Search Results (578)

Teacher professional development (TPD) is increasingly mediated by online platforms, yet emotion analysis in this context remains underdeveloped because teachers’ professional discourse is often reflective, evaluative, and shaped by professional norms. To address this challenge, this study proposes a fine-grained, low-intrusion affective-appraisal analysis framework for TPD-oriented online professional learning that integrates textual evidence with platform interaction logs. The framework retains pleasure, arousal, and dominance from the pleasure–arousal–dominance (PAD) model and introduces utility as an appraisal-related dimension, capturing teachers’ perceived usefulness, value judgment, and professional learning gain. Methodologically, it combines textual representations based on Bidirectional Encoder Representations from Transformers (BERT), intra-week long short-term memory (LSTM) aggregation, interpretable behavioral-log features, and feature-level fusion. Data were collected from an authentic TPD-oriented online course involving 107 pre-service teachers, yielding 1276 teacher-week samples from 4300 texts and 264,028 interaction records. Results show that intra-week sequential modeling improves the macro-averaged F1 score (Macro-F1) over both the term frequency–inverse document frequency plus support vector machine (TF-IDF+SVM) baseline and BERT-based weekly text concatenation, with statistically significant gains over the non-sequential BERT-concat model across all four dimensions. Adding interaction logs improves accuracy across all dimensions and provides complementary process-based evidence, especially for arousal and utility. By linking a four-dimensional affective-appraisal framework with text-log fusion, this study offers a scalable and context-sensitive approach to affective-appraisal analytics in pre-service teacher professional learning. Full article

Home healthcare has recently been promoted in response to the increase in vulnerable people, such as elderly patients who can have difficulty accessing clinics and hospitals in Japan. A characteristic specific to home healthcare is that laboratory tests using specimens are conducted by transport from home to laboratory centers or by point-of-care testing at home. In this case, several issues can lead to inaccurate test values. This narrative literature review summarizes issues in the preanalytical process, a critical phase for ensuring the accuracy of laboratory tests. Specimen collection may not always be smooth in the pathological conditions of some elderly patients and/or in the non-clinic/hospital environments. The preservation of specimens, considering prolonged pre-centrifugation time and storage temperature, can alter the values of various analytes, including blood glucose, potassium, and lactate dehydrogenase. In addition, hemolytic phenomenon caused by insufficient specimen collection, vibration during specimen transport, and excessive milking during fingertip blood sampling can also be an issue. Awareness of the preanalytical process in testing specimens is important for obtaining accurate laboratory tests in home healthcare settings. This comprehensively summarized paper will be helpful in securing test quality and patient care. Full article

In the digital twin (DT) network, effective edge data processing is essential to meet the real-time requirements of DT models. However, edge servers (ESs) are self-interested and have limited computation resources. The virtual content operator (VCO) cannot observe their true computing capabilities, leading to participation reluctance and information asymmetry. To address these challenges, this paper proposes a contract-learning integration method for computing incentive and data offloading. A two-dimensional computation-reward contract incentive mechanism is designed to motivate ESs to provide computation resources for data pre-processing, where both continuous and discrete distributions of ES types are considered. Then, ESs upload the processed results to the VCO for DT model mapping, synchronization, and final construction. Based on the individual rationality and incentive compatibility constraints, the optimal incentive reward and computing resource allocation strategies are analytically derived to maximize the VCO’s utility. Then, based on the signed contracts, a multi-agent double deep Q-network algorithm is developed to jointly optimize the binary data offloading decision, transmission bandwidth, and transmission power for the minimal system delay. The algorithm learns adaptive strategies in the dynamic network environment and mitigates Q-value overestimation. Numerical results demonstrate that the proposed method improves system performance in terms of computing incentive and data offloading. Full article

In BIM-enabled architectural projects, information systems preserve accepted decisions far more reliably than the rejected and non-selected alternatives that shaped them. Drawings, models, specifications and common data environments record what a project became, while the reasons that eliminated competing options are dispersed across meeting notes and revision logs or lost. This asymmetry weakens design coordination, change management and cross-project knowledge reuse. This article proposes a conceptually derived and analytically evaluated recording artefact for recovering these lost decision traces within the phase-transition band from spatial coordination to technical design. A two-gate evaluation logic separates codified screening from stakeholder-mediated review and decouples the procedural location of rejection from the category family that organises its reason. Three loss types are identified: pre-stakeholder invisible loss, trace/version loss and terminal loss. These are linked to six rejection-category families, four process redirection effects and differentiated memory destinations, with a constraint-bearing layer divided into avoidance and comparative branches. A fillable eight-field decision record template, formalised as a single recording-and-routing procedure, is specified for BIM, common data environment and design review workflows, supported by a query specification. The model is illustrated through a constructed hotel-floor decision node and offers a structured basis for retaining the knowledge carried by rejected, revised and valid but non-selected architectural decisions. Full article

The chronic digitalisation deficit within the construction sector induces design anomalies and human errors, leading to a severe erosion of investment profitability. This study aims to implement the automation of resource generation and validation processes, acting as a systemic safety barrier to stabilise analytical workflows. The proposed methodology relies on a Multi-Agent System (MAS) architecture embedded within the n8n environment and powered by Gemini-class language models. The framework integrates a deterministic PostgreSQL database within a Retrieval-Augmented Generation (RAG) architecture, enabling the precise, real-time processing of Construction Law regulations. Applying Chain-of-Thought reasoning alongside structured prompt templates helped eliminate model logic drift, ensuring comprehensive result reproducibility. The deployment of this platform induced a 96% acceleration in the pre-construction phase, reducing the formulation time of Work Breakdown Structure (WBS)/Critical Path Method (CPM) structures from a baseline of 480 min to an average of 20 min. The empirical data demonstrates a radical compression of operational costs (OPEX) concurrent with the marginalisation of the Human Error Probability (HEP) index to a residual level of < 1%. Ultimately, the solution drastically minimised the iterative overhead, confining the design cycle to a single execution while maintaining high level of compliance with the 7R (7 Rights) Logistics Directive. Full article

Sensors and biosensors designed for biomarker detection in biological samples often suffer from performance loss caused by surface fouling, interface blocking, and matrix interference. Although these effects are frequently discussed separately, in real sensing systems they are strongly interconnected and they determine analytical reliability, especially in body fluids like serum, plasma, whole blood, sweat, and other complex media. This review provides a practical and mechanism-oriented overview of how these processes originate, how they differ, and how they ultimately lead to signal drift, reduced sensitivity, false-positive responses, and shortened sensor lifetime. We first discuss the molecular origins of interface failure, including protein adsorption, conditioning film formation, nonspecific binding, ionic strength effects, pH fluctuations, viscosity-related diffusion changes, and electroactive interferents. The impact of these phenomena is then compared across major sensing platforms, including electrochemical, potentiometric, optical, capacitive sensors, field-effect transistors and wearable biosensors. A central part of this review focuses on practical prevention strategies already employed in real biomarker sensing platforms. These include hydration-driven antifouling coatings, zwitterionic and hydrogel interfaces, post-immobilization blocking with bovine serum albumin, mercaptohexanol and ethanolamine, ionophore and membrane engineering in ion-selective electrodes, hydrophobic solid-contact layers for water-layer suppression, regeneration workflows, membrane and microfluidic pre-treatment, and AI-assisted drift correction. By combining advances in materials engineering, surface chemistry, sample handling, and algorithmic correction, this review highlights strategies to improve sensor stability in complex biological fluids. Overall, it offers a practical guide for developing next-generation low-fouling, drift-resistant, and self-correcting sensing systems for reliable biomarker analysis at the point of care. Full article

In this study we describe the refinement process from the first to the second phase of a teaching–learning sequence development and implementation. The TLS comprises several experimental activities that aim to support understanding of Control of Variables Strategy (CVS) reasoning in the context of floating/sinking and properties of magnets. The research was carried out during a science laboratory course in a department of early childhood education. The participants numbered 67 in the first phase of the survey and 45 pre-service early childhood teachers (referred to as student teachers) in the second phase. The analysis is theoretically grounded in Pickering’s model of scientific practice, as adapted in science education, which provides the analytical framework for identifying and categorizing refinement changes. The results showed that the refinements are differentiated from each other according to the factors that guide them. Specifically, the three refinement changes guided by the educational factor were local-guided, i.e., related to a specific activity dealing with the student teachers’ educational needs, and the other two, also driven by the scientific factor, were holistic-open refinements, i.e., related to a set of activities adjusting the TLS to the new scientific trends. These findings contribute to the literature on Teaching-Learning Sequence development by illustrating how theoretically grounded analysis can make refinement processes more explicit and analytically interpretable. Full article

In the rapidly evolving era, with the advancement of AI technology in education, Chat Generative Pre-trained Transformer (ChatGPT) is widely used in education to help students simplify the learning process. In other words, the implementation of ChatGPT makes the learning process more efficient and relevant. This study was conducted to analyze sentiment from social media platforms such as X to determine the impact of ChatGPT’s use in higher education in Indonesia. The research method involves data collection using the data crawling method for the X platform, which is integrated with the RapidMiner application. This sentiment analysis aims to identify trends in positive, negative, and neutral sentiment towards the use of ChatGPT in higher education in Indonesia and Thailand by using the Naive Bayes Classifier classification method and the Cross-Industry Standard Process for Data Mining method to design, execute, and evaluate data analytics projects. This analysis is expected to provide an initial overview of emerging sentiment trends as well as insights into how ChatGPT is perceived in the higher education environment. Overall, the results of this study provide an overview of public perception regarding the influence of ChatGPT in higher education in Indonesia and serve as a foundation for developing policies related to more responsible AI implementation in the academic environment. Full article

Cross-basin tropical cyclone variability may exhibit complex, non-linear dependence structures influenced by large-scale climate modes and potential regime shifts. Reliance on traditional linear correlation measures without accounting for structural changes can therefore lead to misleading interpretations of global storm relationships. This study investigates the regional dependence structures of tropical cyclone counts across six major ocean basins (NA, ENP, WNP, NI, SI, and SP) from 1980 to 2024. We adopt a two-stage analytical framework integrating changepoint detection and copula modeling to address non-stationarity in both marginal distributions and dependence structures. First, we identify a significant structural break in the year 2000 via a penalised likelihood applied jointly to the $d=6$-variate Poisson series, with inter-basin dependence captured by a latent Gaussian process (the construction used by Lund et al. (2025). This is mathematically equivalent to a Gaussian copula with Poisson margins (Genest and Nešlehová (2007)). Then, we apply bivariate copula models separately to the pre- and post-2000 regimes using the randomized probability integral transform with results averaged over 500 replications of the auxiliary uniforms to mitigate randomization noise. The results reveal substantial non-stationarity, most notably a 59% increase in North Atlantic storm frequency and a fundamental reorganization of global dependence structures, while dependence structures evolved from primarily symmetric and weak (dominated by Gaussian and Clayton copulas) to more complex and stronger dependencies (increased Frank and Gumbel copulas). Notably, a statistically significant ($p<0.001$) and strong negative dependence emerged between the Southern Pacific and Northern Indian basins ($\tau =-0.464$) in the recent regime. The inclusion of changepoint detection significantly improves model fit and reveals a fundamental reorganization of global tropical cyclone teleconnections, with enhanced coordination between basins in the contemporary climate regime. Modeling these regimes separately, as opposed to a single stationary period, uncovers a shift towards more complex, tail-dependent copula families (Gumbel, Clayton) in the recent era. These findings have important implications for climate risk assessment, seasonal forecasting, and understanding the impacts of climate change on global storm patterns. The proportion of Gumbel copulas (capturing upper-tail dependence) increased from 7% to 20%, while Gaussian copulas decreased from 53% to 33%, indicating more complex, extreme-value-focused dependencies in the contemporary climate. Due to small sample sizes ($n_1=20$, $n_2=25$), copula and dependence estimates are exploratory, not confirmatory. Interpretations reflect this power constraint, utilizing Benjamini–Hochberg adjustments for significance. Full article

Spectral sensors have expanded the opportunities for the non-destructive monitoring of crops and weeds. However, the lack of standardized and accessible analytical pipelines remains a major limitation for data reproducibility and integration in this field. EasySpectra was developed to address these challenges by providing a unified environment that integrates data import, radiometric calibration, geometric alignment, spectral pre-processing, region-of-interest selection, feature extraction, vegetation index computation, and dataset construction. A graphical user interface guides users through the entire analytical workflow, reducing technical barriers for non-experts. EasySpectra supports heterogeneous data sources, including single-band images, spectral cubes and georeferenced orthomosaics. Across 100 sampled areas, the correction + normalization workflow in EasySpectra produced NDVI values very close to Pix4DFields (0.70 ± 0.052 vs. 0.69 ± 0.055), with a pixel-wise correlation of up to 0.98 and low bias (MBE = 0.05). In an independent UAV dataset, EasySpectra also showed close agreement with WebODM, with NDVI values ranging from 0.09 ± 0.10 to 0.42 ± 0.08 versus 0.08 ± 0.13 to 0.43 ± 0.10, across 13 sampled areas. In addition, hyperspectral species classification using EasySpectra-extracted profiles achieved a Macro F1-score of 0.880, with class-wise accuracies ranging from 0.83 for canola to 0.95 for redroot pigweed. Overall, EasySpectra enables reproducible, transparent, and standardized spectral analysis. Full article

Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in the “Combustion” course, including highly abstract theories, experimental safety risks, and compressed instructional hours. Moving beyond mere technical stacking, the model establishes a closed-loop data ecosystem that integrates “pre-class adaptive diagnosis, in-class contextualized internalization, and post-class personalized transfer,” while deeply embedding engineering ethics and sustainability issues related to carbon neutrality. A one-semester quasi-experimental study (Experimental N = 60, Control N = 60) was conducted, utilizing a triangulated assessment of final exam scores, platform-based behavioral trajectories, and semi-structured interviews. The results showed that the experimental group achieved significantly higher final assessment scores than the control group (82.4 ± 5.7 vs. 73.2 ± 6.9), with normality tests supporting the use of parametric analysis and Analysis of Covariance (ANCOVA) indicating a significant instructional effect after controlling for Grade Point Average (GPA) and pre-test scores. Furthermore, behavioral analysis confirms that the LA mechanism significantly enhances students’ self-regulated learning and engagement by increasing the visibility of the learning process. This study provides an evidence-based reform paradigm for engineering curricula to achieve the synergistic cultivation of knowledge acquisition, competency development, and value alignment within constrained instructional timeframes. Full article

Behavioural analytics remain a dominant approach for modelling learner engagement and predicting performance in digital learning environments. However, existing approaches are largely retrospective, relying on observable behavioural outcomes rather than modelling the underlying cognitive state dynamics that evolve during sustained learning. This study proposes a nonlinear state-space modelling framework that formalises the interaction between cognitive fatigue, attention, and learning as a continuous-time dynamical system. Fatigue is modelled as a latent state governed by load–recovery dynamics, attention is represented as a fatigue-coupled cognitive resource, and learning accumulation is expressed as an attention-mediated process under saturation constraints. The model is discretised and empirically estimated using time-indexed webcam-derived pilot data (N = 63) and further validated using a large-scale intervention dataset (N = 1245). Parameter estimation is performed using regression-based approximation of the discretised state equations, with cluster-robust inference applied to account for intra-session dependencies. The webcam-derived features were pre-processed using temporal windowing and normalisation to ensure consistency across sessions. The swarm-optimised intervention was implemented through adaptive control of instructional load and recovery scheduling, enabling real-time regulation of fatigue progression. Empirical results demonstrate statistically significant model validity, with fatigue dynamics showing moderate explanatory capability (R² = 0.543, p < 0.001) and attention dynamics also significant (R² = 0.499, p = 0.004). At the system level, adaptive intervention significantly reduced fatigue and improved learning performance (t(1244) = 14.34, p < 0.001). The findings suggest a transition from retrospective behavioural modelling toward anticipatory cognitivestate regulation, contributing toward a computational foundation for fatigue-aware adaptive learning systems. Full article

To address the issues of poor seed filling efficiency, low qualified seeding index, and high missed-seeding index in cassava precision planters, this study developed a dual-chamber pre-cut cassava stem filling mechanism. The structure and working principles were analyzed, identifying key factors affecting performance. By employing the discrete element method (DEM) to simulate the interaction between cassava seed stems and the filling mechanism, using statistical analysis to process experimental data, and adopting kinematic and mechanical equilibrium modeling for theoretical analysis, the structure and dimensions of the seed scoop were ultimately optimized. Subsequently, we evaluated the effects of the seed scoop speed in the first filling zone, the seed filling speed ratio, and the seed stem population thickness on performance through multi-factor simulation experiments. Based on the NSGA-II algorithm and the analytic hierarchy process, the optimal parameters were determined as follows: a seed filling speed ratio of 0.78, a seed scoop speed (first filling zone) of 0.6 m/s, and a seed stem population thickness of 290–320 mm. Bench tests under these conditions yielded a 95.31% qualified filling rate (ratio of single-segment cassava stem captured by seed scoop to total stems), 1.89% missed-filling rate (ratio of cassava stem not captured by seed scoop to total stems), and 2.80% double-filling rate (ratio of multi-segment cassava stem captured by seed scoop to total stems). Variety adaptability tests confirmed the mechanism’s robustness for precision planting. These findings offer theoretical guidance for precision planting of stalk-type crops. Full article

Industrial areas, which represent a specific type of urbanised area with an extremely high concentration of material reserves, can be considered key anthropogenic raw material reservoirs in the context of urban mining. Industrial areas, characterised by a high material density and a specific composition of structural systems, show extraordinary potential for providing secondary raw materials with high material and energy value. This increases the need for their systematic evaluation. The aim of the present study was to define the role of the selected industrial area as a strategic node for secondary raw material extraction, to identify the structure and quality of “urban deposits” in the selected location of the Ostrava–Karviná region (CZ), and to provide an analytical framework for its integration into circular planning processes. The methodological approach is based on a combination of pre-demolition audit, material flow mapping, spatial analysis, and structural element characterisation. It is becoming apparent that industrial areas have a high material density and contain significant amounts of recyclable metals, reinforced concrete elements, etc. These stocks are often concentrated in structural systems with predictable geometries, such as serial assembly prefabricated and steel frames, allowing for more accurate estimates of recoverable volumes. The results show that the incorporation of industrial areas into the process of urban mining can significantly reduce the consumption of primary raw materials, mitigate the environmental impacts associated with the extraction of raw materials, and, at the same time, promote the regeneration of industrial areas (or brownfields) through the planned decomposition of structures. The inclusion of urban mining in urban development strategies and the regeneration of industrial sites leads to the prediction that urban mining is one of the key elements for achieving a material-efficient and low-carbon urban environment. Full article

This study examines how individual political predispositions and national mobilisation contexts jointly structure protest participation in contemporary Europe across the pre-pandemic, pandemic and post-pandemic periods. Using data from Rounds 9, 10 and 11 of the European Social Survey (2018–2023), the analytical sample includes 106,106 respondents from 33 countries. Descriptively, protest participation remains a minority behaviour, yet displays pronounced cross-national heterogeneity, with participation rates ranging from below 3% in several Central and Eastern European countries to nearly 20% in the most mobilised contexts and remains remarkably stable across rounds at approximately 8.5%. Building on resource mobilisation theory, political process approaches and New Social Movements perspectives, the analysis conceptualises protest participation not as an isolated behavioural act but as the outcome of interactions between individual resources, evaluative orientations toward democratic institutions and broader mobilisation environments. Logistic regression models, country fixed-effects specifications and multilevel models with random intercepts are used to assess these relationships. At the individual level, political engagement emerges as the strongest predictor of participation: higher political interest is associated with substantially higher protest propensity, while ideological self-placement indicates lower participation among respondents positioned further to the right. Younger age and higher education also increase participation. Lower satisfaction with democracy and stronger perceptions of inequality are consistently associated with protest behaviour, supporting grievance-based interpretations linked to democratic evaluations rather than material deprivation alone. Country fixed-effects and multilevel models confirm that these individual-level associations are robust within countries, while significant between-country variation persists (random-intercept SD = 0.554), indicating that national mobilisation environments shape baseline levels of protest participation. Multilevel results further reveal that protest participation was significantly lower during the pandemic period (Round 10) relative to the pre-pandemic baseline, with only partial recovery in the post-pandemic period. A cross-round comparison demonstrates that the core individual-level associations are stable across all three periods, indicating that these relationships reflect durable structural patterns rather than dynamics specific to any particular mobilisation cycle. Beyond this overall stability, the analysis identifies two theoretically informative exceptions: subjective financial difficulty is significant only in the pre-pandemic period and gender differences in protest participation attenuate over time—patterns consistent with broader shifts in protest repertoires during and after the pandemic. These findings make three contributions to the comparative literature on contentious politics. First, by extending the analysis across three ESS rounds, the study demonstrates the temporal robustness of individual-level determinants of protest—an empirical question rarely addressed in the existing literature. Second, the multilevel design with round fixed effects allows for direct estimation of pandemic-related suppression and post-pandemic recovery in protest activity at the aggregate level. Third, the cross-national scope and temporally structured comparison provide new evidence on how individual political predispositions interact with shifting mobilisation environments across a period of exceptional socio-political strain in Europe. Full article