Search Results (42)

As Industry 5.0 emerges as a human-centric evolution of industrial systems, this study investigates the effectiveness of training interventions in companies aimed at supporting the transition to Industry 5.0, emphasizing human-centric and resilient skill development. Drawing from multiple case studies involving engineers and operators, the research applies both meta-analysis and meta-regression to assess the added value of experiential learning approaches such as Teaching and Learning Factories. In addition, a novel methodology combining quantitative analyses with qualitative interpretation of emerging competences is presented. Principal Component Analysis and classification frameworks are employed to identify and organize key competence clusters along technological, organizational, and social dimensions. Special attention is given to the emergence of human-centered competences such as decision empowerment, which are shown to complement traditional operational capabilities. The findings confirm that experiential training interventions enhance both self-efficacy and adaptive operational readiness, while the use of fusion techniques enables the generalization of results across heterogeneous corporate settings. This work contributes to ongoing discourse on Industry 5.0 readiness by linking training design to strategic company incentives and highlights the role of structured evaluation in informing future policy and implementation pathways. Full article

Packing food into lunch boxes requires the correct portion to be selected. Food items such as fried chicken, eggs, and sausages are straightforward to manipulate when packing. In contrast, deformable objects like spaghetti can give challenges to lunch box packing due to their fragility and tendency to break apart, and the fluctuating weight of noodles. Furthermore, achieving the correct amount is crucial for lunch box packing. This research focuses on self-learned grasping by a robotic arm to enable the ability to autonomously predict and grasp deformable objects, specifically spaghetti, to achieve the correct amount within specified ranges. We utilize deep reinforcement learning as the core learning. We developed a custom environment and policy network along a real-world scenario that was simplified as in a food factory, incorporating multi-sensors to observe the environment and pipeline to work with a real robotic arm. Through the study and experiments, our results show that the robot can grasp the spaghetti within the desired ranges, although occasional failures were caused by the nature of the deformable object. Addressing the problem under varying environmental conditions such as data augmentation can partially help model prediction. The study highlights the potential of combining deep learning with robotic manipulation for complex deformable object tasks, offering insight for applications in automated food handling and other industries. Full article

Serious games are an important tool for education and training that offers interactive and powerful experience. However, a significant challenge lays with adapting a game to meet the specific needs of each player in real-time. The present paper introduces a framework to guide the development of serious games using a phased approach. The framework introduces a level of standardization for the game elements, scenarios and data descriptions, mainly to support portability, interpretability and comprehension. This standardization is achieved through semantic annotation and it is utilized by digital twins to support self-adaptation. The proposed approach describes the game environment using ontologies and specific semantic structures, while it collects and semantically tags data during players’ interactions, including performance metrics, decision-making patterns and levels of engagement. This information is then used by a digital twin for automatically adjusting the game experience using a set of rules defined by a group of domain experts. The framework thus follows a hybrid approach, combing expert knowledge with automated adaptation actions being performed to ensure meaningful educational content delivery and flexible, real-time personalization. Real-time adaptation includes modifying the game’s level of difficulty, controlling the learning ability support and maintaining a suitable level of challenge for each player based on progress. The framework is demonstrated and evaluated using two real-word examples, the first targeting at supporting the education of children with syndromes that affect their learning abilities in close collaboration with speech therapists and the second being involved with training engineers in a poultry meat factory. Preliminary, small-scale experimentation indicated that this framework promotes personalized and dynamic user experience, with improved engagement through the adjustment of gaming elements in real-time to match each player’s unique profile, actions and achievements. Using a specially prepared questionnaire the framework was evaluated by domain experts that suggested high levels of usability and game adaptation. Comparison with similar approaches via a set of properties and features indicated the superiority of the proposed framework. Full article

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article

Background: MSLQ is a self-report instrument that measures motivational orientations and learning strategies of college students and is widely used to measure self-regulated learning. MSLQ has not been translated into Spanish and validated in the Spanish-speaking Latin American population. Objective: The objective of the study is to adapt, validate, and perform a factorial reduction of 9 out of 15 scales of the MSLQ instrument and correlate the scales with the grade point average (GPA) of a sample of health sciences university students. Methods: Nine scales (48 items) of the MSLQ were translated into Spanish and adapted to the Mexican population. Students were invited directly in their classrooms and filled out an electronic questionnaire with personal variables and these nine scales of the MSLQ instrument. We performed exploratory and confirmatory factor analysis (EFA and CFA) and based on the EFA a reduced version of the instrument was proposed. Results: A total of 439 students were included. The CFA showed unacceptable fit parameters with the original scale, therefore an item reduction and rearrangement were performed according to the EFA, and this yielded a reduced version with six scales and 25 items which showed acceptable fit parameters. This new reduced version rearranged the items of the effort regulation scale (ERE) into two different scales newly created in this version: time regulation (TRE) and self-regulation (SRE). The scales that disappeared in the reduced version were: intrinsic goal orientation (IGO), meta-cognitive self-regulation (MSR), and elaboration (ELA). Conclusions: The reduced version showed acceptable fit parameters that included the creation of two new scales (TRE and SRE). In addition, two scales were reduced (TVA and CTH), three scales were modified (MSE, TSE and ERE), two were unmodified (RHE and ORG), and two scales were eliminated (IGO and ELA). Full article

Background/Objectives: The Self-Nanoemulsifying Drug Delivery System (SNEDDS) has been widely applied in oral drug delivery, particularly for poorly water-soluble compounds. The successful development of SNEDDS largely depends on the precise composition of its components. This narrative review provides an in-depth analysis of Quality by Design (QbD), Design of Experiment (DoE), and in silico approach applications in SNEDDS development. Methods: The review is based on publications from 2020 to 2025, sourced from reputable scientific databases (Pubmed, Science direct, Taylor and francis, and Scopus). Results: Quality by Design (QbD) is a systematic and scientific approach that enhances product quality while ensuring the robustness and reproducibility of SNEDDS, as outlined in the Quality Target Product Profile (QTPP). DoE was integrated into the QbD framework to systematically evaluate the effects of predefined factors, particularly Critical Material Attributes (CMAs) and Critical Process Parameters (CPP_S), on the desired responses (Critical Quality Attributes/CQA), ultimately leading to the identification of the optimal SNEDDS formulation. Various DoEs, including the mixture design, response surface methodology, and factorial design, have been widely applied to SNEDDS formulations. The experimental design facilitates the analysis of the relationship between CQA and CMA/CPP, enabling the identification of optimized formulations with enhanced biopharmaceutical, pharmacokinetic, and pharmacodynamic profiles. As an essential addition to this review, in silico approach emerges as a valuable tool in the development of SNEDDS, offering deep insights into self-assembly dynamics, molecular interactions, and emulsification behaviour. By integrating molecular simulations with machine learning, this approach enables rational and efficient optimization. Conclusions: The integration of QbD, DoE, and in silico approaches holds significant potential in the development of SNEDDS. These strategies enable a more efficient, rational, and predictive formulation process. Full article

In the era of Industry 4.0 and the transition toward Industry 5.0, advanced manufacturing is increasingly driven by data analytics, artificial intelligence, and cyber-physical systems. The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. Recent advancements in smart factories emphasize the use of AI-powered process control, enabling machines to self-optimize, self-correct, and even self-retrain to maintain optimal performance. This paper proposes a hybrid control and monitoring framework designed to enhance turning operations by integrating artificial neural networks (ANNs) for predictive modeling and adaptive recalibration. The system leverages machine learning (ML) to improve machining efficiency, tool longevity, and energy consumption optimization. By implementing forward and inverse ANN models, the framework enables real-time estimation of cutting forces and energy consumption, facilitating data-driven decision-making in machining processes. Furthermore, an adaptive recalibration mechanism ensures continuous model updates, allowing the system to dynamically adjust based on evolving machining conditions such as tool wear, material properties, and environmental variations. This research contributes to these advancements by proposing an ANN-based hybrid approach, predictive modeling, and adaptive recalibration. The proposed framework ensures continuous monitoring, automated adjustments, and intelligent decision-making, making it a scalable and adaptable solution for modern machining operations. Full article

This article addresses the validation of an instrument designed to measure organizational leadership practices for inclusion and evaluates the said instrument’s factorial structure and convergent validity. This work responds to the need for quantitative tools to assess how school principals promote inclusion, in recognition of its importance for guaranteeing student access to an equitable quality education at all levels, from early childhood education through high school. The above-referenced instrument was developed based on a comprehensive literature review and consultations with experts, resulting in a 36-item scale structured in six key dimensions: D1: Professional Development; D2: Inclusive Vision; D3: Support for the Teaching–Learning Process; D4: Building Networks; D5: Participation and Dialog, and D6: Resource Management. The validation process included a confirmatory factor analysis that supported the existence of a hierarchical structure of a general factor of leadership for inclusion that determines the aforementioned key dimensions, with adequate fit indices (χ²(588) = 1694.624, p < 0.001, CFI = 0.945, TLI = 0.941, RMSEA = 0.060, SRMR = 0.034) and high internal consistency in the general scale (α = 0.98, Ω = 0.96). In terms of convergent validity, the instrument showed significant and consistent correlations with related constructs such as teacher self-efficacy and pedagogical leadership. This study highlights the importance of leadership for inclusion as a central element of fostering participation and learning in diverse school contexts, by providing a reliable tool for continuous improvement of the school management team’s performance of its functions. Also, it is important input for education policymakers charged with formulating student equity, who recognize the enhanced well-being and active participation in the social environment that result from the greater inclusion of students in their educational communities. Full article

A significant trend in education is the increasing recognition of the need to shift from transmissive teaching to incorporating reflexive practices associated with real-life issues in learning, curriculum, and teaching. Merging Information and Communication Technologies (ICTs) and Metaverse learning technologies in Education for Sustainability (ICT/MeEfS) is critical in responding to current sustainability crises such as climate change. This research article focuses on the factorial structure, reliability, and validity of a teachers’ ICT/MeEfS resilience scale. It examines the predictive value of teacher self-efficacy and transformative teaching beliefs in merging ICTs and education for sustainability. The respondents were 1815 in-service teachers in Indonesia, Malaysia, and Vietnam. The principal component analysis showed a two-factor model (factor 1: “personal ICT/MeEfS resilience” and factor 2: “reflexive practice”), with a significant amount of extracted variance (68.26%). The overall Cronbach’s alpha reliability analysis of the teachers’ resilience scale enabled by ICT/MeEfS was 0.90, indicating a high score and excellent internal consistency. Similarly, the stepwise multiple regression analysis revealed that the two hypothesized predictors, teacher self-efficacy and transformative teaching beliefs, significantly contributed to teachers’ ICT/MeEfS resilience, explaining 73% of its variability. The implications of the research results are discussed in terms of research and in developing the capacity of teachers to embed sustainability issues and SDGs in teaching practices, learning environments, and course curricula enabled by ICTs and Metaverse learning technologies. Full article

This study explores psychological well-being in adolescence through a multidimensional perspective using the Adolescent Students’ Basic Psychological Needs at School Scale, derived from the Self-Determination Theory. The ASBPNSS focuses on three basic psychological needs (Competence, Autonomy, and Relatedness) in adolescence and has not yet been used within the school context in Italy. This study’s main objectives are: (1) to validate a preliminary Italian version of the ASBPNSS; (2) to analyze the association between well-being at school and self-efficacy for self-regulated learning; and (3) to verify whether there are differences by gender. A sample of 395 students (mean age = 17.5; SD = 0.75) completed the ASBPNSS and the Self-Efficacy for Self-Regulated Learning Scale. The factorial structure, composite reliability, and gender invariance of the ASBPNSS were examined. Associations between well-being at school and self-efficacy were tested with structural equation models (CFI = 0.935, TLI = 0.925; RMSEA = 0.054). Measures of well-being were associated with school self-efficacy for self-regulated learning, which predicted Competence (beta = 0.639), Relatedness (beta = 0.350), and Autonomy (beta = 0.309). These relationships were invariant over gender, although girls reported lower latent means in the Relatedness factor. This study highlights the importance of promoting school self-efficacy and well-being in adolescence. Full article

Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied. Full article

Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and Contrastive methods. While Generative methods mainly focus on reconstructing data, Contrastive learning methods refine data representations by leveraging the contrast between each sample and its augmented version. However, existing Contrastive learning methods for anomalous sound detection often have two main problems. The first one is that they mostly rely on simple augmentation techniques, such as time or frequency masking, which may introduce biases due to the limited diversity of real-world sounds and noises encountered in practical scenarios (e.g., factory noises combined with machine sounds). The second issue is dimension collapsing, which leads to learning a feature space with limited representation. To address the first shortcoming, we suggest a diffusion-based data augmentation method that employs ChatGPT and AudioLDM. Also, to address the second concern, we put forward a two-stage self-supervised model. In the first stage, we introduce a novel approach that combines Contrastive learning and masked autoencoders to pre-train on the MIMII and ToyADMOS2 datasets. This combination allows our model to capture both global and local features, leading to a more comprehensive representation of the data. In the second stage, we refine the audio representations for each machine ID by employing supervised Contrastive learning to fine-tune the pre-trained model. This process enhances the relationship between audio features originating from the same machine ID. Experiments show that our method outperforms most of the state-of-the-art self-supervised learning methods. Our suggested model achieves an average AUC and pAUC of 94.39% and 87.93% on the DCASE 2020 Challenge Task2 dataset, respectively. Full article

Video tutorials are a popular means of learning software applications but their design and effectiveness have received little attention. This study investigated the effectiveness of video tutorials for software training. In addition, it examined whether two multimedia design principles, signaling and practice types, contribute to task performance, mental effort, and self-efficacy. The study participants were 114 undergraduate students from a nursing department. A two (no signals vs. signals) × two (video practice vs. video practice video) mixed factorial design was used for testing the main study hypotheses. The analysis revealed a unique contribution of signaling and practice types on task performance and self-efficacy. Contrary to expectations, however, no combined effect of signaling and practice types was found. This paper is concluded with a discussion of the findings and implications for future research. Full article

Recent developments related to non-financial information (NFI) reporting encourage the adoption of a long-term vision approach to sustainable development, which is also behind the definition of global citizens. In turn, the self-determination theory (SDT) describes which elements explain the students’ motivation. Using NFI as the subject and the SDT as the theoretical framework, this paper aims to identify the elements that can explain accounting students’ intrinsic motivation to learn topics related to NFI. Those elements include the different aspects that integrate the concept of a global citizen as well as sociodemographic variables. Data were gathered from a questionnaire to accounting students in Portugal. Through factorial analysis and linear regression, the findings indicate that all the elements underlying the concept of a global citizen, except empathy, are relevant in explaining students’ intrinsic motivation. Conversely, sociodemographic variables were not relevant for this purpose, which may indicate a more integrated perspective of the learning usefulness of topics related to NFI by self-identified students as global citizens. This paper provides insights into how students of an accounting course are intrinsically motivated to acquire skills in NFI reporting, which is particularly relevant to higher education institutions (HEIs), professors, students, and organizations related to accountancy education. Full article

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today’s unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory. Full article