Search Results (7,242)

Limited evidence exists on how teachers contribute to student learning gains in early childhood education. This study draws on the Dynamic Model of Educational Effectiveness (DMEE) and investigates the impact of teacher factors on pre-primary students’ mathematics achievement. It also examines whether the five proposed dimensions—frequency, quality, focus, stage, and differentiation—can clarify the conditions under which these factors influence learning. Using a stage sampling procedure, 463 students and 27 teachers from Greek pre-primary schools were selected. Mathematics achievement was assessed at the beginning and end of the school year, while external observations measured the DMEE factors. Analysis of observation data using multi-trait multilevel models provided support for the construct validity of the measurement framework. Teacher factors explained variation in student achievement gains in mathematics. The added value of using a multidimensional approach to measure the functioning of the teacher factor was identified. Implications of the findings are drawn. Full article

With the significant increase in solar power generation’s proportion in power systems, the uncertainty of its power output poses increasingly severe challenges to grid operation. In recent years, solar forecasting models have achieved remarkable progress, with various developed models each exhibiting distinct advantages and characteristics. To address complex and variable geographical and meteorological conditions, it is necessary to adopt a multi-model fusion approach to leverage the strengths and adaptability of individual models. This paper proposes a photovoltaic power prediction framework based on multi-stage ensemble learning, which enhances prediction robustness by integrating the complementary advantages of heterogeneous models. The framework employs a three-level optimization architecture: first, a recursive feature elimination (RFE) algorithm based on LightGBM–XGBoost–MLP weighted scoring is used to screen high-discriminative features; second, mutual information and hierarchical clustering are utilized to construct a heterogeneous model pool, enabling competitive intra-group and complementary inter-group model selection; finally, the traditional static weighting strategy is improved by concatenating multi-model prediction results with real-time meteorological data to establish a time-period-based dynamic weight optimization module. The performance of the proposed framework was validated across multiple dimensions—including feature selection, model screening, dynamic integration, and comprehensive performance—using measured data from a 75 MW photovoltaic power plant in Inner Mongolia and the open-source dataset PVOD. Full article

Against the backdrop of intensified governance of territorial spatial planning, investigating carbon emissions from the perspective of territorial spatial planning for transport-land use integration holds significant academic and practical value. Taking Cangnan County as the case study, this research first dissects the reciprocal feedback mechanism between regional transport and land use at the territorial spatial planning level, while exploring transport-influencing factors. Subsequently, it constructs an integrated reciprocal feedback system for regional transport and land use by integrating accessibility drivers, cost matrices, and neighborhood weights through land use simulation–prediction models and the four-stage transport model. Finally, based on critical land use factors, diverse development scenarios under this integrated system are formulated; carbon emissions from transport and land use under each scenario are quantified; and their interrelationships are analyzed across multiple dimensions to explore the nexus of carbon emissions in transport–land use integration. Results indicate the following: (1) Integrated feedback enhances model accuracy (Kappa: 0.795→0.893; overall accuracy: 0.893→0.915), facilitating more precise land use simulation. (2) The county’s core construction area demonstrates the highest carbon emissions across all scenarios, meriting prioritized attention. (3) As deduced from the analysis of territorial spatial land use patterns, the significantly higher transport carbon emissions under the ecological protection priority scenario, compared to other scenarios, originate from over-concentrated construction land and imbalanced planning of carbon source land. These findings offer insights for regional planning; policy recommendations for Cangnan County include expanding carbon sink land, scientifically planning carbon source land, optimizing transport structures, and promoting new energy vehicles to advance carbon emission reduction and sustainable development. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

Reaching net-zero greenhouse gas emissions will require broad deployment of carbon capture and storage (CCS), yet significant challenges remain. This paper reviews the main barriers that may hinder or delay widespread CCS adoption, drawing on current projects in various stages of planning, construction, and development. The discussion focuses on technical, economic, social, and regulatory aspects of CCS and identifies several key obstacles. These include the high financial burden on energy production, persistent uncertainties about the long-term behavior of stored CO₂, and the complexity of the regulatory framework governing CCS projects and CO₂ pipelines. Carbon capture, use, and storage (CCUS) remains a major focus of attention in the petroleum industry due to its potential to remove carbon dioxide from the atmosphere or prevent future emissions. Despite this potential, challenges and risks continue to limit progress. Full article

The increasing frequency of extreme weather events and rapid urbanization has exacerbated pluvial flood risks, underscoring the urgent need to strengthen the assessment of pluvial flood resilience in China’s southwestern mountainous regions. Kunming—a plateau basin city—was selected as a case study, and an urban pluvial flood resilience assessment system was developed based on the DPSIR model. The analytic hierarchy process (AHP), entropy method, and game theory-informed combination weighting were applied to determine indicator weights, while the extension cloud model was utilized to quantitatively assess resilience evolution from 2013 to 2022. The results reveal that: (1) Kunming’s pluvial flood resilience experienced a clear three-stage evolution—initial construction (Level II), resilience enhancement (Level III), and resilience reinforcement (Level IV)—reflecting a transition from rudimentary resilience to advanced adaptive capacity; (2) the ranking of primary indicator weights is as follows: Driving Forces > Pressure > State > Response > Impact, with Flood Disaster Risk (P6), Flood Disaster Early Warning Capability (R1), and Topographic and Geomorphological Characteristics (P7) identified as key influencing factors; (3) marked disparities exist across the five dimensions: the Driving Forces dimension demonstrates increasing economic support; the Pressure dimension reflects structural vulnerabilities and climate variability; the State and Impact dimensions advance incrementally through policy implementation; and the Response dimension has substantially improved due to smart city technologies, although persistent gaps in inter-agency emergency coordination remain. This research offers a scientific basis for enhancing pluvial flood resilience in southwestern mountainous cities. Full article

Underwater Object Detection (UOD) based on Synthetic Aperture Sonar (SAS) images is one of the core tasks of underwater intelligent perception systems. However, the existing UOD methods suffer from excessive model redundancy, high computational demands, and severe image quality degradation due to noise. To mitigate these issues, this paper proposes an ultra-lightweight and high-precision underwater object detection method for SAS images. Based on a single-stage detection framework, four efficient and representative lightweight modules are developed, focusing on three key stages: feature extraction, feature fusion, and feature enhancement. For feature extraction, the Dilated-Attention Aggregation Feature Module (DAAFM) is introduced, which leverages a multi-scale Dilated Attention mechanism for strengthening the model’s capability to perceive key information, thereby improving the expressiveness and spatial coverage of extracted features. For feature fusion, the Channel–Spatial Parallel Attention with Gated Enhancement (CSPA-Gate) module is proposed, which integrates channel–spatial parallel modeling and gated enhancement to achieve effective fusion of multi-level semantic features and dynamic response to salient regions. In terms of feature enhancement, the Spatial Gated Channel Attention Module (SGCAM) is introduced to strengthen the model’s ability to discriminate the importance of feature channels through spatial gating, thereby improving robustness to complex background interference. Furthermore, the Context-Aware Feature Enhancement Module (CAFEM) is designed to guide feature learning using contextual structural information, enhancing semantic consistency and feature stability from a global perspective. To alleviate the challenge of limited sample size of real sonar images, a diffusion generative model is employed to synthesize a set of pseudo-sonar images, which are then combined with the real sonar dataset to construct an augmented training set. A two-stage training strategy is proposed: the model is first trained on the real dataset and then fine-tuned on the synthetic dataset to enhance generalization and improve detection robustness. The SCTD dataset results confirm that the proposed technique achieves better precision than the baseline model with only 10% of its parameter size. Notably, on a hybrid dataset, the proposed method surpasses Faster R-CNN by 10.3% in mAP50 while using only 9% of its parameters. Full article

Under the background of global green sustainable development and the urgent need to understand complex regional innovation systems, it is crucial to scientifically assess China’s Tourism Green Innovation Efficiency (TGIE) as a dynamic networked system and reveal its system-level evolution driving mechanism. This article presents the construction of the TGIE evaluation indicator system, measures the inter-provincial TGIE in China in 2011–2023 based on the three-stage super-efficiency SBM-DEA model, analyzes the spatial correlation network characteristics of TGIE by using the motif analysis method and the social network analysis method, and explores the evolutionary driving mechanism by using the time-exponential random graph model (TERGM). The study shows the following: (1) The TGIE of China exhibits a regional distribution pattern characterized by “high in the east and low in the west.” The efficiency of the eastern coastal region is significantly higher than that of the central and western regions, and the overall efficiency shows a fluctuating upward trend. (2) The local structure of China’s TGIE network is dominated by the chain structure, and the partially closed structure is gradually enhanced. It indicates that the bridge role of intermediary nodes in the cross-regional flow of innovation resources is becoming more and more significant. (3) The overall network evolves from a single center to a polycentric collaboration model. High-efficiency regions attract low-efficiency regions to collaborate through high connectivity, and intermediary nodes play a key role in connecting high- and low-efficiency regions. (4) The evolution of China’s TGIE network is driven by both exogenous and endogenous dynamics, showing significant path dependence and path creation characteristics. This study enhances the theoretical framework of complex systems in tourism innovation and offers theoretical support and policy insights for optimizing the network structure of China’s TGIE as a complex adaptive system and maximizing regional cooperation networks. Full article

Advances in satellite miniaturisation have led to a steep rise in the number of Earth-observation platforms, turning the downlink of the resulting high-volume remote-sensing data into a critical bottleneck. Low-Earth-Orbit (LEO) communication constellations offer a high-throughput relay for these data, yet also introduce intricate scheduling requirements. We term the associated task the Remote Sensing Data Transmission in Communication Constellations (DTIC) problem, which comprises two sequential stages: inter-satellite routing, and satellite-to-ground delivery. This problem can be cast as a Hybrid Flow Shop Scheduling Problem (HFSP). Unlike the classical HFSP, every processor (e.g., ground antenna) in DTIC can simultaneously accommodate multiple jobs (data packets), subject to two-dimensional spatial constraints. This gives rise to a new variant that we call the Hybrid Flow Shop Problem with Two-Dimensional Processor Space (HFSP-2D). After an in-depth investigation of the characteristics of this HFSP-2D, we propose a constructive heuristic, denoted NEHedd-2D, and a Two-Stage Memetic Algorithm (TSMA) that integrates an Inter-Processor Job-Swapping (IPJS) operator and an Intra-Processor Job-Swapping (IAJS) operator. Computational experiments indicate that when TSMA is initialized with the solution produced by NEHedd-2D, the algorithm attains the optimal solutions for small-sized instances and consistently outperforms all benchmark algorithms across problems of every size. Full article

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article

Understanding the spatio-temporal evolution of land consolidation is essential for optimizing regional land resource allocation and mitigating human–land conflicts during socio-economic development. This study introduced a novel framework for analyzing such patterns in China. Utilizing a two-decade (2002–2022) prefecture-level city dataset of land consolidation projects in Lianyungang, Jiangsu Province, we developed a “land consolidation intensity” metric and applied quantitative techniques—including land use transfer matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to assess spatio-temporal dynamics and centroid shifts. Key findings included: (1) Land consolidation intensity exhibited distinct stages, evolving from initial development to rapid growth and eventual stabilization, closely aligning with national policy shifts. (2) The primary sources for supplemented cultivated land were ponds, rivers, and tidal flats, followed by grassland, construction land, and forest land, with cultivated land consistently dominating the consolidated landscape. (3) Land consolidation projects distribution concentrated in economic and political centers, with a spatial shift from inland western region towards the eastern coastal region. (4) Gray relational analysis identified economic development as the predominant driver, with policy and social factors providing secondary guidance. This research elucidates the spatio-temporal evolution characteristics of land consolidation at the prefecture-level city and demonstrates the utility of the proposed framework for similar analyses, offering insights relevant to national land use planning and policy formulation. Full article

Background: Zebrafish-based drug discovery systems provide significant advantages over mammalian models for high-throughput in vivo screening. Among these, the NF-κB eGFP reporter system significantly enhances drug discovery in zebrafish by enabling real-time, high-resolution monitoring of pathway activity in live organisms, thereby streamlining mechanistic studies and high-throughput screening. Methods: We developed a novel AI (Quantifish and Orange software)-based zebrafish precision individualized 96-well ZF plates (0–7 dpf) and individualized MT tanks (8 dpf–4 mpf) protocol for the transposon-mediated transgenesis of the NFκB eGFP reporter system. Results: One-cell stage embryos were administered NFκB reporter construct and Tol2 transposase mRNA via microinjection and transferred to separate wells of a 96-well ZF plate. Bright-field and fluorescence images of each well were captured at 5 dpf in the F0, F1, and F2 generations using the automated confocal high-content imager CQ1. The Quantifish software was used for the automated detection and segmentation of zebrafish larval fluorescence intensity in specific regions of interest. Quantitative data on the fluorescence intensity and distribution patterns were measured in Quantifish, and advanced statistical and machine learning methods were applied using Orange. Imaging data with eGFP expression results were assessed to evaluate the efficiency of the transgenic protocol. Discussion: This AI-enhanced precision protocol allows for high-throughput screening and quantitative analysis of NFκB reporter transgenesis in zebrafish, enabling the efficient identification and characterization of stable transgenic lines that exhibit tissue-specific expression of the NF-κB reporter, such as lines with induced expression restricted to the retina following LPS stimulation. This approach streamlines the evaluation of regulatory elements, enhances data consistency, and reduces animal use, making it a valuable tool for zebrafish drug discovery. Full article

We propose a new strategy for constructing Runge–Kutta (RK) methods using evolutionary computation techniques, with the goal of directly minimising global error rather than relying on traditional local properties. This approach is general and applicable to a wide range of differential equations. To highlight its effectiveness, we apply it to two benchmark problems with oscillatory behaviour: the (2+1)-dimensional nonlinear Schrödinger equation and the N-Body problem (the latter over a long interval), which are central in quantum physics and astronomy, respectively. The method optimises four free coefficients of a sixth-order, eight-stage parametric RK scheme using a novel objective function that compares global error against a benchmark method over a range of step lengths. It overcomes challenges such as local minima in the free coefficient search space and the absence of derivative information of the objective function. Notably, the optimisation relaxes standard RK node bounds ($c_i\in [0,1]$), leading to improved local stability, lower truncation error, and superior global accuracy. The results also reveal structural patterns in coefficient values when targeting high eccentricity and non-sinusoidal problems, offering insight for future RK method design. Full article

With the progressive depletion of conventional oil and gas resources and the increasing demand for alternative energy, organic-rich sedimentary rock—oil shale—has attracted widespread attention as a key unconventional hydrocarbon resource. Pyrolysis is the essential process for converting the organic matter in oil shale into recoverable hydrocarbons, and a detailed understanding of its behavior is crucial for improving development efficiency. This review systematically summarizes the research progress on the pyrolysis characteristics of oil shale under laboratory conditions. It focuses on the applications of thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) in identifying pyrolysis stages, extracting kinetic parameters, and analyzing thermal effects; the role of coupled spectroscopic techniques (e.g., TG-FTIR, TG-MS) in elucidating the evolution of gaseous products; and the effects of key parameters such as pyrolysis temperature, heating rate, particle size, and reaction atmosphere on product distribution and yield. Furthermore, the mechanisms and effects of three distinct heating strategies—conventional heating, microwave heating, and autothermic pyrolysis—are compared, and the influence of inherent minerals and external catalysts on reaction pathways is discussed. Despite significant advances, challenges remain in quantitatively describing reaction mechanisms, accurately predicting product yields, and generalizing kinetic models. Future research should integrate multiscale experiments, in situ characterization, and molecular simulations to construct pyrolysis mechanism models tailored to various oil shale types, thereby providing theoretical support for the development of efficient and environmentally friendly oil shale conversion technologies. Full article

This paper explores the rise in voice-based social media as a pivotal transformation in digital communication, situated within the broader era of chatbots and voice AI. Platforms such as Clubhouse, X Spaces, Discord and similar ones foreground vocal interaction, reshaping norms of participation, identity construction, and platform governance. This shift from text-centered communication to hybrid digital orality presents new sociological and methodological challenges, calling for the development of voice-centered analytical approaches. In response, the paper introduces a multidimensional methodological framework for analyzing voice-based social media platforms in the context of surveillance capitalism and AI-driven conversational technologies. We propose a high-level reference architecture machine learning for social science pipeline that integrates digital methods techniques, automatic speech recognition (ASR) models, and natural language processing (NLP) models within a reflexive and ethically grounded framework. To illustrate its potential, we outline possible stages of a PoC (proof of concept) audio analysis machine learning pipeline, demonstrated through a conceptual use case involving the collection, ingestion, and analysis of X Spaces. While not a comprehensive empirical study, this pipeline proposal highlights technical and ethical challenges in voice analysis. By situating the voice as a central axis of online sociality and examining it in relation to AI-driven conversational technologies, within an era of post-orality, the study contributes to ongoing debates on surveillance capitalism, platform affordances, and the evolving dynamics of digital interaction. In this rapidly evolving landscape, we urgently need a robust vocal methodology to ensure that voice is not just processed but understood. Full article