Search Results (3,175)

To develop secure, fast, and interoperable smart substations, it is vital to understand the current situation and potential future directions of the technologies involved. This study presents the evolution and state of the art of the Generic Object Oriented Substation Event (GOOSE) communication protocol, defined by the International Electrotechnical Commission (IEC) 61850 standard. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. This included journal articles published from 2004 to 2025 and conference papers from 2020 to 2025, written in English within Engineering. Only studies primarily focusing on GOOSE, citing it at least ten times, and indexed in the Scopus, IEEE Xplore, and Web of Science databases were included. The quantitative analysis used SciMAT software, complemented by a qualitative analysis. Due to the bibliometric and thematic nature of this review, potential biases were considered at the review level rather than by applying a formal study-level risk-of-bias tool. The final analysis comprised 82 journal articles and 84 conference papers. The results offer a comprehensive mapping of GOOSE research evolution, identify nine main challenges and limitations from the last 22 years, and highlight current research directions. The literature reveals methodological heterogeneity, a predominance of simulation-based approaches, and limited large-scale empirical validation. Full article

Large language model (LLM)-based document translation systems typically treat each segment independently, discarding terminology decisions, human corrections, and discourse cues after each generation step. This stateless approach causes terminology inconsistency across segments, failure to propagate approved post-edits downstream, and redundant prompt-token consumption. Existing solutions—document-level MT, retrieval-augmented generation, and computer-assisted translation (CAT) tools as a general category—address individual aspects but lack a unified, state-aware architecture with provenance, update rules, and rollback semantics. We propose Language Twin, a shared-state architecture that organizes translation projects into seven versioned layers (L0–L6), supporting selective context loading, scoped human-edit propagation, and reversible updates. A pilot study translated three curated English-to-Korean document bundles (17 segments) using GPT-4o with a temperature of 0.3. The Language Twin condition (P1) achieved numerically higher preferred-term accuracy than the strongest baseline (17/21 vs. 14/21; not statistically significant at this sample size) and showed no repeated downstream errors in the monitored set (0/5 vs. 5/5 against the propagation-disabled ablation; Fisher’s exact test: p = 0.008), while reducing prompt tokens by 39.2% relative to full-context loading (A4). In blinded human evaluation (quadratic-weighted κ = 0.71–0.78), P1 achieved the highest terminology rating (4.38/5 vs. 3.97/5) and lowest post-editing time (16.9 s vs. 19.1 s per segment). These pilot-scale results indicate that governed shared state can improve terminology consistency and editing efficiency. Full article

Generative AI (GenAI) is transforming higher education assessments, yet empirical research on students’ lived experiences with GenAI during graded, time-constrained classroom assessments remains scarce. This study investigates how direct experience with GenAI in examinations shapes student perceptions of learning, metacognition, and engagement. Drawing on self-regulated learning research and cognitive load theory, we employed a retrospective pre–post design to analyze qualitative reflections and quantitative data from 90 undergraduate computer science and engineering students. Our qualitative analysis suggests a complex recalibration from idealized expectations of efficiency toward what may be described as a state of measured pragmatism. Interpretive analysis of Post-experience reflections indicates that direct practical engagement appeared to make students more conscious of the need for metacognitive engagement, with a focus on real-time output verification and the restrictive role of time pressure. Concerns regarding assessment authenticity and fairness emerged only after direct engagement. Quantitative results show that although 68.5% preferred the GenAI format, this preference did not correlate significantly with academic performance (r = 0.014, p = 0.89). Those findings suggest that student engagement is driven by pedagogical and professional relevance rather than grade improvement alone. Overall, the findings underscore the need for assessment designs that balance cognitive support with active student monitoring and responsibility. Full article

Chronic kidney disease is rising worldwide, and kidney transplantation remains the preferred modality of kidney replacement therapy. However, long-term graft survival continues to be limited by chronic alloimmune injury, particularly antibody-mediated rejection (ABMR) and its chronic active form. This narrative review synthesizes contemporary evidence on the immunopathogenesis, epidemiology, diagnosis, and management of kidney allograft rejection, with a deliberate focus on studies from the last five years and on United States and European cohorts. We summarize current concepts of T cell–mediated rejection (TCMR), ABMR, mixed and donor-specific antibody (DSA)–negative phenotypes, and the evolution of the Banff classification, highlighting how chronic active ABMR has emerged as a leading cause of death-censored graft loss. We then critically appraise the conventional diagnostic triad of creatinine/eGFR, DSA, and biopsy and review emerging tools, including donor-derived cell-free DNA, urinary chemokines such as CXCL9 and CXCL10, additional blood- and urine-based biomarkers, and biopsy transcriptomics. We also examine how artificial intelligence and machine learning may support digital pathology, multimodal risk prediction, and data integration, while recognizing the current challenges of biological interpretability, external validation, and clinical implementation. Finally, we propose a rejection-focused precision-medicine framework and outline key research gaps, including multicenter validation, trial-ready endpoints, and governance for AI-enabled pathways. Overall, the field is moving from isolated diagnostic signals toward integrated, biologically informed, and clinically actionable approaches to rejection detection and risk stratification. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet, a convolutional model optimized for feature-centric detection, and SigNet, a gated recurrent model designed for sequence-oriented modeling of ordered flow-feature representations. Both models are trained with Cost-Robust Focal Loss (CRF-Loss), a cost-aware objective that penalizes false positives and false negatives according to deployment-specific risk preferences. We evaluate the models on the UNSW-NB15 and CIC-IDS2017 benchmarks using six standard metrics (accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC)), complemented by an analysis of false-positive behavior. On CIC-IDS2017, ForNet achieves precision up to 0.95 and MCC up to 0.93 with AUROC above 0.94, while SigNet shows a stronger recall-oriented profile on UNSW-NB15. In an ablation study, replacing Binary Cross-Entropy with CRF-Loss reduces the false-positive rate by approximately 15–20% and improves robustness-oriented metrics such as MCC by up to 12% on CIC-IDS2017. Rather than claiming universal state-of-the-art performance, the study focuses on performance–risk trade-offs under realistic operational constraints. The results highlight how architectural bias and cost-aware optimisation jointly shape IDS behaviour and offer benchmark-based guidance for interpreting performance–risk trade-offs in lightweight intrusion detection. Full article

To promote the sustainable development of power battery recycling, this study investigates the strategic interplay between altruistic preferences and channel structure. Addressing divergent interests and the dynamic evolution of recycling scale and brand reputation, a differential game model with two state variables is constructed to analyze four decision modes: resale/agency under selfish/altruistic scenarios. The results reveal that altruistic preferences induce Pareto improvements, reconciling the recycler’s utility with the partner’s profit growth. Notably, altruism acts as a moderating mechanism that reshapes channel advantages, enabling the Resale–Altruistic (RA) mode to surpass the agency mode as the system-wide optimal state. Furthermore, a substitutive compensation effect between altruistic preference and revenue-sharing contracts is identified. This research provides a quantitative framework for optimizing behavioral contract design and governance in battery recycling ecosystems. Full article

Against the backdrop of global sustainable development and ecological civilization construction, tourism ecological security at cultural landscape heritage sites faces both opportunities and challenges. This study constructs a cultural landscape heritage tourism ecological security (CLHTES) evaluation system based on the Driver–Pressure–State–Impact–Response (DPSIR) framework. It dynamically assesses CLHTES in the Yangtze River Delta Integrated Demonstration Zone (YRDIDZ) from 2014 to 2023 using the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and linear stretching transformation, identifies obstacle factors with the obstacle degree model, and predicts CLHTES trends for 2024–2030 using a radial basis function (RBF) neural network. Results show that: (1) The CLHTES index in the YRDIDZ presented a three-stage fluctuating upward trend during 2014–2023, with medium-clustered security levels and divergent evolution across the DPSIR criteria layers; (2) CLHTES obstacles feature a multi-level differentiated structure, with rising barriers in D and P layers, the R layer as the future core obstacle, and high-frequency barriers concentrated in cultural and social indicators; (3) Under the assumption of structural continuity in current trajectories, the conditional trend projection suggests that the CLHTES index of the YRDIDZ may sustain a general upward tendency during 2024–2030, with a possibility of approaching Level Ⅶ after 2028; however, these projections should be interpreted as exploratory and scenario-like rather than as robust forecasts, given the short annual series and the absence of exogenous disturbance variables. This study explores tourism-ecology interactions from a social-ecological complex system perspective, supporting synergistic tourism development and ecological protection of cultural landscape heritage. Full article

Bidentate ligands, derived from the 1,3-dicarbonyl framework, play a central role in coordination chemistry, catalysis, and materials science due to their tuneable donor properties and structural versatility. This review examines and compares three closely related ligand classes, β-diketones (O,O′ donors), imino-β-diketones or enaminones (N,O donors), and di-imino-β-diketones or β-diketiminates (N,N′ donors), to elucidate how systematic substitution of oxygen by nitrogen affects structure and properties. The discussion integrates spectroscopic data (NMR and IR), crystallographic findings, electrochemical measurements, and density functional theory (DFT) calculations reported in the literature. Across these systems, tautomerism plays a decisive role, with conjugation-stabilized enol or enamine forms generally preferred in solution and the solid state. Frontier molecular orbital analyses show extensive delocalization over the chelate backbone and, when present, aromatic substituents. Electrochemical studies reveal consistent correlations between experimental reduction potentials and calculated LUMO energies for O,O′-, N,O-, and N,N′-bidentate ligands. Overall, the comparison demonstrates that donor atom substitution within a conserved conjugated scaffold provides a systematic approach to tuning acidity, coordination behaviour, and redox properties, offering a coherent framework for understanding structure–property relationships in 1,3-dicarbonyl-derived chelating ligands. Full article

The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle to reconcile individual user preferences with sustainable consumption objectives, frequently encountering the “information cocoon” effect and lacking interpretability in their decision-making processes. To address these issues, this study proposes a multi-objective, context-aware hotel recommendation framework that integrates text mining, sequential behavior modeling, and reinforcement learning. The framework begins by employing unsupervised learning to extract multidimensional hotel features from online reviews, with an explicit emphasis on comprehensive sustainability metrics. It subsequently applies a dynamic state representation approach that merges long-term and short-term interests with real-time contextual information to accurately reflect evolving consumer needs. Furthermore, a dynamic feature weighting module is incorporated to enhance interpretability and enable context-adaptive evaluation of both commercial and sustainable attributes. The recommendation process is structured as a Markov Decision Process, leveraging a composite reward function comprising diversity penalties and sustainability incentives. Empirical analysis using real-world data validates the framework, demonstrating its contribution to sustainable tourism and achieving recommendation accuracy that surpasses existing benchmark models. Full article

With modern medicine and a better understanding of the ulcerative colitis disease process, there have been many changes in how we manage ulcerative colitis-related dysplasia over the past 20 years. One of the biggest concerns for these patients, given the inflammatory nature of their disease, is the progression from chronic inflammation to cancer. Patients with ulcerative colitis have about a 2.4-fold increased risk of developing colorectal cancer compared to the general population, which is concerning since colorectal cancer is the 2nd leading cause of cancer-related deaths in the United States. Traditionally, surgery was the preferred treatment for ulcerative colitis patients with dysplasia, but now, with advances in surveillance such as high-definition colonoscopy and chromoendoscopy, the management approach is more nuanced. Understanding the risk for different individuals within this patient population is key to comprehensive and personalized care management. In this review article, we will explore risk factors, surveillance methods, and classification. Full article

Elementary music teachers in the United States face many challenges today, including an increasing cultural divide between teachers and students, worsening student behavior, and excessive exposure to technology in children’s lives. These challenges are magnified due to the hundreds of students elementary music teachers see weekly, the lack of teaching and planning time, and inadequate teaching resources, making it difficult to fully understand the culture and learning needs of every child. However, music educators may find culturally sustaining pedagogy (CSP) a useful tool for meeting the needs of a diverse student body. Further, when teachers engage in kid culture, the environments and activities that only children have, there is a plethora of music and media to use that children prefer that can help increase engagement and reduce behavioral problems. In this Perspective article, I provide three sample lessons that model instructional strategies that challenge current systems of power and representation in music education and center student agency through singing, chanting, moving, playing, and creating. Using repertoire that students already know and prefer, such as “Old Town Road,” Fortnite dances, and the song “See You Again”, draws from children’s funds of knowledge. Moving away from the Western art music canon and traditional formal education structures (like standard notation) in favor of learning by ear, peer collaboration, and improvisation decolonizes the curriculum. Critical reflexivity occurs when the teacher acts as a learner, constantly adjusting lessons to ensure student agency and addressing ethical issues, such as the intellectual property rights of creators whose work is used in media like Fortnite. By using melodies, songs, and video game movements children already know, music teachers can use the materials and learning processes in kid culture to engage in culturally sustaining pedagogy. I aim to inspire educators and researchers to reflect on sustaining children’s dynamic, cultural practices and better understand how to authentically bring popular music and media into elementary music lessons to provide a more engaging, relevant, and transformative music education. Full article

The relationship between individual health considerations and environmental outcomes remains insufficiently explored in economic theory. This paper examines how concern for personal health influences private environmental investment and long-run environmental quality. We develop an overlapping generations model in which an individual’s overall health status depends jointly on intrinsic health capital and environmental quality, allowing for limited substitutability between the two. Individuals allocate income between consumption, savings, health investment, and green investment, while environmental quality evolves endogenously through production-driven pollution and both private and public abatement activities. The analysis shows that stronger concern for personal health increases private environmental investment and improves steady-state environmental quality, even though it reduces physical capital accumulation by diverting resources towards health- and environment-related uses. Public environmental spending and improvements in the effectiveness of green initiatives further enhance environmental quality and indirectly stimulate private health investment, despite partially crowding out private green effort. These findings highlight health preferences as an important behavioral channel for environmental sustainability and suggest that policies raising awareness of environmental health risks can effectively complement the standard mechanisms of environmental regulation. Full article

Objectives: The transition to complementary feeding represents an important interval in child nutrition and development. Nutrient demands for growth are high, yet less is known regarding how caregivers make decisions regarding the introduction of solid foods to their infants and what influences their choices and feeding practices. Methods: Semi-structured interviews were conducted via Zoom with caregivers (N = 46, 83% mothers) of typically developing children (6–24 months of age) residing in the United States. A content analytic approach, with consensus coding performed by team members, was undertaken. The Developmental Niche framework guided thematic analysis. Results: Four major themes and four subthemes were identified: (1) Caregivers’ Approach Introducing Solid Foods with Anticipation and Concern, including subthemes of the (a) timing and order of complementary foods (CF) offered to children and (b) foods caregivers avoid offering; (2) Caregivers’ and Children’s Learning, including subthemes of (a) children’s rapid learning and skill development, and (b) the concurrent rapid demands for changes in food parenting; (3) Drivers of Caregivers’ Decisions Related to Offering Solid Foods to their Children; and (4) The Goal of CF: Integration of the Child into Family Mealtimes. Conclusions: Caregivers seek to provide adequate nutrition while balancing children’s health needs with the challenge of encouraging acceptance of family foods and respecting individual preferences. Juggling myriad demands (e.g., time, convenience, other family members, cultural traditions, and expectations), caregivers seek to help their children develop a healthy relationship with food. Full article

Three multicomponent crystals of metformin were investigated to elucidate factors governing crystal architecture. Structures were determined by X-ray diffraction and analyzed using the Atoms-in-Molecules (AIM) approach, focusing on critical points and electron density topology. Three types of crystals were obtained: salt, cocrystal salt solvate and mixed salt with both organic and inorganic anions. Protonation of nitrogen atoms in metformin alters bond lengths and electron density, while strong intramolecular hydrogen bonds in hydrogenmaleate anions stabilize the structures and define the preferred anion geometry. Comparison with monoprotonated metformin revealed similar topological features despite differing protonation states. Mechanochemical synthesis via liquid-assisted grinding (LAG) enabled selective formation of specific crystalline forms, with the solvent type and acid polymorph influencing product distribution. These results highlight the critical roles of protonation, hydrogen bonding, and synthetic methodology in designing and controlling multicomponent metformin crystal structures. Full article