Search Results (118,897)

: Pigeon pea (Cajanus cajan (L.) Millsp.) is a traditional Chinese medicinal plant widely utilized in folk medicine due to its significant pharmacological and nutritional properties. Cajaninstilbene acid (CSA), a stilbene compound derived from pigeon pea leaves, has been extensively investigated since the 1980s. A thorough understanding of CSA’s mechanisms of action and its therapeutic effects on various diseases is crucial for developing novel therapeutic approaches. This paper presents an overview of recent research advancements concerning the biological activities and mechanisms of CSA and its derivatives up to February 2024. The review encompasses discussions on the in vivo metabolism of CSA and its derivatives, including antipathogenic micro-organisms activity, anti-tumor activity, systematic and organ protection activity (such as bone protection, cardiovascular protection, neuroprotection), anti-inflammatory activity, antioxidant activity, immune regulation as well as action mechanism of CSA and its derivatives. The most studied activities are antipathogenic micro-organisms activities. Additionally, the structure–activity relationships of CSA and its derivatives as well as the total synthesis of CSA are explored, highlighting the potential for developing new pharmaceutical agents. This review aims to provide a foundation for future clinical applications of CSA and its derivatives. Full article

Self-compacting concrete (SCC) is a type of concrete that can be poured into complex geometries and dense reinforcement areas without the need for mechanical vibration, exhibiting excellent segregation resistance and flowability. Its adoption in the construction industry has surged in recent years due to its environmental, technical, and economic advantages, including reduced construction time and minimized occupational hazards. The performance of SCC is significantly influenced by the properties of the aggregates used. This study investigates the effects of variations in the coarse-to-fine aggregate ratio and water/binder (w/b) ratio on the fresh, hardened, and durability properties of SCC. A total of eight different SCC mixtures were prepared, utilizing two distinct s/b ratios and four varying fine-to-coarse aggregate ratios. The results indicated that increasing the s/b ratio enhanced fresh state performance but adversely affected mechanical strength and shrinkage behavior. Furthermore, the need for admixture and flow times improved with increasing coarse aggregate content, attributed to the reduction in cohesiveness and viscosity. However, this change did not significantly impact mechanical properties, while high-temperature resistance and shrinkage exhibited an upward trend. Full article

Neuroimaging is revolutionizing our ability to investigate the brain’s structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain using electrodes positioned on the scalp. EEG signals capture both spatial (brain region) and temporal (time-based) data. While a high temporal resolution is achievable with EEG, spatial resolution is comparatively limited. Consequently, capturing both spatial and temporal information from EEG data to recognize mental activities remains challenging. In this paper, we represent spatial and temporal information obtained from EEG signals by transforming EEG data into sequential topographic brain maps. We then apply hybrid deep learning models to capture the spatiotemporal features of the EEG topographic images and classify imagined English words. The hybrid framework utilizes a sequential combination of three-dimensional convolutional neural networks (3DCNNs) and recurrent neural networks (RNNs). The experimental results reveal the effectiveness of the proposed approach, achieving an average accuracy of 77.8% in identifying imagined English speech. Full article

Calcium sulfoaluminate (CSA) cement is recognized as an environmentally friendly alternative to Portland cement (PC) due to its lower carbon footprint and energy requirements. However, traditional CSA cement production faces significant obstacles, including the high cost and regionally constrained availability of bauxite, a key raw material. Utilizing alternative materials in the production process offers a viable approach to address these limitations. This study evaluated the environmental performance of three laboratory-synthesized CSA cements using alternative raw materials sourced through an industrial symbiosis framework. A comparative assessment with PC was conducted, focusing on energy consumption and CO₂ emissions as key environmental performance indicators. The environmental impact of the CSA cements was analyzed using Monte Carlo simulations, a robust statistical approach based on data for the constituent raw materials. This method provides a practical alternative to a full life cycle assessment (LCA) when comprehensive data are not available. The results demonstrate that the CSA cements have significantly lower environmental impacts compared to PC, achieving energy savings of 13–16% and CO₂ emission reductions of 35–48%. These results emphasize the potential of industrial symbiosis to enable more sustainable CSA cement production while addressing raw material constraints. In addition, this approach highlights the wider applicability of industrial symbiosis frameworks in the construction industry, contributing to a zero-waste future and supporting global climate goals. Full article

The remarkable increase in published medical imaging datasets for chest X-rays has significantly improved the performance of deep learning techniques to classify lung diseases efficiently. However, large datasets require special arrangements to make them suitable, accessible, and practically usable in remote clinics and emergency rooms. Additionally, it increases the computational time and image-processing complexity. This study investigates the efficiency of converting the 2D chest X-ray into one-dimensional texture representation data using descriptive statistics and local binary patterns, enabling the use of feed-forward neural networks to efficiently classify lung diseases within a short time and with cost effectiveness. This method bridges diagnostic gaps in healthcare services and improves patient outcomes in remote hospitals and emergency rooms. It also could reinforce the crucial role of technology in advancing healthcare. Utilizing the Guangzhou and PA datasets, our one-dimensional texture representation achieved 99% accuracy with a training time of 10.85 s and 0.19 s for testing. In the PA dataset, it achieved 96% accuracy with a training time of 38.14 s and a testing time of 0.17 s, outperforming EfficientNet, EfficientNet-V2-Small, and MobileNet-V3-Small. Therefore, this study suggests that the dimensional texture representation is fast and effective for lung disease classification. Full article

The lack of in-depth analysis on the reservoir characteristics and the paleoenvironmental conditions of the Niutitang Formation in the study area has led to an unclear understanding of its geological background. In this study, core samples from well SZY1 were selected, and X-ray diffraction (XRD), scanning electron microscopy (SEM), and quantitative elemental analysis were employed to systematically investigate the reservoir properties and paleoenvironment of the shales. The results indicate that the Niutitang Formation shales form a low-porosity, low-permeability reservoir. By utilizing indicators such as the chemical index of alteration (CIA) and elemental ratios, the study delves into the paleoclimate and paleoproductivity of the region. The (La/Yb)_n ratio is approximately 1, indicating a rapid deposition rate that is beneficial for the accumulation and preservation of organic matter. The chondrite-normalized and North American Shale Composite (NASC)-normalized rare earth element (REE) distribution patterns of the shales show consistent trends with minimal variation, reflecting the presence of mixed sources for the sediments in the study area. Analysis reveals that the Niutitang Formation shales are enriched in light rare-earth elements (LREEs) with a negative europium anomaly, and the primary source rocks are sedimentary and granitic, located far from areas of seafloor hydrothermal activity. The Ni_EF and Cu_EF values suggest high paleoproductivity, and the shales were deposited in an anoxic-reducing environment. The depositional environments of the Marcellus and Utica shales in the United States, the Wufeng-Longmaxi black shales in the Changning area of the Sichuan Basin, and the shales in the study area are similar, characterized by anoxic reducing conditions and well-developed fractures. The thermal evolution degree of the study area is relatively moderate, currently in the peak gas generation stage, with the reservoir quality rated as medium to high, indicating good potential for hydrocarbon accumulation and promising exploration prospects. Full article

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies. Full article

Numerical analysis of a U-type solid oxide fuel cell stack was performed using computational fluid dynamics to investigate the effects of stack capacities and fuel/air utilization rates on the internal flow uniformity. The results indicated that increasing the fuel/air utilization rate improved the gas flow uniformity within the stack for the same stack capacity. The uniformity in the anode fluid domain was better than that in the cathode fluid domain. Furthermore, the flow uniformity within the stack was associated with the percentage of pressure drop in the core region of the stack. The larger the percentage of pressure drop in the core region, the more uniform the flow inside the stack. Additionally, under a fuel utilization rate of 75%, the computational results exhibited excessively high fuel utilization rates in the top cell of a 3 kWe stack, indicating a potential risk of fuel depletion during actual stack operation. Full article

Foodborne illnesses and mortalities persist as a significant global health issue. The World Health Organization estimates that one out of every ten individuals becomes ill following the consumption of contaminated food. However, in the age of digitalization and technological progress, more and more data and data evaluation technologies are available to counteract this problem. A specific challenge in this context is the efficient and beneficial utilization of the continuously increasing volume of data. In pursuit of optimal data utilization, the objective of the present study was to develop a Multi-Criteria Decision Analysis (MCDA)-based assessment scheme to be prospectively implemented into an overall artificial intelligence (AI)-supported database for the autonomous risk categorization of food incident reports. Such additional evaluations might help to identify certain novel or emerging risks by allocating a level of risk prioritization. Ideally, such indications are obtained earlier than an official notification, and therefore, this method can be considered preventive, as the risk is already identified. Our results showed that this approach enables the efficient and time-saving preliminary risk categorization of incident reports, allowing for the rapid identification of relevant reports related to predefined subject areas or inquiries that require further examination. The manual test runs demonstrated practicality, enabling the implementation of the evaluation scheme in AI-supported databases for the autonomous assessment of incident reports. Moreover, it has become evident that increasing the amount of information and evaluation criteria provided to AI notably enhances the precision of risk assessments for individual incident notifications. This will remain an ongoing challenge for the utilization and processing of food safety data in the future. Full article

Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery’s characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs. Full article

The routine assessment of pilot fatigue is paramount to ensuring aviation safety. However, current designs of pilot fatigue factors often lack the comprehensiveness needed to fully account for the dynamic and cumulative nature of fatigue. To bridge this gap, this study introduces a biomathematical fatigue model (BFM) that leverages system dynamics theory, integrating a dynamic feedback mechanism for fatigue information. The novelty of this approach lies in its capability to continuously capture and model fatigue fluctuations driven by varying operational demands. A comparative analysis with international methodologies for evaluating cumulative fatigue on weekly and monthly scales demonstrates that the proposed BFM effectively reproduces variations in pilot fatigue characteristics. Moreover, the pilot fatigue coefficient derived from the model provides a robust differentiation of fatigue profiles across diverse work types, making it particularly suitable for estimating cumulative fatigue over monthly intervals. This BFM-based approach offers valuable insights for the strategic planning of flight schedules and establishes an innovative framework for utilizing BFMs in fatigue management. By employing a scientifically grounded evaluation method rooted in system dynamics and the BFM, this study rigorously assesses cumulative pilot fatigue, confirming the model's accuracy in replicating fatigue patterns and validating the efficiency and reliability of the derived fatigue coefficient. Full article

This research explored the application of cotton straw fiber in asphalt mixtures, aiming to optimize the asphalt mixtures’ performance. Firstly, 17 experiments were designed using Response Surface Methodology (RSM). Subsequently, the Box–Behnken Design (BBD) was used to examine how the asphalt content, fiber length, and cotton straw fiber content interacted to affect the modified asphalt mixes’ pavement performance. Based on the experimental findings, performance prediction models were created to direct optimization. The optimized design was then validated through pavement performance tests and bending fatigue tests. The findings revealed that cotton straw fiber content, length, and asphalt content significantly influence the performance of modified asphalt mixtures. The inclusion of cotton straw fibers enhanced various properties of the mixtures. When the fiber content was set at 0.3%, fiber length at 6 mm, and asphalt content at 5.3%, the response indicators, including Marshall stability, dynamic stability, flexural strength, and freeze–thaw strength ratio, were measured at 12.246 kN, 2452.396 times/mm, 12.30 MPa, and 92.76%, respectively. These results indicate that the cotton-straw-fiber-modified asphalt mixture achieved optimal performance while meeting regulatory requirements. Additionally, fatigue tests showed that the cotton-straw-fiber-modified asphalt mixture exhibited superior fatigue resistance compared with the SBS-modified asphalt mixture. The maximum error between the RSM predictions and the experimental measurements was within 10%, demonstrating the accuracy of the predictive models in estimating the impact of different factors on asphalt mixture performance. The application of RSM in designing and optimizing cotton-straw-fiber-modified asphalt mixtures proved to be highly effective, offering valuable insights for utilizing cotton straw fibers in road construction. Full article

Background: Coleoptera is the second-most significant insect group associated with decomposing carcasses, yet its role in the decomposition process and postmortem colonization following envenomation is poorly understood. Purpose of the Study: This study aimed to investigate the effects of the venoms from Cerastes cerastes and Naja haje on the decomposition of rabbit carcasses while evaluating the main beetle taxa attracted to these decaying remains. Methods: Three groups of rabbits, each with five individuals, were utilized. The first group was injected with the venom of Cerastes cerastes, the second with Naja haje venom, and the control group received 0.85% physiological saline before euthanasia with CO₂. Results: Four decomposition stages (fresh, bloating, decay, and dry) with durations varying based on venom type and carcass condition were observed. A total of 647 individual beetles of six species (Necrobia rufipes, Attagenus sp., Dermestes frischii, D. maculatus, Bledius sp., and Apentanodes sp.) belonging to four families (Cleridae, Dermestidae, Staphylinidae, and Tenebrionidae) were collected and identified. D. maculatus was the most abundant species. Fewer beetles were attracted to carcasses envenomed with N. haje compared to the other groups. Conclusions: Envenomation by snake venom influences the attraction and succession rate of necrophilous coleopterans to carcasses, which is important for forensic investigations. Full article

Microalgae are known for their efficient removal of hexavalent chromium (Cr(VI)) through biosorption and bioaccumulation, yet the subsequent release of Cr(VI) upon cell death remains a challenge. The reduction of Cr(VI) to the less toxic trivalent chromium [Cr(III)] is another critical remediation strategy that mitigates the risk of Cr(VI) re-release, but research on microalgal reduction of Cr(VI) is scarce. In this study, a microalgal strain designated as MQ-1 was isolated from chromium-contaminated mine effluent, demonstrating the capability to tolerate and remove Cr(VI). Phylogenetic analysis revealed that MQ-1 is closely related to the genus Chlorella; hence, it is classified as Chlorella sp. MQ-1. This strain exhibited robust growth at Cr(VI) concentrations below 2 mg/L, achieving a removal rate higher than 82% for initial Cr(VI) concentrations between 0.5 and 1 mg/L after a 5-day incubation period. Mechanistic studies revealed that MQ-1 promoted the removal of Cr(VI) mainly through intracellular bioreduction and bioaccumulation processes, in which more than 60% of Cr(VI) was reduced to the less toxic Cr(III) and stocked in the cells. A two-stage cultivation strategy, involving initial biomass accumulation followed by Cr(VI) treatment, significantly enhanced the removal efficiency, which was further accelerated under illuminated conditions. Notably, MQ-1 cultures with initial OD₆₈₀ values of 4 and 6 accomplished 84.28% and 91.31% Cr(VI) removal from 2 mg/L solutions, respectively, within 30 hours under light exposure. These findings highlight the potential of MQ-1 to utilize renewable solar energy to reduce Cr(VI) and to mitigate the risk of its re-release into the environment. This characteristic positions MQ-1 as a potentially sustainable and cost-effective solution for Cr(VI) remediation and suggests its significant potential for large-scale implementation in bioremediation strategies aimed at Cr(VI)-contaminated waters. Full article

Seafood preservation techniques have evolved from ancient methods to modern innovations like canning, freezing, and surimi production. Canning in the 19th century introduced airtight containers, while commercial freezing technologies like flash freezing extended shelf life. Surimi pastes in the 20th century led to affordable imitation seafood products. Emerging technologies continue to enhance seafood preservation methods. Moreover, the integration of digital technology, automation, and data sharing, known as Industry 4.0, is transforming various industries. This integration encompasses blockchain technology, automation, robotics, and big data analytics, aiming to enhance production, sustainability, traceability, and efficiency in fish processing. With a focus on the seafood market dynamics affecting these advances, this research was conducted with the aim to understand how technical breakthroughs in the seafood business are dispersed and implemented across different nations. We aim to determine the correspondence between the technological sophistication of machinery in seafood processing companies and map it across different countries across the globe to obtain an understanding of the generation of technology used in prominence. Variations in adoption rates and technological trends reflect regional market dynamics. The Seafood Expo ASIA 2023 study looked at the use of Industry 4.0 technologies, operational procedures, and technology adoption in the global seafood processing industry. Notably, countries like Norway, the Republic of Korea, Spain, Turkey, and the Netherlands have rapidly embraced Industry 4.0 technologies. The market factors driving these technological advancements across different countries include rising consumer demand for sustainable seafood, economic incentives, and global competition. A correspondence analysis was employed to analyze the correspondence between countries and the level of technological sophistication in the machinery used. We successfully mapped the level of technology utilized in machinery across global seafood processing companies, providing insights into the technological advancements shaping the industry. Full article