Search Results (577)

Combining multiple industrial waste streams as alkali-activated precursors provides compositional complementarity but also creates complex interactions that cannot be explained by bulk chemistry or final performance alone. This review examines precursor compatibility through three coupled processes: dissolution, elemental competition, and phase selection. Precursor reactivity is interpreted hierarchically in terms of bulk composition, potentially reactive composition, and the composition actually dissolved under specific reaction conditions. In multi-source systems, asynchronous release of Ca, Si, Al, Mg, and associated anions continuously modifies pore-solution chemistry, saturation states, nucleation, and interfacial transport. Dissolution therefore becomes mutually coupled, while released species compete for incorporation into binding gels and secondary phases. Their temporal availability, transport, and structural accommodation ultimately govern phase selection and spatial heterogeneity. Because increased strength or heat release alone is not a unique signature of chemical coupling, precursor compatibility should be established by linking changes in precursor-specific reaction kinetics with effective elemental supply and partitioning, phase evolution, and macroscopic response. This mechanism-based perspective reframes precursor compatibility as a conditional compatibility window, and shifts multi-source alkali-activated materials design from empirical precursor proportioning toward coordination of reaction time windows, elemental supply, and targeted phase formation under specified activator chemistry, pH, temperature, precursor proportion, curing conditions, and reaction time. Future studies should quantify time-resolved elemental balances and phase-competition boundaries and integrate them with reaction–transport models to identify robust compatibility windows under realistic precursor variability. Full article

Classifying blue basin trap captures of Tropinota hirta according to environmental conditions can support timely and data-driven management of this flower-feeding pest in almond orchards; however, imbalanced class distributions pose important challenges for machine-learning models. This study proposes an interpretable PSO-XGBoost with Partial SMOTE framework for classifying T. hirta abundance levels derived from blue basin trap captures using temporal and meteorological variables. From a biomimetic perspective, Particle Swarm Optimization (PSO) is a bio-inspired optimization algorithm that emulates the collective search and information-sharing behavior observed in natural swarms, translating principles of biological collective intelligence into a computational optimization process. The dataset consisted of 256 trap observations categorized into High (H), Low (L), and Medium (M) abundance classes, with 180, 55, and 21 observations, respectively, indicating substantial class imbalance. Eleven temporal and meteorological variables were used as predictors, while PestCount, representing the observed number of T. hirta adults captured in the blue traps and used to derive the target classes, was excluded from the predictor set to prevent target leakage. The proposed framework combines controlled minority-class oversampling using Partial SMOTE, bio-inspired PSO for XGBoost hyperparameter optimization, and rule extraction for model interpretability. A Partial-SMOTE ratio ablation analysis identified L = 0.80 and M = 0.40 as the most favorable class-specific oversampling configuration, avoiding complete class balancing while improving overall and class-sensitive predictive performance. Performance was evaluated using stratified five-fold cross-validation, with oversampling applied exclusively to the training folds to prevent information leakage. The proposed method achieved a mean Accuracy of 89.45% and a mean Weighted F1-score of 88.79%, representing the best overall performance among the evaluated methods. The ablation analysis showed that PSO-based optimization improved conventional XGBoost, while the proposed Partial-SMOTE configuration outperformed both class-weighted XGBoost and PSO-XGBoost without oversampling. Robustness was further assessed using 10-times repeated stratified five-fold cross-validation, where the proposed method achieved 87.51 ± 3.75% Accuracy, 86.94 ± 3.71% Weighted F1-score, and 71.60 ± 7.40% Macro F1-score. Friedman and post-hoc Wilcoxon signed-rank tests with Holm correction confirmed statistically significant performance differences in favor of the proposed method. In addition, human-readable IF–THEN rules with bounded temporal and meteorological conditions were extracted from the optimized model to improve interpretability. The results indicate that integrating a biomimetic swarm-intelligence optimization strategy with controlled oversampling, blue-trap monitoring, and interpretable machine learning provides a promising framework for predicting T. hirta abundance levels and supporting environmentally compatible monitoring and pest-management decision-making in almond orchards. Full article

Electrochemical oxidation (EO) is a versatile, advanced oxidation process (AOP) for the treatment of persistent and recalcitrant pollutants in wastewater. This critical review examines recent advances in EO, with emphasis on oxidation mechanisms, electrode materials, reactive species, reactor configurations, operational parameters, and energy efficiency. Direct electron-transfer reactions and indirect oxidation mediated by electrogenerated reactive species are discussed in relation to pollutant degradation, mineralization, and process selectivity. Attention is given to active and non-active anodes, classified according to their interaction with electrogenerated oxidizing species and their catalytic behavior toward organic pollutant oxidation, as well as to electrode stability and oxygen evolution competition. The effects of parameters such as current density applied potential, pH, electrolyte composition, conductivity, temperature, mass transfer, and electrolysis time on treatment performance and energy consumption are critically evaluated. The review also examines the integration of EO with microbial electrochemical technologies, anaerobic digestion, hydrogen production, and other resource-recovery pathways, distinguishing direct electrochemical energy recovery from downstream energy generation. Photo-electrooxidation is considered as a complementary light-assisted electrochemical approach. Importantly, the available evidence does not yet support generalized claims of positive net energy recovery, highlighting the need for complete system-level energy balances. Finally, key challenges related to real wastewater, electrode durability, transformation products, toxicity, scale-up, techno-economic feasibility, and environmental sustainability are discussed, together with future perspectives for efficient and scalable EO-based wastewater treatment. Full article

Artificial intelligence (AI) is increasingly transforming the game industry by reshaping development processes, production workflows, and career structures. In this study, we present a scoping literature review examining the impact of AI on game development and employment within the industry, with particular attention to the perspectives of displacement, task transformation, and productivity effects. Drawing on academic research and industry reports published between 2020 and 2026, while retaining earlier foundational literature where relevant, this study analyzes how AI technologies such as generative AI, machine learning, procedural content generation, and large language models are integrated across major stages of game production, including game art, production management, programming, and live operations. Our findings indicate that AI can support efficiency, accelerate development cycles, and enable new forms of personalization and creative experimentation. At the same time, AI adoption may alter workforce dynamics by increasing the automation potential of routine and entry-level tasks, especially in areas such as asset creation and quality assurance. Rather than fully replacing workers, however, AI more commonly reconfigures job roles and increases demand for hybrid skill sets that combine technical knowledge, creativity, and AI literacy. Our study also identifies emerging opportunities in AI-assisted design, data analytics, and machine learning integration. Despite these benefits, AI adoption introduces important challenges related to employment polarization, intellectual property, workforce adaptation, ethical governance, and market concentration. We conclude that AI is fundamentally reshaping the game industry and highlight the need for balanced strategies that support innovation while preserving creative diversity, sustainable career pathways, and equitable access to technological opportunities. Overall, the strongest evidence concerns AI capabilities, workflow changes, and task-level transformation, whereas direct evidence of actual employment outcomes and long-term occupational change remains limited and uneven. Full article

People often recognize spoken words more accurately and more quickly than written words. From the perspective of active inference under the free-energy principle, the present study develops a formal account of this speaking-over-writing advantage in production-modality retrieval. We modeled retrieval under spoken and written production-modality states, together with the selection of an early or late response policy according to its expected sensory consequences. Sixteen university students spoke or wrote words presented on a computer screen and later judged the modality in which each word had been produced. Responses were followed by explicit visual feedback indicating whether each judgment was correct or incorrect, thereby providing an observable sensory consequence of the recognition response. Response times were implemented as early and late policies within a partially observable Markov decision process (POMDP). The model included modality-specific likelihood parameters linking response timing to feedback outcomes. Spoken words were identified more quickly and accurately than written words, and early responding was more strongly associated with correct feedback under the speaking state than under the writing state. The POMDP was compared with a simpler Variational-Laplace model with a logistic observation function. Bayesian model selection favored the POMDP, indicating that the POMDP provided a better balance of fit and complexity. These findings show how production modality, response timing, and expected sensory consequences can be represented within a common generative framework. The framework provides a basis for future extensions that model uncertainty about production modality, preserve continuous response timing, incorporate learning across trials, and compare active-inference accounts with alternative models of recognition memory. Full article

Previous studies have consistently demonstrated associations between time perspective and chronotype, but the mechanisms underlying these associations remain poorly understood. Drawing on the view that time perspective manifests in behaviours that can shape bedtime routines, which in turn affect the state component of chronotype, the present study examined whether sleep hygiene mediates the relationship between time perspective and chronotype. Among 455 adults who self-reported time perspective, sleep hygiene, and chronotype, eveningness was associated with poorer sleep hygiene, lower future and past-positive orientations, higher past-negative orientation, and greater deviation from a balanced time perspective. Furthermore, poor sleep hygiene was associated with lower future and lower past-positive orientations, higher present-fatalistic orientation, and greater deviation from a balanced time perspective. Mediation analyses demonstrated that the associations of lower future and past-positive orientations, higher past-negative, and greater deviation from a balanced time perspective with chronotype were partly mediated by poor sleep hygiene. These findings suggest that the indirect associations between time perspective and chronotype through sleep hygiene are consistent with the proposed theoretical mechanism. The results align with the view that chronotype incorporates a state component that represents a potential target of sleep hygiene interventions aimed at shifting this component toward morningness for some individuals. Full article

This paper examines the relationship between tourism flows and gender equality in the 27 member states of the European Union (EU27). Drawing on a labor market and structural change perspective, we argue that tourism, as a labor-intensive and female-intensive service activity, may be associated with higher gender equality through employment, economic independence and human capital channels, while occupational segregation and poor job quality may weaken this link. Using a balanced panel dataset for 2013–2023 (297 country-year observations), with effective estimation samples of 2015–2023 or 2016–2023 owing to lagged regressors, we estimate country fixed-effects specifications with panel-generalized least squares (with and without cross-section weights), robust M-estimation and a backward variable selection procedure. Tourism flows appear to be positively associated with the Gender Equality Index (GEI) of the European Institute for Gender Equality (EIGE) in all 15 specifications, conditional on human capital, full-time equivalent employment, gender segregation, GDP per capita and R&D; the association is statistically significant at the 5% level in ten specifications and at or around the 10% level in the remaining five. The estimated elasticity is small (about 0.01) and considerably weaker than those of human capital and employment, and there is some evidence, although not robust across all specifications, that the association is stronger in the EU27 periphery, where gender equality is lower and tourism demand higher. Given the observational design, the findings are interpreted as conditional associations rather than causal effects. They suggest that tourism development in peripheral member states may complement, but not substitute for, policies targeting tertiary education, women’s full-time employment and job quality in tourism. Full article

Short-term power load forecasting plays a crucial role in the operation scheduling, energy management, and security assessment of smart grids, as its accuracy directly affects the economic efficiency and reliability of power systems. However, power load series are characterized by strong nonlinearity, non-stationarity, and multi-scale temporal dependencies, which make it difficult for traditional forecasting models to achieve both high accuracy and strong generalization under complex load scenarios. In recent years, deep learning models have demonstrated promising performance in load forecasting; nevertheless, their effectiveness is highly dependent on hyperparameter configurations. Manual hyperparameter tuning is not only time-consuming but also prone to premature convergence to suboptimal solutions. To overcome these challenges, this study develops an AMS-GRO-based short-term power load forecasting framework. By improving the original Gold Rush Optimizer (GRO), three adaptive strategies are integrated into the proposed optimizer, namely the current-to-pbest/1 mutation strategy, adaptive elite-guided search strategy, and success-rate-based adaptive strategy selection mechanism. These improvements jointly enhance the global exploration capability, local exploitation ability, and strategy adaptability of the optimizer. Subsequently, the proposed AMS-GRO is employed to perform global hyperparameter optimization for a Dilated BiGRU-Attention model, forming the AMS-GRO–Dilated BiGRU forecasting framework. Key hyperparameters, including the learning rate, number of hidden units, attention dimension, and regularization coefficient, are adaptively optimized. Extensive numerical experiments were conducted on the 30-dimensional CEC2017 suite and the 10- and 20-dimensional CEC2022 suites using 30 independent runs. According to the Friedman test, AMS-GRO achieved mean ranks of 1.37, 1.33, and 1.25, respectively, ranking first in all three experimental settings. In the short-term power load forecasting experiment, AMS-GRO–Dilated BiGRU achieved an MAE of 21.09, MAPE of 0.0177, MSE of 791.10, and R² of 0.9746. Compared with the unoptimized Dilated BiGRU model, these results correspond to reductions of 13.0%, 13.7%, and 21.5% in MAE, MAPE, and MSE, respectively, together with a 0.70-percentage-point improvement in R². From a symmetry perspective, the proposed framework balances global exploration and local exploitation through adaptive strategy selection, while the Dilated BiGRU exploits bidirectional temporal symmetry to capture multi-scale load patterns. These results demonstrate that the proposed framework provides competitive optimization performance and improves forecasting accuracy on the investigated load dataset. Full article

The rapid expansion of renewable energy sources and the growing adoption of electric passenger vehicles (EVs) necessitate intelligent energy management strategies to ensure grid stability and operational efficiency. Achieving a sustainable energy system requires very high shares of renewable energy, potentially up to 100%. To balance fluctuations in renewable generation and electricity demand, energy storage technologies are essential. Stationary battery storage is an effective solution, but battery production is associated with significant emissions and resource consumption. From a sustainability perspective, minimizing additional battery production is therefore desirable. Vehicle-to-grid (V2G), which enables bidirectional energy exchange between EVs and the power grid, offers a promising alternative by utilizing batteries that are already installed in electric vehicles instead of deploying additional stationary storage systems. This increases resource efficiency and reduces the environmental impact of energy storage deployment. At the same time, automated charging technologies can further improve the sustainability of future mobility and energy systems by increasing the usability of electric vehicles and optimizing charging infrastructure utilization, particularly in long-term parking scenarios such as airports. As vehicle automation advances, the relevance of automated charging solutions will continue to grow. However, it remains unclear whether automation will support or hinder the implementation of vehicle-to-grid (V2G) concepts and their potential contribution to a more sustainable transport and energy sector. This paper therefore provides a comprehensive overview of the recent advancements in automated charging systems and evaluates their suitability for enabling grid-supportive V2G applications. Using a weighted point rating system, different technologies are systematically assessed across relevant EV use cases. The results highlight both synergies and trade-offs between automation and V2G, offering insights into how automated charging technologies can be designed to maximize their contribution to sustainable mobility and energy systems. Full article

In adaptive learning environments, accurate prediction of learner performance is essential for delivering personalized educational support. Logistic regression-based models such as PFA, AFM, and DAS3H have demonstrated strong predictive capabilities; however, they face two complementary limitations: high computational cost due to Q-matrix density and a predictive accuracy ceiling inherent to their linear formulation. This study proposes a hybrid algorithm that addresses both limitations simultaneously. The Fast E-learning Recommendation (FER) method is first applied to reduce Q-matrix complexity by aggregating all knowledge components associated with each item into a single combined skill. From an algorithmic perspective, this transformation reduces training time complexity from $O\left(I\times K\right)$ to $O\left(I\right)$, where $I$ is the number of items and $K$ is the number of knowledge components, yielding substantial computational savings while preserving the conjunctive cognitive structure of the original Q-matrix. XGBoost is then integrated into the simplified models to enhance predictive performance by capturing non-linear relationships that logistic regression cannot capture. The proposed hybrid framework is evaluated on four real-world educational datasets. Experimental results demonstrate that the FER-XGBoost approach achieves a favorable balance between computational efficiency and predictive accuracy: in several configurations, it not only maintains but also slightly improves predictive performance while significantly reducing execution time compared to standard baseline models. Full article

Reconstructing long-term spatiotemporal processes from unstructured historical records presents a fundamental challenge in historical Geographic Information Systems (GIS). Local chronicles contain rich information regarding the timing, location, and context of historical events. However, their narrative form hinders systematic spatiotemporal interpretation and timeline mapping. To address this challenge, this paper proposes an integrated framework for timeline mapping using local chronicles, which includes (1) a dimension-importance evaluation method to identify the most relevant analytical dimension for visualization, (2) a large language model (LLM)-based workflow for extracting, matching, and clustering historical events from unstructured texts with respect to the identified analytical dimension, and (3) a multi-perspective timeline mapping approach to reconstruct and visualize spatiotemporal narratives. The framework is demonstrated through a case study in Dengfeng, China, where historical educational records from local chronicles are employed to reconstruct the long-term spatiotemporal evolution of educational equity. The case study demonstrates that (a) structural balance is identified as the most relevant analytical dimension, guiding subsequent event mining; (b) the LLM-based workflow extracts and structures 721 historical events, with 415 events matched to the identified dimension and clustered into three thematic groups of institutional plurality, school system reform, and spatial reorganization; and (c) the multi-perspective timeline maps generated from these clusters effectively reveal a long-term, coherent process of structural reconfiguration, illustrating the framework’s capacity to transform narrative historical records into interpretable spatiotemporal narratives. The proposed framework provides a transferable historical GIS workflow for timeline mapping using local chronicles and offers a practical approach for reconstructing spatiotemporal processes from unstructured historical texts in various geographical contexts. Full article

Healthcare organizations must sustain the quality of clinical care while accounting for it to patients and families, staff, regulators, funders, and the wider community. Although Total Quality Management (TQM) and the Balanced Scorecard (BSC) are among the most widely adopted management tools globally, how their integration can help healthcare organizations discharge the obligations imposed by clinical governance remains underexplored. Drawing on literature spanning healthcare management, management accounting, clinical governance, public accountability, and organizational resilience, this conceptual paper develops a quality governance framework. The four perspectives of the BSC are reconceptualized as accountability forums, each addressing a distinct constituency. At the same time, top management commitment is separated from the operational enablers of TQM and reconstituted as a strategic leadership layer. An integration model derives its activity set transparently from the management accounting literature while reconstructing it for healthcare, pairing each activity with the indicators through which it becomes visible to a specified forum. The resulting distinction between governance-process and performance indicators allows integration embedded in governance structure to be distinguished from integration adopted in name. The framework then locates the capacities to anticipate, monitor, respond, and learn within this architecture, with the measurement layer supplying monitoring capacity. Conditions under which the framework may distort conduct or conceal disparities are specified, and testable propositions are advanced. Full article

Background/Objectives: Ensemble perception enables the visual system to rapidly extract summary statistics from multiple objects. However, the temporal dynamics of ensemble average representations and their dependence on magnocellular-biased (M-biased) and parvocellular-biased (P-biased) visual information remain unclear. This study examined when ensemble face-gender information could be decoded under these two stimulus conditions. Methods: Twenty-four healthy adults performed a two-alternative forced-choice gender judgment task with neutral face ensembles and single faces. M-biased stimuli were low-luminance-contrast grayscale faces, whereas P-biased stimuli were isoluminant red–green faces. Time-resolved multivariate pattern analysis, temporal generalization, and cross-condition generalization characterized gender-related EEG dynamics. Results: Behaviorally, responses were faster for P-biased than M-biased stimuli and for single faces than ensembles, with no significant accuracy differences. Balanced Integration Scores were significantly higher for P-biased than M-biased stimuli in both ensemble and single-face conditions. M-biased ensemble gender information was reliably decodable during early (95–300 ms) and late processing (775–930 ms), with significant early temporal generalization. P-biased face ensembles showed no significant cluster-corrected time-resolved decoding but exhibited significant temporal generalization during middle-to-late processing. Under M-biased stimulation, ensemble gender information generalized to single-face judgments from 690–995 ms. Conclusions: Ensemble face-gender information showed a multistage temporal organization. M-biased ensemble information became decodable early. Late cross-condition generalization further indicated that ensemble and single-face judgments shared gender-discriminative task information. These findings characterize the temporal evolution of ensemble gender information under pathway-biased visual stimulation and provide a temporal perspective on ensemble perception. Full article

The fast development of Internet of Things (IoT) gadgets has brought both a new level of connectivity and automation, at the same time making the networks more vulnerable to more sophisticated cyber attacks. Existing intrusion detection systems have a number of fundamental limitations, such as extreme class imbalance in network traffic data, inadequate feature interaction modeling, inability to train on dynamic attack patterns, heavy reliance on fully labeled data, weak evaluation capabilities, and limited interpretability. To deal with these issues, this research employs a data balancing mechanism with an autoencoder to reduce class imbalance by learning meaningful latent representations of the minority attack classes. A Parallel Hybrid single-step BiLSTM-FCNN (PH-BiLSTM-FCNN) model is then proposed as a single network to learn contextual dependencies and nonlinear discriminative features simultaneously along parallel learning pathways. To further enhance adaptability and convergence stability, a Q-learning Optimized PH-BiLSTM-FCNN (Q-PH-BiLSTM-FCNN) is introduced, enabling dynamic optimization of training behavior based on feedback-driven interactions. In addition, an Entropy-based Active Learning on PH-BiLSTM-FCNN (EAL-PH-BiLSTM-FCNN) is developed to significantly reduce labeling requirements by selectively querying the most informative samples. Furthermore, a blockchain layer with smart contracts is embedded consistently in all of the proposed models to ensure tamper-resistant logging, transparent validation, and reliable recording of training and evaluation results. From an algorithm design perspective, the proposed framework is structured as a set of verifiable procedures for PH-BiLSTM-FCNN training, Q-learning optimization, and entropy-based active sample selection. Its algorithmic behavior and robustness are evaluated through execution-time analysis, 10-fold cross-validation, and permutation feature importance, ensuring both predictive reliability and interpretability. Experimental results demonstrate that the proposed PH-BiLSTM-FCNN, Q-PH-BiLSTM-FCNN, and EAL-PH-BiLSTM-FCNN models consistently outperform state-of-the-art FCNN, Bi-LSTM, GRU, LSTM, LR models, achieving improvements of 4.80%, 13.52%, and 6.85% in accuracy, 4.81%, 13.53%, 6.86% in recall, and 5.18%, 18.44%, and 8.52% in precision recall-area under the curve, respectively. These results affirm that the proposed framework provides improved detection performance, statistical reliability, and interpretability while addressing practical deployment limitations in IoT security settings. Full article

Graph neural networks are increasingly used for river water-quality forecasting because they can represent interactions among monitoring locations that conventional site-wise time-series models cannot capture. However, the usefulness of graph learning depends strongly on whether graph structure reflects the hydrological processes governing pollutant transport. This review examines recent advances in river-network graph learning from the perspectives of graph construction, dynamic connectivity, propagation delay, physical constraints, and model validation. Existing approaches are organized into geometric, correlation-based, hydrological topology, transport-weighted, dynamic, and physics-informed graphs. To clarify their increasing physical content, we propose a graph physical-fidelity ladder from H0 to H6 and evaluate validation strength independently on a V0–V3 axis, thereby separating what a graph represents from how rigorously that representation is tested. Current studies commonly rely on fixed adjacency structures, while variations in discharge, flow direction, travel time, tributary contributions, reservoir regulation, and pollutant-specific transformation remain incompletely represented. Particular attention is given to dynamic edge updating, event- and pollutant-adaptive graphs, travel-time-aware message passing, mass-conserving architectures, and cross-basin representation learning. We further argue that predictive accuracy alone is insufficient for establishing hydrological credibility. Learned connectivity and edge importance should be tested against flow direction, transport time, mass balance, and structural counterfactuals, including edge reversal, deletion, weight perturbation, and dynamic-graph freezing. Future progress will depend on matching the complexity of graph representations to the physical claims they support and on evaluating predictive skill together with structural validity, physical consistency, uncertainty, and transferability. Such hydrologically faithful graph learning could provide more reliable and operationally defensible river water quality forecasts. Full article