Search Results (7,117)

Herringbone gear pairs are critical in high-speed aerospace transmissions, where windage power loss significantly impacts efficiency and thermal management. This study proposes a prediction method that decomposes the total windage loss into five components based on structural features: the tooth, end, circumferential, and relief groove surface losses for both gears, and the meshing extrusion loss. Theoretical models for each component are established to form a complete prediction method using fluid–structure interaction principles. CFD simulations analyze the velocity, pressure, and energy fields around the gear pair, with windage loss integrated via fluid torque on gear surfaces. Results indicate that windage loss escalates rapidly and becomes non-negligible when the driving gear speed exceeds 7000 rpm. The prediction model demonstrates strong agreement with CFD simulations, with a maximum relative error of 13.6%. Analysis reveals that the driving gear contributes the largest share of the total gear pair loss, with meshing extrusion accounting for 20.1–23.6%. For a single herringbone gear, the tooth surface is the primary source of loss (~83%), followed by the end surface (~8%), while relief groove and circumferential losses remain below 10%. This research provides a validated theoretical foundation for optimizing efficiency and thermal control in high-speed aerospace gear systems. Full article

Photocatalytic reduction of CO₂ to produce high-value chemical fuels is a research hotspot for sustainable development, yet its integration into undergraduate experimental teaching is hindered by a high risk, high cost, and shortage of large-scale instruments. Herein, a Fe₂TiO₅–polydopamine (PDA) S-scheme heterojunction photocatalyst was fabricated via in situ self-polymerization, and its structure, photoelectric properties, and CO₂ reduction mechanism were systematically characterized. Under visible light, the heterojunction delivers a CO production rate of 14.1 μmol·g⁻¹·h⁻¹ (6.6 times that of pure Fe₂TiO₅) with 94.2% cyclic stability. More importantly, this work constructs a virtual–real hybrid experimental teaching mode (virtual simulation pre-training + offline practical verification) for inorganic and environmental chemistry experiments, developing a virtual simulation platform with six modules (laboratory safety, instrument introduction, experimental principle, 3D simulation, virtual assessment, and after-school thinking). This mode solves the teaching bottlenecks of high-risk operation and inaccessible large-scale characterization (in situ XPS and CO₂-BET), standardizes experimental operations, and deepens students’ understanding of photocatalytic mechanisms. This study not only provides a high-efficiency photocatalyst for CO₂ reduction but also offers a replicable virtual–real integration paradigm for inorganic chemistry experimental teaching reform. Full article

The article is devoted to developing methodological approaches to multi-criteria resource optimization of technological solutions in Nature Use Projects, considering the growing shortage of water and energy resources, climate change, and post-war transformation of Ukraine’s agricultural sector. The need to transition from traditional technical and economic optimization models to integrated assessment approaches, which consider ecological, resource, and economic aspects of the project implementation effectiveness, is substantiated. The methodological basis of the study is a combination of Multi-Criteria Decision-Making and the Water-Energy-Food Nexus concept, enabling the necessary adaptive management and formalizing the process of project decision-making under multifactor uncertainty. A set of indicators of resource-ecological and economic efficiency is proposed, including indicators of productivity, weather and climate risk, resource use, environmental reliability, investment attractiveness, etc. A key feature of this approach is the transformation of resource-ecological indicators into a value form, ensuring their integration with economic indicators within a single optimization model. Based on a machine experiment for the conditions of the Kherson region, an assessment of the effectiveness of various irrigation regimes, which differ from the project irrigation regime in terms of watering and irrigation norms, in terms of their level of provision with water and energy resources, was carried out. It was determined that, under the studied conditions, in dry years (p = 70%), the permissible deficit threshold is approximately 30%, achieving a compromise between economic efficiency and environmental acceptability. Adaptive management of irrigation regimes has been shown to reduce the resource intensity of production without a significant loss of productivity. This creates a basis for revising outdated design standards, which focused on 100% satisfaction of water needs, in favor of adaptive models that account for the real resource potential of the territory. This approach transforms irrigation from a resource-intensive industry into a tool for sustainable territorial development, where the priority is the efficiency of each cubic meter of water and kilowatt-hour of energy used, rather than gross collection. It has been proven that the implementation of resource optimization as a basic principle of natural resource project management contributes to increasing the efficiency of natural capital use, minimizing ecological risks, and ensuring the sustainable development of the agricultural sector. The obtained results can be used to substantiate engineering solutions in projects for the restoration and modernization of water management and land reclamation systems in Ukraine. Full article

Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products. Full article

High-performance flexible electronics require soft materials that combine mechanical robustness with efficient ionic conduction. In conventional ionogels, however, these requirements often conflict: dense networks improve strength but reduce the free volume and mobility needed for ion transport. This review provides a critical overview of recent progress in cellulose-based ionogels, with emphasis on design principles for decoupling mechanical and conductive properties. We discuss how cellulose precursors, crosslinking architectures (hydrogen bonding, covalent networks, and metal-ion coordination), and processing histories determine gel structure and mechanical integrity. We then highlight strategies that mitigate the trade-off, including precursor engineering, phase-separated networks, double-network architectures, crystallization-induced reorganization, and anisotropic assembly. Representative applications in flexible sensors, flexible energy-storage devices, and soft actuators are also summarized. This review offers a practical framework for designing cellulose-based soft functional materials with robust mechanics and sustained ionic conductivity. Full article

International research and policy frameworks underscore the value of mandatory energy regulations in reducing energy demand and greenhouse gas (GHG) emissions in the built environment. However, Global South (GS) countries experience several challenges in effectively implementing building energy efficiency codes (BEECs), as codes are either absent, unevenly adopted or inconsistently enforced. A poor alignment with the specific climatic, socio-economic and construction realities further limits the potential of BEECs to support GS climate resilience. This research aims to identify opportunities to enhance building energy regulatory practices by exploring recent progress in the field. It also systematically evaluates existing mandatory BEECs in the GS to identify models and principles that could guide the development of more effective codes, specifically for GS countries without BEECs. It is hypothesised that the mandatory BEECs currently implemented in GS countries can be analysed using contextually relevant criteria to reveal common regulatory patterns, strengths, and shortcomings, thereby informing a climate-responsive framework suited to GS realities. This research implemented a two-tiered literature review. After determining the broad regulatory context, an exploratory review of the current state of the art in BEEC research was conducted. These publications (primarily 2016–2025) were obtained via a systematic query in Scopus. Following the exploratory review, this study performed a Systematic Quantitative Literature Review (SQLR) to assess mandatory BEECs from 18 GS countries. The findings reveal that BEECs are useful for delivering energy-efficient buildings in the real world. However, ample opportunities exist to improve their comprehensiveness in context and coverage. Improving regulatory implementation systems and structures, along with robust stakeholder engagement, can support better BEEC design and enforcement. To address the need for contextualised BEECs, the SQLR helped develop a taxonomy by comparing the mandatory codes. This research also introduces the Sustainable Level Indicator Model, Matrix, and Map (SLIM³) prototype, proposed as a decision-support tool, and hosted on an interactive online platform, thereby potentially contributing to real-world building energy regulatory practices. The SLIM³ tool organises the mandatory BEECs into a coherent, accessible framework that could assist GS decision-makers in benchmarking existing and new codes, identifying gaps and prioritising contextually appropriate improvements, thus contributing to a more resource-efficient built environment. Full article

Sustainability has become a core guiding principle for high-quality urban development. As a key dimension of urban resilience, economic resilience is of utmost importance. It directly relates to a city’s capacity to maintain stable operations and sustainable development when confronting shocks, serving as a crucial foundation for safeguarding economic health and public welfare. Through bolstering risk management, accelerating industrial upgrading, and enhancing the efficiency of resource allocation, financial technology (fintech) empowers urban economic resilience. This plays a pivotal role in accelerating the transition to new engines of national economic growth and promoting sustainable urban economic development. This paper selects panel data from 281 prefecture-level cities covering the period from 2009 to 2023 to examine the impact of fintech on urban economic resilience. It further examines the moderating role of industrial agglomeration in this relationship, analyzes heterogeneity in urban economic resilience, and investigates the spatial spillover effects of fintech on it. The results demonstrate that fintech significantly promotes the enhancement of urban economic resilience. This finding remains valid after multiple robustness tests and endogeneity treatments are conducted; the role of fintech in promoting urban economic resilience is more pronounced in cities with a higher degree of industrial agglomeration, and fintech can generate spatial spillover effects, leading to a marked improvement in the economic resilience of neighboring areas. Full article

This paper builds upon a previous study suggesting an optimization procedure for rotation sequences by introducing a fourth factor in Euler-type decompositions, thus allowing for an additional degree of freedom used both as a variational parameter and a means to avoid the gimbal lock singularity. Here, an analogous result is derived for generic rigid motions, which is of potential interest in 3D robot manipulators, aircraft, and spacecraft using gimbals to navigate in space. The idea is based on Kotelnikov’s principle of transference, which extends the properties of pure rotations to arbitrary Galilean transformations, interpreted as screw motions. To do that in practice, it is convenient to use dual quaternions or their projective version, referred to as dual Rodrigues’ vectors. With this approach, the explicit solutions are easy to extend and therefore optimization is rather straightforward: we show, both analytically and with numerical examples, that factorizing motion into sequences of four consecutive screws is, in general, significantly more energy-efficient compared to using three. 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

Remote sensing (RS) imagery often suffers from non-uniform atmospheric scattering, resulting in severe contrast degradation, detail blurring, and spectral distortion. While recent advanced State Space Models (SSMs) offer efficient long-range modeling, they frequently struggle with spectral–spatial coupling interference and lack explicit physical constraints, leading to over-smoothed textures and color biases in high-reflectance regions. In this paper, we propose PhysWave-SSN, a Physics-Inspired Frequency-Decoupled Network specifically designed for high-fidelity RS image dehazing. The architecture employs a task-adaptive frequency-specific screening strategy to effectively isolate structural details from atmospheric interference. Specifically, we first introduce a Frequency-Aware Selection Gate (FASG) that unifies adaptive channel screening with physical transmission estimation, enabling precise recalibration of frequency components. To bridge the gap between physical scattering principles and state space representation learning, we develop a Physics-Informed SSM (PI-SSM), where the discretization step size of Mamba is dynamically modulated by the estimated haze density. This mechanism allows the model to adaptively adjust its spatial receptive field according to local degradation levels, enhancing physical interpretability. Furthermore, a Luminance-Adaptive Fusion Module (LAFM) is presented to protect high-reflectance land covers and maintain spectral consistency. Extensive experiments on multiple RS datasets demonstrate that PhysWave-SSN achieves superior performance, notably attaining a maximum PSNR gain of 2.49 dB while ensuring high structural and spectral fidelity. Full article

Flanged connections are a critical joining method in modern industrial production, making the detection of bolt loosening in flanges a vital step to ensure industrial safety. Current research on bolt loosening detection in flanges mainly focuses on flat-face flanges without gaskets, while studies on bolted pipe flanges containing gaskets are relatively limited. To achieve bolt loosening detection in such gasketed pipe flanges, this paper analyzes the influence of bolt loosening on wave propagation in the gasket based on the stress wave principle and finite element simulation, and employs the hammer impact method to realize the detection of bolt loosening degree in pipeline flanges. The optimal knock force and hammer head material for the bolt loosening detection experiments were determined experimentally. Through comparative experiments, the Support Vector Machine—Recursive Feature Elimination (SVM-RFE) model was identified as being more accurate and efficient in assessing the degree of bolt loosening. Furthermore, the model was optimized by incorporating feature enhancement and cost-sensitive learning, thereby providing a reliable methodological solution for the rapid identification of bolt loosening severity in pipeline flanges. Full article

To address the issues associated with traditional multi-point surveying processes in the dead-end cutting for oil and gas pipelines—such as cumbersome procedures, high error rates, lengthy emergency repair cycles, and difficulties in ensuring welding precision—an infrared line drawing device has been developed that enables rapid positioning, long-distance high-precision alignment, and accurate marking of cutting locations. This paper establishes mathematical models for the centering deflection mechanism and the marking mechanism, and derives theoretical solutions for key structural parameters. Thirteen finite element models were constructed using Abaqus to simulate operating conditions involving different pipe diameters and link lengths. A variance-based uniformity metric was employed to quantify structural stress stability, and optimal parameters were determined based on the principle that smaller variance indicates more uniform stress distribution and closer to ideal component service life. The results indicate that the optimal length of the three mounting bolts is 85 mm, with a maximum deflection angle of 9.25°, which meets the requirements. A spring extension of 5 mm for the marking pen can accommodate the compensation needs for marking on DN300 to DN500 pipes. An optimal set of connecting rod parameters across pipe diameters has been determined, with a 240 mm connecting rod capable of covering more than 75% of operating conditions. This device and its parameters are expected to contribute to first-pass compliance and reduce downtime, providing efficient and precise technical support for the maintenance and emergency repair of oil and gas pipelines. Full article

Against the backdrop of deep integration of technological innovation and industrial innovation, this study constructs a theoretical model incorporating market and government behavior. Utilizing panel data from 284 prefecture-level cities across China from 2013 to 2023, it empirically analyzes the roles and mechanisms of efficient market and proactive government in facilitating the deep integration. Findings indicate that the market’s “push–pull mechanism” promotes the deep integration of technological and industrial innovation, enhancing output performance. However, market mechanism exhibits diminishing marginal output performance: beyond a certain threshold, the force to drive further performance improvements weakens. The government’s role can positively influence the market mechanism’s ability to drive deep integration of technological innovation and industrial innovation, and growth in output performance. In coordinating government and market efforts, the following principles should be observed: as market mechanism matures, gradually enhance technological innovation by allocating human capital to research activities, particularly basic research; formulate industrial policies that progressively prioritize science and technology innovation activities; and advance the development of public goods, such as technology transfer and commercialization platforms. To advance the deep integration of technological and industrial innovation, policy implications include consistently leveraging market mechanisms, coordinating government and market efforts to design policies for capital and talent mobility, and promoting the construction of conversion platforms like concept-validation centers and science incubators. Full article

Biological cellular structures exhibit a high degree of systematic organization in both morphological configuration and functional coordination, providing important biomimetic insights for urban spatial organization. To address issues in traditional high-density residential areas, such as homogeneous spatial structures and insufficient accessibility of public spaces, this study proposes a planning method for five-minute living circle residential areas based on a biomimetic cellular structure within the framework of space syntax theory. Taking a residential area in Wuhan, China, as a case study, a cell-like spatial structure model was constructed. Convex space analysis, axial analysis, and visibility analysis were conducted using Depthmap software to quantitatively evaluate key syntactic indicators, including integration, connectivity, mean depth, and choice. The results show that, compared with the original planning scheme, the biomimetic cellular planning model significantly optimized the spatial structure of the residential area by relying on the functionally synergistic mechanisms of selective permeability of the cell membrane, whole-area permeation of the cytoplasm, central regulation of the nucleus, distributed coordination of organelles, and efficient transport through cellular microfilaments. In the sample living circle, the overall integration increased from 1.27 to 1.64, the mean depth decreased from 3.79 to 3.18, and spatial connectivity increased from 3.74 to 5.44. Meanwhile, the synergy of the road network increased from 0.44 to 0.86, indicating marked improvements in spatial accessibility, connectivity, and the degree of coordination within the spatial structure. In addition, the visibility analysis showed that the pedestrian aggregation capacity of the public core space was enhanced, and the spatial vitality of public activity spaces in the residential area was improved. The findings demonstrate that the spatial organization model based on biomimetic cellular principles can effectively enhance spatial efficiency and social vitality in five-minute living circle residential areas, providing a quantifiable design method and theoretical framework for bio-inspired urban planning. Full article

The widespread use of herbicides in agriculture results in their accumulation in the environment, which has a negative impact on non-target biota. One way to reduce environmental risks while maintaining the effectiveness of plant protection products is to apply supramolecular chemistry principles to agricultural practices. Although pillar[n]arenes are used in the production of sensors and antidotes for pesticides, their influence on the herbicidal properties and ecotoxicity of herbicides toward aquatic organisms and higher plants has hardly been studied. The effect of pillar[5]arenes on the herbicidal activity of 2,4-dichlorophenoxyacetic acid (2,4-D) and glyphosate (Glyp), as well as the ecotoxicity of the resulting binary systems toward Ceriodaphnia affinis and Paramecium caudatum, was assessed for the first time. The association constants of pillar[5]arenes with Glyp (logK_a = 3.92–4.06) were an order of magnitude higher than the corresponding values for 2,4-D (logK_a = 2.66–3.06) with the stoichiometry of 1:1. The formation of stable associates (143–177 nm) with negative zeta potential values (from −20.9 to −7.8 mV) was demonstrated for the pillar[5]arene/herbicide systems. Low phytotoxicity of pillar[5]arenes against Chlorella vulgaris was shown. The addition of pillar[5]arenes to 2,4-D reduced the wheat (Triticum aestivum L.) germination index by 4.5-fold compared to the pure herbicide. Forming associates between decamethoxypillar[5]arene and Glyp increased the LC₁₀ by more than twofold compared to the individual herbicide against Paramecium caudatum and Ceriodaphnia affinis. It was demonstrated that combining pillar[5]arenes with Glyp can reduce ecotoxicity while partially preserving or selectively modifying phytotoxicity. The results obtained in this study are encouraging for the development of materials and supramolecular systems that could boost agricultural efficiency while reducing its environmental impact. Full article