Search Results (3,915)

This study investigated the cortical activation patterns and functional connectivity underlying human decision-making by comparing two distinct purchasing orientations: other-oriented consumption (OOC) and self-oriented consumption (SOC), using functional near-infrared spectroscopy (fNIRS) as a wearable neuroimaging modality. The results revealed significant temporal concentration differences in $∆\mathrm{H}\mathrm{b}\mathrm{O}$ under the OOC condition in Ch06 ($p$< 0.05). The 15 fNIRS channels were mapped to seven anatomically defined regions of interest ($\mathrm{ROIs}$) to better capture regional activation patterns and functional network properties. While global network metrics showed no significant differences, seed-based connectivity analysis revealed that the OOC condition elicited significantly stronger functional connectivity between the medial prefrontal cortex (${\mathrm{R}\mathrm{O}\mathrm{I}}_4$) and the left lower PFC (${\mathrm{R}\mathrm{O}\mathrm{I}}_6$, $p$ < 0.05, $d$ = 0.45). In summary, while the overall network efficiency remained stable across conditions, our findings highlight a spatially specific enhancement in functional connectivity centered on the PFC, indicating an increased cognitive load from engaging in complex social cognitive processes. These findings advance the understanding of neural correlates underlying human decision-making and demonstrate the utility of wearable monitoring using fNIRS for capturing cognitive state differences in human-centered decision contexts. Full article

Gold-enriched silica-listvenite rock from the Kaymaz Gold Deposit (KGD) was investigated to determine the effect of regional tectonism and Eocene granite intrusion on gold mineralization. The questions “is granite a heat–fluid source or a lithologic barrier?” and “how does regional tectonism affect gold mineralization?” remain unclear. This study aims to clarify these questions via field studies, core sample observations, petrography, ore microscopy, scanning electron microscopy (SEM), XRD, and fluid inclusion analyses; these methods were applied to samples collected from four different sites within the KGD (1—Damdamca, 2—Karakaya, 3—Mermerlik, and 4—Kızılağıl). The highest-grade gold mineralization is present in the listvenite rock in the fault-controlled contact zone between serpentinite and granite, whereas granite hosts minor gold and silver enrichments near the contact. The orientations of contacts are compatible with the NW-SE-trending Eskişehir fault zone in Karakaya and the NE-SW-trending tear faults in Damdamca. Listvenite is silica-rich and has high iron oxy-hydroxide content, while granite is argilized and silicified along the contact with listvenite. Native gold grains were found between the quartz minerals of listvenite and granite. The adsorption of gold by goethite ± lepidocrocite has been observed in the listvenite samples of Mermerlik. Chromite, Ni-sulfide minerals, pyrite, arsenopyrite, galena, native silver, acanthite, iodargyrite, and goethite ± lepidocrocite are the other detected ore minerals. Secondary Cr-Fe-Mn oxide minerals were detected in a granite sample via SEM analyses. The data indicates that listvenitization-causing fluid partially remobilized these metals along with Au and reprecipitated them in the granite during mineralization. The homogenization temperatures (Th) (°C) of fluid inclusions vary between 116 and 393 °C, and the Th (°C) distribution indicates multi-phase mineralization. The Th (°C) values of listvenite and silicified granite are quite similar, which indicates that the same hydrothermal fluid circulated in both lithologies. The low salinity values (1.2–5.4%) indicate that the hydrothermal fluid was derived predominantly from meteoric water. The liquid–vapor ratios of inclusions and quartz textures indicate non-boiling conditions. Gold enrichment in the KGD developed in relation to the circulation of hydrothermal fluids along the faults. The KGD shows typical fluid inclusions, alteration properties, and mineral paragenesis of low-sulfidation-type epithermal deposits. Our study data indicates that meteoric water-rich hydrothermal fluid circulated along the fault zones, dissolved Au and other related elements from the serpentinite, and reprecipitated in the listvenite-altered granite. Granite acts as an impermeable barrier, leading to the circulation of hydrothermal fluids through the contact. Supergene activities affect the mineralization in both Mermerlik and Kızılağıl. No evidence indicating the magmatic origin of gold mineralization was observed. Full article

Urban Air Mobility (UAM) has emerged as a promising solution to alleviate urban congestion and support low-carbon transportation by utilizing low-altitude airspace. However, its large-scale deployment requires governance mechanisms that simultaneously address environmental impacts, social acceptance, and institutional coordination. Existing studies have not yet provided an operational Environmental, Social, and Governance (ESG)-based decision framework for UAM governance. This study develops and empirically validates an ESG-oriented governance model for UAM integration into urban development. A mixed-method approach was adopted, including literature and policy analysis to identify 22 execution-level factors, a questionnaire survey of industry practitioners and experts (N = 307), and the Analytic Hierarchy Process (AHP) combined with expert consultation to determine priority weights. The results show that the Governance dimension has the highest importance (38.72%), followed by Social (32.15%) and Environmental (29.13%). Laws and regulations, standard certification, and digital management constitute the core institutional foundations for UAM deployment. Privacy protection and social acceptance are the dominant social concerns, while noise pollution represents the most critical environmental constraint. Across all dimensions, standard certification, privacy, noise control, management framework, and digital management are the highest-weighted factors. The proposed framework provides a practical ESG-based decision tool to support policy prioritization and sustainable UAM implementation in rapidly urbanizing regions. Full article

Salt-bearing foreland fold–thrust belts represent a critical tectonic system for ultra-deep hydrocarbon exploration. In the Kalasu structural belt of the Kuqa Depression—characterized by the “four extremes” of ultra-high temperature, pressure, salinity, and stress—conventional single-detachment models fail to adequately resolve the complex subsalt structures. To address this challenge, this study integrates high-resolution 3D seismic data, field outcrop observations, well logs, balanced cross-sections, and particle image velocimetry (PIV)-monitored physical modeling to propose a ramp–flat multi-detachment model. Our results demonstrate that deformation is governed by four regional detachment horizons: gypsum-salt layers, thick mudstones, coal-bearing strata, and the basement, which vertically partition the basin into six tectonic units: supra-salt, salt, subsalt, supra-coal, coal, and sub-coal basement. The structural architecture is controlled by five key factors: (1) paleo-uplift geometry, (2) distance from the South Tianshan orogenic front, (3) orientation of basin-bounding faults, (4) regional stress regime (pure compression versus transpression), and (5) rheological contrasts among detachment layers. The kinematic evolution follows a progressive sequence: basement-involved thrusting → multi-level ramp–flat detachment folding → cover detachment. Three primary trap levels are identified—subsalt, supra-coal, and sub-coal—hosting six distinct trap styles: pop-up anticlines, imbricate faulted anticlines, structural triangle zones, fault-bend fold anticlines, supra-coal anticlines, and inter-coal/sub-coal anticlines. Notably, under transpressional stress, oblique paleo-uplifts control the formation of enigmatic “fish-scale” arcuate trap belts composed of fault-bend fold anticlines. Full article

Against the backdrop of digital transformation, industrial integration between modern services and advanced manufacturing has become an important driver of sustainable industrial development. Nevertheless, existing studies have mainly examined its direct effects, while paying insufficient attention to temporal path dependence, spatial spillovers, and the underlying transmission mechanisms. Using panel data for 29 Chinese provinces from 2005 to 2024, this study investigates how industrial integration affects manufacturing upgrading in China within a dynamic spatial econometric framework. To this end, a dynamic Spatial Durbin Model, spatial mediation analysis, and instrumental-variable estimation are employed. The empirical results indicate that industrial integration significantly promotes manufacturing upgrading. In the benchmark model, a 1% increase in the coupling-coordination index between modern services and advanced manufacturing is associated with an approximately 0.121% increase in the manufacturing upgrading index. Manufacturing upgrading also shows strong temporal persistence, as reflected by a lagged dependent variable coefficient of 0.878. The decomposition of spatial effects further reveals that industrial integration produces both local promotion effects and cross-regional spillovers, with a direct effect of 0.135 and an indirect effect of 0.156. In addition, mechanism analysis shows that innovation efficiency serves as an important transmission channel linking industrial integration to manufacturing upgrading. These findings imply that industrial integration can support sustainable development by improving resource allocation efficiency, strengthening innovation capacity, and promoting more coordinated regional industrial development. This study enriches the literature on industrial integration and manufacturing upgrading from a dynamic spatial perspective and provides policy-relevant evidence for the design of differentiated and sustainability-oriented industrial integration strategies. Full article

This article examines the emergence of entrepreneurial universities within China’s innovation-driven development agenda, with Shenzhen used as a regional case through which to analyse this process. Drawing on the Triple Helix literature and its later Quadruple and Quintuple Helix extensions, this study uses a qualitative case-study design that combines policy and archival analysis, descriptive questionnaire evidence from 132 respondents, and 42 semi-structured interviews with university, industry, government and venture-capital actors. The analysis shows how Shenzhen’s innovation capacity has been built through the interaction of firm-led technological upgrading, enabling municipal governance and a gradual repositioning of universities. Rather than following the university-centred pattern often associated with mature Western innovation systems, Shenzhen displays a hybrid Helix configuration in which universities acquire entrepreneurial functions through talent provision, external partnerships, practice-oriented knowledge exchange and organisational adaptation. This article therefore contributes to debates on entrepreneurial universities by explaining how such institutions can develop in late-developing, industry-led regions where conventional research infrastructure is initially limited. It also offers policy implications for strengthening sustainable university entrepreneurship, cross-sector coordination and regional innovation resilience in emerging economies. Full article

Discrete memristive chaotic maps are promising for secure communications due to their digital compatibility, yet existing designs face limitations, including narrow hyperchaotic ranges and a single type of chaotic attractor. This paper proposes a family of 2D hyperchaotic maps by coupling a discrete exponential memristor with four 1D seed maps. Theoretical analysis reveals that the exponential memristor induces non-hyperbolic fixed points and periodicity with respect to the memristor’s initial charge, enabling controlled coexistence of both homogeneous and heterogeneous multistable attractors. Numerical simulations show two positive Lyapunov exponents (LEs) and broad hyperchaotic regions; the memristor-coupled Sine map achieves a maximum LE of 0.4963 and spectral entropy (SE) of 0.8915, outperforming representative cosine- and quadratic-based benchmarks. A pseudorandom number generator (PRNG) passes all National Institute of Standards and Technology (NIST) SP 800-22 tests. STM32F407-based hardware experiments confirm physical realizability, and an image encryption application demonstrates near-ideal entropy (7.9883) and strong differential attack resistance. These results establish the discrete exponential memristor as an effective nonlinearity for enriching chaos complexity and hardware-oriented security primitives. Full article

Coal-fired power units remain integral to electricity supply in many regions while facing increasingly stringent environmental expectations. Bridging reliable generation with sustainability requires more than end-of-pipe controls; it demands continuous intelligence embedded in plant operations. This study introduces an industry-oriented monitoring framework that transforms historical operational records into actionable foresight, enabling on-the-fly orchestration of combustion conditions to anticipate sulfur oxide (SOx) concentrations. Leveraging 919 empirical data points collected in 2019 from Unit 8 of the Taichung Thermal Power Plant, the framework integrates robust data governance, targeted feature curation, and a neural network-based analytics core. Eight process variables—sulfur content, coal feed rate, fixed carbon, grinding rate, calorific value, excess air, air flow, and boiler efficiency—emerge as the most influential drivers through systematic selection and feature importance attribution. The resulting forecasting module exhibits near-perfect alignment with observed emissions (R² = 0.99), enabling near-real-time guidance for setpoint adjustments and facilitating compliance strategies under varying load and fuel-quality conditions. Beyond accuracy, the system is architected for scalability and portability, aligning with Industry 4.0 paradigms by coupling continuous sensing, data-driven decision support, and stakeholder transparency. By reframing emission oversight as a proactive, intelligent service rather than a static reporting function, the proposed approach advances operational resilience, regulatory compliance, and community trust, with direct implications for resource efficiency and circular economy initiatives across heavy industry. The framework reduces potential SOx emissions and improves energy utilization efficiency under varying operational conditions. This approach contributes to environmental sustainability by enabling proactive emission reduction and cleaner production practices. It supports regulatory compliance and aligns with global sustainability goals, including SDG 7 and SDG 13. Full article

Hybrid steel–PVA fiber-reinforced concrete offers promise for enhancing both load-bearing capacity and deformation capacity. However, the coupled effects of fiber parameters and volume-fraction combinations on compressive strength (σc) and peak strain (εc) are still not fully understood. A unified, interpretable, and engineering-oriented quantitative framework is still lacking. This study compiled experimental data from 26 published literature, building a multi-source database consisting of 397 datasets for σc and 203 datasets for εc. Based on this database, a comprehensive analytical framework was proposed, including model prediction, SHAP-based interpretation, Monte Carlo marginalization, synergy-gain window determination, and dual-objective mix-proportion optimization. For σc prediction, LightGBM achieved the highest test-set R² (0.9783), whereas CatBoost showed more robust error control (MAE = 2.7409 MPa). CatBoost was therefore selected as the base model for the subsequent interpretation analysis. For εc prediction, Bayesian-optimized CatBoost achieved the best test performance (R² = 0.9659, MAE = 0.0218, RMSE = 0.0358), while the transfer-learning model reached a comparable accuracy level (R² = 0.9650). SHAP analysis revealed that σc is mainly governed by matrix mix-proportion factors and steel fiber volume fraction, whereas εc is more sensitive to S/B and PVA-related variables. The mean synergy-gain maps generated via Monte Carlo marginalization and two-dimensional grid evaluation further showed clear differences between the two targets. Positive synergy in σc was highly localized. Its maximum mean synergy gain was 4.7949 MPa at (Steel, PVA) = (1.875%, 2.000%). By contrast, εc exhibited a wider positive-synergy region, with a peak value of 0.0141629 at (0.38%, 1.62%). Therefore, the engineering output of this study is not a single optimal mix point. Instead, it is a set of candidate windows for different performance targets, together with boundary-risk identification and priorities for experimental validation. Full article

This study provides a mapping of the current state of electromobility education in Mexico, aiming to identify gaps and opportunities in developing specialized talent for the energy transition and sustainable mobility. A scoping review was conducted using a structured methodological framework, involving searches across official platforms, academic institutions, and government sources, with specific criteria to select programs related to electric vehicle technology and sustainable transport. The results reveal a concentration of programs in the northern and central industrial regions, while southern and southeastern areas remain underrepresented, creating territorial disparities. Additionally, specialized programs were identified, alongside gaps in emerging areas such as battery recycling, renewable energy integration, and policy and regulatory training. The review highlights that, although Mexico has a substantial workforce, there are significant deficiencies in advanced, innovation-oriented training, limiting the country’s participation in more specialized segments of global EV value chains. It is concluded that strengthening academia–industry collaboration, expanding curricula in strategic domains, and investing in postgraduate education and research are essential steps to support a more inclusive and sustainable electromobility transition. Full article

To address the large variability in existing pavement distress in expressway reconstruction and expansion projects in Zhejiang Province, China, a differentiated overlay design and decision-making method based on multi-index evaluation was proposed using the Ningbo section of the Yongtaiwen Expressway as a case study. Based on 3D ground-penetrating radar (GPR), falling weight deflectometer (FWD), and field coring tests, the existing pavement was classified into five conditions: intact pavement, slight and severe surface-layer distress, and slight and severe base-layer distress. For pavements with surface-layer distress, two alternative overlay schemes were designed. Scheme I was defined as a performance-oriented scheme using high-performance SMA/Superpave asphalt layers and an ATB-25 transition layer where necessary to improve fatigue resistance and coordinated structural performance. Scheme II was defined as an economy-oriented scheme using conventional AC layers and crack-resistant or bonding measures to reduce construction cost while maintaining adequate structural capacity. An ABAQUS-based temperature–load coupled finite element model considering the temperature-sensitive viscoelastic characteristics of asphalt layers was established to analyze the mechanical responses and service lives of the overlay schemes, and the entropy weight–TOPSIS method was used for multi-objective comprehensive decision-making. The results showed that temperature–load coupling markedly increased the tensile strain at the bottom of the asphalt overlay and was a key controlling factor in design. All schemes satisfied the 15-year design requirement, while the base-layer fatigue life of the performance-oriented scheme (Scheme I) was generally no lower than that of the cost-oriented scheme (Scheme II), indicating better long-term service reliability. In addition, the relative closeness coefficients of Scheme I under slight and severe surface-layer distress were 0.586 and 0.546, respectively, both higher than those of the cost-oriented scheme. The proposed method can effectively balance technical performance and life-cycle cost and provides a useful reference for differentiated overlay design in similar expressway reconstruction and expansion projects in hot–humid regions. Full article

Transparent and comparable evaluation of ecological restoration outcomes is essential for advancing performance-based environmental governance and ESG-aligned ecological compensation. However, existing grassland monitoring approaches in semi-arid regions often rely on single vegetation indices, which fail to capture ecosystem structure, functional recovery, and temporal dynamics. To address these limitations, this study proposes a process-oriented Restoration Index (RI) based on multi-source remote sensing data. By integrating spectral, textural, and phenological indicators, together with topographic and climatic factors, derived from Sentinel-2 and Landsat time-series imagery, the framework characterizes vegetation productivity, community structure, and seasonal ecological processes within a unified analytical framework. A case study in the Xilingol grassland of Inner Mongolia shows that different management strategies, including grazing exclusion, reseeding, and rotational grazing, are associated with distinct restoration trajectories and recovery performance. The results indicate that the RI captures both spatial heterogeneity and temporal evolution of ecosystem recovery, while the normalization procedure improves the relative comparability of restoration assessment results within the adopted framework. Quantitative evaluation shows positive agreement with field observations, providing preliminary support for the applicability of the approach within the study area. Overall, the RI framework provides a scalable and policy-relevant basis for ecological restoration assessment and may support ecological compensation evaluation, environmental auditing, and more transparent restoration governance. Full article

Window glazing systems play a critical role in building energy performance, particularly in office buildings with large window-to-wall ratios, which introduce complex trade-offs between energy consumption, thermal comfort, and visual comfort. This study develops a two-stage optimization-assessment framework to assess glazing performance across six climate regions defined by the TS 825 standard in Türkiye. In the first stage, a genetic algorithm-based multi-objective optimization approach was employed to minimize annual energy consumption (heating, cooling, and daylight-linked lighting) and thermal discomfort hours. In the second stage, the resulting Pareto-optimal solutions were further evaluated and ranked according to spatial disturbing glare (sDG) performance using annual glare simulations. The results show that energy-optimal solutions are not necessarily visually acceptable, highlighting the limitations of single-criterion approaches. While static low-e glazing provides competitive energy performance under several climate conditions, it may lead to increased glare risk, particularly at high window-to-wall ratios and in sun-exposed orientations. Dynamic glazing systems, although not consistently superior in energy terms, offer a more balanced performance when glare is considered, especially in colder climates. These findings emphasize the need for a climate-based, multi-criteria, and integrated approach to glazing selection. Full article

Under wartime conditions, conceptual approaches to organizing the education system are changing, and the means of achieving goals are being modified. All of this affects the development of infrastructural provision for the educational network and simultaneously requires adequate management. The state, as the main subject of social management, employs management theory and practice of competent (professional) business leadership. This approach not only allows it to survive but also to develop in the objectively existing competitive environment. It has been determined that the main elements of educational management (EM) organization include the quality of intellectual resources, analysis of internal and external environments, analysis, selection and implementation of educational system (ES) development strategies and evaluation and control of their execution. Attention is focused on forming an ecologically oriented society through the lens of knowledge transfer, with a focus on the irrational use of natural resources across various spheres of human activity, energy resource deficits, and sustainable development tasks in Ukraine. A central place in this process is assigned to organizing project activities and to forming an ecologically oriented worldview among future specialists trained by educational institutions at various levels and forms of ownership. The analysis of educational management (EM) models shows that the project-investment model remains relevant. Trends in quantitative indicators of EM and ecological projects in Eastern European countries have been analyzed, based on which conclusions have been formulated that reflect the current state of ecological education development and demonstrate existing changes, challenges, and prospects. A visualized flowchart of optimizing the organization of higher education through the prism of an environmentally friendly society has been developed, with four blocks highlighted: methodological, organizational, analytical, and resultant. It has been determined that knowledge transfer from universities to communities should become a priority in the state’s post-war reconstruction, ensuring the socio-economic development of regions, including strengthening Ukraine’s energy independence. The practical significance of the obtained results lies in developing recommendations for implementing the integration of educational management (new functions) and project activities in educational institutions, which can be used when forming their development strategies, establishing international partnerships in the educational sphere, as well as for developing state programs to support the development of Ukraine’s economic, ecological, and social policy. Full article

To address the strong reliance on manual operations and the low efficiency of egg shape index (ESI) phenotyping in layer breeding, this study proposed an ESI measurement method based on an improved YOLOv12n-seg model. Ghost Bottleneck modules were introduced into the backbone to reduce model complexity. In addition, a boundary-aware loss combining Binary cross entropy (BCE), Dice, and Boundary Loss was designed to improve mask quality. Based on the segmentation results generated by YOLO-Ghost, principal component analysis was employed to extract the orientation and scale of the principal axes of the segmented regions. The major and minor axes of the pixel-level masks were then obtained, and their ratio was used as the measured ESI value. Compared with YOLOv12n-seg, YOLO-Ghost reduced the number of model parameters and computational cost by 39.86% and 17.58%, respectively, while increasing the frame rate by 40.91%. The model achieved an mAP@0.50–0.95 of 92.10%, BF1 of 86.28%, and BIoU of 74.99%. Compared with other instance segmentation models, YOLO-Ghost achieved a precision of 99.96%, a recall of 99.69%, and a detection speed of 454.55 f/s. For ESI estimation, the predicted values showed good agreement with manual measurements, with an R² of 0.8184, MAE of 0.03219, and RMSE of 0.03681. The results indicate that the proposed method can achieve non-contact, automated, and accurate measurement of ESI, and provides technical support for high-throughput automated phenotypic data collection in layer breeding. Full article