Search Results (1,693)

A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials. Full article

Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and invasive. While vision-based systems offer a more accessible alternative, they have not been extensively explored for eye-writing recognition. Additionally, the natural instability of eye movements and variations in writing styles result in inconsistent signal lengths, which reduces recognition accuracy and limits the practical use of eye-writing systems. To address these challenges, we propose a novel vision-based eye-writing recognition approach that utilizes a webcam-captured dataset. A key contribution of our approach is the introduction of a Discrete Fourier Transform (DFT)-based length normalization method that standardizes the length of each eye-writing sample while preserving essential spectral characteristics. This ensures uniformity in input lengths and improves both efficiency and robustness. Moreover, we integrate a hybrid deep learning model that combines 1D Convolutional Neural Networks (CNN) and Temporal Convolutional Networks (TCN) to jointly capture spatial and temporal features of eye-writing. To further improve model robustness, we incorporate data augmentation and initial-point normalization techniques. The proposed system was evaluated using our new webcam-captured Arabic numbers dataset and two existing benchmark datasets, with leave-one-subject-out (LOSO) cross-validation. The model achieved accuracies of 97.68% on the new dataset, 94.48% on the Japanese Katakana dataset, and 98.70% on the EOG-captured Arabic numbers dataset—outperforming existing systems. This work provides an efficient eye-writing recognition system, featuring robust preprocessing techniques, a hybrid deep learning model, and a new webcam-captured dataset. Full article

The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article

The stochastic and undetermined nature of longwall coal mining results from the complex interaction between geological-mining and technical-organizational factors. This interaction causes variability in key parameters of the production process. This article presents three stochastic models developed on the basis of probability density functions, which describe selected process parameters. These mathematical functions serve as the foundation for effective stochastic models, enabling analysis of complex mining operations. The methodology employed in the study involves empirical data collection, statistical analysis, and stochastic simulation, carried out under both laboratory and field conditions. The results include empirical probability functions for output, delays, and crew-dependent productivity, offering insights into process variability and its impact on performance. Each method is characterized by its theoretical foundations, algorithmic structure, and application areas. The models have been validated through statistical tests and operational field data and can be applied as decision-support tools in both scientific research and industrial management. Given the extensive nature of the described methods, the article provides a comprehensive reference list for readers interested in further exploration and practical implementation in mining engineering. Full article

The increasing digitization of design processes has profoundly transformed the role of sketching in industrial design, integrating it with advanced technologies such as artificial intelligence (AI). This paper presents an innovative methodology for automotive design that combines the intuitive power of sketching, both traditional and digital, with the structured approach of Stylistic Design Engineering (SDE) and the capabilities of generative AI. The study investigates how AI can enhance and accelerate key phases of the design process, including ideation, style analysis, and development, by generating design variations and optimizing the transition from initial concepts to re-fined digital models. Through case studies integrating manual sketching, digital tools, and AI, this research demonstrates how this approach not only pre-serves the designer’s creativity but also improves efficiency and precision. The core contribution of this work lies in the development of a circular and iterative framework that balances creative exploration with methodological rigor, enabling significant reductions in time and cost while fostering innovation. The results underscore the potential of this integrated approach to drive a paradigm shift in automotive design and broader industrial design practices. By bridging creative ideation and systematic development, this methodology offers valuable applications not only in aesthetic design but also in engineering design contexts, where sketching can aid in defining and optimizing functional solutions from the earliest stages. Full article

Industrial microorganisms have a wide range of applications in biomanufacturing, energy production, environmental protectionpharmaceutical development, etc. Synthetic biology has revolutionised the field of industrial microorganisms by designing, constructing and optimising biological systems. The aim of this study is to discuss the key technologies of synthetic biology in industrial microorganisms and their application prospects. Gene editing technology, one of the core tools of synthetic biology, enables researchers to precisely modify microbial genomes to optimise their metabolic pathways or introduce new functions. Metabolic engineering, as an important direction for the application of synthetic biology in industrial microorganisms, enables the efficient synthesis of target products by optimising and reconstructing the metabolic pathways of microorganisms. The development of high-throughput screening and automated platforms has enabled large-scale gene editing and metabolic engineering experiments. The application of synthetic genomics promises to develop microbes with highly customised functions. However, there are still many challenges in this field, and future research still requires interdisciplinary collaboration to drive the application of synthetic biology in industrial microorganisms to new heights. Full article

Artificial intelligence (AI) is profoundly transforming heritage tourism through the incorporation of technological solutions that reconfigure the ways in which cultural heritage is conserved, interpreted, and experienced. This article presents a critical and systematic review of current AI applications in this field, with a special focus on their impact on destination management, the personalization of tourist experiences, universal accessibility, and the preservation of both tangible and intangible assets. Based on an analysis of the scientific literature and international use cases, key technologies such as machine learning, computer vision, generative models, and recommendation systems are identified. These tools enable everything from the virtual reconstruction of historical sites to the development of intelligent cultural assistants and adaptive tours, improving the visitor experience and promoting inclusion. This study also examines the main ethical, technical, and epistemological challenges associated with this transformation, including algorithmic surveillance, data protection, interoperability between platforms, the digital divide, and the reconfiguration of heritage knowledge production processes. In conclusion, this study argues that AI, when implemented in accordance with principles of responsibility, sustainability, and cultural sensitivity, can serve as a strategic instrument for ensuring the accessibility, representativeness, and social relevance of cultural heritage in the digital age. However, its effective integration necessitates the development of sector-specific ethical frameworks, inclusive governance models, and sustainable technological implementation strategies that promote equity, community participation, and long-term viability. Furthermore, this article highlights the need for empirical research to assess the actual impact of these technologies and for the creation of indicators to evaluate their effectiveness, fairness, and contribution to the Sustainable Development Goals. Full article

Parkinson’s disease (PD) is a progressive neurological disorder marked by the gradual loss of dopamine-producing neurons in the brain. This neuronal degradation causes motor symptoms such as tremors, stiffness, and slowness of movement. Despite decades of research, current treatments remain limited to symptom management, highlighting the urgent need for deeper insights into PD mechanisms and new therapeutic approaches. Among model organisms, zebrafish (Danio rerio) have emerged as a valuable tool for PD research due to the possibility of genetic manipulation. Zebrafish can be engineered to carry human PD-associated mutations, such as those in α-synuclein, LRRK2, and Parkin, enabling researchers to study the molecular and cellular basis of the disease. Additionally, exposure to neurotoxins like MPTP and paraquat allows scientists to replicate PD-like symptoms in zebrafish, supporting drug screening and behavioural analysis. This review summarises the key advantages and limitations of zebrafish as a model for PD, compares it with rodent models, and discusses recent advances and future directions that may improve translational outcomes in PD therapy and personalised medicine. Full article

This study investigates the impact of increased extrusion ram speed—achieved by utilizing liquid nitrogen as a die cooling agent—on the mechanical properties of a 6060-aluminum alloy. Mechanical characterization of the extruded profiles was performed using both tensile and nanoindentation tests. In addition, nanoindentation was employed to evaluate creep behaviour and to extract key parameters, such as the steady-state creep strain rate. The findings indicate that while the enhanced ram speed has a minimal influence on Ultimate Tensile Strength (UTS) and Yield Tensile Strength (YTS), it has a more noticeable effect on elongation. Finite Element Analysis (FEA) was used in conjunction with nanoindentation data to model the mechanical behaviour of the alloy, showing good agreement with experimental tensile test results. This confirms the effectiveness of FEA-assisted nanoindentation as a reliable tool for mechanical assessment. Moreover, the results demonstrate that creep displacement is significantly influenced by the increased ram speed. However, the steady-state creep strain rate remained largely unaffected by variations in ram speed with the use of liquid nitrogen as a coolant. Notably, the creep stress exponent (n) was found to increase with higher ram speeds enabled by liquid nitrogen cooling. Full article

Urban streetscapes are among the most frequently encountered spatial environments in daily life, and their restorative visual features have a significant impact on well-being. Although existing studies have revealed the relationship between streetscape environments and perceived restorativeness, there remains a lack of scalable, data-driven methods for quantifying such perception at the street level. This study proposes an interpretable and replicable framework for predicting streetscape restorativeness by integrating semantic segmentation, perceptual evaluation, and machine learning techniques. Taking Liwan District of Guangzhou as a case study, street-view images (SVIs) were collected and processed using the Mask2Former model to extract the following five key visual metrics: greenness, openness, enclosure, walkability, and imageability. Based on the Perceived Restorativeness Scale (PRS), an online questionnaire was designed from four dimensions (fascination, being away, compatibility, and extent) to score a random sample of images. A random forest model was then trained to predict the perceptual levels of the full dataset, followed by K-means clustering to identify spatial distribution patterns. The results revealed that there were significant differences in visual characteristics among high, medium, and low restorativeness street types. The proposed framework enables scalable, data-driven evaluation of perceived restorativeness across diverse urban streetscapes. By embedding perceptual metrics into large-scale urban analysis, the framework offers a replicable and efficient approach for identifying streets with low restorative potential—thus providing urban planners and policymakers with a novel tool for prioritizing street-level renewal, improving public well-being, and supporting perception-oriented urban design without the need for labor-intensive fieldwork. Full article

Cannabidiol (CBD) and its derivatives show interesting therapeutic potential, including antioxidant, anti-inflammatory, and anticancer properties; however, their clinical translation remains a complex task due to physicochemical restrictions such as low water solubility, high lipophilicity, and instability under light, oxygen, and high temperatures. Polymeric encapsulation has emerged as a promising strategy to overcome these challenges, offering protection against environmental degradation, improved bioavailability, and controlled release. Natural and synthetic polymers, both biocompatible and biodegradable, provide versatile matrices for CBD delivery, enabling nanoparticle formation, targeted transport, and enhanced pharmacokinetics. This review highlights the structural characteristics of CBD, its interaction mechanisms with polymeric matrices such as hydrogels, electrospun nanofibers, biodegradable microparticles, thin films, and lipid-polymer hybrid systems, and the principal encapsulation techniques, such as emulsion solvent evaporation, electrospinning, and supercritical fluid technologies, that facilitate stability and scalability. Furthermore, material characterization approaches, including microscopy, thermal, and degradation analyses, are discussed as tools for optimizing encapsulation systems. While notable advances have been made, key challenges remain in achieving reproducible large-scale production, ensuring regulatory compliance, and designing smart polymeric carriers personalized for specific therapeutic contexts. By addressing these gaps, polymer-based encapsulation may unlock new opportunities for CBD in pharmaceutical, nutraceutical, and therapeutic applications, providing a guide for future innovation and translation into effective patient-centered products. Full article

Risk assessment for moisture safety—particularly in the context of future climate scenarios—is not yet a routine component of building design practices. Key challenges include: (1) uncertainty over who is responsible for conducting assessments, (2) ambiguity regarding the appropriate timing, and (3) a lack of clear guidance on integrating climate data into the process. To meet the challenges, this article explores and evaluates the development of a national climate adaptation framework for designing moisture-resilient buildings in alignment with projected future climate conditions and the requirements of the Norwegian Planning and Building Act. In noteworthy detail the article presents the general approach/steps followed in the research and the qualitative climate risk assessment elements to be considered in the design process of buildings. The framework has been co-produced with the Norwegian construction industry and public sector and introduces structured checklists and division of responsibilities (architects, engineers, etc.) to clarify and operationalize this. The mainstreaming of climate adaptation requires further refinement and broader integration of climate indices into building guidelines. These indices enable more accurate moisture performance predictions and help eliminate unsuitable solutions for specific zones. The framework—reinforced by tools such as the SINTEF Building Research Design Guides (Byggforskserien)—offers a comprehensive, evolving approach to moisture resilience, dependent on ongoing tool development, clarified roles, and wider uptake of climate-sensitive risk assessments. Full article

Objective: False lumen (FL) thrombosis plays a key role in aortic remodeling after Frozen Elephant Trunk (FET) surgery, yet current imaging assessments are limited to categorical classifications. This study aimed to evaluate an unsupervised artificial intelligence (AI) algorithm based on a variational autoencoder (VAE) for automated, continuous quantification of FL thrombosis using serial computed tomography angiography (CTA). Methods: In this retrospective study, a VAE model was applied to axial CTA slices from 30 patients with aortic dissection who underwent FET surgery. The model encoded each image into a structured latent space, from which a continuous “thrombus score” was developed and derived to quantify the extent of FL thrombosis. Thrombus scores were compared between postoperative and follow-up scans to assess individual remodeling trajectories. Results: The VAE successfully encoded anatomical features of the false lumen into a structured latent space, enabling unsupervised classification of thrombus states. A continuous thrombus score was derived from this space, allowing slice-by-slice quantification of thrombus burden across the aorta. The algorithm demonstrated robust reconstruction accuracy and consistent separation of fully patent, partially thrombosed, and completely thrombosed lumen states without the need for manual annotation. Across the cohort, 50% of patients demonstrated an increase in thrombus score over time, 40% a decrease, and 10% remained unchanged. Despite these individual differences, no statistically significant change in overall thrombus burden was observed at the group level (p = 0.82), emphasizing the importance of individualized longitudinal assessment. Conclusions: The VAE-based method enables reproducible, annotation-free quantification of FL thrombosis and captures patient-specific remodeling patterns. This approach may enhance post-FET surveillance and supports the integration of AI-driven tools into personalized aortic imaging workflows. Full article

The study of fractals has a long history in mathematics and signal analysis, providing formal tools to describe self-similar structures and scale-invariant phenomena. In recent years, cognitive science has developed a set of powerful theoretical and experimental tools capable of probing the representations that enable humans to extend hierarchical structures beyond given input and to generate fractal-like patterns across multiple domains, including language, music, vision, and action. These paradigms target recursive hierarchical embedding (RHE), a generative capacity that supports the production and recognition of self-similar structures at multiple scales. This article reviews the theoretical framework of RHE, surveys empirical methods for measuring it across behavioral and neural domains, and highlights their potential for cross-domain comparisons and developmental research. It also examines applications in linguistic, musical, visual, and motor domains, summarizing key findings and their theoretical implications. Despite these advances, the computational and biological mechanisms underlying RHE remain poorly understood. Addressing this gap will require linking cognitive models with algorithmic architectures and leveraging the large-scale behavioral and neuroimaging datasets generated by these paradigms for fractal analyses. Integrating theory, empirical tools, and computational modelling offers a roadmap for uncovering the mechanisms that give rise to recursive generativity in the human mind. Full article

Biological control with beneficial bacteria offers a sustainable alternative to synthetic agrochemicals for managing plant pathogens and enhancing plant health. However, bacterial biocontrol agents (BCAs) remain underexploited due to regulatory hurdles (such as complex registration timelines and extensive dossier requirements) and limited strain characterization. Recent advances in omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have strengthened the bioprospecting pipeline by uncovering key microbial traits involved in biocontrol. Genomics enables the identification of biosynthetic gene clusters, antimicrobial pathways, and accurate taxonomy, while comparative genomics reveals genes relevant to plant–microbe interactions. Metagenomics uncovers unculturable microbes and their functional roles, especially in the rhizosphere and extreme environments. Transcriptomics (e.g., RNA-Seq) sheds light on gene regulation during plant-pathogen-bacteria interactions, revealing stress-related and biocontrol pathways. Metabolomics, using tools like Liquid Chromatography–Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance spectroscopy (NMR), identifies bioactive compounds such as lipopeptides, Volatile Organic Compounds (VOCs), and polyketides. Co-culture experiments and synthetic microbial communities (SynComs) have shown enhanced biocontrol through metabolic synergy. This review highlights how integrating omics tools accelerates the discovery and functional validation of new BCAs. Such strategies support the development of effective microbial products, promoting sustainable agriculture by improving crop resilience, reducing chemical inputs, and enhancing soil health. Looking ahead, the successful application of omics-driven bioprospection of BCAs will require addressing challenges of large-scale production, regulatory harmonization, and their integration into real-world agricultural systems to ensure reliable, sustainable solutions. Full article