Search Results (8,176)

Guinea pig (Cavia porcellus) meat is valued for its nutritional quality and ease of production. Marination is a key value-adding process, but it is limited by its long duration. Therefore, technologies that accelerate marination and improve tenderness are needed. The objective of this study was to evaluate the effect of ultrasound application time and temperature on the physicochemical parameters and yield of guinea pig meat during marination. The marination solution contained 1.9% NaCl, 1.9% acetic acid, and 0.51% oregano essential oil. Ultrasound treatment (200 W) was applied for 15–120 min, while static treatments were conducted at 20, 30, and 40 °C. Ultrasound-assisted marination of guinea pig meat improved NaCl uptake, reduced acetic acid content, and improved water-holding capacity. Moderate temperatures (40 °C) minimized weight loss, and short ultrasound times preserved color and texture. However, prolonged ultrasound exposure led to myofibrillar disruption and increased weight loss. Correlation analysis revealed that pH, influenced by NaCl and acetic acid, had a significant impact on moisture, texture, and water-holding capacity. Overall, the controlled application of ultrasound and temperature effectively optimized marination efficiency, enhanced functional properties, and preserved the quality of guinea pig meat. Full article

The increasing production of fruit and vegetable by-products from the food processing industry presents both environmental challenges and opportunities for valorisation as sources of bioactive compounds. These by-products, including peels, seeds, pomace, and leaves, are rich in polyphenols, carotenoids, dietary fibres, glucosinolates, phytosterols, and essential oils, which exhibit antioxidant, anti-inflammatory, antimicrobial, and prebiotic activities. Recent advances in green extraction technologies, including ultrasound-, microwave-, supercritical fluid-, and cold plasma-assisted extraction, allow for an efficient and sustainable recovery of these compounds, while preserving their bioactivity. Incorporation of by-product-derived extracts into functional foods and nutraceuticals offers health-promoting benefits and supports circular bioeconomy strategies. However, challenges remain in standardisation, safety assessment, and regulatory approval, among others. This review summarises current progress and outlines future directions for the sustainable utilisation of fruit and vegetable by-products in health-oriented applications. Full article

Brain–Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices by interpreting brain wave patterns associated with specific motor imagery tasks, which are derived from EEG signals. Although BCIs allow applications such as robotic arm control and smart assistive environments, they face major challenges, mainly due to the large variation in EEG characteristics between and within individuals. This variability is caused by low signal-to-noise ratio (SNR) due to both physiological and non-physiological artifacts, which severely affect the detection rate (IDR) in BCIs. Advanced multi-stage signal processing pipelines, including efficient filtering and decomposition techniques, have been developed to address these problems. Additionally, numerous feature engineering techniques have been developed to identify highly discriminative features, mainly to enhance IDRs in BCIs. In this review, several pre-processing techniques, including feature extraction algorithms, are critically evaluated using deep learning techniques. The review comparatively discusses methods such as wavelet-based thresholding and independent component analysis (ICA), including empirical mode decomposition (EMD) and its more sophisticated variants, such as Self-Adaptive Multivariate EMD (SA-MEMD) and Ensemble EMD (EEMD). These methods are examined based on machine learning models using SVM, LDA, and deep learning techniques such as CNNs and PCNNs, highlighting key limitations and findings, including different performance metrics. The paper concludes by outlining future directions. Full article

Magnesia-dolomite refractories have emerged as sustainable alternatives to traditional carbon- or chromium-containing linings in steelmaking and cement industries. Their outstanding thermochemical stability, high refractoriness, and strong basic slag compatibility make them suitable for converters, electric arc furnaces (EAF), and argon–oxygen decarburization (AOD) units. However, their practical application has long been constrained by hydration and thermal shock sensitivity associated with free CaO and open porosity. Recent advances, including optimized raw material purity, fused co-clinker synthesis, nano-additive incorporation (TiO${}_2$, MgAl${}_2$O${}_4$ spinel, FeAl${}_2$O${}_4$), and improved sintering strategies, have significantly enhanced density, mechanical strength, and hydration resistance. Emerging technologies such as co-sintered magnesia–dolomite composites and additive-assisted microstructural tailoring have enabled superior corrosion resistance and extended service life. This review provides a comprehensive analysis of physicochemical mechanisms, processing routes, and industrial performance of magnesia–dolomite refractories, with special emphasis on their contribution to technological innovation, decarbonization, and circular economy strategies in high-temperature industries. Full article

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions. Full article

Background/Objectives: Thoracic trauma remains a leading cause of trauma-related illness and death. Despite advances in imaging, ventilation strategies, and surgical fixation, its management remains a topic of debate, with varying practices across hospitals. Current Gaps: Although surgical stabilization of rib fractures (SSRF) has shown a mortality benefit in cases of flail chest and in elderly patients, its indications for non-flail cases remain uncertain. Analgesia strategies are evolving, and epidural remains the gold standard; however, it is limited by contraindications. In contrast, regional blocks, such as the erector spinae plane block (ESPB) and serratus anterior plane block (SAPB), are emerging as safer alternatives to opioid and thoracic epidural analgesia (TEA). Artificial intelligence (AI) is transforming imaging interpretation and risk stratification; however, its integration into daily trauma care is still in its early stages of development. Perspective: This article examines the integration of surgical innovation, regional anesthesia, and AI-powered diagnostics as integral components of future thoracic trauma care. We emphasize the importance of standardized surgical criteria, multimodal pain management approaches, and AI-assisted decision-making tools. Conclusions: Thoracic trauma care is shifting toward a personalized, multidisciplinary, and technology-enhanced approach. Incorporating evidence-based SSRF, advanced pain management techniques, and AI-supported imaging can help reduce mortality, enhance recovery, and optimize resource utilization. Full article

The bio-conversion of agricultural biomass into value-added products via solid-state fermentation (SSF) represents a cost-effective and eco-friendly approach, though it is often limited by low efficiency and prolonged processing times. While low-intensity magnetic fields (LMFs) have shown potential to enhance microbial metabolism and improve mass and heat transfer during SSF, the effects of conventional inhomogeneous magnetic fields remain inconsistent and may even cause localized microbial damage due to uneven field distribution. In this study, we designed and optimized a Helmholtz coil system capable of generating a highly homogeneous low-intensity magnetic field to overcome this limitation. Through electromagnetic simulation and experimental validation, an optimized aluminum profile-supported coil configuration was developed, achieving an average magnetic field intensity of 142.77 G under 70% power load with high spatial homogeneity (maximum deviation: ±1.32%). Applied to the solid-state fermentation of peanut meal, the homogeneous LMF treatment (40 G, 4 h) significantly increased peptide content by 77.76% compared to non-treated samples, and by 42.95% over traditional inhomogeneous LMF treatment. This work establishes homogeneous magnetic-field-assisted SSF as a novel, efficient, and scalable bioprocessing strategy, providing both a robust technological framework and new insights into the role of field uniformity in the magneto-fermentation of agricultural biomass. Full article

Introduction: Stroke remains one of the leading causes of long-term disability worldwide. Post-stroke motor recovery depends on neuroplasticity, which is stimulated by intensive, repetitive, and task-specific training. Modern technologies such as robotic rehabilitation (RR), virtual reality (VR), functional electrical stimulation (FES), brain–computer interfaces (BCIs), and non-invasive brain stimulation (NIBS) offer novel opportunities to enhance rehabilitation. They operate through sensory feedback, neuromodulation, and robotic assistance which promote neural reorganization and motor relearning. Neurobiological Basis of Motor Recovery: Mechanisms such as long-term potentiation, mirror neuron activation, and cerebellar modulation underpin functional reorganization after stroke. Literature Review Methodology: A narrative review was conducted of studies published between 2005 and 2025 using PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar. Randomized controlled trials, cohort studies, and systematic reviews assessing the efficacy of these modern technologies were analyzed. Literature Review: Evidence indicates that RR, VR, FES, BCIs, and NIBS improve upper and lower limb motor function and strength, and enhance activities of daily living, particularly when combined with conventional physiotherapy (CP). Furthermore, integrated rehabilitation technologies (IRT) demonstrate synergistic neuroplastic effects. Discussion: Modern technologies enhance therapy precision, intensity, and motivation but face challenges related to cost, standardization, and methodological heterogeneity. Conclusions: RR, VR, FES, BCIs, NIBS, and IRT are effective complements to CP. Early, individualized, and standardized implementation can optimize neuroplasticity and functional recovery. Full article

Within the field of assisted reproductive technologies (ARTs), embryologists regularly face the critical task of identifying embryos with the highest likelihood of implantation and survival. To help aid and standardize this practice, many embryo selection strategies have been developed to give the best chance of pregnancy success. Over the years, there has been a large increase in experimental studies conducted within this area of research. This increase has allowed for the formation of significant and plausible theories of embryo development, especially in cases where the most prominent factors seem identical. These advancements have both expanded the typical process of traditional treatments and have even paved the way for new techniques. The exact combination of all these relevant factors has not been fully elucidated into a single all-encompassing scheme for embryo decision. Morphological, genetic, and developmental indicators are well-studied individually, but the exact methods that should be prioritized in each scenario may change with respect to an individual patient. Deciding whether factors like age, egg quality, lifestyle choices, or previous medical history should alter methods of embryo ranking can result in conflict, especially in the case where a choice is being made between two similar embryos. This article reviews the conventional methods along with emerging technologies that provide the tools for embryologists to evaluate and rank embryos with high implantation potential (HIP). By showcasing these methods, including their respective benefits and drawbacks, this article provides information to allow clinicians to make effective decisions by integrating multiple approaches to embryo selection. Full article

Recent breakthroughs in machine learning, artificial intelligence, and the emergence of large datasets have made the integration of eye tracking increasingly feasible not only in computing but also in many other disciplines to accelerate innovation and scientific discovery. These transformative changes often depend on intelligently analyzing and interpreting gaze data, which demand a substantial technical background. Overcoming these technical barriers has remained an obstacle to the broader adoption of eye tracking technologies in certain communities. In an effort to increase accessibility that potentially empowers a broader community of researchers and practitioners to leverage eye tracking, this paper presents an open-source software platform: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze), designed to offer comprehensive descriptive and predictive analytical support. Firstly, BEACH-Gaze provides sequential gaze analytics through window segmentation in its data processing and analysis pipeline, which can be used to achieve simulations of real-time gaze-based systems. Secondly, it integrates a range of established machine learning models, allowing researchers from diverse disciplines to generate gaze-enabled predictions without advanced technical expertise. The overall goal is to simplify technical details and to aid the broader community interested in eye tracking research and applications in data interpretation, and to leverage knowledge gained from eye gaze in the development of machine intelligence. As such, we further demonstrate three use cases that apply descriptive and predictive gaze analytics to support individuals with autism spectrum disorder during technology-assisted exercises, to dynamically tailor visual cues for an individual user via physiologically adaptive visualizations, and to predict pilots’ performance in flight maneuvers to enhance aviation safety. Full article

Smart furniture represents a growing field that integrates Internet of Things (IoT), embedded systems and assistive technologies, yet lacks a comprehensive synthesis of its components and applications. This PRISMA-guided systematic review analysed 35 studies published between 2014 and 2024, sourced from PubMed, Web of Science and Scopus. The included studies presented prototypes of smart furniture that used IoT, sensors or automation. The focus was on extracting data related to technological configurations, functional uses, validation methods, maturity levels and commercialisation. Three technological pillars emerged, data collection (n = 31 studies), transmission/processing (n = 30), and actuation (n = 22), often combined into multifunctional systems (n = 14). Health monitoring was the dominant application (n = 15), followed by environmental control (n = 8) and assistive functions for older adults (n = 8). Validation methods varied; 37% relied solely on laboratory testing, while 20% only involved end-users. Only one solution surpassed Technology Readiness Level (TRL) 7 and is currently on the market. Current research remains pre-commercial, with gaps in AI integration, long-term validation, and participatory design. Smart furniture shows promise for healthcare and independent living, but requires standardised evaluation, ethical data practices, and co-creation to achieve market readiness. Full article

Gastrointestinal disorders, such as colorectal cancer (CRC), pose a substantial health burden worldwide, showing increased incidence rates across different age groups. Detecting and removing polyps promptly, recognized as CRC precursors, are crucial for prevention. While traditional colonoscopy works well, it is vulnerable to specialist errors. This study suggests an AI-based diminutive sessile polyp localization assistant utilizing the YOLO-V8 family. Comprehensive evaluations were conducted using a diverse dataset that was assembled from various available datasets to support our investigation. The final dataset contains images obtained using two imaging methods: white light endoscopy (WLE) and narrow-band imaging (NBI). The research demonstrated remarkable results, boasting a precision of 96.4%, recall of 93.89%, and F1-score of 94.46%. This success can be credited to a meticulously balanced combination of hyperparameters and the specific attributes of the comprehensive dataset designed for the colorectal polyp localization in colonoscopy images. Also, it was proved that the dataset selection was acceptable by analyzing the polyp sizes and their coordinates using a special matrix. This study brings forth significant insights for augmenting the detection of diminutive sessile colorectal polyps, thereby advancing technology-driven colorectal cancer diagnosis in offline scenarios. This is particularly beneficial for gastroenterologists analyzing endoscopy capsule images to detect gastrointestinal polyps. Full article

Sperm cryopreservation is a key technique in assisted reproductive technologies (ART), livestock breeding, fertility preservation, and wildlife conservation. However, the freeze–thaw process induces significant oxidative stress through the production of reactive oxygen species (ROS) by mitochondria, which can lead to impaired sperm motility, membrane damage, DNA fragmentation, and reduced fertilization potential. MitoQ is a mitochondria-targeted antioxidant consisting of a ubiquinone moiety conjugated to triphenylphosphonium (TPP⁺). MitoQ selectively accumulates in the mitochondrial matrix, where it efficiently scavenges reactive oxygen species (ROS) at their point of origin. This targeted action helps preserve mitochondrial function, sustain ATP production, and inhibit apoptotic signaling. Extensive experimental evidence across diverse species, including bulls, rams, boars, humans, dogs, and goats, shows that MitoQ supplementation during cryopreservation enhances post-thaw sperm viability, motility, membrane integrity, and DNA stability. Optimal dosing between 50 and 150 nM achieves these benefits without cytotoxicity, although higher doses may paradoxically increase oxidative damage. Compared to conventional antioxidants, MitoQ offers superior mitochondrial protection and enhanced preservation of sperm bioenergetics. Future directions involve exploring synergistic combinations with other cryoprotectants, advanced delivery systems such as nanoparticles and hydrogels, and detailed mechanistic studies on long-term effects. Overall, MitoQ represents a promising adjunct for improving sperm cryopreservation outcomes across clinical, agricultural, and conservation settings. Full article

South America harbors one of the world’s richest biodiversities, yet its wildlife faces escalating threats from climate change and anthropogenic pressures. Biobanking different types of cells and tissues represents an important strategy to preserve genetic diversity and support conservation efforts in the long run. This review highlights the main challenges, opportunities, and future perspectives for biobanking as a conservation tool in South America. Key challenges include technical standardization, funding, and integration with conservation policies. Despite these barriers, recent advances demonstrate the growing importance of biobanking as a complementary tool for safeguarding endangered species and strengthening long-term conservation strategies in the region. The integration of biotechnological approaches into conservation programs positions biobanks as pivotal tools for advancing wildlife management and safeguarding the unique biodiversity of South America. Full article

With the development of large language model (LLM) technology, AI-assisted education systems are gradually being widely used. Learning Path Recommendation (LPR) is an important task in personalized instructional scenarios. AI-assisted LPR is gaining traction for its ability to generate learning content based on a student’s personalized needs. However, the native-LLM has the problem of hallucination, which may lead to the inability to generate learning content; in addition, the evaluation results of the LLM on students’ knowledge status are usually conservative and have a large margin of error. To address these issues, this work proposes a novel approach for LPR enhanced by knowledge tracing (KT) and LLM. Our method operates in a “generate-and-retrieve” manner: the LLM acts as a pedagogical planner that generates contextual reference exercises based on the student’s needs. Subsequently, a retrieval mechanism constructs the concrete learning path by retrieving the top-N most semantically similar exercises from an established exercise bank, ensuring the recommendations are both pedagogically sound and practically available. The KT plays the role of an evaluator in the iterative process. Rather than generating semantic instructions directly, it provides a quantitative and structured performance metric. Specifically, given a candidate learning path generated by the LLM, the KT model simulates the student’s knowledge state after completing the path and computes a knowledge promotion score. This score quantitatively measures the effectiveness of the proposed path for the current student, thereby guiding the refinement of subsequent recommendations. This iterative interaction between the KT and the LLM continuously refines the candidate learning items until an optimal learning path is generated. Experimental validations on public datasets demonstrate that our model surpasses baseline methods. Full article