Search Results (5,688)

Neoproterozoic iron formations (NIFs), deposited during Cryogenian glaciation events, are critical for understanding early Earth oxidation events and the evolution of glacial–interglacial environments. Apatite, a common accessory mineral in iron formations, holds significant implications for sedimentary environments and diagenetic processes, but these aspects remain underexplored. This study focuses on the Hengyang NIF in the Nanhua Basin, South China. Using whole-rock geochemistry and major and trace element analysis of apatite, we investigate the environmental significance of apatite and associated diagenetic processes. Our results show that the Hengyang NIF are formed through the mixing of low-temperature hydrothermal fluids, seawater, and terrigenous detrital materials, with hydrothermal contributions increasing progressively from the bottom to the top of the iron formation layers. Whole-rock geochemical proxies indicate that the depositional water column evolved from relatively oxidizing to weakly oxidizing conditions. The study further demonstrates that the rare earth element patterns in apatite, characterized by middle rare earth element enrichment, are primarily controlled by porewater chemistry during diagenesis. In contrast, Ce anomalies and the V/Cr and V/(V + Ni) ratios in apatite, which are strongly influenced by fluid–rock interactions and magnetite recrystallization, no longer reliably reflect the primary depositional environment. The Th/U ratio in apatite, due to its geochemical stability, aligns with whole-rock trends and serves as a more reliable redox proxy. Based on these findings, we propose a three-stage depositional-diagenetic model: the early and late stages are characterized by high-energy, rapid sedimentation with minimal diagenetic modification, while the middle stage is dominated by low-energy, stagnant conditions with slow sedimentation rates, leading to prolonged diagenesis and significant decoupling of mineral geochemical signatures. This study emphasizes the need to distinguish between sedimentary and diagenetic signals when using mineral geochemical proxies to reconstruct paleoenvironments and provides new insights into the genesis of Neoproterozoic iron formations. Full article

This study explains how strategy–execution gaps become self-reinforcing during digital process transformation in layered manufacturing organizations. Drawing on an embedded qualitative process study of a large Chinese brewery’s transformation (2020–2024), we triangulate 10 semi-structured interviews across hierarchical levels with longitudinal public disclosures to reconstruct the initiative timeline and trace mechanisms across change phases. The analysis shows that platform-based process governance can scale faster than shared meaning and dialog, producing frontline sensemaking gaps and formalistic, top-down communication. These conditions thin employee voice and weaken feedback closure, which in turn erodes the legitimacy of organizational diagnosis and fragments implementation support. As interface problems are handled through local workarounds, management intensifies visibility-based monitoring, further suppressing voice and reinforcing the execution gap. We develop an organizational development process model that centers feedback closure and diagnosis legitimacy as bridging mechanisms linking soft change dynamics (meaning, trust, voice) with hard digital governance (process standards, data infrastructures, monitoring). The model offers actionable implications for leaders to build closure and legitimate diagnosis as operational capabilities throughout transformation. Full article

Forest Rights Mortgage Loans (FRMLs) have grown quickly in China’s collective forest areas, even though the basic conditions for this type of lending remain far from ideal. In many places, forest holdings are small and scattered, property rights are complex and not fully consolidated, and channels for disposing of collateral are limited. Under these circumstances, the Fulin Loan Model (FLM) in Fujian provides a useful case for understanding how forest-rights lending can still function in practice. Drawing on fieldwork, semi-structured interviews, and process tracing, this study explores both how the model was established and how it has been sustained over time. The analysis suggests that the FLM is neither a straightforward market-based lending tool nor merely a top-down policy arrangement. Rather, it relies on a more mixed form of governance in which local government support, banking procedures, and village-level social relations are brought together through specific organizational arrangements. These arrangements help lower the costs of early institutional experimentation, distribute and manage lending risks, and translate locally rooted trust into a form of credit support that formal financial institutions can recognize. As a single-case study, the FLM points to one possible way in which rural finance can be made workable under conditions of incomplete markets and strong social embeddedness. Full article

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe. Full article

Arid Central Asia (ACA) is a major source of atmospheric dust in the Northern Hemisphere; however, the evolutionary models and driving mechanisms of Holocene aeolian activity in this region remain debated. Based on 13 reliable AMS ¹⁴C dates from the Sayram Lake SLM2009 sediment core, this study reconstructs the Holocene sequence in aeolian activity through end-member modeling analysis (EMMA). It evaluates its relationship with regional atmospheric circulation. Four end-members were identified from base to top: EM1, with a modal grain size of 7.58 μm, represents low-energy suspension deposition; EM2 (26.30 μm) reflects lacustrine hydrodynamic processes; while EM3 (52.48 μm) and EM4 (416.86 μm) serve as proxies for regional aeolian activity. The results indicate that aeolian activity was relatively strong during the early Holocene (reaching peaks at 11.7–11.2 and 9.2–8.1 cal ka BP), significantly intensified during the mid-Holocene (7.3–5.3 cal ka BP), and gradually weakened in the late Holocene (since 4.0 cal ka BP). Comparison of the aeolian record from Lake Sayram with Greenland ice cores, North Atlantic ice-rafted debris events, and the GISP2 K⁺ record indicates that variations in aeolian activity in arid Central Asia are closely linked to the Northern Hemisphere climate system. We propose that these variations were primarily modulated by large-scale atmospheric circulation, driven by the synergistic interaction between the Siberian High and the mid-latitude westerlies. Full article

The prediction of dynamic responses of offshore wind turbine foundations under wind-wave-current multi-field coupled loads is the cornerstone of safety in offshore wind power engineering. The currently widely adopted equivalent load application method, while computationally efficient, simplifies loads into concentrated forces applied at the pile top and tower top, neglecting fluid-structure dynamic interaction mechanisms, which leads to deviations in response predictions. To overcome this limitation, this paper proposes a high-precision bidirectional fluid-structure interaction numerical framework. The fluid domain employs computational fluid dynamics (CFD) to construct an air-seawater two-phase flow model, utilizing the standard k-ε turbulence model and nonlinear wave theory to accurately simulate complex marine environments. The solid domain establishes a wind turbine-stratified seabed system via the finite element method (FEM), describing soil-rock mechanical properties based on the Mohr-Coulomb constitutive model. Comparative studies indicate that the equivalent static method significantly underestimates the displacement response of pile foundations, particularly under the extreme shutdown conditions examined in this study. This value should be interpreted as a case-specific observation rather than a universal deviation, and the discrepancy may vary with sea state, wind speed, current velocity, and wind–wave misalignment, thereby leading to non-conservative estimates of stress distribution. In contrast, the fluid-structure interaction method can reveal key physical processes such as local flow acceleration and wake–interference effects around the tower and the parked rotor under shutdown conditions, and the nonlinear interaction and resistance-increasing mechanisms between waves and currents. This model provides a reliable tool for safety assessment and damage evolution analysis of wind turbine foundations under extreme marine conditions, promoting the transformation of offshore wind power structure design from empirical formulas to mechanism-driven approaches. Full article

In this study, a non-typical luminescent organosilicone was synthesized through a click reaction and used as a cross-linker to cure hydroxyl-terminated dimethylsilicone oil at room temperature via the sol–gel process, followed by application as a coating on a glass surface. This organosilicone film functions effectively as a luminescent down-shifting (LDS) material. Additionally, the presence of methyl groups and voids in the structure imparts a low refractive index, allowing it to serve as an anti-reflective (AR) layer. Optical and structural analyses on organosilicone-coated glass samples were conducted, and the dual-functional layer was applied to the glass cover of a perovskite solar panel to evaluate its performance. The coating not only enhanced light transmission as an AR layer but also converted UV light into blue light, which was absorbed by the solar cell. The results indicated improved solar panel performance, particularly in short-circuit current (Isc), external quantum efficiency (EQE) in the UV wavelength range, and overall efficiency. Overall, this material is a promising candidate for solar panel applications owing to maximized UV absorption for LDS, preserved transparency of the top cover glass, and room-temperature gelation, which facilitates repair of the dual-functional coating. Full article

Digital transformation is reshaping societies by promoting the adoption of advanced technologies. Moreover, the digitization of public services has become an important focus for governments. In this paper, digital government performance indicators are analyzed to improve the efficiency of digitizing public services. Based on this awareness, the seven main criteria and twenty-one sub-criteria are determined. Then, a fuzzy decision-making framework is proposed to evaluate digital government performance across 165 countries as alternatives. To the best of our knowledge, limited studies have investigated an integrated clustering-based fuzzy decision-making framework for evaluating digital government performance. The intuitionistic trapezoidal fuzzy number-based analytical hierarchy process (ITFNAHP), a part of the introduced framework, is developed to find the weights of the main criteria and sub-criteria. Digital technologies, innovation, and the economy are the most significant criteria for digital government operations. The k-means clustering method is then employed to group the alternatives. The four clusters are obtained from the clustering technique. Next, the technique of order preference similarity to ideal solution (TOPSIS) is introduced to rank the digital governments of each cluster. Switzerland, Rwanda, North Macedonia, and Eswatini are the top choices among others in each cluster, respectively. Additionally, a sensitivity analysis is conducted considering the ten different situations. In addition, the managerial and policy implications are discussed, including the achievement of Sustainable Development Goals (SDGs). Full article

This paper investigates the formation mechanism and key influencing factors of freckle defects that arise during the directional solidification of a novel third-generation nickel-based single crystal superalloy turbine blade. A combined experimental and multi-physics numerical simulation approach was adopted. The results indicate that freckle formation primarily originates from solutal convection, which subsequently triggers a cascade of processes, including the development of convection-induced segregation channels, flow-driven dendrite fragmentation, and the migration and aggregation of dendrite fragments. The severity of freckling is closely dependent on both the casting’s position within the furnace and its local geometric characteristics. Castings located in regions with poorer heating conditions exhibit lower temperature gradients and slower solidification rates, significantly increasing their susceptibility to freckle formation. Similarly, on a given casting, the side subjected to less favorable heating is more prone to freckle initiation. The freckle number varies non-monotonically along the blade height, increasing from 3 to a maximum of 16, with a temporary decrease near the platform and a final reduction near the top. This trend is mainly attributed to thickness-dependent channel segregation, as well as freckle propagation into the interior and coalescence at higher positions. This study provides a crucial theoretical basis for understanding the formation mechanism of freckle defects in nickel-based single crystal superalloys and offers valuable guidance for optimizing blade manufacturing processes, reducing solidification defects, and enhancing blade quality and service performance. Full article

This paper compares the daily return volatility by four leading international indices: JSE Top 40, FTSE 100, Nikkei 225 and S&P/ASX 200. The return series are modelled in ARMA process, where ARMA(1,3) values are taken for JSE Top 40 and S&P/ASX 200, ARMA(0,0) for FTSE 100, and ARMA(1,2) for Nikkei 225. The volatility is modelled in APARCH and GJR-GARCH (e.g., under various conditional distributions including Student-t (STD), skewed Student-t (SSTD), generalised error distribution (GED), skewed generalised error distribution (SGED), and generalised hyperbolic distribution (GHYD)). Model selection results based on information criteria indicate that the APARCH models outperform their GJR-GARCH counterparts in all cases. In particular, the ARMA(p,q)-APARCH(1,1) with SSTD is most suitable for the JSE Top 40 and the FTSE 100. The model that best describes the Nikkei 225 is an ARMA(1,2)–APARCH(1,1) model with SGED, and the S&P/ASX 200 fits an ARMA(1,3)-APARCH(1,1) model with GHYP. Among the indices, the FTSE 100 has the highest volatility persistence, while the Nikkei 225 responds more quickly to shocks. This out-of-sample forecasting test shows that ARMA(p,q)-APARCH(p,q) provides more accurate volatility predictions, especially for JSE Top 40 and S&P/ASX 200 investors. Full article

Within the framework of behavioral ecology and conservation, individual recognition plays a critical role in the research on wild social animals at the individual level. Traditional identification methods often rely on long-term field experience or invasive physical tagging. Recent advances in deep learning enable non-invasive individual recognition under natural conditions; however, the effectiveness of facial detection and identification depends on species-specific facial characteristics, environmental conditions, and dataset scale. In this study, we used 3385 images from 18 identified wild Tibetan macaques (Macaca thibetana) to develop an individual recognition system, TMacaque-FaceNet, integrating You Only Look Once (YOLO) for face detection and a Vision Transformer (ViT) for individual classification. The results showed that the Tibetan macaque face detector achieved a mAP@0.5 of 0.971, with a precision of 0.974 and a recall of 0.931. The individual recognizer for the wild Tibetan macaque social group achieved a top-1 accuracy of 96.33% on the test set. On an event-wise (temporal holdout) validation set comprising 90 images (5 images per individual), the recognizer achieved a top-1 accuracy of 95.56%. Gradient-weighted attention rollout analyses further revealed that the model focused on biologically meaningful facial regions, supporting the interpretability of the recognition process. Our results provide a new automated facial recognition method to non-invasively monitor Tibetan macaque individuals in natural environments. It provides a practical tool to facilitate automated behavioral observation, social network analysis, and long-term population monitoring of wild non-human primates. Full article

Prompt 3D mapping of grain storage is essential for effective management. However, standard mapping algorithms encounter a number of challenges, with the typical granary environment containing dust, grain piles, and narrow aisles. A single robotic agent is not able to provide complete area coverage, and most multi-robot approaches involve re-scanning the same areas due to a lack of explicit viewpoint-based task allocation processes. In order to overcome the above issues, we propose an air–ground collaborative exploration system for complex multi-grain pile scenarios. Exploration redundancy can be reduced by estimating the advantages of viewpoints through ray tracing and assigning the tops of the grain piles to aerial robots with ground vehicles in lower regions and narrow aisles. In order to manage dense dust (5–15 mg/m³), the quality-aware fusion strategy evaluates the reliability of the distance and point density of the sensing to reduce the influence of degraded aerial depth data. Moreover, mapping relies on LiDAR data to ensure mapping quality. A mechanism for re-scanning to enable coverage-driven exploitation of insufficiently explored regions is subsequently proposed. The simulation results show that the design achieved a grain pile coverage of 97.2%, with the total exploration time reduced by 20.1% over single-robot baselines. The results indicate that viewpoint-aware task allocation and dust-sensitive perception fusion can offer a practical solution for autonomous inspection in GPS-restricted, dust-rich industrial environments, such as granary facilities. Full article

Growing Angus steers (n = 20) were blocked by weight and randomly assigned to one of two treatment groups: Control group (CON, n = 10) fed a feedlot ration ad libitum, or a ruminally protected hydrogenated fat-embedded calcium gluconate (HFCG) treatment group (HFCG, n = 10), which was fed the control ration top-dressed at 16 g/head/day for 55 days. During the slaughter process, digesta samples were collected from the cecum, colon, and rectum. Acetate concentrations were greater in the cecal and rectal digesta of steers (p ≤ 0.05) in the HFCG treatment group. Propionate concentrations were greater in the cecal, colonic, and rectal (p ≤ 0.05) digesta of steers in the HFCG treatment group. Butyrate concentrations were greater (p = 0.098) in the colon digesta of steers in the HFCG treatment group; however, they were not different in the cecal and rectal digesta. To determine the microbial composition within each section of the hindgut, DNA was extracted, and 16S rRNA gene sequencing was performed. Data were analyzed using a General Linear Model with dietary treatment as the main effect. Species richness in the cecum, colon, and rectum was not different between treatments. Erysipelotrichaceae, Peptostreptococcaceae, and Atopobiaceae abundances were increased (p ≤ 0.05) in the cecal bacterial community of steers in the HFCG group, while a significant decrease (p ≤ 0.05) in Rikenellaceae and Muribaculaceae abundances was recorded within the same bacterial community. In the colon bacterial community of steers in the HFCG group, Ruminococcaceae and Muribaculaceae abundances were elevated (p ≤ 0.05), while there was a reduction (p ≤ 0.05) in Lachnospiraceae, Erysipelotrichaceae, Atopobiaceae, and Peptostreptococcaceae abundances. Paeniclostridium, Romboutsia, and Turicibacter abundances were increased (p ≤ 0.05) in the cecal bacterial community of steers in the HFCG group, while there was a decrease (p ≤ 0.05) in Rikenellaceae_RC9 _gut_group abundance within the same bacterial community. In the colon microbiota of steers in the HFCG group, Turicibacter abundance was decreased (p ≤ 0.05). Supplementing growing steers with HFCG impacted some members of the bacterial populations, which have important roles in gut homeostasis and health, along with the formation of beneficial end-products in the gastrointestinal tract. Full article

The development and operation of cooperative digital library systems face management problems that do not always have a complete solution. These cooperative systems currently need to incorporate new artificial intelligence solutions to develop intelligent services for decision-making and recommendation mechanisms for which there is not an integral management solution. Indeed, the management of these intelligent services is outside the scope of traditional management systems for digital collections. This paper proposes using an intelligent management framework to address these issues. Basically, we use a top-down design approach based on six abstractions and four refinement techniques to design a management model that integrates suitable cooperative models, system behaviors, software architecture and processes to manage cooperative digital library systems that incorporate intelligent services. The work is novel since it concentrates on the major management problems found with concrete cooperative scenarios. And the intelligent management frameworks are reviewed and evolved to solve these management problems with digital library systems. Further, the approach is evaluated, designing an intelligent management framework for cooperative, intelligent digital library systems used in a government organization for current digital transformations. Finally, a qualitative analysis methodology is used with this case study, collecting relevant data with a questionnaire, and showing and discussing major results. These results demonstrate, for example, that our participants consider our approach a clear, feasible and useful solution for digital transformations. Further, they would strongly recommend our solution for other government organizations. Full article

A stereo vision framework is designed to improve disparity estimation in occluded and boundary regions, targeting autonomous driving scenarios. The proposed architecture combines frozen Distilled Internally Normalized Optimized Version 2 features with a modular three-stage attention fusion strategy, which consists of bottom-up semantic propagation, top-down detail enhancement, and cross-view attention mechanisms. These stages jointly enforce semantic consistency, structural integrity, and accurate correspondence modeling. The fused features are then processed by an Iterative Geometry Encoding and Volumetric regression-based disparity estimation module for multi-stage regression and iterative refinement. A three-phase training pipeline is employed, including pretraining on SceneFlow, fine-tuning on virtual Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) benchmarks, and adaptation to the KITTI and ETH Zurich 3D benchmark dataset. The model achieves an out-of-center, non-occluded pixel error of 7.45% on KITTI2012 and a D1-all error of 4.10% on KITTI2015. Beyond quantitative performance, the proposed method produces visually superior disparity maps. The enhancements of boundary sharpness, occlusion completion, and structural coherence demonstrate the strong potential of the proposed algorithm for real-world deployment in dynamic and complex environments. Full article