Search Results (2,546)

The rising global demand for renewable energy and the urgency of mitigating climate change have positioned biofuels, particularly sugarcane ethanol, at the forefront of sustainability and conservation debates. Although promoted as a renewable alternative, sugarcane cultivation can cause habitat loss, biodiversity decline, soil degradation, and water contamination. This study presents a bibliometric assessment of 217 publications addressing the biodiversity impacts of sugarcane production, based on searches in the Web of Science Core Collection for papers published between 1998 and 2023. Using the bibliometrix package in R, we identified key publication trends, collaboration networks, and thematic structures. Between 1998 and 2006, no studies were returned by our searches, after which research activity increased substantially, peaking in 2021. Brazil, the world’s largest sugarcane producer, was the most frequent contributor to scientific output, while other major sugarcane producers, such as Thailand and India, showed limited engagement. Thematic mapping of the studies returned by our searches revealed three clusters: (1) cross-cutting themes linking sugarcane, biodiversity, and sustainability; (2) niche themes on pest and soil dynamics; and (3) emerging themes on the ecological role of bats in sugarcane landscapes. Overall, the findings highlight the growing academic engagement in reconciling bioenergy development with biodiversity conservation. Full article

The rapid advancement of generative artificial intelligence (GenAI) has significantly impacted educational and professional practices, presenting both opportunities and challenges. This study explores the integration of GenAI into a systems engineering seminar, aiming to develop essential GenAI skills and enhance disciplinary knowledge. Two hypotheses guide this research: (H1) engaging with GenAI in research and design activities improves student proficiency in using GenAI, and (H2) engaging with GenAI in design activities related to advanced disciplinary knowledge improves their understanding and use. The study employs a case study approach combined with a survey, involving 26 graduate students in a systems engineering seminar. Students were encouraged to use GenAI tools for all tasks, including literature reviews, presentations, and a drone design challenge. Data was collected through recorded presentations and student interactions with GenAI tools. Data analysis involved systematic coding and thematic analysis of presentations, student–GenAI interactions, and survey responses, with triangulation across multiple data sources to ensure validity. The findings indicate that the students effectively learned about GenAI tools, demonstrated gradual improvements in using tools, criticized and selected among them, and even built a new GenAI tool. They demonstrated improved critical thinking and creativity, as evidenced by their ability to critically assess GenAI outputs and apply them to practical challenges like the drone design task. One student developed a custom GenAI tool by training ChatGPT-4o for specialized modeling tasks. The integration of GenAI in educational settings through self-directed learning, peer presentations, and design challenges appears to enhance learning experiences by fostering critical thinking and creativity. The evidence suggests that GenAI tools, when used with appropriate validation and critical assessment, may serve as valuable aids in developing engineering skills and addressing complex problems. Best practices in teaching about GenAI are provided. Full article

Ageing results in the progressive loss of near vision, known as presbyopia, which impacts individuals and society. Existing corrective methods offer only partial compensation and do not restore dynamic focusing at varying distances. This work presents a closed-loop correction system for presbyopia, employing biopotential signals from the ciliary muscle and an artificial neural network to predict the eye’s accommodative state in real time. Non-invasive contact lens electrodes collect biopotential data, which are preprocessed and classified using a multi-layer perceptron. The classifier output guides a control system that adjusts an external focus-tunable lens, enabling both accommodation and disaccommodation similar to a young eye. The system demonstrated an accuracy of 0.79, with F1-scores of 0.78 for prediction of accommodation and 0.77 for disaccommodation. Using the system in two presbyopic subjects, near visual acuity improved from 0.28 and 0.38 to 0.04 and −0.03 logMAR, while distance acuity remained stable. Despite challenges such as signal quality and individual variability, the findings demonstrate the feasibility of restoring near-natural accommodation in presbyopia using neuromuscular signals and adaptive lens control. Future research will focus on system validation, expanding the dataset, and pre-clinical testing in implantable devices. Full article

This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO₂(t + 1), PM₁₀(t + 1), and SO₂(t + 1)—in the Bay of Algeciras, a highly complex region located in southern Spain. Hourly data related to air quality, meteorological conditions, and maritime traffic were collected from 2017 to 2019 across multiple monitoring stations distributed throughout the bay, enabling the analysis of diverse forecasting scenarios. The output variable was segmented into four distinct, non-overlapping quartiles (Q1–Q4) to capture different concentration ranges. AE models demonstrated greater accuracy in predicting moderate pollution levels (Q2 and Q3), whereas SAE models achieved comparable performance at the lower and upper extremes (Q1 and Q4). The results suggest that stacking AE layers with varying degrees of sparsity—culminating in a supervised output layer—can enhance the model’s ability to forecast pollutant concentration indices across all quartiles. Notably, Q4 predictions, representing peak concentrations, benefited from more complex SAE architectures, likely due to the increased difficulty associated with modelling extreme values. Full article

The global demand for strategic minerals like scheelite is growing rapidly due to technological advancements and emerging industries, making it a key global resource. However, there is a lack of integrated research on utilization technology of scheelite from a global perspective and exploring its future development direction. Bibliometric methods have been widely applied due to their advantages in the analysis of qualitative and quantitative literature information. Based on 1137 publications from the Web of Science Core Collection spanning 1999 to 2024, this study systematically examines the global and Chinese research trajectories and emerging frontiers in scheelite resource utilization technologies. A paradigm shift from fundamental geological and material property studies to green beneficiation, low-carbon metallurgy, and intelligent process optimization has been revealed. Key global research hotspots include flotation separation, surface chemistry regulation, LA-ICP-MS micro-analysis, and photoluminescence properties, whereas China has developed distinctive strengths in complex polymetallic ore separation, leaching kinetics, and tailings valorization. Chinese institutions contribute over 54% of worldwide output, with Central South University leading in publication volume, collaboration networks, and academic impact. Future efforts should prioritize intelligent process control, the efficient separation of complex polymetallic ores, and the high-value recovery of secondary resources. Full article

Background and objectives: Automated evaluation of open-ended responses remains a persistent challenge, particularly when consistency, transparency, and reproducibility are required. While large language models (LLMs) have shown promise in rubric-based evaluation, their reliability across multiple evaluators is still uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about interpretability and system robustness. This study introduces GraderAssist, a graph-based, rubric-guided, multi-LLM framework designed to ensure transparent and reproducible automated evaluation. Methods: GraderAssist evaluates a dataset of 220 responses to both technical and argumentative questions, collected from undergraduate computer science courses. Six open-source LLMs and GPT-4 (as expert reference) independently scored each response using two predefined rubrics. All outputs—including scores, feedback, and metadata—were parsed, validated, and stored in a Neo4j graph database, enabling structured querying, traceability, and longitudinal analysis. Results: Cross-model analysis revealed systematic differences in scoring behavior and feedback generation. Some models produced more generous evaluations, while others aligned closely with GPT-4. Semantic analysis using Sentence-BERT embeddings highlighted distinctive feedback styles and variable rubric adherence. Inter-model agreement was stronger for technical criteria but diverged substantially for argumentative tasks. Originality: GraderAssist integrates rubric-guided evaluation, multi-model comparison, and graph-based storage into a unified pipeline. By emphasizing reproducibility, transparency, and fine-grained analysis of evaluator behavior, it advances the design of interpretable automated evaluation systems with applications in education and beyond. Full article

Reducing dietary crude protein (CP) while sustaining growth performance and minimizing nitrogen emissions is a critical challenge in swine production. Beyond growth efficiency, the influence of low-protein diets (LPDs) on meat quality traits, gut microbiota, and systemic metabolism in finishing pigs remains insufficiently understood. In this study, 180 healthy crossbred finishing pigs (Duroc × Liangguang Small Spotted; initial body weight 85.49 ± 4.90 kg) were assigned to three dietary regimens for 35 days (six replicate pens per treatment, ten pigs per pen, male/female = 1:1): Control (CON, 15.5% CP), Low-Protein 1 (LP1, 14.5% CP), and Low-Protein 2 (LP2, 13.5% CP). Growth performance and nutrient digestibility were not impaired by protein reduction. Notably, LP1 pigs exhibited thicker backfat (p < 0.05), while LP2 pigs showed decreased concentrations of specific fatty acids (C12:0–C22:1n9) and essential amino acids (aspartic acid, glutamic acid, lysine) compared with LP1 (p < 0.05), indicating that dietary protein levels affected muscle composition. Cecal microbiota analysis revealed distinct shifts, with Prevotella spp., Faecalibacterium spp., and Plesiomonas spp. enriched in CON, whereas LP1 promoted Eubacteriaceae spp., Christensenellaceae spp., and Clostridia spp. (p < 0.05). Serum metabolomics further distinguished groups: LP1 reduced bile secretion and cholesterol metabolism pathways (p < 0.05) and LP2 further suppressed cholesterol metabolism and primary bile acid biosynthesis (p < 0.05), with a trend toward reduced phenylalanine metabolism (p = 0.07). Collectively, these findings demonstrate that moderate dietary protein reduction, when balanced with essential amino acids, maintains growth, reduces nitrogen output, and beneficially alters muscle composition, gut microbiota, and host metabolic pathways, offering nutritional strategies to enhance pork quality and promote sustainable pig production. Full article

Online radiation dose monitoring is critical for the safe operation of accelerators. Although commercial dose monitors are well-developed, integrating an ionization chamber directly within a small-sized Betatron magnet remains challenging. In this study, we designed an air ionization chamber tailored for real-time dose monitoring in a small-sized Betatron. We selected aluminum for the chamber wall based on structural and integration requirements, designed the cavity geometry, and developed the associated charge collection and sampling circuits. Using a standard reference PTW ionization chamber, we calibrated the output voltage of the chamber against X-ray dose rates and conducted stability tests. The results show that there is a very good linear relationship between the output voltage of the ionization chamber and the X-ray dose rate. The relative standard deviation of the dose rate data within a 10 min working cycle is 3.25%, and the dose rate data shows good consistency with the standard reference ionization chamber. The ionization chamber can ensure operational safety for a small-sized Betatron and offer guidance for similar applications. Full article

At present, energy harvesting technologies are gradually replacing batteries and have become a research hotspot as power sources for low-power components in wearable electronic devices. To collect and utilize the energy generated by skiers during the process of pushing against the skis, a piezoelectric energy harvesting system (PEHS) for self-powered ski boots was proposed and designed to supply power for low-power wearable devices. The output voltage of the PEHS was modeled and simulated using the finite element method, and the causes of the simulation results were analyzed. An energy harvesting experiment of the prototype was conducted under loading conditions using a universal testing machine. Under a uniform sinusoidal load of 800 N at 1 Hz, the prototype of the PEHS for self-powered ski boots achieved a maximum output power of 57.44 mW with an optimal matching load resistance of 404 kΩ. A skiing tester wearing the self-powered ski boots conducted real-motion experiments, performing three different actions: (1) alternating single-foot stepping for propulsion, (2) alternating left and right ski edge stepping for propulsion, and (3) alternating forefoot and heel stepping for propulsion. The instantaneous peak voltages measured in these tests were statistically analyzed, and the corresponding peak power values were calculated through theoretical computation to be 6.48 ± 0.27 mW, 4.47 ± 0.21 mW, and 13.21 ± 0.48 mW for the three actions, respectively (expressed with a 95% confidence interval). Full article

The climate-smart agriculture (CSA) approach, a sustainable alternative to conventional practices in agriculture, supports three main pillars: increasing productivity, resilience, and greenhouse gas (GHG) mitigation through the adoption of climate-smart practices (CSPs). Effective CSA assessment tools are needed to evaluate the impact of and support the broader adoption of CSPs. This study addresses this need by developing the RAPTURE (Resilient Agricultural Practices for Transforming Uncertain and Resource-Scarce Environments) tool. The RAPTURE tool was developed through five steps, which included collecting data on CSA definitions, existing practices and classifications, climatic conditions of the study areas, and the mathematical equations used to assess CSPs—all of which were stored in databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was adopted to guide the selection and inclusion of 222 studies from the Web of Science database, forming the basis for the development of the RAPTURE tool. The first step of RAPTURE synthesizes simple and complex definitions of CSA from the database of 35 definitions. For the second and third steps, an updated classification of the CSPs was developed using a database with 78 CSPs, and a weather conditions database created from areas where CSPs have been studied and implemented was also provided, respectively. The fourth step of the RAPTURE tool includes a database containing the input and output variables necessary for the assessment of CSPs’ impacts, which is essential for the selection of an assessment method. The fifth and last step of the tool contains the assessment methods available, including 24 mathematical methods documented and synthesized. An application of RAPTURE using agricultural data from Florida in 2022 and 2023, and considering an increase of 20% with the implementation of CSPs, showed better productivity and rain-use efficiency. While previous studies have shown that adopting CSPs in agriculture provides several benefits, such as better agricultural production, higher carbon sequestration, the application of the RAPTURE tool in assessing CSPs also demonstrates their ability to increase productivity and resource-use efficiency. Full article

High-throughput phenotyping has become essential for plant breeding programs, replacing traditional methods that rely on subjective scales influenced by human judgment. Machine learning (ML) computer vision systems have successfully used convolutional neural networks (CNNs) for image segmentation, providing greater flexibility than thresholding methods that may require carefully staged images. This study compares two quantitative image analysis methods, rule-based thresholding using the magick package in R and an instance-segmentation pipeline based on the widely used Mask-R-CNN architecture, and then compares the output of each to two different sets of human evaluations. Walnuts were collected over three years from over 3000 individual trees maintained by the UC Davis walnut breeding program. The resulting 90,961 kernels were placed into 100-cell trays and imaged using a 20-megapixel Basler camera with a Sony IMX183 sensor. Quantitative data from both image analysis methods were highly correlated for both lightness (L*; r² = 0.997) and size (r² = 0.984). The thresholding method required many manual adjustments to account for minor discrepancies in staging, while the CNN method was robust after a rapid initial training on only 13 images. The two human scoring methods were not highly correlated with the image analysis methods or with each other. Pixel classification provides data similar to human color assessments but offers greater consistency across different years. The thresholding approach offers flexibility and has been applied to other color-based phenotyping tasks, while the CNN approach can be adapted to images that are not perfectly staged and be retrained to quantify more subtle kernel characteristics such as spotting and shrivel. Full article

In micro inertial measurement units (MIMUs), the zero bias, scale factor error, and non-orthogonal error in both gyroscopes and accelerometers will lead to cumulative errors in inertial navigation computation. This paper proposes a rapid, self-contained calibration method for estimating the MIMU output model based on residual velocity measurement, which significantly reduces calibration time and enhances estimation accuracy without requiring high-precision turntables or external references. First, a comprehensive output model of the MIMU is established. Subsequently, a self-contained calibration model based on a Kalman filter is developed, utilizing residual velocity and the difference between gravity-integrated velocity and inertial navigation velocity. Then, an oriented rotation scheme is designed by a self-developed spherical rotation platform, and the observability for parameters in the MIMU output model is analyzed. Finally, the simulation results indicate that the parameters in the MIMU output model can be successfully estimated within 390 s, achieving an estimation accuracy exceeding 85%. The static and dynamic scenario navigation experiment results demonstrate the effectiveness of the proposed self-contained calibration. Collectively, the proposed method provides a rapid, convenient, and self-contained calibration solution for MIMUs. Full article

Triboelectric nanogenerators (TENGs) have emerged as a promising technology for harvesting mechanical energy via contact electrification (CE) at diverse interfaces, including solid–liquid, liquid–liquid, and gas–liquid phases. This review systematically explores fluid-based TENGs (Flu-TENGs), introducing a foundational and novel classification framework based on direct versus indirect charge transfer to the charge-collecting electrode (CCE). This framework addresses a critical gap by providing the first unified analysis of charge transfer mechanisms across all major fluid interfaces, establishing a clear design principle for future device engineering. We comprehensively compare the underlying mechanisms and performance outcomes, revealing that direct charge transfer consistently delivers superior energy conversion—with specific studies achieving up to 11-fold higher current and 8.8-fold higher voltage in solid–liquid TENGs (SL-TENGs), 60-fold current and 3-fold voltage gains in liquid–liquid TENGs (LL-TENGs), and 34-fold current and 10-fold voltage enhancements in gas–liquid TENGs (GL-TENGs). Indirect mechanisms, relying on electrostatic induction, provide stable Alternating Current (AC) output ideal for low-power, long-term applications such as environmental sensors and wearable bioelectronics, while direct mechanisms enable high-efficiency Direct Current (DC) output suitable for energy-intensive systems including soft actuators and biomedical micro-pumps. This review highlights a paradigm shift in Flu-TENG design, where the deliberate selection of charge transfer pathways based on this framework can optimize energy harvesting and device performance across a broad spectrum of next-generation sensing, actuation, and micro-power systems. By bridging fundamental charge dynamics with application-driven engineering, this work provides actionable insights for advancing sustainable energy solutions and expanding the practical impact of TENG technology. Full article

Odontobutis potamophila, a small benthic carnivorous fish endemic to the Yangtze River basin, holds considerable ecological and commercial value. However, overfishing and habitat degradation have led to a severe decline in its wild population. A lack of quantitative reproductive data has further hampered effective conservation and resource management. To address this, we conducted monthly sampling, collecting a total of 894 individuals from Nansi Lake between August 2017 and July 2018. By integrating gonadal histological staging, gonadosomatic index (GSI) analysis, logistic regression, and fecundity assessments, we provide a foundational understanding of the species’ reproductive biology. The annual sex ratio was 1.06:1, with a temporary female bias in April (2.14:1) shifting due to male nest-guarding behavior. Both sexes reached maturity at one year and approximately 73.6 mm in length. Spawning occurred from March to June, peaking in May (GSI = 28.92%). Absolute fecundity ranged 2306 ± 1430 eggs and correlated positively with body size and age, while relative fecundity stabilized after age two. Individuals aged two years and older contributed over 80% of total egg production, reflecting a strategy of early maturation with high reproductive output at older ages. This study aims to systematically understand the reproductive biology of O. potamophila. These results support science-based measures such as Covering the entire window from gonadal maturation to fry dispersal, an annual fish ban established from March to June, a minimum catch size of 80 mm, and improved broodstock management for aquaculture and conservation efforts aimed at this and related benthic fishes in shallow lake ecosystems. Full article

Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research. Full article