Search Results (59)

This study investigates the influence of inclination on a submerged plate (SP) device acting as both a breakwater (BW) and a wave energy converter (WEC) subjected to representative regular and realistic irregular waves of a sea state across 11 inclination angles. Numerical simulations were conducted using ANSYS Fluent. Regular waves were generated by Stokes’s second-order theory, while the WaveMIMO technique was employed to generate irregular waves. Using the volume of fluid (VOF) method to model the water–air interaction, both approaches generate waves by imposing their vertical and horizontal velocity components at the inlet of the wave flume. The SP’s performance as a BW was analyzed based on the upstream and downstream free surface elevations of the device; in turn, its performance as a WEC was determined through its axial velocity beneath the plate. The results indicate that performance varies between regular and irregular wave conditions, underscoring the importance of accurately characterizing the sea state at the intended installation site. These findings demonstrate that the inclination of the SP plays a critical role in balancing its dual functionality, with certain configurations enhancing WEC efficiency by over 50% while still offering relevant BW performance, even under realistic irregular sea conditions. Full article

In response to the escalating global energy demands, the optimization of energy efficiency has emerged as a linchpin for sustainable development. This study considers the potential of energy conservation and emission reduction in one of the most economically vibrant and resource-intensive regions in China, the Yangtze River Economic Belt, encompassing 11 provinces and cities. The SBM-Undesirable model is used to measure the energy efficiency and analyze the temporal-spatial distribution. Moran’s I is employed to analyze the overall spatial pattern and local regional differences in energy efficiency. The systematic analysis shows that the temporal fluctuation exists in the development of energy efficiency, and the average of the Yangtze River Economic Belt exhibits a development pattern of “downstream > midstream > upstream” from the spatial perspective. The upstream region would require way more effort than others to decarbonize and improve efficiency. At the municipal level, the overall energy efficiency of 11 provinces and cities fails to reach an efficient state, and potential for improvement exists. Moreover, the potential model of energy conservation and emission reduction is constructed. We further explore the pathways of energy efficiency improvement for each region in the Yangtze River Economic Belt, including pathways of “High-Efficiency Type”, “High Emission Reduction Potential”, and “Extensive Development Type”. Full article

The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations from Space (WOfS) platform, to quantify the hydrological impacts of GERD’s three filling phases (2019–2022) on Sudan’s Roseires Dam. Using Sentinel-2 satellite data processed through the Open Data Cube framework, we analyzed water extent changes from 2018 to 2023, capturing pre- and post-filling dynamics. Results show that GERD’s water spread area increased from 80 km² in 2019 to 528 km² in 2022, while Roseires Dam’s water extent decreased by 9 km² over the same period, with a notable 5 km² loss prior to GERD’s operation (2018–2019). These changes, validated against PERSIANN-CDR rainfall data, correlate with GERD’s filling operations, alongside climatic factors like evapotranspiration and reduced rainfall. The study highlights the potential of Earth Observation (EO) technologies to support transparent, data-driven transboundary water governance. Despite the Cooperative Framework Agreement (CFA) ratified by six upstream states in 2024, mistrust persists due to Egypt and Sudan’s non-ratification. We propose enhancing the Nile Basin Initiative’s Decision Support System with EO data and AI-driven models to optimize water allocation and foster cooperative filling strategies. Benefit-sharing mechanisms, such as energy trade from GERD, could mitigate downstream losses, aligning with the CFA’s equitable utilization principles and the UN Watercourses Convention. This research underscores the critical role of EO-driven frameworks in resolving Nile Basin conflicts and achieving Sustainable Development Goal 6 for sustainable water management. Full article

To investigate the spatiotemporal distribution characteristics of nitrogen and phosphorus in the water and sediment of the Danjiangkou Reservoir, the source of the Middle Route of China’s South-to-North Water Diversion Project, we designed a year-long monitoring program. The water and sediment samples were collected from 13 sampling points in the upstream and downstream areas over the year. The results revealed significant spatial heterogeneity in N and P concentrations, with higher levels of total nitrogen, nitrate nitrogen, and nitrite nitrogen in the upstream area compared to the downstream area (p < 0.01). Total phosphorus was also significantly higher in the upstream area (p < 0.05). Seasonal variations were observed, with TN and TP levels peaking in February and August, respectively. The TN:TP ratio indicated a severe P-limited state in most periods, transitioning to a co-limited state of N and P during summer. Sediment analysis showed that TN and TP concentrations were higher in the upstream area, with no significant differences between upstream and downstream on an annual basis, exhibiting strong stoichiometric internal stability. However, seasonal differences were noted, particularly in February and November. This study highlights the complex interactions between water and sediment, emphasizing the role of sediment resuspension, water flow, and seasonal changes in nutrient dynamics. These findings provide a scientific basis for the management and protection of water quality in the Danjiangkou Reservoir, ensuring its role as a critical water source for the South-to-North Water Diversion Project. Full article

In the Amazon, nearly one million people remain without reliable access to electricity. Moreover, the rural electricity grid is a mostly single-phase, ground-return type, with poor energy quality and high expenses. This study examines very low-head micro-hydropower (MHP) sites in the Amazon, emphasizing the integration of multiple axial-flow turbines. It includes an analysis of flow duration curves and key curves, both upstream and downstream, to design an MHP plant with multiple units targeting maximized energy yield. The presence of multiple turbines is crucial due to the substantial annual flow variation in the Amazon rivers. One contribution of this work is its scalable framework for ultra-low-head and high flow variability in small rivers, which is applicable in similar hydrological configurations, such as those typical of the Amazon. The design applies the minimum pressure coefficient criterion to increase turbine efficiency. Computational Fluid Dynamics (CFD) simulations forecast turbine efficiency and flow behavior. The CFD model is validated using experimental data available in the literature on a similar turbine, which is similarly used in this study for cost reasons, with discrepancies under 5%, demonstrating robust predictions of turbine efficiency and head behavior as a function of flow. This study also explores the implications of including inlet guide vanes (IGVs). We use a case study of a small bridge in Vila do Janari, situated in the southeastern part of Pará state, where heads range from 1.4 to 2.4 m and turbine flow rates span from 0.23 to 0.92 m³/s. The optimal configuration shows the potential to generate 63 MWh/year. Full article

The fundamental principles of “sustainable development” and “green” promoted by ESG align with the concept of “green and sustainable” development. Enhancing enterprise ESG is a methodical endeavor that necessitates enterprises to possess ESG investment capabilities, coordinate many stakeholders, and leverage the influence of prominent market players. State-owned enterprises (SOEs) possess a specific level of support within a nation’s economy. SOEs serve as a fundamental pillar of China’s socialist economic system with distinctive characteristics, significantly influencing business conduct and reinforcing corporate value orientation. Consequently, the capacity of SOEs to assume a strategic leadership role in enhancing supply chain ESG performance is of paramount importance for the general elevation of ESG standards among Chinese enterprises. Limited research has investigated the transmission effect of the ESG performance among chain enterprises from a supply chain viewpoint, particularly regarding the pivotal role of SOEs in enhancing the ESG performance of these entities. This article examines the influence of SOEs’ ESG performance on the ESG performance of supply chain enterprises, focusing on the spillover effects of SOEs’ ESG performance within the supply chain context. It investigates how SOEs lead upstream and downstream enterprises in enhancing their ESG performance, aiming to address the existing cognitive gap in this area and provide substantial evidence for pertinent theories and practices. This article, employing an empirical research methodology, discovers that the ESG performance of state-owned supply chain core enterprises significantly enhances the ESG performance of enterprises in a supply chain, while non-state-owned supply chain core enterprises do not exhibit this effect. Furthermore, research indicates that this effect is asymmetric: when the supply chain core enterprise is a SOE and the enterprises in the supply chain are non-state-owned, the leading effect is more pronounced, and this effect is more powerful for upstream enterprises. The heterogeneity test reveals that the impact of the ESG performance is more pronounced in larger state-owned supply chain core enterprises that have been publicly listed for an extended duration and operate in highly competitive markets. The conclusions of this essay address the deficiencies of current research and provide significant practical implications for the development of green supply chains in the contemporary era. Full article

Traffic sign classification (TSC) based on deep neural networks (DNNs) plays a crucial role in the perception subsystem of autonomous driving systems (ADSs). However, studies reveal that the TSC system can make dangerous and potentially fatal errors under adversarial attacks. Existing defense strategies, such as adversarial training (AT), have demonstrated effectiveness but struggle to generalize across diverse attack scenarios. Recent advancements in self-supervised learning (SSL), particularly adversarial contrastive learning (ACL) methods, have demonstrated strong potential in enhancing robustness and generalization compared to AT. However, conventional ACL methods lack mechanisms to ensure effective defense transferability across different learning stages. To address this, we propose a robust pre-training–fine-tuning algorithm for meta-adversarial defense (RPF-MAD), designed to enhance the sustainability of adversarial robustness throughout the learning pipeline. Dual-track meta-adversarial pre-training (Dual-MAP) integrates meta-learning with ACL methods, which improves the generalization ability of the upstream model to different adversarial conditions. Meanwhile, adaptive variance anchoring robust fine-tuning (AVA-RFT) utilizes adaptive prototype variance regularization to stabilize feature representations and reinforce the generalizable defense capabilities of the downstream model. Leveraging the meta-adversarial defense benchmark (MAD) dataset, RPF-MAD ensures comprehensive robustness against multiple attack types. Extensive experiments across eight ACL methods and three robust fine-tuning (RFT) techniques demonstrate that RPF-MAD significantly improves both standard accuracy ($SA$) by 1.53% and robust accuracy ($RA$) by 2.64%, effectively enhances the lifelong adversarial resilience of TSC models, achieves a 13.77% improvement in the equilibrium defense success rate ($EDSR$), and reduces the attack success rate ($ASR$) by 9.74%, outperforming state-of-the-art (SOTA) defense methods. Full article

Industrial Internet Platforms (IIPs) provide critical value for supply chain management. The motivation for value creation and capture by supplier firms increasingly depends on acquiring and processing customer-oriented information, with geographical distance to customers being a key factor in assessing costs and benefits. This study investigates whether firms’ IIP adoption increases or decreases the geographical distance to their customers, utilizing supply chain data and digital transformation metrics from China’s A-share listed firms in the new energy vehicle industry. The study finds that an increased adoption level of IIPs by focal firms leads to a greater average geographical distance to their customers. This conclusion remains valid after addressing endogeneity concerns and performing several robustness checks. Moreover, the effect of IIP adoption on expanding the geographic scope of sales is significant when the focal firm is more specialized in the supply chain, more socially embedded, located in China’s eastern region, or acts as an upstream/downstream complement to complete vehicle manufacturers. No significant difference in the impact of IIP adoption is observed between private and state-owned enterprises. This study combines platform innovation and transaction cost theories, offering new insights and policy implications regarding the spatial impact of digital technology adoption in advanced manufacturing and related service industries. Full article

To overcome the limitations of rigid body limit equilibrium methods in earth rock dam slope stability analysis, this study develops a system reliability framework using the finite element strength reduction method (FEM-SRM). An elastoplastic finite element model simulates dam construction and impoundment, identifying potential slip pathways. Each pathway, treated as a parallel system of shear-failed elements, is analyzed via the response surface method to derive explicit limit state functions. Reliability indices are computed using an improved first-order second-moment method, while interdependencies are assessed through stepwise equivalent linearization. System reliability is determined using Ditlevsen’s narrow bound method. Applied to a 314 m earth rockfill dam, three critical slip pathways were identified: upstream shallow (reliability index is 6.94), upstream deep (reliability index is 6.87), and downstream deep (reliability index is 7.44), with correlation coefficients between 0.26 and 0.89. The system reliability index (6.81) significantly exceeds the code target (4.2), highlighting the method’s ability to integrate material randomness, stress-strain nonlinearity, and multi-slip interactions. This framework provides a robust probabilistic approach for high earth rock dam stability assessment, enhancing engineering safety evaluations. Full article

With the rise of online education platforms, the vast number of accumulated data conceal patterns regarding the evolution of students’ knowledge states. Tracking a student’s knowledge state through their historical interaction records is known as knowledge tracing, a critical task in intelligent educational systems. Current research predominantly focuses on designing high-performance networks to enhance knowledge state tracking capabilities, often employing simplistic methods for question embedding, such as one-hot encoding or graph-based representations. This paper proposes a pre-training model for multi-concept question embedding (Pre-training Multi-concept Question Embedding, PMQE), aimed at providing a robust upstream tool for knowledge tracing and intelligent education fields. We first leverage the textual information in the dataset, representing concepts using their names or descriptive text embeddings. We then utilize the structural information from the question–concept graph, applying graph convolutional networks to derive question embeddings that integrate both textual semantics and structural information. Additionally, auxiliary information (such as question difficulty) is utilized for joint model optimization. The model outputs include question embeddings, question–concept weight matrices, and question difficulty, which can be used for knowledge tracing and other downstream tasks. Experiments conducted on two real-world datasets and multiple models validate the effectiveness of our approach. Full article

This study investigates the time-resolved aerodynamics in the cavity regions of a full-scale, high-speed, low-pressure turbine stage representative of geared turbofan engines. The turbine stage is tested in the von Karman Institute’s short-duration rotating facility at different purge rates (PR) injected through the upstream hub cavity. Spectra from the shroud and downstream hub cavity show perturbations linked to blade passing frequency and rotor speed. Asynchronous flow structures associated with ingress/egress mechanisms are observed in the rim seal of the purged cavity. At 0% PR, the ingress region extends to the entire rim seal, and pressure fluctuations are maximized. At 1% PR, the instability is suppressed and the cavity is sealed. At 0.5%, the rim-seal instability exhibits multiple peaks in the spectra, each corresponding to a state of the instability. Kelvin–Helmholtz instabilities are identified as trigger mechanisms. A novel technique based on the properties of the cross-power spectral density is developed to determine the speed and wavelength of the rotating structures, achieving higher precision than the commonly used cross-correlation method. Moreover, unlike the standard methodology, this approach allows researchers to calculate the structure characteristics for all the instability states. Spectral analysis at the turbine outlet shows that rim-seal-induced instabilities propagate into regions occupied by secondary flows. A methodology is proposed to quantify the magnitude of the induced fluctuations, showing that the interaction with secondary flows amplifies the instability at the stage outlet. Full article

Although various studies on nitrous oxide as a prospective green propellant have been recently explored, a polyethylene nitrous oxide catalytic decomposition hybrid thruster was barely demonstrated due to an inordinately high catalyst preheating time of a heater, which led to the destruction of components. Therefore, hydrogen peroxide was used as a preheatant, a substance to preheat, with a dual-catalyst bed. The thruster with polyethylene (PE) as a fuel, N₂O as an oxidizer, H₂O₂ as the preheatant, Ru/Al₂O₃ as a catalyst for the oxidizer, and Pt/Al₂O₃ as a catalyst for the preheatant was arranged. A preheatant supply time of 10 s with a maximum catalyst bed temperature of more than 500 °C and without combustion and an oxidizer supply time of 20 s with a burning time of approximately 15 s were decided. Because the catalyst bed upstream part for decomposing the preheatant was far from the post-combustion chamber, the post-combustion chamber pressure increased and the preheatant mass flow rate decreased after a hard start during the preheatant supply time. Moreover, because the catalyst bed upstream part primarily contributed to preheating, the maximum catalyst bed temperature was less than the decomposition temperature of the preheatant during the preheatant supply time. Additionally, because the catalyst bed downstream part for decomposing the oxidizer was far from the post-combustion chamber, the post-combustion chamber pressure decreased and then increased during a transient state in the oxidizer supply time. Full article

Tunnel fires often lead to vehicles being trapped inside, causing the “blocking effect”. In this work, fire plume behavior and the maximum ceiling temperature rise in a curved tunnel with blocked vehicles under longitudinal ventilation conditions are studied numerically. The results show that, in curved tunnels, the fire plume in the quasi-stable state exhibits dynamic deflections between the concave and convex walls of the tunnel, so the location of high-temperature zones varies accordingly. The flow field structure in the near field of the blockage and the fire source is complex but can be decoupled into four characteristic sub-structures, i.e., the free shear layer, recirculation I above the vehicle blockage, recirculation II behind the downstream of the blockage, and recirculation III at the top of the tunnel. Recirculation I and II pull the fire plume upstream, while free shear layer and recirculation III pull the flame downstream. The final plume deflection direction depends on the relative strengths of these two pulling forces. As the longitudinal air velocity increases, the plume deflection direction changes from downstream to upstream of the fire source, forming the “downstream tilt—touch the ceiling above the fire source—upstream tilt” mode, resulting in the maximum ceiling temperature rise fluctuating in a decreasing-increasing-decreasing trend. Moreover, the higher the blocking ratio, the lower the critical air velocity required to induce the transition of the plume deflection directions, e.g., a critical wind speed of 3 m/s for a blockage ratio of 0.46 and a critical wind speed of 1 m/s for a blockage ratio of 0.62. Finally, a semi-empirical equation of the maximum ceiling temperature rise in curved tunnels, considering both longitudinal wind and the vehicle blocking ratio, is proposed and validated. This work highlights the multi-dimensional and non-stable plume behavior pattern in a complex tunnel fire scenario, thus providing a deeper understanding to improve the classical tunnel fire dynamic system. Full article

This study analyzes the patent landscape of Saudi Arabia from the announcement of Saudi Vision 2030 in late April 2016 to September 2024, utilizing the Patsnap database to evaluate patent grants across various organizations. The findings reveal a gradual increase in patent registrations, with Saudi Aramco leading in patent grants, followed by King Faisal University (KFU), King Fahd University of Petroleum and Minerals (KFUPM), King Abdullah University of Science and Technology (KAUST), and King Abdulaziz University (KAU). SABIC, a prominent industry player in Saudi Arabia, has registered most of its patents using its European Head Office address and holds extensive collaborations with international partners, generating numerous patents. The analysis identifies the top patent offices where KSA organizations seek protection, including the United States Patent and Trademark Office (USPTO), Saudi Authority for Intellectual Property (SAIP), European Patent Office (EPO), the China National Intellectual Property Administration (CNIPA), and the German Patent and Trade Mark Office. However, the limited number of registrations at the SAIP highlights a need for improvement. The primary application domains encompass borehole/well accessories, measurement devices, organic chemistry, computing, and chemical/physical processes. The landscape reveals that Saudi Aramco and KFUPM focus predominantly on upstream and downstream technologies, while KAU, KFU, and KAUST concentrate on life sciences. Key findings indicate a significant increase in patent activity since the vision announcement, suggesting a growing focus on innovation within Saudi Arabia. However, the concentration of patents among a few major players (Saudi Aramco and SABIC) and the underrepresentation of patents filed with the Saudi Authority of Intellectual Property (SAIP) highlight areas for improvement. This study emphasizes the necessity to support small and medium enterprises (SMEs) and healthcare research institutions to foster broader participation in innovation and protect novel technologies. This research contributes valuable insights into the current state of patenting activities in Saudi Arabia and outlines opportunities for enhancing the country’s innovation ecosystem. Full article

The historic New York City (NYC) Watershed Memorandum of Agreement signed in 1997, which established the Watershed Protection and Partnership Program (WPPP), aimed to safeguard water quality for downstream NYC communities and enhance community vitality within the upstream watershed. Up to now, the focus has been on maintaining water quality, with less attention on the socioeconomic and demographic well-being of the upstream communities. This study bridges this gap by examining the socioeconomic and demographic trends within the upstream watershed communities. We collected census data from the U.S. Census Bureau and American Community Survey (ACS) from 1990, 2000, 2010, and 2020. The data were collected primarily for the Catskill-Delaware Watershed region, as well as the five immediate watershed counties, New York State, the U.S., and two rural counties similar in population density to the Watershed to serve as comparison areas. This study analyzed demographic factors and economic, and community characteristics to identify trends and shifts over time. Our findings reveal the demographic shift towards an older retiree population, decreasing labor force, higher unemployment rates, higher disparities in income distribution, and longer commute times in the Watershed compared to our comparison areas. The Watershed also experiences higher home vacancies than the comparison areas, suggesting a shift towards seasonal or second-home use of properties. While this study offers insights into socioeconomic and demographic shifts in watershed communities, its reliance on census data may limit the precision in establishing causal links between watershed protection measures and these shifts. Incorporating qualitative analysis in future research will deepen our understanding of these relationships. Full article