Search Results (1,228)

The low participation of men in primary education is a persistent and structural phenomenon that cannot be adequately understood through homogeneous views of teachers’ motivations and experiences. This study is conducted in the Chilean context, which is characterized by a highly feminized teaching workforce and persistent challenges related to working conditions, social valuation of teaching, and teacher retention. It aims to analyze profiles of male primary school teachers, considering their motivations, perceptions, and the meanings they attribute to the teaching profession. A sequential explanatory mixed-methods design (QUAN → qual) was employed. First, 144 male in-service primary teachers completed the FIT-Choice scale and a latent class analysis was conducted. Subsequently, in-depth interviews were carried out with an intentionally selected subsample of 20 teachers, which were analyzed using qualitative content analysis. Three distinct motivational profiles were identified: altruistic, pragmatic, and critical. The qualitative findings complemented these profiles, highlighting the influence of personal trajectories and working conditions on teachers’ career choice and retention in the profession. Overall, the findings suggest that policies for training, support, and professional induction must recognize teacher heterogeneity and promote inclusive working environments, moving beyond approaches that focus exclusively on increasing the number of men in primary education. Implications for the design of policies aimed at attracting and retaining male primary school teachers are discussed. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Critical minerals are central to industrial strategies in the Global South, but evidence on how such policies reshape domestic production is limited. This paper maps Indonesia’s nickel ecosystem before and after the 2014 export ban using input–output multipliers and labor intensity from the 2010, 2016, and 2020 input–output tables. We provide a descriptive account of nickel’s evolving economic trajectory during the downstreaming push. Three patterns stand out. Forward linkages declined from 16 to 8 and backward linkages moved from 75 to 73, suggesting a narrower structure with greater specialization in higher value, more capital-intensive activities. Output multipliers rose most in sectors that support the electric vehicle supply chain, including professional and technical services, machinery, fabricated metals, transport equipment, energy, and finance. In contrast, the labor multiplier fell from about 6514 to 3366 jobs per IDR 1 trillion of final demand, implying a higher value added alongside lower employment intensity. Overall, downstreaming appears to work through structural concentration and growth in complementary sectors rather than broad-based diversification. Complementary policies in skills, regional development, and energy infrastructure are therefore critical for inclusive industrial transformation. Full article

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article

Kazakhstan has aggressively pursued the internationalization of higher education, evidenced by the strategic Bolashak scholars’ program, adoption of the Bologna Process, and expanded academic mobility. In this paper, we argue that these efforts, while structurally significant, have yielded results that have prioritized institutional outputs over intercultural learning outcomes. To achieve genuine modernization, internationalization must move beyond technical compliance and be grounded in the cultivation of intercultural competence and citizenship. We review the trajectory of Kazakhstan’s educational history, development, and reforms and conclude that current efforts lack the cohesion and theoretical grounding necessary to foster globally engaged, interculturally competent citizenship. We narratively review selected international case studies of higher education institutions that have developed intercultural competence and citizenship programs to develop cross-case themes and practices. Consequently, we suggest a contextualized paradigm for developing intercultural competence within Kazakhstani higher education. We present a series of theoretical, practical, and institutional suggestions tailored for Kazakhstani higher education institutions to consider. Ultimately, intercultural competence in Kazakhstan must begin with a critical exploration of national and local values to engage the global community from a “glocalized,” culturally resonant, and decolonized standpoint. Full article

In the fabrication process of pipelines for petrochemical and other industries, weld reinforcement is often excessive and adversely affects subsequent processes such as anticorrosion treatment and surface coating. Weld reinforcement must be removed through a grinding process. Welding deformation and fit-up errors often lead to highly irregular weld geometries, which makes robotic grinding difficult and causes the task to still heavily rely on manual operation. To address this issue, this study proposes an automatic weld recognition and grinding trajectory planning method based on 3D visualization and deep learning. A weld recognition network, termed WSR-Net, has been developed based on an improved PointNet++ architecture with a cross-attention mechanism, achieving a segmentation accuracy of 98.87% and a mean intersection over union of 90.71% on the test set. An intrinsic shape signature (ISS) key point selection algorithm with orthogonal slicing-based pruning optimization is developed to robustly extract key weld ridge points that characterize the weld trend on rugged weld surfaces. According to the height differences between the weld and the adjacent base metal surfaces, the grinding reference surface is fitted using the weld contour through the moving least-squares method. The ridge line points are projected onto the grinding reference surface along the local normal to generate the expected grinding trajectory points. The grinding trajectory that meets the process constraints is generated through reverse layer slicing. Grinding experiments demonstrate that the proposed WSR-Net achieves robust segmentation performance for both planar and curved surface welds. With the reverse layered trajectory planning method, the proposed method enables high-precision automatic grinding of complex spatially curved surface welds. The results show that the final grinding mean error is 0.316 mm, which satisfies the preprocessing requirements for subsequent processes. The proposed method provides a feasible technical method for the intelligent grinding of spatially curved surface welds. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

The effectiveness of social engineering schemes, such as phishing, depends significantly on the victim’s emotional state, which is intentionally moved by the attacker toward fear, sadness, and disgust through time pressure, threats, or messages about potential losses, which weaken cognitive control. EEG datasets that simultaneously contain basic emotions and realistic phishing scenarios are lacking. Therefore, in some cases, stress-based biophysiological datasets obtained using the Trier Social Stress Test (TSST) are used for neurophishing modeling. The TSST exhibits phasic dynamics: a transition from a neutral state to a peak in fear, followed by an increase in sadness and a partial recovery to a neutral state, highlighting fear and sadness as key components of social stress. The interval of maximum fear probability is interpreted as the window of greatest vulnerability to phishing, when it is critical to consciously pause, verify information across independent channels, and avoid impulsive actions. The suggested hybrid neural network model, WS-KAN-EEGNet, is trained on five emotions and applied to these recordings, generating temporal trajectories of state probabilities with high accuracy, forming a reliable basis for future industrial solutions to ensure a secure digital space. Full article

Rehabilitation robotics faces the challenges of aligning engineering design with patient-specific needs. Most existing controllers in rehabilitation robots often constrain motion to fixed paths or provide only passive guidance, limiting user engagement and adaptability. This study proposes a novel active-assistive mode controller that integrates a virtual tunnel-based force generation mechanism with a dynamic moving-window technique for tracking activities of daily living (ADL) trajectories. Unlike conventional impedance controllers, the proposed method dynamically adjusts the virtual tunnel in real time, permitting voluntary upper-limb movement within a safe operational range while preventing excessive deviation. The system was implemented on a wearable two-degree-of-freedom (DOF) upper-limb exoskeleton equipped with drive and integrated sensor units. Experimental results demonstrated that decreasing the guidance force ($F_{gf}$) increased tracking errors, from $1°$ at 100% $F_{gf}$ to $5°$ at 30% $F_{gf}$, indicating greater voluntary participant motion. Peak actuator torques correspondingly decreased from 14.75 to 13.43 Nm (elbow) and from 4.14 to 2.48 Nm (wrist), confirming the controller’s capability to modulate robotic assistance according to user effort. Tests with 30 healthy participants demonstrated the effectiveness of guidance along predefined ADL trajectories, validating the controller’s potential for patient-centered rehabilitation. Full article

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article

Moving beyond traditional sociodemographic models, this study investigates the psychometric drivers of lexical change. Using Swiss German as a case study, we compare historical data from the Sprachatlas der deutschen Schweiz (1939–1958) with a recent large-scale app-based survey (N = 1013) to quantify trajectories over the past century. We identify four distinct mechanisms: exogenous convergence (Schmetterling), endo-normative leveling (Rande), endogenous innovation and divergence (schlittschuhlaufen), and diachronic persistence (Stäge). For the locally rooted speakers in our dataset, structural analysis indicates that traditional variables carry less weight than expected. While age remains the primary vertical predictor, psychological factors outperform traditional variables (e.g., gender, social networks) in this environment of ubiquitous exposure. Multivariate models demonstrate that lexical choices are strongly influenced by individual disposition: traits such as agreeableness accelerate the adoption of supraregional forms, whereas a strong local identity functions as a “brake” against standardization. Ultimately, while macro-factors create the pressure for change, individual micro-factors determine whether it takes hold. A speaker’s attitude acts as a “filter” and their personality as a “gate,” deciding whether they accept or resist new forms. These findings challenge purely structural accounts, suggesting that for these locally rooter speakers, even without high physical mobility, lexical change is shaped by a psychometric architecture. Full article

This article addresses the problem of finding the shortest route for Automated Guided Vehicles (AGVs) in a production environment with constrained battery state-of-charge (SoC) and time-dependent operating conditions. The route map is divided into a uniform grid containing stationary obstacles and two types of dynamic obstacles: human, for which AGV transportation is prohibited, and inanimate (moving objects), which impose a penalty function. A key contribution of the proposed methodology is the introduction of a battery residual charge matrix, which embeds cell-level energy feasibility directly into the grid-based environment representation by determining minimum admissible SoC constraints and accounting for transition-dependent energy costs. This matrix restricts the set of traversable cells under low-energy conditions, enabling energy-aware route feasibility evaluation during both initial planning and adaptive replanning. The proposed approach is based on the A* and D* Lite algorithms, providing shortest-path construction that explicitly integrates battery SoC into the spatio-temporal cost function. To avoid collisions in a multi-agent environment during routing, a simplified hybrid scheme with M* elements performs local coordination and adaptive trajectory replanning. The effectiveness of the proposed methodology was assessed using travel time, temporal complexity, and spatial complexity metrics. Simulation results on a $10\times 10$ grid showed that agents with sufficient battery completed routes of 8 and 11 cells with travel times of 7.2 to 10.7 conventional units. A critically low-energy agent was initially unable to move, but after adjusting the minimum SoC constraint, all agents completed their routes with travel times up to 11.4 conventional units, demonstrating the direct impact of energy constraints on system performance. Additional experiments with varying agent counts and SoC thresholds confirmed reliable balancing of route feasibility and energy constraints across configurations. Full article

While Western theology typically locates omniscience in a personal Creator-God, Indian philosophy presents a notable spectrum. This article traces the dialectical arc of omniscience (sarvajñāna) across major Indian philosophical traditions, arguing that what appears as an epistemological question—“who knows everything?”—is ultimately an ontological puzzle about the nature of consciousness itself. Moving from the Vedic oscillation between cosmic personhood (Puruṣa Sūkta) and primordial uncertainty (Nāsadīya Sūkta), through the Upaniṣadic internalization of omniscience as Self-knowledge (ātmajñatā), the article examines how Nyāya-Yoga affirms divine omniscience as a logical and soteriological necessity, how Mīmāṃsā displaces it onto an impersonal authorless text, and how Jainism and Buddhism reappropriate it as a perfected human achievement. The final section demonstrates that both Sāṃkhya’s isolation (kaivalya) and Advaita Vedānta’s non-dual realization ultimately transcend encyclopedic omniscience, revealing that authentic liberation requires not the possession of maximal information but a transformation from representational object-knowledge to non-objectifying awareness. Together, these trajectories constitute Indian philosophy’s most enduring contribution to the global philosophy of religion: the recognition that the “All” cannot be an object of knowledge, because it is the very condition for any knowledge whatever. Full article

Ensuring the rights of involuntary resettlers is fundamental to a law-based state and essential for achieving social equity and sustainable development. However, institutional improvement depends not only on the intent of top-level design but also on the capacity for dynamic adaptation amid evolving social contexts. Moving beyond the predominant research focus on policy design principles, this study investigates the dynamic evolution of China’s reservoir resettlement rights protection policies from 1949 to 2025. We first constructed a corpus of 32 core policy documents. Employing a bibliometric analysis within a multi-dimensional framework, we statically examined the developmental patterns of these policies. Subsequently, we applied the Punctuated Equilibrium Theory (PET) to dynamically analyze their policy changes, identifying a trajectory marked by both long-term stability and significant punctuations. Our findings reveal that over 76 years, the policy process has undergone two major equilibrium periods and two critical punctuation nodes, demonstrating a clear pattern of “protracted stability interspersed with short bursts of rapid transformation.” The policy image has correspondingly evolved through four distinct stages: “Administratively Mobilized Resettlement,” “Development-Oriented Resettlement,” “Harmonious Society for Resettlers,” and “Common Prosperity.” The study argues that this evolution is driven by the interplay of shifting central government attention, the occurrence of focusing events, and the reinforcement of evolving Policy Images, which collectively broadened the policy venue and led to non-linear institutional change. Based on these findings, the paper recommends: first, adopting a dynamic approach to policy formulation; second, maintaining sustained political commitment and robust institutional safeguards; and third, fostering multi-stakeholder consultation and collaborative governance mechanisms. These strategies are essential to more effectively secure the multifaceted rights of reservoir resettlers. Full article

Background/Objectives: Successful interception of moving targets requires combining predictive control, which anticipates future target states, and reactive control, which compensates for ongoing sensory discrepancies. How these components evolve over time and are distributed across gaze and manual behavior remains unclear. We aimed to explore the time-resolved dynamics of predictive control during continuous interception and to dissociate eye and hand contributions. Methods: Human participants intercepted a moving target in a two-dimensional arena using a joystick while eye movements were recorded. Target speed was systematically varied, and visual information was selectively reduced by occluding either the target or the user-controlled cursor. Predictive control was assessed using two complementary metrics: a geometric strategy index capturing moment-to-moment spatial lead or lag relative to target motion, applied separately to gaze and manual trajectories, and root mean square error (RMSE) computed relative to current and forward-shifted target positions to quantify predictive alignment. Results: Successful interception was characterized by structured, speed-dependent transitions between predictive and reactive control rather than a fixed strategy. Predictive alignment emerged early and was dynamically reweighted as temporal constraints increased. Gaze and manual behavior showed complementary but partially dissociable predictive signatures. Occluding the target decreased predictive alignment, whereas occluding the user-controlled cursor had comparatively minor effects, indicating strong reliance on internal state estimation rather than continuous visual feedback of the effector. Conclusions: Predictive and reactive control are continuously and dynamically reweighted during interception. Their interaction unfolds within single trials and depends on target dynamics and sensory availability. These findings provide quantitative evidence for time-resolved coordination between anticipatory and feedback-driven control mechanisms in goal-directed behavior. Full article