Search Results (226)

α-L-fucosidases (EC 3.2.1.51) are of particular interest due to their ability to cleave terminal α-L-fucose residues from glycoconjugates, a property associated with numerous biological and therapeutic effects. They have also been investigated for their potential use in glycan remodeling, disease biomarker analysis, and particularly as therapeutic agents in the context of fucosidosis, a rare lysosomal storage disorder, caused by a deficiency in α-L-fucosidase activity. However, limitations in enzyme availability, stability, and substrate specificity highlight the need for novel and more efficient enzyme sources. Bifidobacterium castoris (B. castor is) is a newly identified species first discovered in the beaver gut microbiota in 2019. Phylogenetic studies have revealed its advanced metabolic capacity, and genomic analyses have demonstrated its extensive carbohydrate metabolism potential. This research article focuses on the recombinant production and biochemical characterization of a novel α-L-fucosidase from B. castoris LMG (Laboratorium voor Microbiologie Gent) 30937, predicted to belong to glycoside hydrolase family 29 (GH29) according to Universal Protein Resource (UniProt) annotation. Under optimized reaction conditions the recombinant α-L-fucosidase exhibited a specific activity of 0.264 U/mg to pNP-Fuc (4-Nitrophenyl-α-L-fucopyranoside). The results indicate that the enzyme is active in the pH range of 3.0–8.0 and temperatures of 24–42 °C, but its optimum conditions are the slightly acidic pH of 5.5 and the elevated temperature of 42 °C. This profile suggests that the enzyme is adapted to acidic intestinal-like environments. This novel enzyme expands the GH29 α-L-fucosidase repertoire and offers a promising new candidate for future biotechnological applications. Full article

This study examines the effectiveness of the strategic session format in teaching sustainable development within a university ecology course, with a particular focus on fostering student engagement. A pedagogical experiment was conducted with first-year undergraduate students, who were divided into four stakeholder groups—Ecologists, Developers, Residents, and Authorities—to work on the following question: “What should a sustainable city of the future be like?” Team roles were assigned based on a diagnostic survey assessing individual collaboration styles. The online session was structured in two stages, combining small-group discussions and plenary meetings, and was moderated by third-year students. The collaboration was supported by digital tools, including online boards and structured templates. Data collection involved student surveys, discussion transcripts, and moderator observations. The results indicate that students preferred the interactive strategic session format over conventional instruction methods. Participants demonstrated high levels of engagement, an ability to analyze complex sustainability issues, and a willingness to reconcile differing stakeholder perspectives. The findings also revealed areas for improvement, which informed further adjustments to the format. This paper offers a documented example of using the strategic session as an educational tool for sustainable development, aligning with active learning principles. It highlights the format’s potential for interdisciplinary learning and its adaptability through accessible digital platforms. Full article

This paper explores the theoretical foundations and practical applications of the Japanese concept of ikigai in the field of career counselling and study choice, with particular emphasis on its potential implementation in Slovenia’s educational system. Unlike traditional guidance models that primarily focus on the alignment of abilities and interests, ikigai represents a holistic framework integrating values, competencies, social contribution, and economic sustainability. The paper develops a novel conceptual model of ikigai coaching, applicable across three educational phases and structured into five implementation steps. It situates ikigai within broader motivational and existential theories, while also addressing the challenges of intercultural adaptation and risks of oversimplification or commercialisation. Empirical insight is provided through an expert interview with Professor Rutger ThielenTielen (Breda University of Applied Sciences, BUAS), and limitations as well as directions for future research are critically examined. The original contribution of this article lies in its contextualisation of ikigai within the Slovenian educational and cultural framework, where such approaches have not yet been systematically applied or academically evaluated. By integrating theoretical reflection, a structured coaching model, and empirical insight, the paper advances the academic debate on meaning-oriented career counselling and provides a culturally sensitive proposal for enriching guidance practices in Slovenia. Full article

Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields. Full article

With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. However, traditional CNN models still have deficiencies in the extraction of early weak fault features and the suppression of high noise. In response to these problems, this paper proposes a convolutional neural network (SAWCA-net) that integrates spectrum-guided dynamic variable-width convolutional kernels and dynamic interactive time-domain–channel attention mechanisms. In this model, the spectrum-adaptive wide convolution is introduced. Combined with the time-domain and frequency-domain statistical characteristics of the input signal, the receptive field of the convolution kernel is adaptively adjusted, and the sampling position is dynamically adjusted, thereby enhancing the model’s modeling ability for periodic weak faults in complex non-stationary vibration signals and improving its anti-noise performance. Meanwhile, the dynamic time–channel attention module was designed to achieve the collaborative modeling of the time-domain periodic structure and the feature dependency between channels, improve the feature utilization efficiency, and suppress redundant interference. The experimental results show that the fault diagnosis accuracy rates of SAWCA-Net on the bearing datasets of Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) reach 99.15% and 99.64%, respectively, which are superior to the comparison models and have strong generalization and robustness. The visualization results of t-distributed random neighbor embedding (t-SNE) further verified its good feature separability and classification ability. Full article

Beliefs about one’s abilities are powerful predictors of success. Self-efficacy is a basic belief every human should have, as it reflects the confidence that one can achieve one’s goals. As this belief can change over time and depends on one’s self-reflection competence, it is defined as a skill. Academic self-efficacy extends beyond the classroom, shaping how students approach problems, set goals, and respond to challenges. There have been many attempts to create an instrument for measuring different types of self-efficacy, from general self-efficacy about life to self-efficacy to solve specific mathematical tasks. The purpose of this study was to translate, test, and adapt the Academic Self-Efficacy Scale to a sample of Latvian adolescents. The sample comprises 360 adolescents, ranging from 13-year-old sixth-grade pupils to first-year university students. The Academic Self-Efficacy Scale was validated by confirmatory factor analysis, which demonstrated excellent model fit and good item loadings. The Academic Self-Efficacy Scale demonstrated weak to moderate correlations with self-reported achievements in literature, language, and diligence. The strongest correlations were between academic self-efficacy and mathematics. Academic self-efficacy explained 23% of achievement distribution in mathematics. Achievement in mathematics together with diligence explained 32% of self-efficacy distribution. The validated scale demonstrated good reliability, convergence, and incremental validity, and the scale’s reliability and unidimensionality were approved. Full article

Occupancy detection is vital for improving energy efficiency, automation, and security in smart buildings. Reliable detection systems enable dynamic control of lighting, heating, ventilation, air conditioning, and security operations, leading to substantial cost savings and enhanced occupant comfort. However, accurately detecting occupancy using environmental sensor data remains challenging. Existing machine learning and deep learning models, such as Random Forests, convolutional neural networks, and recurrent neural networks, often struggle to capture both fine-grained local patterns and long-range temporal dependencies, limiting their generalization to complex, real-world occupancy patterns. To address these challenges, we propose Translution, a novel hybrid Transformer-based architecture specifically designed for occupancy detection from multivariate sensor time-series data. Translution combines multi-scale convolutional encoding to extract local temporal features, self-attention mechanisms to model long-range dependencies, and an adaptive gating mechanism that dynamically selects relevant features to improve robustness and generalization. We trained Translution on 8143 samples and evaluated it on two distinct subsets of the University of California, Irvine (UCI) Occupancy Detection Dataset: one with shorter, more consistent time spans (2804 samples) and another covering longer, more varied occupancy cycles with abrupt changes and different lighting/ventilation conditions (9752 samples). Evaluating these diverse subsets, which represent both typical and challenging real-world scenarios, explicitly strengthens Translution’s generalizability claim, demonstrating its ability to detect occupancy across varied temporal patterns and environmental conditions accurately. Our results demonstrate that Translution achieves 98.5% accuracy, 97.3% F1-score, and 98.55% area under the receiver operating characteristic curve, significantly outperforming traditional machine learning and deep learning baselines. These findings highlight Translution’s potential as a highly accurate and stable solution for real-time occupancy detection in diverse smart building environments. Full article

Background/Objectives: The Wolf Motor Function Test (WMFT) is a standardised assessment tool used to evaluate upper limb (UL) performance in individuals with acquired brain injury (ABI). It consists of 15 timed movement tasks, two strength measures, and a functional ability scale that assesses the quality of movement from 0 to 5. This study aimed to translate and culturally adapt the WMFT for Spanish-speaking individuals with ABI. Methods: The translation and cultural adaptation process followed established guidelines and involved researchers from the Rey Juan Carlos University (URJC) and from the Investigación en Terapia Ocupacional (InTeO) group. A joint committee of experts from both research groups unified two previous versions into the final Spanish version of the WMFT. The pilot study included 60 ABI survivors, who were evaluated for the clarity and usability of the adapted test. Descriptive statistical analysis was conducted to evaluate participant characteristics and test performance, with the results summarised for both the less-affected and most-affected UL. Results: The final version of the tool features inclusive language and a unified administration procedure. In the pilot study, execution times were longer when using the most-affected UL, particularly for tasks involving object manipulation, while grip strength was lower. Conclusions: The Spanish version of the WMFT is a suitable tool for evaluating UL function in ABI survivors and shows promising clinical and research implications. Full article

Given that grinding robots are easily affected by internal and external disturbances when machining complex surfaces with high precision, in this study, an adaptive robust impedance control method combining a radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. In a Cartesian coordinate system, we first use the universal approximation ability of the RBFNN to accurately identify and actively compensate for complex unknown disturbances in robot dynamics online. Then, an improved sliding mode impedance controller, which uses robust sliding mode control to effectively suppress the influence of RBFNN identification error and residual disturbance on trajectory tracking and ensure the accuracy of impedance control, is implemented. This approach improves the control performance and overcomes the inherent chattering phenomenon of the traditional sliding mode. Full article

Conventional immunotherapy, including immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, has revolutionized cancer therapy over the past decade. Yet, the efficacy of these therapies is limited by tumor resistance, antigen escape mechanisms, poor persistence, and T-cell exhaustion, particularly in the treatment of solid tumors. The emergence of unconventional immunotherapies offers novel opportunities by leveraging diverse immune cell subsets and synthetic biologics. This review explores various immunotherapy platforms, including gamma delta T cells, invariant natural killer T cells, mucosal-associated invariant T cells, engineered regulatory T cells, and universal CAR platforms. Additionally, it expands on biologics, including bispecific and multispecific antibodies, cytokine fusions, agonists, and oncolytic viruses, showcasing their potential for modular engineering and off-the-shelf applicability. Distinct features of unconventional platforms include independence from the major histocompatibility complex (MHC), tissue-homing capabilities, stress ligand sensing, and the ability to bridge adaptive and innate immunity. Their compatibility with engineering approaches highlights their potential as scalable, efficient, and cost-effective therapies. To overcome translational challenges such as functional heterogeneity, immune exhaustion, tumor microenvironment-mediated suppression, and limited persistence, novel strategies will be discussed, including metabolic and epigenetic reprogramming, immune cloaking, gene editing, and the utilization of artificial intelligence for patient stratification. Ultimately, unconventional immunotherapies extend the therapeutic horizon of cancer immunotherapy by breaking barriers in solid tumor treatment and increasing accessibility. Continued investments in research for mechanistic insights and scalable manufacturing are key to unlocking their full clinical potential. Full article

In the Hexaflex model of Acceptance and Commitment Therapy (ACT), psychological flexibility refers to the ability to openly embrace difficult thoughts and emotions while acting in alignment with personal values. In contrast, psychological inflexibility involves rigid avoidance and control strategies that hinder adaptive functioning. Although previously studied, more culturally relevant evidence is needed to inform interventions that promote well-being and mental health among Latin American students. This study explored manifestations of psychological flexibility and inflexibility in 15 undergraduate students from the University of Magdalena in Colombia (mean age = 20.13 years; 53.33% female) through semi-structured, face-to-face interviews (~45 min each). Data were analyzed using Interpretative Phenomenological Analysis (IPA), focusing on how participants described and made sense of their experiences. A total of 25 emergent themes were identified and grouped into 12 subordinate themes, mapped onto the 6 core ACT processes. The participants reported efforts to control or avoid distressing internal experiences, often resulting in difficulty acting in accordance with their values. The findings highlight a recurring ambivalence between avoidance and acceptance, and barriers to committed action, underscoring the dynamic interplay between flexibility and inflexibility. These results support the relevance of ACT-based interventions, such as structured group sessions that foster acceptance, mindfulness, and values-based behavior. Integrating this training into counseling and academic support services could enhance students’ well-being and performance. Future research should examine these dynamics longitudinally and across diverse contexts. Full article

To address the issues of information redundancy, limited feature representation, and empirically set parameters in rolling bearing fault diagnosis, this paper proposes a Multi-Entropy Screening and Optimization Temporal Convolutional Network (MESO-TCN). The method integrates feature filtering, network modeling, and parameter optimization into a unified diagnostic framework. Specifically, ensemble empirical mode decomposition (EEMD) is combined with a hybrid entropy criterion to preprocess the raw vibration signals and suppress redundant noise. A kernel-extended temporal convolutional network (ETCN) is designed with multi-scale dilated convolution to extract diverse temporal fault patterns. Furthermore, an improved whale optimization algorithm incorporating a firefly-inspired mechanism is introduced to adaptively optimize key hyperparameters. Experimental results on datasets from Xi’an Jiaotong University and Southeast University demonstrate that MESO-TCN achieves average accuracies of 99.78% and 95.82%, respectively, outperforming mainstream baseline methods. These findings indicate the method’s strong generalization ability, feature discriminability, and engineering applicability in intelligent fault diagnosis of rotating machinery. Full article

The COVID-19 pandemic brought unprecedented challenges, particularly for older adults. They were identified as a high-risk group. While research has primarily focused on health measures, less is known about their adaptation processes during this period in the UK and Colombia. This study explores “how older adults in the UK and Colombia adapted during the health crisis after one year”. We conducted interviews with 29 participants in the UK and 32 participants in Colombia, aged 63–95, about their experiences one year after the pandemic. We analysed their anonymised transcripts using constructivist grounded theory. The pandemic highlighted older adults’ ability to learn new skills in the face of adversities. Some found new goals; others found pleasure in optimising existing skills and tasks. Some compensated for the lack of social connectivity by intensifying hobbies. We identified three broad ways older adults adapted. Cognitive adaptation included acceptance, positive reframing, and religious trust. Emotional regulation was experienced not only through deep freeze, weather impact, social support, religion, pet companionship but also emotional struggles. Finally behavioural adaptation was enacted through routine modification, use of virtual technologies, intertwined cognitive–emotional–behavioural adaptation, and previous experiences. However, adaptation varied, with some individuals struggling to adapt, highlighting that while adaptation is possible for some, it is not universal among all older adults. Full article

Generative AI tools offer opportunities for enhancing learning and assessment, but raise concerns about equity, academic integrity, and the ability to critically engage with AI-generated content. This study explores these issues within a psychology-oriented postgraduate programme at a UK university. We co-designed and evaluated a novel AI-integrated assessment aimed at improving critical AI literacy among students and teaching staff (pre-registration: osf.io/jqpce). Students were randomly allocated to two groups: the ‘compliant’ group used AI tools to assist with writing a blog and critically reflected on the outputs, while the ‘unrestricted’ group had free rein to use AI to produce the assessment. Teaching staff, blinded to group allocation, marked the blogs using an adapted rubric. Focus groups, interviews, and workshops were conducted to assess the feasibility, acceptability, and perceived integrity of the approach. Findings suggest that, when carefully scaffolded, integrating AI into assessments can promote both technical fluency and ethical reflection. A key contribution of this study is its participatory co-design and evaluation method, which was effective and transferable, and is presented as a practical toolkit for educators. This approach supports growing calls for authentic assessment that mirrors real-world tasks, while highlighting the ongoing need to balance academic integrity with skill development. Full article

Deep learning has recently achieved remarkable progress in hyperspectral image (HSI) classification. Among these advancements, the Transformer-based models have gained considerable attention due to their ability to establish long-range dependencies. However, the quadratic computational complexity of the self-attention mechanism limits its application in hyperspectral image classification (HSIC). Recently, the Mamba architecture has shown outstanding performance in 1D sequence modeling tasks owing to its lightweight linear sequence operations and efficient parallel scanning capabilities. Nevertheless, its application in HSI classification still faces challenges. Most existing Mamba-based approaches adopt various selective scanning strategies for HSI serialization, ensuring the adjacency of scanning sequences to enhance spatial continuity. However, these methods lead to substantially increased computational overhead. To overcome these challenges, this study proposes the Hyperspectral Spatial Mamba (HyperSMamba) model for HSIC, aiming to reduce computational complexity while improving classification performance. The suggested framework consists of the following key components: (1) a Multi-Scale Spatial Mamba (MS-Mamba) encoder, which refines the state-space model (SSM) computation by incorporating a Multi-Scale State Fusion Module (MSFM) after the state transition equations of original SSMs. This module aggregates adjacent state representations to reinforce spatial dependencies among local features; (2) our proposed Adaptive Fusion Attention Module (AFAttention) to dynamically fuse bidirectional Mamba outputs for optimizing feature representation. Experiments were performed on three HSI datasets, and the findings demonstrate that HyperSMamba attains overall accuracy of 94.86%, 97.72%, and 97.38% on the Indian Pines, Pavia University, and Salinas datasets, while maintaining low computational complexity. These results confirm the model’s effectiveness and potential for practical application in HSIC tasks. Full article