Search Results (4,785)

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

Against the backdrop of rapid modernization and tightening agricultural resource constraints, coordinating urbanization and grain production is a key challenge for China. Using panel data from 30 Chinese provinces from 2004 to 2023, this study applies the coupling coordination degree (CCD) model and a panel vector autoregression model to examine the spatiotemporal coupling characteristics and interaction mechanisms among new-type urbanization (NTU), cultivated land use efficiency (CLUE), and food security (FS). The results show that these three systems have gradually evolved toward coordinated development, with major grain-producing regions consistently leading and entering a moderate coordination stage earlier than other regions. Spatially, CCD exhibits significant positive spatial autocorrelation, characterized by stable “High–High” agglomeration in Northeast China and “Low–Low” agglomeration in southern provinces. Dynamic analysis indicates that system fluctuations are mainly driven by internal inertia, while inter-system interactions are also significant; NTU promotes CLUE, and CLUE and FS exhibit bidirectional causality with complex feedback effects. This study argues for promoting urban–rural factor mobility, advancing green and technology-enabled land use, implementing region-specific development strategies, and establishing a “human–land–grain” early-warning mechanism to safeguard food security during urban expansion. Full article

Universities must prepare future professionals with critical thinking skills to effectively address complex social and environmental challenges. In engineering degrees, while technical competences are strongly developed, the acquisition of ethical and social skills remains challenging within the framework of traditional subjects. This paper explores how the integration of the Sustainable Development Goals (SDGs), following a competence-based educational model, can contribute to the development of ethical, social, and sustainability-related competences in an engineering degree. A set of activities, exercises, and tasks grounded in real professional contexts was designed to encourage students to explore sustainable solutions to social and environmental problems, supported by experiential learning and visible thinking routines. These activities were coherently aligned through interdisciplinary coordination among professors teaching in the degree. The results indicate that the proposed approach was positively received by both professors and students, who valued its contribution to personal and professional development. Students demonstrated enhanced critical thinking and greater awareness of the social and environmental implications of engineering decisions. This work aims to support and inspire educators seeking to integrate SDGs into their teaching by offering a feasible, transferable, and easy-to-implement framework for embedding ethical, social and sustainability-related competences in engineering teaching. Full article

Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000–2022). The model demonstrated robust predictive capability at the provincial scale (R² = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R² ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (−8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies—specifically, securing irrigation during flowering and deploying cooling interventions during fruit development—providing a data-driven basis for climate-smart management in tropical agriculture. Full article

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

Planning in robotics represents an ongoing research challenge, as it requires the integration of sensing, reasoning, and execution. Although large language models (LLMs) provide a high degree of flexibility in planning, they often introduce hallucinated goals and actions and consequently lack the formal reliability of deterministic methods. In this paper, we address this limitation by proposing a hybrid Sense–Plan–Code–Act (SPCA) framework that combines perception, LLM-based reasoning, and symbolic planning. Within the proposed approach, sensory information is first transformed into a symbolic description of the world in Planning Domain Definition Language (PDDL) using an LLM. A heuristic planner is then used to generate a valid plan, which is subsequently converted to code by a second LLM. The generated code is first validated syntactically through compilation and then semantically in simulation. When errors are detected, local corrections can be applied and the process is repeated as necessary. The proposed method is evaluated in the OpenAI Gym MiniGrid reinforcement learning environment and in a Gazebo simulation on a UR5 robotic arm using a curriculum of tasks with increasing complexity. The system successfully completes approximately 71–75% of tasks across environments with a relatively low number of simulation iterations. Full article

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m². Full article

The successful attainment of the Sustainable Development Goals (SDGs) necessitates robust monitoring frameworks capable of tracking progress toward tangible outcomes while capturing dynamic sustainability trajectories. However, existing SDG evaluation methods suffer from three critical limitations: (1) misalignment between global targets and national priorities, which undermines contextual relevance; (2) fragmented assessments that neglect holistic integration of social, economic, and ecological dimensions, thereby obscuring systemic interdependencies; and (3) insufficient longitudinal analysis, which restricts insights into temporal patterns of sustainable development and hinders adaptive policymaking. To address these gaps, we employed China’s 31 provinces as a case study and constructed an SDG indicator framework comprising 178 metrics—harmonizing global SDG benchmarks with China’s national development priorities. Using official statistics and open-source data spanning 2006–2021, we evaluate longitudinal SDG scores for all 17 goals (SDGs 1–17). Additionally, we developed a composite SDG index that considers the coupling coordination degree of the social–economic–ecological system and evaluated the index value under different economic region settings. Finally, we developed a two-threshold model to analyze the dynamic evolution of SDG conditions, incorporating temporal sustainability (long-term development resilience) and action urgency (short-term policy intervention needs) as dual evaluation dimensions. This model was applied to conduct a longitudinal analysis (2006–2021) across all 31 Chinese provinces, enabling a granular assessment of regional SDG trajectories while capturing both systemic trends and acute challenges over time. The results indicate that China’s social SDG performance improved substantially over the 2006–2021 period, achieving a cumulative increase of 126.53%, whereas progress in ecological SDGs was comparatively modest, with a cumulative growth of only 23.93%. Over the same period, the average composite SDG score across China’s 31 provinces increased markedly from 0.502 to 0.714, reflecting a strengthened systemic alignment between regional development trajectories and national sustainability objectives. Further analysis shows that all provinces attained a status of “temporal sustainability with low action urgency” throughout the study period, highlighting China’s overall progress in sustainable development. Nevertheless, pronounced regional disparities persist: eastern provinces developed earlier and have consistently maintained leading positions; central and northeastern regions exhibit broadly comparable development levels; and western regions, despite severe early-stage lagging, have demonstrated accelerated growth in later years. Our study holds substantial significance by integrating multi-dimensional indicators—spanning ecological, economic, and social dimensions—to deliver a holistic, longitudinal perspective on sustainable development. Full article

While the international influence of the Programme for International Student Assessment (PISA) on education policy debates is well recognized, the degree to which PISA findings drive actual policy reforms and classroom practices remain debated. Using PISA as a case, this article examines how educational research is translated into policy responses and practices in German federalism, focusing specifically on immigrant students—a key group within German education reform discourse. It analyzes the reflection of PISA findings from the 2000, 2018, and 2022 assessments on immigrant student performance in the resolutions of the Standing Conference of Ministers of Education and Cultural Affairs, the process of implementation by the federal states (Länder), and the effect on school-level practice. Framed by research knowledge mobilization theory, the article investigates the relationships among research production, mediation, and usage, clarifying the interplay between educational research, policy, and practice in Germany’s federal system. Historical analysis exposes consistent gaps between research-derived recommendations and binding, actionable change at both policy and practice levels, often due to challenges in developing evidence-based and consistently applied policy measures across the Länder. The article concludes with practical recommendations for improving the impact of interdisciplinary, policy-oriented research on policy and practice, considering the complexities of Germany’s federal governance. Full article

The global phase portrait (GPP) classification of polynomial planar Hamiltonian systems with finitely many isotropic points is a challenging problem. Only homogeneous Hamiltonian systems of degrees up to five have been dealt with in existing literature. In this paper, through a polar coordinate compactification, we prove that the GPP of a homogeneous planar Hamiltonian system is uniquely determined by the phase portrait around its isotropic point, referred to as the local phase portrait (LPP). Thus, the global classification can be reduced to the local classification. Secondly, two distinct approaches, topological index analysis and algebraic factorization, are proposed to establish both the local classification and the global one. And finally, the corresponding physical flows are discussed, and the consistency of results from the two approaches is validated through four examples. Full article

Against the backdrop of rapid urbanization, megacities have become crucial drivers of development. As the country with the largest number of megacities (seven in total), China is confronted with significant challenges such as population–resource–environment conflicts, which render high-quality development an imperative pursuit. This study employs a system dynamics approach to assess high-quality development in China’s megacities. It analyzes interactions among economic growth, technological innovation, environmental quality, and livelihood security under policy regulation, clarifying their evolutionary mechanisms and constructing a model to project the high-quality development index (HQDI) and coupling coordination degree (CCD) among subsystems. Findings reveal an upward trend in both HQDI and CCD across the seven megacities, with notable stratification. Beijing, Shanghai, and Shenzhen form the top echelon, leveraging financial and technological resources, driven by science and green development. Guangzhou and Chongqing constitute the second tier, supported by regional integration and industrial clusters, while Chengdu and Tianjin form the third echelon via regional strategic transformations. In coordinated development, Shanghai, Beijing, Shenzhen, and Guangzhou lead with multi-link synergy, whereas Chengdu, Chongqing, and Tianjin advance industry–ecology–livelihood coordination through regional strategies. This study offers insights for overcoming development bottlenecks, optimizing policies, and enhancing urban governance to foster a coordinated, high-quality development pattern. Full article

The hydrogen gasochromic phenomenon offers a new strategy for real-time sensing technologies for hydrogen leakage to ensure hydrogen safety. However, the limited recovery kinetics impede the cycling and further practical applications. Herein, we designed a series of PtAg-decorated WO₃ nanowires via the chemical reduction deposition method, which could exhibit obvious and reversible color changes for H₂ detection. With the assistance of Ag, the oxygen adsorption and dissociation were accelerated; then, the sample could exhibit a constant rapid recovery rate. The crystalline Pt-Ag/WO₃ nanowires could attain a 50% recovery degree within 52 s, and the recovery time of the Pt-Ag/WO₃ sample was reduced to one fifth that of Pt/WO₃. This study provides a fundamental solution to the challenge of slow recovery kinetics in H₂ gasochromic crystalline materials. Full article

The Pacific sleeper shark (Somniosus pacificus) is a long-lived, deep-water, sub-polar species that exhibits flexible foraging strategies, likely combining scavenging with active predation on a broad range of prey, yet their role in marine food webs and impact on commercial species remain undetermined. Tracking the location of Pacific sleeper sharks in Alaskan coastal waters is extremely challenging given the predominantly aphotic depths that these sharks occupy, often in spatially constrained and critically under-sampled regions: deep, steep-flanked, convoluted fjords of Prince William Sound (PWS). From the first ever, year-long depth and temperature records recovered from archiving pop-up satellite-linked transmitters (n = 7), we characterized the residence distributions, depth, and thermal habitat for sharks within the PWS fjords and identified seasonal and temporal variation in habitat use. Depths recorded from the seven sharks ranged from 3 to 572 m, and pop-up tag locations suggested a high degree intra-annual residency within western PWS. Ambient water temperatures ranged from 2.65 to 11.1 °C, with little deviation from the median of 5.9 °C. Seasonal patterns emerged within and across individuals relative to the variation in vertical movements, ambient temperatures, and horizontal movements that could reflect resource-oriented strategies. The high degree of residency combined with extensive use of the water column facilitates the use of physically recoverable, high-resolution behavioral and environmental samplers on Pacific sleeper sharks. This adaptive sampling using Pacific sleeper sharks as platforms of opportunity may in turn enable the use of Pacific sleeper sharks as climate and ecosystem sentinels. Full article

Silica-based sorbents covalently modified with polyhedral boron clusters represent a promising platform for highly selective separation materials, yet robust and synthetically accessible immobilization protocols remain underdeveloped. In this work, novel sorbents based on commercially available silica gels functionalized with closo-dodecaborate anions were synthesized and systematically characterized. Two immobilization strategies were compared: direct nucleophilic addition of surface aminopropyl groups to the nitrilium derivative (Bu₄N)[B₁₂H₁₁NCCH₃] and sol–gel condensation of a pre-formed boron-containing APTES-derived silane. Covalent attachment via amidine bond formation was confirmed by solution and MAS ¹¹B NMR spectroscopy, IR spectroscopy, elemental analysis/ICP-OES, and SEM. The direct grafting route afforded a boron loading of 4.5 wt% (≈20% of the theoretical capacity), with the efficiency limited by electrostatic repulsion between anionic amidine fragments on the negatively charged silica surface, whereas the APTES route gave lower absolute loading (0.085 mmol/g) due to the low specific surface area of the coarse silica support. Despite the moderate degree of functionalization, the resulting boron cluster–modified silica gels are attractive candidates for specialized chromatographic applications, where the unique hydrophobic and dihydrogen-bonding properties of closo-dodecaborates may enable selective retention of challenging analytes and motivate further optimization of surface morphology and immobilization conditions. Full article

Globally, more than 60,000 abandoned open-pit mines have been identified. Most of these sites lack effective management or ecological restoration measures. As a result, they pose substantial environmental and socioeconomic challenges. Against this backdrop, the reuse of quarry wastelands has emerged as a critical strategy for improving resource efficiency and promoting sustainable development in mining regions. Current domestic research mainly concentrates on ecological restoration techniques for abandoned quarry sites. However, systematic methods for prioritizing and ranking alternative reuse models remain limited. This study investigated four quarry reuse models: agricultural production, ecological protection, recreation-based education, and new energy development. The analysis integrated site suitability (${\mathrm{U}}_1$) with residents’ demands (${\mathrm{U}}_2$). Four representative quarry sites in Jiawang District, Xuzhou City, were selected as case studies. Based on coupled matching analysis, a priority identification method for quarry site reuse models was developed. Results indicated divergent prioritization between site suitability and resident demand. Site suitability composite values ranged from 3.9548 to 6.3094. Qishan and Kanshan sites demonstrated high suitability for recreation-based education and agricultural production, while the Dongshan site showed the highest ecological protection suitability. Suitability for emerging energy applications was generally low across all sites. Resident demand composite values showed significant variation across the four models. Recreation-based education demand (${\mathrm{U}}_2$ ranging from 0.3273 to 0.3778) substantially exceeded the other three land use types, with residents generally harbouring a degree of reluctance towards new energy development models. After coupling these factors, the original site suitability rankings were restructured: Qishan and Dongshan were selected for the recreation-based education model; Kanshan for agricultural production; and Changshan for ecological protection. This study offers insights for the diversified utilization of abandoned quarries in rural areas and provides a reference for ecological restoration and transformative development in mining regions. Full article