Search Results (18,685)

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R² compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R² values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability. Full article

The large-scale production of waste plastics has brought serious environmental pollution problems and its recycling and high value-added utilization technology remains a global challenge. Therefore, this study uses waste polyethylene (PE) to prepare high-performance polyethylene composite modified asphalt (HPEA), solving the problem of poor stability and low temperature performance of traditional plastic modified asphalt, while achieving high value-added utilization of waste plastics. A high-performance polyethylene composite modifier (HPE) was prepared through mechanochemical and thermochemical interactions. Then HPEA with different HPE content and styrene-butadiene-styrene (SBS) modified asphalt (SBSMA) with different SBS content were prepared. Compare and analyze the conventional performance, storage stability, anti-aging performance and microscopic properties of HPEA and SBSMA. The results are as follows: (1) the conventional performance of HPEA is comparable to, or superior to, that of SBSMA. The addition of HPE resulted in a significant decrease in asphalt penetration. The modification effect achieved by adding 3–5% SBS to Kunlun 70# asphalt is equivalent to that achieved by incorporating 4–6% HPE. (2) HEPA exhibits good storage stability and no obvious segregation phenomenon. When the HPE content changes from 4% to 8%, the maximum difference in 48 h softening point of HPEA is 1.1 °C, which is significantly smaller than the 48 h softening point difference of SBSMA when the SBS content changes from 3% to 5%. (3) When HPE attains a specific concentration, HPEA can exhibit an anti-aging performance that is comparable to, or superior to, that of SBSMA. (4) The infrared spectrum of HPEA closely resembles that of SK70# matrix asphalt. The modification of HPEA primarily involves physical blending, with HPE undergoing development and re-crosslinking within the system, leading to interactions between smaller particles and asphalt, resulting in the formation of a relatively stable three-dimensional spatial structure. Full article

This paper presents a quaternion-based robust sliding-mode controller for quadrotors operating under significant wind disturbances. The proposed control method improves the reliability and efficiency of quadrotor control by eliminating the singularity problem inherent in the Euler angle method. The quadrotor dynamics and wind environment are modeled, and dynamic analysis is performed via numerical simulation. A realistic wind model is used, similar to a combination of deterministic and statistical models. The Lyapunov stability theory is utilized to prove the convergence and stability of the proposed control system. The simulation results demonstrate that the quaternion-based controller enables the quadrotor to follow the desired path and remain stable, even under external wind disturbances. Specifically, both position and attitude converge to the desired values within 10 s, demonstrating stable performance despite the challenging wind disturbances in both scenarios. Scenario 1 features turbulence with an average wind speed of 12 m/s and changing wind directions, while Scenario 2 models an environment with wind speeds that change abruptly and discretely over time, coupled with temporal variations in wind direction. Additionally, a comparative analysis with the conventional PD controller highlights the superior performance of the proposed RSMC controller in terms of trajectory tracking, stability, and energy efficiency. The rotor speeds remain within a reasonable and hardware-feasible range, ensuring practical applicability. Full article

Background and Objectives: Acute respiratory distress syndrome (ARDS) is a severe form of acute lung injury with high mortality, characterized by hypoxemic respiratory failure and diffuse lung damage. Despite advancements in care, no definitive biomarkers have been established for ARDS diagnosis and prognostic stratification. Soluble receptor for advanced glycation end-products (sRAGE), a marker of alveolar epithelial injury, has shown promise as a prognostic indicator in ARDS. This study evaluates sRAGE’s utility in predicting 28-day mortality. Materials and Methods: A retrospective cohort study was conducted at a tertiary care ICU in Serbia from January 2021 to June 2023. Adult patients meeting the Berlin definition of ARDS were included. Exclusion criteria included pre-existing chronic respiratory diseases and prolonged mechanical ventilation before diagnosis. Serum sRAGE levels were measured within 48 h of ARDS diagnosis using enzyme-linked immunosorbent assay (ELISA). Clinical severity scores, laboratory markers, and ventilatory parameters were recorded. Logistic regression and survival analyses were used to assess the prognostic value of sRAGE for 28-day mortality. Results: A cohort of 121 patients (mean age 55.5 years; 63.6% male) was analyzed. Non-survivors exhibited higher median sRAGE levels than survivors (5852 vs. 4479 pg/mL, p = 0.084). The optimal sRAGE cut-off for predicting mortality was >16,500 pg/mL (sensitivity 30.4%, specificity 86.9%). Elevated sRAGE levels were associated with greater disease severity and an increased risk of 28-day mortality in ARDS patients, highlighting its potential as a prognostic biomarker. The main findings, while indicative of a trend toward higher sRAGE levels in non-survivors, did not reach statistical significance. Conclusions: The main findings, while indicative of a trend toward higher sRAGE levels in non-survivors, did not reach statistical significance (p = 0.084). sRAGE demonstrates potential as a prognostic biomarker in ARDS and has moderate correlation with 28-day mortality. Integrating sRAGE with other biomarkers could enhance risk stratification and guide therapeutic decisions. The retrospective design limits the ability to establish causation, underscoring the need for multicenter prospective studies. Full article

The food industry generates substantial waste, contributing to environmental challenges, such as pollution and greenhouse gas emissions. Utilizing by-products, particularly fruit peels that are rich in fiber, antioxidants, and vitamins, presents a sustainable approach to reducing waste, while enhancing the nutritional value of food products. Specifically, peach peel can be used to produce gluten-free flours, with increased fiber content and antioxidant properties. Extrusion technology is a highly effective method for developing these functional flours, as it improves digestibility, reduces anti-nutrients, and enhances nutrient bioavailability. This study investigates the potential of combining corn flour with peach peel flour, derived from Royal Summer peachs (RSF), at different concentrations (0%, 5%, and 15%). A factorial experimental design was utilized to evaluate the impact of RSF incorporation on the proximate composition, antioxidant capacity, and functional properties of the flour. The results indicate that flours containing 15% RSF demonstrated significant improvements in terms of the dietary fiber content (5.90 g per 100 g⁻¹) and antioxidant capacity (ABTS^•+ 745.33 µmol TE per 100 g⁻¹), meeting the “source of fiber” labelling requirements. The glycemic index of the 15% RSF flour was reduced to 78.09 compared to non-enriched flours. The functional properties of the flour, such as swelling and gelation capacities, were also enhanced with RSF incorporation. These findings highlight the potential of RSF-enriched flours in regard to the development of sustainable, health-promoting, plant-based, and gluten-free flours. Full article

Background: Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) are the most prevalent subgroup among NETs and include heterogeneous tumors characterized by different clinical behavior and prognosis. The NETest is a tool based on real-time PCR combined with deep learning strategies to specifically identify tumors with a neuroendocrine genotype. Despite the promising results achieved regarding its utility in the field of GEP-NETs, the NETest has not yet entered into routine clinical practice. Methods: We performed a systematic review aimed at summarizing available evidence on the application of the NETest in both the diagnosis and the prognostic stratification of GEP-NETs. Results: We identified five studies evaluating the diagnostic role of the NETest and nine studies evaluating its prognostic value. The NETest emerged as a reliable biomarker for GEP-NET diagnosis with an accuracy higher than 90%, regardless of tumor stage and grade. However, according to some studies, the NETest showed a low specificity, mainly attributed to interferences with other gastro-intestinal malignancies. In terms of prognostic value, the NETest correlated with the detection of residual disease after surgery in six studies. The NETest was also associated with patients’ survival outcomes, namely progression-free survival (PFS) and overall survival (OS) in three studies. Conclusions: According to current systematic review, the value of the NETest both for diagnosis and for prognosis of GEP-NET emerged as robust across different studies. Further prospective analysis on larger GEP-NET series is encouraged to validate this tool, improving patients’ diagnosis, management, and follow-up. Full article

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts. Full article

Gampsocleis gratiosa Brunner von Wattenwyl, 1862, is a type of omnivorous chirping insect with a long history of artificial breeding. It has high economic value and is also an excellent orthopteran model organism. In this study, 12 types of culture media combined with 16S rRNA sequencing were employed to isolate 838 bacterial strains from the gut of G. gratiosa. After sequence comparison, a total of 98 species of bacteria were identified, belonging to 3 phyla, 5 classes, 11 orders, 20 families, and 45 genera. Firmicutes and Proteobacteria accounted for the majority (92.86%). At the order level, Enterobacteriaceae, Bacillales, and Lactobacillales predominated (79.59%). At the genus level, Klebsiella (11.22%) and Enterococcus (7.14%) predominated. This study also enumerated the strain morphological, physiological and biochemical properties of 98 species of bacteria, including colony morphology, Gram staining, bacterial motility test, temperature gradient growth, pH gradient growth, citrate utilization test, temperature oxidase test, contact enzyme test, methyl red test, V-P test, indole test, gelatin liquefaction test, nitrate reduction test, hydrogen sulfide test, starch hydrolysis test, cellulose decomposition test, esterase (corn oil) test and antibiotic susceptibility testing. Additionally, 16 antibiotics were utilized to test the bacterial susceptibility of the strains. This study explored the types and community structure of some culturable microorganisms in the intestinal tract of G. gratiosa and recorded their physiological characteristics. These data reflect the physiological functions of the intestinal microorganisms of G. gratiosa and provide support for subsequent research on the interaction mechanism between microorganisms and their hosts. Full article

Beetroot (Beta vulgaris), a root vegetable known for its vivid natural color and nutritional profile, is a source of a wide range of bioactive compounds, including betalains, phenolics, vitamins, and antioxidants. These bioactive compounds are associated with many health-promoting properties, including antihypertensive, antioxidant, anti-inflammatory, and anticancer effects. The beetroot processing industry produces substantial by-products abundant in phytochemicals and betalains, presenting valuable opportunities for utilization. Therefore, it can replace synthetic additives and enhance the nutritional value of foods. By reducing waste and supporting a circular economy, beetroot by-products improve resource efficiency, cut production costs, and lessen the food industry’s environmental impact. Beetroot and its by-products are rich in phytochemicals that provide various wellness advantages. They support cardiovascular health, inhibit microbe-induced food spoiling, aid liver function, and reduce inflammation and oxidative stress. This paper presents a detailed review of current knowledge on beetroot and its by-products, focusing on their biochemical components, extraction and stabilization techniques, health benefits, and potential applications in the food industry. It underscores the versatility and importance of red beetroot and its derivatives, advocating for further research into optimized processing methods and innovative uses to enhance their industrial and nutritional value. By providing valuable insights, this review aims to inspire food scientists, nutritionists, and the agricultural sector to integrate beetroot and its by-products into more sustainable and health-oriented food systems. Full article

Abstract: Hydropower is a vital strategic component of China’s clean energy development. Its construction and optimized water resource allocation are crucial for addressing global energy challenges, promoting socio-economic development, and achieving sustainable development. However, the optimization scheduling of cascade hydropower stations is a large-scale, multi-constrained, and nonlinear problem. Traditional optimization methods suffer from low computational efficiency, while conventional intelligent algorithms still face issues like premature convergence and local optima, which severely hinder the full utilization of water resources. This study proposed an improved whale optimization algorithm, the Black-winged Differential-variant Whale Optimization Algorithm (BDWOA), which enhanced population diversity through a Logistic-Sine-Cosine combination chaotic map, improved algorithm flexibility with an adaptive adjustment strategy, and introduced the migration mechanism of the black-winged kite algorithm along with a differential mutation strategy to enhance the global search ability and convergence capacity. The BDWOA algorithm was tested using test functions with randomly generated simulated data, with its performance compared against five related optimization algorithms. Results indicate that the BDWOA achieved the optimal value with the fewest iterations, effectively overcoming the limitations of the original whale optimization algorithm. Further validation using actual runoff data for the cascade hydropower station optimization scheduling model showed that the BDWOA effectively enhanced power generation efficiency. In high-flow years, the average power generation increased by 8.3%, 6.5%, 6.8%, 4.1%, and 8.2% compared to the five algorithms while achieving the shortest computation time. Significant improvements in power generation were also observed in normal-flow and low-flow years. The scheduling solutions generated by the BDWOA can adapt to varying inflow conditions, offering an innovative approach to solving complex hydropower station optimization scheduling problems. This contributes to the sustainable utilization of water resources and supports the long-term development of renewable energy. Full article

This study examines the impact of seabed conditions on image segmentation for seabed target images acquired via side-scan sonar during sea experiments. The dataset comprised cylindrical target images overlying on two seabed types, mud and sand, categorized accordingly. The deep learning algorithm (U-NET) was utilized for image segmentation. The analysis focused on two key factors influencing segmentation performance: the weighting method of the cross-entropy loss function and the combination of datasets categorized by seabed type for training, validation, and testing. The results revealed three key findings. First, applying equal weights to the loss function yielded better segmentation performance compared to pixel-frequency-based weighting. This improvement is indicated by Intersection over Union (IoU) for the highlight class in dataset 2 (0.41 compared to 0.37). Second, images from the mud area were easier to segment than those from the sand area. This was due to the clearer intensity contrast between the target highlight and background. This difference is indicated by the IoU for the highlight class (0.63 compared to 0.41). Finally, a network trained on a combined dataset from both seabed types improved segmentation performance. This improvement was observed in challenging conditions, such as sand areas. In comparison, a network trained on a single-seabed dataset showed lower performance. The IoU values for the highlight class in sand area images are as follows: 0.34 for training on mud, 0.41 for training on sand, and 0.45 for training on both. Full article

With the advancement of the construction of urban underground spaces, it is inevitable that new tunnels will pass through existing pipelines. To ensure the safety and stability of these pipelines, it is essential to strictly control the impact of shield tunneling. The hardening soil model with small-strain stiffness (HSS) comprehensively accounts for the small-strain behavior of soil, and the calculated results are closer to the values measured in engineering compared to those of other models. Consequently, it has been widely adopted in the development and utilization of underground spaces. The selection of parameters for the HSS model is particularly critical when performing numerical simulations. This article establishes the proportional relationships between the small-strain moduli of the HSS model in the loess region of Xi’an through standard consolidation tests, triaxial consolidation drained shear tests, and triaxial consolidation drained loading−unloading shear tests. Additionally, an empirical formula for the static lateral pressure coefficient applicable to loess was derived and validated through engineering examples. Full article

The widespread adoption of dynamic adaptive streaming (DAS) has revolutionized the delivery of high-quality internet multimedia content by enabling dynamic streaming quality adjustments based on network conditions and playback capabilities. While numerous reviews have explored DAS technologies, this study differentiates itself by focusing on Quality of Experience (QoE)-oriented optimization in cloud-edge collaborative environments. Traditional DAS optimization often overlooks the asymmetry between cloud and edge nodes, where edge resources are typically constrained. This review emphasizes the importance of dynamic task and traffic allocation between cloud and edge nodes to optimize resource utilization and maintain system efficiency, ultimately improving QoE for end users. This comprehensive analysis explores recent advances in QoE-driven DAS optimization strategies, including streaming models, implementation mechanisms, and the integration of machine learning (ML) techniques. By contrasting ML-based DAS approaches with traditional methods, this study highlights the added value of intelligent algorithms in addressing modern streaming challenges. Furthermore, the review identifies emerging research directions, such as adaptive resource allocation and hybrid cloud-edge solutions, and underscores potential application areas for DAS in evolving multimedia systems. With the aim of serving as a valuable resource for researchers, practitioners, and decision-makers in addressing the challenges of resource-constrained edge environments and the need for QoE-centric solutions, this comprehensive analysis endeavors to promote the development, implementation, and application of DAS optimization. Acknowledging the crucial role of DAS optimization in improving the overall QoE for the end users, we hope to facilitate the continued advancement of video streaming experiences in the cloud-edge collaborated environment. Full article

In this study, 45 alternative green materials for building walls were experimentally produced, utilizing renewable (epoxidized sesame oil), natural (clay), and waste (Seyitömer fly ash) resources. These materials were evaluated based on key technical properties such as mass, tensile-compressive strength, and thermal conductivity, all of which are essential for construction and insulation applications. Subsequently, theoretical modeling was conducted for the material coded SE45, which demonstrated the lowest thermal conductivity. Through mathematical calculations, the theoretical thermal conductivity value was determined with a deviation of +5.88%. Furthermore, 48 alternative scenarios were designed for three different building envelope types (internally insulated, externally insulated, and sandwich), using commonly used building insulation materials alongside the sesame oil-based green material with the lowest thermal conductivity (SE45). Energy performance evaluations were conducted by analyzing temperature distributions along the walls of all designed scenarios using ANSYS simulations under the climatic conditions of Ankara, Turkey. Full article

The catalytic conversion of carbon dioxide (CO₂) into carbon monoxide (CO) via the reverse water–gas shift (RWGS) reaction offers a promising pathway toward a sustainable carbon cycle. However, the competing Sabatier reaction presents a significant challenge, underscoring the need for highly efficient catalysts. In this study, we developed a novel catalyst comprising cobalt-oxide-supported nickel-hydroxide nanoparticles (denoted as Co@Ni). This catalyst achieved a remarkable CO production yield of ~5144 μmol g⁻¹ at 573 K, with a CO selectivity of 77%. These values represent 30% and 70% improvements over carbon-supported Ni(OH)₂ (Ni-AC) and CoO (Co-AC) nanoparticles, respectively. Comprehensive physical characterizations and electrochemical analyses reveal that the exceptional CO yield of the Co@Ni catalyst stems from the synergistic electronic interactions between adjacent active sites. Specifically, cobalt-oxide domains act as electron donors to Ni sites, facilitating efficient H₂ splitting. Additionally, the oxygen vacancies in cobalt oxide enhance CO₂ adsorption and promote subsequent dissociation. These findings provide critical insights into the design of highly efficient and selective catalysts for the RWGS reaction, paving the way for advancements in sustainable carbon utilization technologies. Full article