Search Results (587)

Members of the genus Nemipterus are economically important fish species distributed in the tropical and subtropical Indo-West Pacific region. The majority of species in this genus inhabit waters with sandy–muddy substrates on the continental shelf, although different species are found at slightly varying water depths. In this study, we sequenced seven species within the genus Nemipterus after identifying the specimens using complementary morphological analysis and DNA barcoding. Each species yielded over 40,000,000 clean reads, totaling over 300,000,000 clean reads across the seven species. A total of 276,389 unigenes were obtained after de novo assembly and a total of 168,010 (60.79%) unigenes were annotated in the protein database. The comprehensive functional annotation based on the KOG, GO, and KEGG databases revealed that these unigenes are mainly associated with numerous physiological, metabolic, and molecular processes, and that the seven species exhibit similarity in these aspects. By constructing a phylogenetic tree and conducting divergence time analysis, we found that N. bathybius and N. virgatus diverged most recently, approximately during the Neogene Period (14.9 Mya). Compared with other species, N. bathybius and N. virgatus are distributed in deeper water layers. Therefore, we conducted selection pressure analysis using these two species as the foreground branches and identified several environmental-responsive genes. The results indicate that genes such as aqp1, arrdc3, ISP2, Hip, ndufa1, ndufa3, pcyt1a, ctsk, col6a2, casp2 exhibit faster evolutionary rates during long-term adaptation to deep-water environments. Specifically, these genes are considered to be associated with adaptation to aquatic osmoregulation, temperature fluctuations, and skeletal development. This comprehensive analysis provides valuable insights into the evolutionary biology and environmental adaptability of threadfin breams, contributing to the conservation and sustainable management of these species. Full article

Minor cereals are an important part of the Chinese grain industry, accounting for about 8 percent of the country’s total grain-growing area. Minor cereals include millet, buckwheat, Panicum miliaceum, and other similar grains. Influenced by topographical and climatic factors, the distribution of minor cereals in China is mainly concentrated in the plateau and hilly areas north of the Yangtze River. In addition, there are large concentrations of minor cereals in Inner Mongolia, Heilongjiang, and other areas with flatter terrain. However, the level of mechanized harvesting in these areas is still low, and there is little research on the whole process of mechanized harvesting of minor cereals. This paper aims to discuss the current status of the minor cereal industry and its mechanization level, with particular attention to the challenges encountered in research related to the mechanized harvesting of minor cereals, including limited availability of suitable machinery, high losses, and low efficiency. The article provides a comprehensive overview of the key technologies that must be advanced to achieve mechanized harvesting throughout the process, such as header design, threshing, cleaning, and intelligent modular systems. It also summarizes current research progress on advanced equipment for mechanized harvesting of minor cereals. In addition, the article puts forward suggestions to promote the development of mechanized harvesting of minor cereals, focusing on aspects such as crop variety optimization, equipment modularization, and intelligentization technology, aiming to provide a reference for the further development and research of mechanized harvesting technology for minor cereals in China. Full article

Against the backdrop of the energy revolution, global energy demands are rising. Solar–wind hybrid energy storage plants (SWHESPs) are undoubtedly a research hotspot in this field for enhancing energy efficiency. However, the primary challenge in constructing SWHESPs is site selection. This paper aims to comprehensively investigate the site selection process for SWHESPs and determine the optimal site scientifically and objectively by considering various aspects, including technology, society, environment, and economy. This study employs a literature review and the Delphi method to establish the site selection index system of SWHESPs. The triangular fuzzy number (TFN) is used in relative similarity as an objective weight, while the Decision-Making Test and Evaluation Laboratory (DEMATEL) is used as a subjective weight. The comprehensive weights are computed using the Lagrange optimization method. Additionally, the options are ranked and evaluated using Geographic Information System (GIS) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods based on prospect theory. The study also performs comparative and sensitivity analyses to confirm the effectiveness of the proposed methods. Proper siting can optimize the efficiency of resource use, which not only helps achieve more efficient use of clean energy but also promotes local economic development and job creation. Full article

In the present study, the coaxial waterjet-assisted nanosecond laser drilling of micro-holes in C_f/SiC composites, coupled with nanosecond laser drilling in air for fabricating micro-holes with high aspect ratios, were investigated. The surface morphology, reaction products, and micro-hole shapes were thoroughly examined. The results reveal that, for the coaxial waterjet-assisted nanosecond laser drilling of micro-holes in the C_f/SiC composite, the increasing of waterjet velocity enhances the material removal rate and micro-hole depth, but reduces the micro-hole diameter and taper angle. The coaxial waterjet isolates the laser-ablated region and cools down the corresponding region rapidly, leading to the formation of a mixture of SiC, SiO₂, and Si on the surface. As the coaxial waterjet velocity increases, the morphology of residual surface products changes from a net-like structure to individual spheres. Coaxial waterjet-assisted nanosecond laser drilling, with a waterjet velocity of 9.61 m/s, achieves micro-holes with a good balance between efficiency and quality. For the fabrication of micro-holes with a high aspect ratio in C_f/SiC composites, micro-holes fabricated by nanosecond laser drilling in air exhibit obvious taper features, which should be ascribed to the combined effects of spattering slag, plasma, and energy dissipation. The application of coaxial waterjet-assisted nanosecond laser drilling on micro-holes fabricated by laser drilling in air effectively expands the hole diameter. The fabricated micro-holes have very small taper angles, with clean wall surfaces and almost no reaction products. This approach, combining nanosecond laser drilling in air followed by coaxial waterjet-assisted nanosecond laser drilling, offers a promising technique for fabricating high-quality micro-holes with high aspect ratios in C_f/SiC composites. Full article

The growing demand for clean, reliable and efficient power supply has driven the adoption of renewable energy sources in the package of distributed generation (DG) at the distribution segment of the power system. Despite advancements in DG allocation methodologies, a significant research gap exists regarding the simultaneous evaluation of DG sizing, location and power factor optimization, and their economic implications. This study presents the Mountain Gazelle Optimizer (MGO), a recent optimization approach to address the challenges of sizing, locating, and optimizing the power factor of multi-type DG units in a radial distribution network (RDN). In this work, the MGO is employed to reduce voltage variations, reactive power losses, real power losses, and costs while improving the bus voltage in the RDNs. The methodology involves extensive simulations across multiple scenarios covering one to three DG allocations with varying power factors (unity, fixed, and optimal). Key performance metrics evaluated included real and reactive loss reductions, voltage profile index (VPI), voltage stability index (VSI), and cost reductions due to energy losses compared to base cases. The proposed approach was implemented on the standard 33- and 69-bus networks, and the findings demonstrate that the MGO much outperforms other optimization approaches in the existing literature, realizing considerable decreases in real power losses (up to 98.10%) and reactive power losses (up to 93.38%), alongside notable cost savings. This research showcases the critical importance of optimizing DG power factors, a largely neglected aspect in most prior studies. In conclusion, this work fills a vital gap by integrating power factor optimization into the DG allocation framework, offering a comprehensive approach to enhancing the electricity distribution networks’ dependability, efficacy, and sustainability. Full article

Access to safe, clean drinking water is a critical challenge across many resource-constrained settings, especially in developing economies. Large-scale water treatment technologies are often available in urban areas, whereas such centralized systems are unavailable in rural and remote areas due to high infrastructure costs, rugged terrains, and maintenance challenges. To address this challenge, point-of-use (PoU) water treatment systems can fill this critical gap. This study critically evaluates the role low-cost PoU water treatment solutions play as a promising alternative to address water access and quality aspects in remote rural areas. The study explores the present state of global water sources, the challenges of water scarcity and pollution, and the limitations of existing large-scale treatment technologies. It highlights the motivation behind PoU systems and provides an in-depth analysis of various low-cost technologies, including operational principles, performance efficiency, and economic viability. Embedded in this study is a concise evaluation of the sustainability of these solutions in addressing water access and quality challenges in resource-limited regions. Finally, the study proposes solutions and perspectives on improving PoU systems and scale-up of the systems for large-scale applications to facilitate increased access to clean and safe water. Full article

The link between ESG and financial performance is still under debate. In this study, we explore which aspects of ESG specifically drive market valuations through both systematic and idiosyncratic risk channels. We analyze the impact of the three core ESG pillars, 10 subcategories, and associated controversies on market valuations in the energy sector. This analysis reveals that the environmental factor has a stronger impact (regression coefficient = 0.05) than the governance factor (regression coefficient = 0.003), emphasizing the need to prioritize environmental performance in ESG strategies. The positive coefficients for environmental resource use (0.005) and innovation (0.008) indicate that investments in efficiency and clean technologies are beneficial, while the negative coefficient for emissions (−0.004) underscores the risks associated with poor emissions management. These findings suggest that environmental risks currently outweigh governance risks for the energy sector, reinforcing the importance of aligning governance practices with environmental goals. To maximize ESG effectiveness, energy firms should focus on measurable improvements in resource efficiency, innovation, and emissions reduction and transparently communicate this progress to stakeholders. The evidence suggests that energy firms approach the ESG landscape differently, with sustainability leaders benefiting from higher valuations, particularly when ESG efforts are aligned with core competencies. However, many energy companies under-invest in value-creating environmental initiatives, focusing instead on emission management, which erodes value. While they excel in emission control, they lag in innovation, missing opportunities to enhance valuations. This underscores the potential for ESG risk analysis to improve portfolio performance, as sustainability can both create value and mitigate risks by factoring into valuation equations as both risks and opportunities. This study uniquely contributes to the ESG–financial performance literature by disentangling the specific ESG dimensions that drive market valuations in the energy sector, revealing that value is created not through emission control but through strategic alignment with eco-innovation, governance, and social responsibility. Full article

This study applied data analysis techniques to analyze work-related accidents in Brazil’s mining sector from 2019 onward, identifying key risks and patterns. Using public datasets from governmental sources, it categorized accidents by the type of injury, causal agents, and affected body parts. The methodology employed included data cleaning, processing, and the development of interactive visualizations using advanced analytical tools, such as Python and Power BI, to facilitate data interpretation. Among the most significant events, the Brumadinho tailings dam collapse in 2019 emerged as a major outlier, substantially affecting multiple aspects of the analysis. This single incident accounted for 71.7% of all work-related fatalities recorded during the four-year period under study, highlighting its disproportionate impact on the dataset. This study also examined the main causes and consequences of mining accidents and facilitated the creation of victim profiles based on gender and age group, incorporating psychological theories regarding risk perception. It was concluded that, although the mining sector represents a small fraction of all work-related accidents in Brazil, the proportion of accidents relative to the number of workers in the sector is substantial, highlighting the need for stricter occupational safety management. The results can guide regulations and help companies and institutions to create safer, more sustainable mining policies. The methodology proved to be highly suitable, indicating its potential for application in safety analysis across other sectors. Full article

The safe removal of residual flammable contaminants from vertical mixer blades is a crucial challenge in aerospace propellant production. While robotic cleaning has become the preferred solution due to its precision and operational safety, the complex helical geometry of mixer blades presents significant challenges for robotic systems, primarily in three aspects: (1) dynamic sub-region division, requiring simultaneous consideration of functional zones and residue distribution, (2) ensuring path continuity across surfaces with varying curvature, and (3) balancing time–energy efficiency in discontinuous cleaning sequences. To address these challenges, this paper proposes a novel robotic cleaning path planning method for complex curved surfaces. Firstly, we introduce a blade surface segmentation approach based on the k-means++ clustering algorithm, along with a sub-surface patch boundary determination method using parameterized curves, to achieve precise surface partitioning. Subsequently, robot cleaning paths are planned for each sub-surface according to cleaning requirements and tool constraints. Finally, with total cleaning time as the optimization objective, a genetic algorithm is employed to optimize the path combination across sub-facets. Extensive experimental results validate the effectiveness of the proposed method in robotic cleaning path planning. Full article

Countries worldwide are focused on the objective of zero emissions by 2050. However, the accelerated implementation of clean technologies has had some drawbacks, remarkably those related to safety issues. Liquefied petroleum gas (LPG) emerges as a transition fuel in this context, considering the following two aspects. First, LPG is a fuel that has environmental advantages compared to other fossil fuels, so the extension of coverage as a replacement fuel is a key factor. Second, LPG has a well-developed storage and transportation infrastructure that can be used, sometimes without modifications, for clean fuels, helping their implementation. Therefore, the safety analysis and the study of the consequences related to the hazards of LPG is a current subject that contributes, through all the tools reviewed in this article, to not only reduce the risks of this fuel but also to connect with the safety issues of clean fuels. This review article provides a comprehensive overview through consequence modeling tools, highlighting computational fluid dynamics (CFD) and machine learning to pave the way for the full implementation of clean fuels that will power the future of humanity. Full article

Fermenting fruit and vegetable juices with probiotic bacteria is becoming a popular way to create functional drinks, offering an alternative to traditional dairy-based probiotic products. These plant-based juices are naturally rich in nutrients that help support the growth and activity of various probiotic strains. They also meet the rising demand for lactose-free, vegan, and clean-label options. This review looks at the key microbiological, nutritional, and sensory aspects of probiotic fermentation in juice. Common probiotic groups like Lactobacillus, Bifidobacterium, Lactococcus, Bacillus, and Streptococcus show different abilities to adapt to juice environments, affecting properties such as antioxidant levels, shelf life, and taste. The review also explores how factors like pH, sugar levels, heating, and storage can influence fermentation results. New non-thermal processing methods that help maintain probiotic survival are also discussed. Since fermented juices can sometimes develop off-flavors, this paper looks at ways to improve their taste and overall consumer appeal. Finally, future directions are suggested, including personalized nutrition, synbiotic products, and advanced encapsulation technologies. Overall, probiotic fermentation of fruit and vegetable juices shows strong potential for developing a new generation of healthy and appealing functional foods. Full article

Under the strategic objectives of carbon peaking and carbon neutrality, it is inevitable for large-scale integration of renewable energy into thermal power units. Nevertheless, improving the capacity of these units for flexible peak shaving is necessary on account of the intermittent and instability of renewable energy. As a novel combustion technology, self-preheating combustion technology offers enormous merits in this aspect, with increasing combustion efficiency (η) and controlling NO_x emissions simultaneously. Considering production and operation cost, installation difficulty and environmental pollution, this study innovatively proposed a compact fluidized modification device (FMD) on the basis of this technology and explored the influences of buffer tank and operation load on operation stability, fuel modification, combustion characteristics and NO_x emissions on an MW grade pilot-scale test platform. Afterwards, the comparative analysis on performance disparities was further launched between FMD and traditional self-preheating burner (TSB). Adding the buffer tank enhanced operation stability of FMD and improved its modification conditions, and thus promoted NO_x emission control. Optimal modification efficiency was realized at medium and high loads, respectively, for high-volatile and low-volatile coals. As load increased, η increased for high-volatile coal, but with NO_x emissions increasing. In comparison, this condition reduced NO_x emissions with high η for low-volatile coal. Compared to TSB, FMD demonstrated more conspicuous advantages in stable operation and fuel modification. Simultaneously, FMD was more conducive to realizing clean and efficient combustion at high temperatures. In industrial applications, appropriate FMD or TSB should be picked out grounded in diverse application requirements. By optimizing burner structure and operational parameters, original NO_x emissions decreased to a minimum of 77.93 mg/m³ with high η of 98.59% at low load of 30%. Full article

Increasing the proportion of clean energy within the energy structure is a crucial strategy for achieving energy transformation. Hydrogen and ammonia, as leaders in clean energy technologies, have garnered significant global attention. The combination of hydrogen and ammonia has emerged as a novel form of energy storage, transportation, and conversion; however, the safety aspects of their application process warrant closer attention. Research on hydrogen safety has been conducted extensively, with particular focus on the leakage, diffusion, combustion, and explosion processes. Both theoretical research and engineering applications have advanced significantly. In particular, hydrogen detection technology, primarily based on electrical measurement, has matured considerably, while schlieren imaging-based flow field visualization technology is progressing steadily. In contrast, safety research concerning ammonia remains in its early stages. Research on the leakage and diffusion characteristics of ammonia predominantly focuses on liquid ammonia, with a strong emphasis on engineering applications. Studies on the combustion and explosion characteristics of ammonia primarily address flame parameters and the combustion development laws. Ammonia serves as an efficient hydrogen storage medium. The conversion process involving hydrogen and ammonia will occur simultaneously in both time and space. Current research has not adequately addressed the safety concerns associated with the application process of hydrogen–ammonia mixtures. Future research on the safety of hydrogen–ammonia application processes should focus on the diffusion characteristics and combustion and explosion behaviors, as well as the development of electrical measurement detection technologies and optical flow field visualization techniques for hydrogen–ammonia mixtures. Full article

Biopolymers are revolutionizing the materials landscape, driven by a growing demand for sustainable alternatives to traditional petroleum-based materials. Sourced from biological origins, these polymers are not only environment friendly but also present exciting solutions in healthcare, packaging, biosensors, high performance, and durable materials as alternatives to crude oil-based products. Recently, biopolymers derived from plants, such as lignin and cellulose, alongside those produced by bacteria, like polyhydroxyalkanoates (PHAs), have captured the spotlight, drawing significant interest for their industrial and eco-friendly applications. The growing interest in biopolymers stems from their potential as sustainable, renewable materials across diverse applications. This review provides an in-depth analysis of the current advancements in plant-based and bacterial biopolymers, covering aspects of bioproduction, downstream processing, and their integration into high-performance next-generation materials. Additionally, we delve into the technical challenges of cost-effectiveness, processing, and scalability, which are critical barriers to widespread adoption. By highlighting these issues, this review aims to equip researchers in the bio-based domain with a comprehensive understanding of how plant-based and bacterial biopolymers can serve as viable alternatives to petroleum-derived materials. Ultimately, we envision a transformative shift from a linear, fossil fuel-based economy to a circular, bio-based economy, fostering more sustainable and environmentally conscious material solutions using novel biopolymers aligning with the framework of the United Nations Sustainable Development Goals (SDGs), including clean water and sanitation (SDG 6), industry, innovation, and infrastructure (SDG 9), affordable and clean energy (SDG 7), sustainable cities and communities (SDG 11), responsible production and consumption (SDG 12), and climate action (SDG 13). Full article

Data cleaning remains one of the most time-consuming and critical steps in modern data science, directly influencing the reliability and accuracy of downstream analytics. In this paper, we present a comprehensive evaluation of five widely used data cleaning tools—OpenRefine, Dedupe, Great Expectations, TidyData (PyJanitor), and a baseline Pandas pipeline—applied to large-scale, messy datasets spanning three domains (healthcare, finance, and industrial telemetry). We benchmark each tool on dataset sizes ranging from 1 million to 100 million records, measuring execution time, memory usage, error detection accuracy, and scalability under increasing data volumes. Additionally, we assess qualitative aspects such as usability and ease of integration, reflecting real-world adoption concerns. We incorporate recent findings on parallelized data cleaning and highlight how domain-specific anomalies (e.g., negative amounts in finance, sensor corruption in industrial telemetry) can significantly impact tool choice. Our findings reveal that no single solution excels across all metrics; while Dedupe provides robust duplicate detection and Great Expectations offers in-depth rule-based validation, tools like TidyData and baseline Pandas pipelines demonstrate strong scalability and flexibility under chunk-based ingestion. The choice of tool ultimately depends on domain-specific requirements (e.g., approximate matching in finance and strict auditing in healthcare) and the magnitude of available computational resources. By highlighting each framework’s strengths and limitations, this study offers data practitioners clear, evidence-driven guidance for selecting and combining tools to tackle large-scale data cleaning challenges. Full article