Search Results (128)

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0. Full article

To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature complementarity. Firstly, we construct a parallel architecture comprising a Transformer semantic parsing branch and a Convolutional Neural Network (CNN) detail capturing pathway, achieving collaborative optimization of global context modeling and local feature extraction. Furthermore, a Pooling-Cooperative Convolutional Module was designed, which enhances the model’s capability in detail extraction and boundary localization through weighted centroid features of dental structures and a latent edge extraction module. Finally, a Semantic Transformation Module and Interactive Fusion Module are constructed. The Semantic Transformation Module converts geometric detail features extracted from the CNN branch into high-order semantic representations compatible with Transformer sequential processing paradigms, while the Interactive Fusion Module applies attention mechanisms to progressively fuse dual-stream features, thereby enhancing the model’s capability in holistic dental feature extraction. Experimental results demonstrate that the proposed method achieves an IoU of 91.49% and a Dice coefficient of 94.54%, outperforming current segmentation methods across multiple evaluation metrics. Full article

Grounded in the theory of metacognitive prediction error minimization, this study is the first to propose and empirically validate the mechanism of implicit metacognitive predictive processing by which bodily interaction influences the Aha! experience. Three experimental groups were designed to manipulate the level of temporal synchrony in bodily interaction: Immediate Mirror Group, Delayed Mirror Group, and No-Interaction Control Group. A three-stage experimental paradigm—Prediction, Execution, and Feedback—was constructed to decompose the traditional holistic insight task into three sequential components: solution time prediction (prediction phase), riddle solving (execution phase), and self-evaluation of Aha! experience (feedback phase). Behavioral results indicated that bodily interaction significantly influenced the intensity of the Aha! experience, likely mediated by metacognitive predictive processing. Significant or marginally significant differences emerged across key measures among the three groups. Furthermore, fNIRS results revealed that low-frequency amplitude during the “solution time prediction” task was associated with the Somato-Cognitive Action Network (SCAN), suggesting its involvement in the early predictive stage. Functional connectivity analysis also identified Channel 16 within the reward network as potentially critical to the Aha! experience, warranting further investigation. Additionally, the high similarity in functional connectivity patterns between the Mirror Game and the three insight tasks implies that shared neural mechanisms of metacognitive predictive processing are engaged during both bodily interaction and insight. Brain network analyses further indicated that the Reward Network (RN), Dorsal Attention Network (DAN), and Ventral Attention Network (VAN) are key neural substrates supporting this mechanism, while the SCAN network was not consistently involved during the insight formation stage. In sum, this study makes three key contributions: (1) it proposes a novel theoretical mechanism—implicit metacognitive predictive processing; (2) it establishes a quantifiable, three-stage paradigm for insight research; and (3) it outlines a dynamic neural pathway from bodily interaction to insight experience. Most importantly, the findings offer an integrative model that bridges embodied cognition, enactive cognition, and metacognitive predictive processing, providing a unified account of the Aha! experience. Full article

Speaker verification (SV) is an exceptionally effective method of biometric authentication. However, its performance is heavily influenced by the effectiveness of the extracted speaker features and their suitability for use in resource-limited environments. Transformer models and convolutional neural networks (CNNs), leveraging self-attention mechanisms, have demonstrated state-of-the-art performance in most Natural Language Processing (NLP) and Image Recognition tasks. However, previous studies indicate that standalone Transformer and CNN architectures present distinct challenges in speaker verification. Specifically, while Transformer models deliver good results, they fail to meet the requirements of low-resource scenarios and computational efficiency. On the other hand, CNNs perform well in resource-constrained environments but suffer from significantly reduced recognition accuracy. Several existing approaches, such as Conformer, combine Transformers and CNNs but still face challenges related to high resource consumption and low computational efficiency. To address these issues, we propose a novel solution that enhances the Transformer model by introducing multi-scale convolutional attention and a Global Response Normalization (GRN)-based feed-forward network, resulting in a lightweight backbone architecture called the lightweight simple transformer (LST). We further improve LST by incorporating the Res2Net structure from CNN, yielding the Res2Former model—a low-parameter, high—precision SV model. In Res2Former, we design and implement a time-frequency adaptive feature fusion(TAFF) mechanism that enables fine-grained feature propagation by fusing features at different depths at the frame level. Additionally, holistic fusion is employed for global feature propagation across the model. To enhance performance, multiple convergence methods are introduced, improving the overall efficacy of the SV system. Experimental results on the VoxCeleb1-O, VoxCeleb1-E, VoxCeleb1-H, and Cn-Celeb(E) datasets demonstrate that Res2Former achieves excellent performance, with the Large configuration attaining Equal Error Rate (EER)/Minimum Detection Cost Function (minDCF) scores of 0.81%/0.08, 0.98%/0.11, 1.81%/0.17, and 8.39%/0.46, respectively. Notably, the Base configuration of Res2Former, with only 1.73M parameters, also delivers competitive results. Full article

The development of ultrahigh-concentration photovoltaic (UHCPV) systems plays a pivotal role in advancing sustainable solar energy technologies. As the demand for clean energy grows, the need to align concentrated photovoltaic (CPV) system design with high-efficiency solar cell production becomes critical for maximizing energy yield while minimizing resource use. Despite some experimental efforts in UHCPV development, there remains a gap in integrating Fresnel lens-based systems with the comprehensive thermal modeling of key components in improving system sustainability and performance. To bridge this gap and promote more energy-efficient designs, a detailed numerical model was established to evaluate both the thermal and optical performance of a UHCPV system. This model contributes to the sustainable design process by enabling informed decisions on system efficiency, thermal management, and material optimization before physical prototyping. Through COMSOL Multiphysics simulations, the system was assessed under direct normal irradiance (DNI) ranging from 400 to 1000 W/m². Optical simulations indicated a high theoretical optical efficiency of ~93% and a concentration ratio of 1361 suns, underscoring the system’s potential to deliver high solar energy conversion with minimal land and material footprint. Moreover, the integration of thermal and optical modeling ensures a holistic understanding of system behavior under varying ambient temperatures (20–50 °C) and convective cooling conditions (heat transfer coefficients between 4 and 22 W/m².K). The results showed that critical optical components remain within safe temperature thresholds (<54 °C), while the receiver stage operates between 78.5 °C and 157.4 °C. These findings highlight the necessity of an effective cooling mechanism—not only to preserve system longevity and safety but also to maintain high conversion efficiency, thereby supporting the broader goals of sustainable and reliable solar energy generation. Full article

Although structural aluminium alloys have many advantages (low self-weight, corrosion resistance, 100% recyclable), they are associated with some conservative design methods in Eurocode 9. Conservative reductions in aluminium’s mechanical properties in the welded connection zone and the limitations of extruded aluminium members (the relatively small dimensions and uniform shape of the profile over the length) significantly limit the use of aluminium in load-bearing structures. This paper summarises the background, planned activities, and preliminary results of the ongoing REAL-fit project. The aim of the project is to conduct comprehensive interdisciplinary research on the feasibility of applying innovative automated (robotic) welding technologies and reliable design methods for aluminium welded members, joints, and entire structural systems. In this paper, the shortcomings of the current design approach are identified, and experimental, numerical, and reliability-based methodology for possible improvements is proposed. Furthermore, the project considers the integration of the advanced direct design method (DDM) with the methods of life cycle assessment (LCA) and life cycle cost analysis (LCCA) as a possible direction for establishing a more holistic evaluation framework. This is precisely one of the project’s ultimate goals, which will assess the reliability and sustainability of economical aluminium structures throughout their life cycle. Full article

Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, En) and randomness (via Hyper-entropy, He), yet existing similarity measures often neglect the stochastic dispersion governed by He. To address this gap, we propose HECM-Plus, an algorithm integrating Expectation (Ex), En, and He to holistically model geometric and probabilistic uncertainties in cloud models. By deriving He-adjusted standard deviations through reverse cloud transformations, HECM-Plus reformulates the Hellinger distance to resolve conflicts in multi-expert evaluations where subjective ambiguity and stochastic randomness coexist. Experimental validation demonstrates three key advances: (1) Fuzziness–Randomness discrimination: HECM-Plus achieves balanced conceptual differentiation (δC₁/C₄ = 1.76, δC₂ = 1.66, δC₃ = 1.58) with linear complexity outperforming PDCM and HCCM by 10.3% and 17.2% in differentiation scores while resolving He-induced biases in HECM/ECM (C₁–C₄ similarity: 0.94 vs. 0.99) critical for stochastic dispersion modeling; (2) Robustness in time-series classification: It reduces the mean error by 6.8% (0.190 vs. 0.204, *p* < 0.05) with lower standard deviation (0.035 vs. 0.047) on UCI datasets, validating noise immunity; (3) Design evaluation application: By reclassifying controversial cases (e.g., reclassified from a “good” design (80.3/100 average) to “moderate” via cloud model using HECM-Plus), it resolves multi-expert disagreements in scoring systems. The main contribution of this work is the proposal of HECM-Plus, which resolves the limitation of HECM in neglecting He, thereby further enhancing the precision of normal cloud similarity measurements. The algorithm provides a practical tool for uncertainty-aware decision-making in multi-expert systems, particularly in multi-criteria design evaluation under conflicting standards. Future work will extend to dynamic expert weight adaptation and higher-order cloud interactions. Full article

Previous research and classroom practices have focused on dispelling shame, assuming that it negatively impacts self-efficacy and performance, and overlook the potential for shame to facilitate learning. To investigate this gap, we designed an intervention with 132 tertiary education students (45.46% male, 64.4% European ethnicity) spanning diverse undergraduate majors to show how and why designing for experiences of shame and appropriately regulating them can differentially impact learning. Shame was induced through autobiographical recall, imagination, and failure-driven problem-solving before randomly assigning students to three conditions: two with explicit tips for either decreasing shame or maintaining shame (experimental groups) and one with no-regulation tips (control). Students worked on an introductory data science problem deliberately designed to lead to failure before receiving canonical instruction. Manipulation checks triangulating self-reported and facial expression analysis data suggested that shame was successfully regulated in the intended direction, depending on the condition. Our results, drawing on mixed-methods analyses, further suggested that relative to students decreasing shame, those who maintained shame during initial problem-solving had (i) similar post-test performance on a non-isomorphic question and improved performance on the transfer question, evidenced by accuracy in solving applied data science and inference tasks; (ii) complete reasoning across all post-test questions, as evidenced by elaborations justifying the usage of graphical and numerical representations across those tasks; and (iii) use of superior emotion regulation strategies focused on deploying attention to the problem and reappraising its inherently challenging nature with an approach orientation, as evidenced by a higher frequency of such codes derived from self-reported qualitative data during the intervention. Decreasing shame was as effective as not engaging in explicit regulation. Our results suggest that teaching efforts should be channeled to facilitate experiencing emotions that are conducive to goals, whether they feel pleasurable or not, which may inevitably involve emoting both positive and negative (e.g., shame) in moderation. However, it is paramount that emotional experiences are not merely seen by educators as tools for improved content learning but as an essential part of holistic student development. We advocate for the deliberate design of learning experiences that support, rather than overshadow, students’ emotional growth. Full article

Polymers, as metamaterials, represent an emerging area of research, where designed microstructural geometries lead to the competitively superior mechanical properties in these materials. Such materials are quite broadly used in energy absorption, impact protection, and biomedical systems. Polymeric metamaterials improve energy absorption and impact protection compared to traditional materials by leveraging their engineered microstructural geometries, which enhance their ability to dissipate energy and withstand impacts more effectively. However, for them to perform optimally, the relations between their micro and macro geometrical configurations need to be clearly understood. This paper reviews the significance of multi-scale modeling as one of the effective approaches for linking these differences. Considering the most recent developments in all these methods, including polymeric electronic materials at the microscale, mesoscale cellular structure, and at macroscale computational engineering, this paper underscores the holistic cross-fertilization of these methods. Some of their prominent uses include different engineering applications, such as structures for impact attenuation and load-carrying systems, demonstrating multi-scale procedures as promising tools to solve engineering problems. However, issues such as technical difficulties and the integration of experimental data within nonlinear and time-dependent considerations lead to challenges in the modeling of polymeric metamaterials. This review concludes with the identification of new trends such as the incorporation of artificial intelligence (AI) within modeling processes, as well as sustainability aspects which help to overcome existing constraints while allowing for great development opportunities. Along with the information of core research questions and gaps, this paper seeks to provide a systematic framework of technologies and applications that can be the basis for future research and development of polymeric metamaterials. Full article

The conservation tillage method is a more holistic method introduced in Hungary two decades ago. Its environmental benefits in agriculture were widely studied and documented. The impact of conservation tillage on soil compaction and penetration resistance remains debated, necessitating further research to clarify its long-term effects in different soil types and cropping systems. The present study evaluates the impact on soil penetration resistance following 16 years of implementation of six distinct tillage practices. The study was conducted at Józsefmajor Experimental and Training Farm (JM) of the Hungarian University of Agriculture and Life Sciences near Hatvan. The study employed a randomized complete block design (RCBD) to evaluate six distinct tillage methods. These methods encompassed disking (D) at 12–14 cm depth, shallow cultivation (SC) at 18–20 cm depth, no-tilling (NT), deep cultivation (DC) at 22–25 cm depth, loosening (L) at 40–45 cm depth, and plowing (P) at 28–30 cm depth. In this study, soil compaction was assessed by measuring soil penetration resistance (SPR) at different depths (0–50 cm) and periods of the cropping year. Disking and NT significantly increased SPR between 10 and 20 cm, likely due to increased soil densification and reduced porosity in the absence of deep soil disturbance. While under sunflower cropping season significantly higher SPR was measured. In March 2021, the SPR at D and NT differed significantly from other measurement dates (September, October, November, and April). Regarding the difference between the depths, SPR increased with increasing depths in all treatment plots. The study findings revealed that NT and D tillage methods significantly increased soil penetration resistance in both cropping years, whereas L and P reduced SPR and enhanced the soil moisture storage potential of the soil particularly for the sunflower cropping period. The significance of the Spearman correlations observed suggested that SPR could be a valuable indicator of root growth potential under certain tillage conditions. Based on our results, we recommend the adoption of occasional deep soil loosening for reduced tillage systems (SC, D, DC, and NT) for both wheat and sunflower. This will create a compact-free zone for greater crop root proliferation, nutrient access, and SMC storage. Full article

Post-earthquake fires (PEFs) pose a significant secondary hazard in earthquake-prone regions, compounding the destruction caused by seismic events and threatening structural safety. This review explores the interplay between seismic damage and fire resistance, focusing on ignition sources such as damaged utility systems and overturned appliances, and their cascading effects on structural integrity. Advanced performance-based design approaches are evaluated, emphasizing the integration of probabilistic risk assessments, sequential analysis, and hybrid fire simulations to address multi-hazard scenarios. Key findings of current studies reveal that seismic damage, including spalling, cracking, and loss of fireproofing, substantially reduces the fire resistance of materials like steel and reinforced concrete, exacerbating structural vulnerabilities. Despite advancements, critical gaps persist in experimental data, probabilistic modeling, and comprehensive performance-based design guidelines for PEF scenarios. Addressing these deficiencies requires enhanced data collection, improved modeling techniques, and the integration of PEF considerations into building codes. This study provides a comprehensive review of PEF damage assessment and underscores the need for a holistic, multi-hazard design paradigm to enhance structural resilience and ensure safety in regions subject to seismic and fire risks. These insights provide a foundation for future research and practical applications aimed at mitigating the compounded effects of earthquakes and fires. Full article

Traditional blockchain consensus mechanisms, such as Proof of Work (PoW) and Proof of Stake (PoS), face significant challenges related to the centralisation of validators and miners, environmental impact, and trustworthiness. While PoW is highly secure, it is energy-intensive, and PoS tends to favour wealthy stakeholders, leading to validator centralisation. Existing mechanisms lack fairness, and the aspect of sustainability is not considered. Moreover, it fails to address social trust dynamics within validator selection. To bridge this research gap, this paper proposes Proof of Friendship (PoF)—a novel consensus mechanism that leverages social trust by improving decentralisation, enhancing fairness and sustainability among the validators. Unlike traditional methods that rely solely on computational power or financial stakes, PoF integrates friendship-based trust scores with geo-location diversity, transaction reliability, and sustainable energy adoption. By incorporating a trust graph, where validators are selected based on their verified relationships within the network, PoF mitigates the risks of Sybil attacks, promotes community-driven decentralisation, and enhances the resilience of the blockchain against adversarial manipulation. This research introduces the formal model of PoF, evaluates its security, decentralisation, and sustainability trade-offs, and demonstrates its effectiveness compared to existing consensus mechanisms. Our investigation and results indicate that PoF achieves higher decentralisation, improved trustworthiness, reduced validator monopolisation, and enhanced sustainability while maintaining strong network security. This study opens new avenues for socially aware blockchain governance, making consensus mechanisms more equitable, efficient, and environmentally responsible. This consensus mechanism demonstrates a holistic approach to modern blockchain design, addressing key challenges in trust, performance, and sustainability. The mechanism is tested theoretically and experimentally to validate its robustness and functionality. Processing latency (PL), network latency (NL) [transaction size/network speed], synchronisation delays (SDs), and cumulative delay per transaction are 85 ms, 172 ms, 1802 ms, [PL + NL + SD] 2059 ms, respectively. Full article

Education is key in promoting sustainable development across various sectors, including nutrition. Teachers play a critical role in shaping the mindset and skills of future generations, enabling them to effectively address global challenges. By integrating sustainability into their initial training, future teachers will have a greater understanding of the complexity of issues such as food security, environmental conservation, and social equity. It will also enable them to design teaching proposals that are in line with this complexity for their professional future. The objectives of this study are to show a curricular proposal that integrates healthy and sustainable nutrition contents into a degree in early childhood education and to assess university students’ competencies in sustainability issues. This is a pre-experimental quantitative study with a sample of second-year students enrolled in a subject called “Childhood, Health, and Nutrition” that lasted for one semester in three consecutive academic years. The results showed the effective integration of the project-oriented learning strategy as a teaching–learning methodology for the design of healthy and sustainable nutrition workshops for children aged 4–5. The workshops were presented in a simulated school context at the SDG Student Congress held at the university, and the sustainability competencies of these future early childhood teachers were assessed by a multidisciplinary team of experts using a specific rubric. The data obtained revealed a medium–high level of competency development in all three academic years analysed. This is a preliminary study that offers an example of how to integrate sustainability in a holistic manner linked to healthy nutrition contents aimed at training future teachers. Full article

To address the insufficient bandwidth and message response delays in FlexRay dynamic segments within automotive communication networks, this study proposes an optimized message scheduling strategy based on the FlexRay dynamic segment (DSMSS). By holistically integrating multi-dimensional parameters—including message length, deadline, remaining processing time, and Automotive Safety Integrity Level (ASIL)—the strategy introduces a dynamic frame ID priority allocation mechanism. Leveraging dynamic programming, this approach systematically optimizes message transmission sequences. Furthermore, a new compensation scheduling method is proposed to prevent the continuous delay of low-priority messages and achieve priority transmission within the compensation period after high-priority tasks. Guided by ISO 26262 standards, electronic control units (ECUs) are classified, and an experimental platform simulating an automotive chassis control system is established using the FlexRay bus topology. The verification is performed using the CANoe.FlexRay simulation tool and the VN8970 hardware interface. The experimental results demonstrate that, compared to the conventional Earliest Deadline First (EDF) algorithm, the DSMSS strategy achieves a 28.1% improvement in bandwidth utilization and a 9.4% reduction in worst-case response time when transmitting 20 dynamic messages. This study addresses communication system asymmetry through balanced supply–demand scheduling, significantly enhancing real-time FlexRay performance and resource efficiency. The findings provide theoretical and technical foundations for designing efficient, robust communication architectures in intelligent connected vehicles, advancing practical solutions for bandwidth-constrained automotive networks. Full article

This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. This integration aims to capture local-level morphological features effectively while analyzing global spatial relationships more comprehensively. While the CNN structure excels at discerning fine morphological details of insects, the ViT’s self-attention mechanism enables a holistic evaluation of their overall configurations. Several data preprocessing steps were implemented to enhance the model’s performance, including data augmentation techniques and strategies to ensure class balance. In addition, hyperparameter optimization contributed to more stable and robust model training. Experimental results indicate that the ConvViT model outperforms commonly used benchmark architectures such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViT, achieving a classification accuracy of 93.61%. This hybrid approach improves accuracy and strengthens generalization capabilities, delivering steady performance during training and testing phases, thereby increasing its reliability for field applications. The findings highlight that the ConvViT model achieves high efficiency in pest detection by integrating local and global feature learning. Consequently, this scalable artificial intelligence solution can support sustainable agricultural practices by enabling the early and accurate identification of pests and reducing the need for intensive pesticide use. Full article