceeding the unfiltered baseline of 63.7% ($\Delta =+19.5$ percentage points (pp), $t=4.02$, $p=0.002$). The vector-based input filter achieves perfect detection performance (Precision $=1.000$, Recall $=1.000$, F1 $=1.000$). Cross-domain validation on 201 Russian-language provocative queries confirms generalizability (Recall $=0.985$, LLM compliance among triggered queries $=0.977$). The operational Time-to-Compliance for enforcing new rules was reduced from hours (model fine-tuning) to under five seconds (vector indexing). These findings suggest that enterprise AI safety requires an architectural shift from model-centric training to system-centric control, complemented by system-prompt-level anti-inference governance. We conclude that AVI offers a scalable, cost-effective, and statistically validated framework for auditable AI compliance, independent of the underlying model provider. Full article

Rectangular sinking wells are widely used in underground and hydraulic engineering, where maintaining vertical alignment during construction is essential for safe serviceability and long-term performance. Even moderate inclination may cause eccentric transfer of the vertical load to the concrete plug, leading to non-uniform contact stresses beneath the base. This study presents a closed-form analytical framework for assessing the admissible tilt of rectangular sinking wells based on stress redistribution under one-axis inclination. The well is modeled as a rigid body, and the resulting load eccentricity is related to the shaft height and tilt angle. Classical eccentric compression theory is then used to derive explicit expressions for the maximum and minimum base stresses, from which a serviceability-based admissibility criterion is formulated. The obtained solution provides the allowable inclination directly as a function of shaft height, plan dimensions, and an adopted stress amplification factor. Parametric analyses show that the admissible tilt decreases with increasing shaft height and increases with larger base dimensions. A distinct directional anisotropy is observed for rectangular plans, whereas square wells recover symmetric behavior with identical limits in both orthogonal directions. This identifies square geometry as the symmetry-preserving limit case of the proposed model. Sensitivity analyses further demonstrate the influence of admissible stress amplification on permissible inclination levels. The proposed formulation offers a transparent screening tool for construction monitoring and post-construction assessment, while also illustrating how geometric symmetry reduction governs the mechanical response of inclined foundation systems. Full article

Low-temperature stress significantly limits plant growth, development, and productivity, posing a major environmental constraint. The potato (Solanum tuberosum L.) is particularly vulnerable to low temperatures, underscoring the crucial need to enhance cold tolerance in potato breeding efforts for sustainable production. Basic leucine zipper (bZIP) transcription factors serve as central regulators of plant developmental processes and stress responses; however, their functional role in cold tolerance in tetraploid potato remains poorly understood. Here, we report a systematic characterization of the bZIP gene family in tetraploid potato and provide preliminary evidence that StbZIP104 enhances plant cold tolerance. A total of 191 StbZIP genes were identified and classified into 11 subfamilies, exhibiting uneven chromosomal distribution and expansion primarily driven by whole-genome and segmental duplication. Promoter cis-element analysis, together with GO and KEGG enrichment analyses, indicated that StbZIP genes are broadly associated with hormone signaling, stress responses, signal transduction, and environmental adaptation. Expression profiling under low-temperature treatment revealed eight cold-inducible StbZIP genes (log₂FC ≥ 1 and FDR < 0.05), among which StbZIP104 was strongly induced (log₂FC ≥ 2) and showed 5.36-fold higher expression in highly cold-resistant cultivars than in cold-sensitive cultivars. Subcellular localization confirmed that StbZIP104 is a nuclear-localized protein. Functional validation confirmed that overexpressing StbZIP104 notably improved cold tolerance in transgenic Samsun NN tobacco (Nicotiana tabacum cv. Samsun NN). This was supported by heightened superoxide dismutase and peroxidase activities, increased levels of soluble protein and soluble sugars, and decreased malondialdehyde content compared to the wild type under cold stress. This study establishes a basis for the functional characterization of the bZIP gene family in tetraploid potato and serves as a theoretical reference for understanding the mechanisms that govern cold tolerance in this species. Full article

As a key indicator of low-carbon economic transformation, the influencing factors of carbon productivity (CP) have attracted considerable academic attention. However, the study of the role of artificial intelligence (AI) technology innovation is comparatively confined. Using China’s prefecture-level-and-above cities as the sample, this study measures regional AI technology innovation based on AI patent stocks and empirically examines its impact on carbon productivity. The principal findings of this paper are as follows: (1) AI technology innovation boosts urban carbon productivity through three channels: enhancing green innovation, reducing transaction costs, and increasing AI attention. (2) The regional heterogeneity analysis shows that this positive impact of AI technology innovation on carbon productivity exerts a stronger facilitating effect on eastern regions, resource-dependent cities, and central cities. The heterogeneity analysis at the technological level further provides evidence of the effect of AI technology innovation on carbon productivity varying along different tiers of technological development, innovation mode, and innovation role. (3) The analysis identifies the energy structure as a pivotal threshold variable governing the efficacy of AI innovation in bolstering carbon productivity. Notably, crossing the threshold of clean energy penetration triggers an escalating positive feedback loop between AI innovation and carbon productivity. (4) Estimation of temporal effect dynamics via non-parametric panel model shows that the impact of AI technology innovation on CP exhibits phased characteristics. The coefficient became significantly positive in 2010 and peaked in 2015, after which its effect gradually weakened. This study provides comprehensive empirical evidence for understanding the relationship between AI technology innovation and CP and provides policy references for the use of AI technology to promote the coordinated achievement of economic growth and carbon reduction. Full article

We develop an information-geometric framework for describing quantum state-update processes associated with measurement and statistical distinguishability. The approach is based on the quantum relative entropy and the quantum Fisher information metric, which together induce a natural Riemannian geometry on the manifold of quantum states. Using the second-order expansion of relative entropy, we show how the Fisher metric governs the local structure of distinguishability between nearby states and defines a corresponding thermodynamic length. This geometric structure provides an effective description of finite quantum state transitions in terms of fluctuation geometry and information-space distance. The formalism is applied to thermal two-level systems and harmonic oscillator states, illustrating how the Fisher metric encodes susceptibilities, fluctuations, and geometric transition costs. We also discuss the relation between thermodynamic length, dissipation bounds, and optimal paths in state space. Within this framework, wave function collapse is interpreted not as a microscopic dynamical mechanism, but as an effective state-update process that admits a geometric characterization in the manifold of density operators. The resulting perspective unifies concepts from quantum information theory, thermodynamics, and differential geometry within a common operational framework based on statistical distinguishability. Possible connections with quantum speed limits, entanglement geometry, and holographic relations between relative entropy and gravitational dynamics are briefly discussed. Full article

Runway microtexture is a key parameter governing pavement friction. In recent years, several microtexture assessment methods have been developed; however, understanding of microtexture evolution under operational conditions, as well as the effects of maintenance techniques, remains limited. In this study, a runway at an Australian airport was investigated using laser profilometry. Measurements were conducted across multiple transverse sections, including aircraft touchdown and mid-runway zones. Microtexture deterioration rates were evaluated based on the estimated number of tire–pavement contacts, and aggregate polishing was assessed at different locations. Measurements were also performed after rubber contamination removal and rejuvenation treatments. The results indicate that approximately 25% of total microtexture reduction can be attributed to surface polishing, with a lower contribution in touchdown zones due to the protective effect of rubber deposits. A non-linear degradation trend was observed in touchdown zones, where approximately 1100 tire contacts reduced average microtexture roughness from 18 μm to 11 μm. Rubber removal effectively restored microtexture close to its original levels across the runway width. A rejuvenation treatment with a covering of fine sand initially improved microtexture; however, rapid deterioration occurred due to loss of the sand coating. These findings improve the understanding of microtexture evolution under operational runway conditions, albeit only at a case study level, and support more effective runway maintenance planning and intervention strategies. Full article

This study examines the preservation of cultural landscapes in Asian heritage villages, using the Qiaonan Village in China as a case study. The study proposes an integrated model that combines macro-level planning, meso-level governance and micro-level community participation. Key findings show that only 32% of residents perceive the distribution of tourism benefits as fair, while a GIS analysis revealed a 28% increase in commercial land use within the heritage core between 2019 and 2022, indicating rising commercialisation pressures. The study explores the tensions between heritage conservation and tourism-driven development, with a focus on spatial integrity and local identity. It suggests that co-management and equitable benefit-sharing could strike a balance between economic growth, preservation, and community well-being. Rather than offering validated solutions, the research provides a diagnostic lens and generates hypotheses for other heritage villages. The transferability of these findings depends on local governance capacity, regulatory clarity, and the stage of tourism development, factors that will require systematic assessment in future comparative research. Full article

Market-based environmental regulations are increasingly vital for driving green transitions. As a major construction economy and the world’s leading carbon emitter, China launched its Carbon Emission Trading System (CETS) to advance dual-carbon goals and pilot decarbonization in high-emission sectors. Using 2009–2021 data on A-share listed construction enterprises, this study employs a propensity score matching difference-in-differences (PSM-DID) approach to assess CETS’ impact on corporate Environmental, Social, and Governance (ESG) performance. Results show that CETS significantly improves construction enterprises’ ESG performance. Mechanism analysis identifies green technology innovation as a key transmission channel, with government subsidies positively moderating this effect. Heterogeneity analyses reveal stronger policy effects among state-owned enterprises and firms in eastern regions. These findings remain robust under alternative specifications, matching methods, and higher-order fixed effects. This study offers micro-level evidence on how market-based carbon regulations shape corporate sustainability through ESG, informing China’s carbon market refinement and global market-driven decarbonization efforts. Full article