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Large Language Models (LLMs) now occupy a prominent role in science, engineering, and higher education. Their capacity to generate step-wise solutions, conceptual explanations, and problem-solving pathways creates new opportunities—but also new risks—for Chemical Engineering learners. Despite widespread informal use, few empirical studies have evaluated LLM performance using a systematically designed dataset mapped directly to Bloom’s Taxonomy. This study evaluates the competency of ChatGPT in solving Chemical Engineering problems mapped to Bloom’s Taxonomy. A diverse dataset of undergraduate-level problems spanning six cognitive domains was used to assess the model’s reasoning across ascending levels of cognitive complexity. Each response was evaluated for accuracy and categorized into five error types. Although ChatGPT demonstrated considerable potential across a range of topics, the analysis also revealed important challenges and limitations that inform best practices for integrating LLMs into Chemical Engineering education. Results show significant differences in ChatGPT performance across Bloom levels, revealing three distinct tiers of capability. Strong performance was observed at lower cognitive levels (Remember–Apply), while substantial degradation occurred at Analyze, Evaluate, and specially Create. The findings provide a nuanced, empirically grounded understanding of current LLM capability limits, with practical recommendations for educators integrating LLMs into engineering curricula. Full article

The management of operating rooms (ORs) is one of the most studied topics in operations research applied to healthcare. In particular, scheduling elective surgeries in a pediatric and teaching hospital can be a challenge because disruptions occur frequently. The aim of our research was to create a mathematical programming model to schedule day hospital (DH) patients, considering possible disruptions and defining how to best manage the rescheduling process. Our study originates from a collaboration between a high-volume pediatric surgery department and operations research practitioners. The possible disruptions we consider are emergencies and same-day cancellations of planned hospital operations. Elective DH surgeries are scheduled considering the waiting list and the patients’ clinical priorities, generating a nominal schedule. This schedule is optimized in conjunction with a series of back-up schedules to guarantee that OR activity immediately recovers in case of a disruption. An ILP-based approach to the problem is proposed. We enumerate a representative subset of the possible emergency and no-show scenarios, and for each of them a back-up plan is designed. The approach reschedules patients, minimizing disruptions with respect to the nominal schedule, and applies an as-soon-as-possible policy in case of emergencies to ensure that all patients receive timely care. The approach is shown to be effective in managing disruptions, ensuring that the waiting list is managed properly, with a balanced mix of urgent and less urgent patients. It provides an effective solution for scheduling patients in a pediatric hospital, considering the unique features of such facilities. Full article

Material circularity in construction requires material information at the end of life for the trading of materials. Different digital technologies (DTs) are essential for such information management. This research aims to review key aspects of developing a blockchain-based material passports (MPs) system when integrating with key DTs used for MPs. This research is based on a critical literature review, with an integrative approach that synthesises both academic and grey literature. The literature search was initiated using chosen keywords relevant to the topic to first identify the key literature. This was followed by using a snowballing technique to expand the search with further relevant literature. Building Information Modelling (BIM), digital twin (DTw) and blockchain technology (BCT) were identified as key technologies for material information management. BIM and DTw are central to the management process as all the information created and collected is modelled, visualised, analysed and stored using BIM platforms. However, existing MP platforms utilising centralised databases to store data were found to be unreliable for managing material data in an industry like construction with a dispersed supply chain and typically longer lifecycle. BCT was realised as necessary for information management in construction, as it allows us to manage information in a more decentralised, transparent and immutable manner. Furthermore, examining current research about blockchain application for information management in construction led to the conclusion that, although the studies on blockchain-based MP platforms covering the entire industry supply chain prevail, the management of material data at the built asset level throughout its lifecycle using such MP systems is underexplored. Thus, building on the literature review, a conceptual model of blockchain-based MP system is proposed in this paper, describing integration with BIM and DTw, and with relevant processes and actors to manage MP information throughout the building lifecycle. Acknowledging the limitations of a subjective literature review, the conceptual model and the ideas are proposed as a foundation for further research and develop MP system with empirical validation. Although theoretically, this study identifies the suitability of blockchain technology for managing product lifecycle information in industry like construction and provides ground for further theoretical research for planning and policy required for blockchain-based MP development and implementation. Full article

Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article

Digital transformation in construction enterprises is frequently constrained by risks that are heterogeneous, context-dependent, and described with inconsistent terminology across studies and practice. Prior research has predominantly relied on expert judgment or narrative reviews to summarize such risks, which limits reproducibility and makes it difficult to iteratively expand the indicator set when new evidence becomes available. To address these challenges, this study develops a hybrid risk identification framework that integrates unsupervised topic modeling with structured inductive coding. Latent Dirichlet Allocation (LDA) is employed to extract latent semantic patterns from a systematically screened body of literature on construction digital transformation, consolidating dispersed risk expressions into coherent thematic units. The Gioia methodology is then applied to inductively structure these themes into a hierarchical risk indicator system, ensuring traceability from textual evidence to conceptual indicators and enhancing interpretability for construction management applications. Rather than enumerating isolated risk events, the proposed framework conceptualizes digital transformation risk in construction enterprises as a set of interacting structural conditions that shape risk exposure across project stages and organizational boundaries. By shifting risk identification from event-based listings to a structural and condition-oriented representation, this study provides a transferable foundation for subsequent causal modeling and multi-criteria risk evaluation in construction digital transformation. Full article

Elastic wave scattering in polycrystalline materials has been a long-lasting topic in seismology and physical acoustics. Numerous analytical scattering models have been reported for polycrystals with random grain orientations. However, the elastic wave scattering in polycrystals with a preferred grain orientation (crystallographic texture) has not been well studied. This study develops a general ultrasonic scattering model that correlates the scattering coefficients and attenuation coefficients with orientation distribution coefficients (ODCs) for polycrystalline materials with a crystallographic texture. These models are valid for aggregates of triclinic grains with arbitrary texture symmetry. Since different terminologies for orientation distribution functions (ODFs) are adopted in quantitative texture analysis, the relations between different terminologies are also summarized in this study. Furthermore, for two special cases—hexagonal polycrystalline materials with a fiber texture and cubic polycrystalline materials with orthotropic texture symmetry—explicit expressions for the ultrasonic backscattering coefficient through ODCs are derived. The explicit relationship between ultrasonic backscattering and ODCs not only manifests how the individual texture coefficients impact ultrasonic scattering but also makes it possible to determine ODCs up to the eighth order experimentally from ultrasonic scattering measurements. This type of forward model also can be applied to the microstructure characterization of textured polycrystals. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

As China’s demographic transition deepens and fertility rates continue to decline, childbearing has shifted from a private family matter to a salient public issue. Social media platforms have become key arenas in which fertility-related concerns are articulated, negotiated, and publicly constructed. This study analyzes fertility-related user-generated content (UGC) from three major Chinese platforms—Sina Weibo, Douyin, and Toutiao—collected between 30 June and 31 December 2024. Using BERTopic-based topic modeling, sentiment quantification, and cross-platform comparison, the study examines how fertility discourse is thematically organized and emotionally expressed across different platform environments. The results reveal clear platform differentiation. Douyin primarily foregrounds individualized and relational narratives embedded in everyday family life, Toutiao emphasizes gender-neutral, macro-social and policy-oriented interpretations, while Sina Weibo centers on gender relations, institutional arrangements, and rights-based debate. Sentiment analysis indicates that fertility discourse on all three platforms exhibits an overall negative emotional orientation, though with varying intensity. Rather than reflecting uniformly pessimistic fertility attitudes, this negative bias is interpreted as a product of platformized public discourse. The study proposes an emotional filtering mechanism to explain how fertility-related emotions are selectively distributed across communicative spaces: problem-oriented and conflict-laden expressions are more likely to gain visibility in open public platforms. By integrating a platformization perspective, this study demonstrates how platform-specific communication logics shape both the thematic configuration and emotional structure of fertility discourse, offering new insights into the mediated construction of fertility concerns in contemporary China. Full article

With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control (MPC) emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning (RL) and deep reinforcement learning (DRL) have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems. Full article

The rapid expansion of corporate sustainability reporting has increased transparency requirements while raising concerns about greenwashing driven by selective, narrative-based disclosure. This study assesses the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage for a sample of 204 large firms operating in Central and Eastern Europe in 2023. Using natural language processing techniques, the analysis constructs a Greenwashing Severity Index (GSI) that captures discrepancies between firms’ ESG self-representation and external public narratives. The index combines ESG-specific focus measures, sentiment analysis, TF–IDF-based term weighting, and topic modeling to quantify imbalances in ESG communication. Results indicate moderate but widespread greenwashing across countries, industries, and firm sizes, with substantial heterogeneity linked to differences in regulatory maturity and stakeholder scrutiny. Higher alignment between corporate disclosures and external narratives is observed among larger firms and in sectors subject to stronger public accountability, while finance, aviation, and online commerce exhibit higher greenwashing severity. A propensity score matching analysis further shows that firms with imbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behavior rather than substantive sustainability engagement. Overall, the findings demonstrate that transparency alone is insufficient to ensure credible ESG communication, highlighting the need for EU sustainability governance to move beyond disclosure-based compliance toward digitalized, data-driven monitoring frameworks that systematically integrate external information sources to curb strategic ESG misrepresentation and enhance corporate accountability under evolving regulatory regimes. Full article

The topic of chaotic thresholds for piecewise linear discontinuous (PWLD) systems with multiple-well potentials is a persistent topic in the research of a number of authors. In this article we investigate the chaos of a modified piecewise linear discontinuous (MPWLD) system. The model, containing N free parameters, could be of interest to specialists working in this area. With a specially developed software product, we generate the Melnikov equation $M\left(t\right)=0$ and examine all its zeros. This opens up an opportunity for researchers to correctly understand and formulate the classical Melnikov criterion for the possible occurrence of chaos in dynamical systems. Several simulations are composed. We also demonstrate some specialized modules for investigating the dynamics of the proposed model. Intriguing and new generalizations made through probabilistic constructions are considered. Full article

There has been a surge in interest in and implementation of motion capture (MoCap)-based lessons in animation, creative education, and performance training, leading to an increasing number of studies on this topic. While recent studies have summarized these developments, few have been conducted that synthesize existing findings into a theoretical framework. Building upon the Cognitive Affective Model of Immersive Learning (CAMIL), this study proposes the Cognitive Affective Model of Motion Capture Training (CAMMT) as a theoretical and research-based framework for explaining how MoCap fosters creative cognition in computer animation practice. The model identifies six affective and cognitive constructs: Control and Active Learning, Reflective Thinking, Perceptual Motor Skills, Emotional Expressive, Artistic Innovation, and Collaborative Construction that describe how MoCap’s technological affordances of immersion and interactivity support creativity in animation practice. The findings indicate that instructional and design methods from less immersive media can be effectively adapted to MoCap environments. Although originally developed for animation education, CAMMT contributes to broader theories of creative design processes by linking cognitive, affective, and performative dimensions of embodied interaction. This study offers guidance for researchers and designers exploring creative and embodied interaction across digital performance and design contexts. Full article

Background: This study examined how patients and caregivers perceive and experience AI-based care technologies through text mining analysis. The goal was to identify major themes, sentiments, and value-oriented interpretations embedded in their narratives and to understand how these perceptions align with key dimensions of patient-centered care. Methods: A corpus of publicly available narratives describing experiences with AI-based care was compiled from online communities. Natural language processing techniques were applied, including descriptive term analysis, topic modeling using Latent Dirichlet Allocation, and sentiment profiling based on a Korean lexicon. Emergent topics and emotional patterns were mapped onto domains of patient-centered care such as information quality, emotional support, autonomy, and continuity. Results: The analysis revealed a three-phase evolution of care values over time. In the early phase of AI-mediated care, patient narratives emphasized disruption of relational care, with negative themes such as reduced human connection, privacy concerns, safety uncertainties, and usability challenges, accompanied by emotions of fear and frustration. During the transitional phase, positive themes including convenience, improved access, and reassurance from diagnostic accuracy emerged alongside persistent emotional ambivalence, reflecting uncertainty regarding responsibility and control. In the final phase, care values were restored and strengthened, with sentiment patterns shifting toward trust and relief as AI functions became supportive of clinical care, while concerns related to depersonalization and surveillance diminished. Conclusions: Patients and caregivers experience AI-based care as both beneficial and unsettling. Perceptions improve when AI enhances efficiency and information flow without compromising relational aspects of care. Ensuring transparency, explainability, opportunities for human contact, and strong data protections is essential for aligning AI with principles of patient-centered care. Based on a small-scale qualitative dataset of patient narratives, this study offers an exploratory, value-oriented interpretation of how relational care evolves in AI-mediated healthcare contexts. In this study, care-ethics values are used as an analytical lens to operationalize key principles of patient-centered care within AI-mediated healthcare contexts. Full article

Photovoltaic (PV) power forecasting has become a key tool for the intelligent management of electrical grids. Since the largest source of error in PV power forecasting originates from uncertainties in solar irradiance prediction, improving the accuracy of solar irradiance forecasts has emerged as an active research topic. This study evaluates multiple random tree-based model versions using a challenging dataset collected at globally distributed stations, spanning elevations from sea level to nearly 4000 m and covering a wide range of climate classes. The originality of the study lies in the synergistic contribution of two elements: the innovative inclusion of diffuse irradiance among the predictors and a comparative analysis of forecast quality across lowland and mountainous locations. In such environments, accurate solar resource forecasting is particularly important for the intelligent management of stand-alone PV systems deployed at high altitudes and in remote, off-grid areas. Overall, the results identify Extremely Randomized Trees (XTRc) as the best-performing model. XTRc achieves Skill Scores ranging from 0.087 to 0.298 across individual stations. The model accuracy remains high even at mountain stations, provided that sky-condition variability is low. Full article

In many empirical studies, researchers face challenges when addressing sensitive topics as respondents may be reluctant to provide truthful answers due to privacy concerns. Traditional direct survey methods often yield biased or unreliable estimates in such contexts. The randomized response technique offers a robust alternative by improving data validity while protecting respondent confidentiality. This paper proposes a novel quantitative three-stage randomized response model, introducing a new Horvitz–Thompson (HT)-type estimator for estimating the means of two sensitive variables under a general sampling design. Simulation studies indicate that the proposed estimator can achieve lower bias and mean squared error (MSE) compared to other existing estimators in the literature. Additionally, an empirical investigation was conducted using data from Shahid Chamran University of Ahvaz to estimate the average rates of exam cheating and cigarette consumption among students under a simple random sampling scheme, further demonstrating the practical utility of the proposed approach. Full article