Search Results (42,711)

The carob tree (Ceratonia siliqua L.) is receiving growing attention for its agro-industrial potential, particularly due to its seeds, which are the source of locust bean gum (LBG), a galactomannan-rich polysaccharide with wide applications in food and pharmaceutical industries. Efficient dehusking of carob seeds is critical to maximize LBG purity and yield, yet current industrial methods pose environmental concerns and lack robust quality control tools. In this study, we demonstrate the use of Diffuse Reflectance Spectroscopy (DRS) and Kubelka–Munk (KM) modeling as a rapid, non-destructive technique to assess dehusking efficiency. By combining spectral data from four complementary spectrometers (450–1800 nm), we identified key reflectance and absorbance features capable of distinguishing raw, industrially treated, and laboratory-dehusked seeds. Notably, our laboratory-treated seeds exhibited a considerably lower reflectance in the NIR plateau (800–1400 nm) compared to raw and industry-treated seeds, and their KM-reconstructed skin showed enhanced absorption bands at 960, 1200, and 1400 nm, consistent with more complete husk removal and improved light penetration. Principal Component Analysis revealed tighter clustering and lower variability in lab-processed seeds, indicating superior process reproducibility. These results establish DRS as a scalable, green analytical tool to support quality control and optimization in carob processing. Full article

Background: The growth of digital patient feedback presents a new opportunity for healthcare quality monitoring. This study addresses the need to automatically classify the content of patient reviews to identify primary sources of dissatisfaction. Objective: The purpose of this study is to develop a machine learning algorithm for classifying negative patient reviews into two core categories: medical content (M—pertaining to diagnosis, treatment, and outcomes) and organizational support (O—pertaining to logistics, cost, and communication). We aim to identify which type of concern prevails and to analyze variations across cities, patient gender, and medical specialties. Methods: A database of 18,680 negative patient reviews (rated 1 star) was compiled from the Russian aggregator infodoctor.ru for the period from July 2012 to August 2023. A training set was created using an independent annotation procedure with three experts. A logistic regression model was trained to classify reviews into M and O categories, demonstrating an accuracy of 88.5%. Results: The analysis revealed a significant structural shift in Moscow, where since 2021, medical (M) complaints began to prevail over organizational (O) ones. This trend was not observed in St. Petersburg or other major Russian cities. Notably, in St. Petersburg, M-type reviews were more common within the most represented medical specialties, whereas O-type reviews consistently dominated in other cities. Gender differences were most pronounced in St. Petersburg, where women were more frequently authors of M reviews and men of O reviews. Conclusions: The developed algorithm provides a valuable tool for the automated monitoring of patient feedback. It enables healthcare managers to distinguish between clinical and service-related issues, facilitating targeted improvements in medical service quality and patient satisfaction. Full article

This paper explores the evolution of first-generation solar cells by analysing the selection and engineering of materials that led to innovations. It also addresses the potential of using materials other than silicon and issues related to innovative recycling technologies. The paper presents the evolution of the Romanian photovoltaic sector and assesses the life cycle of photovoltaic panels, focusing on the recovery of high-quality raw materials and their reintroduction into the production process to improve the circular economy in this field. As the number of installed panels grows exponentially, so does the need to manage waste efficiently at the end of their life cycle. Photovoltaic panel recycling is slowly but surely becoming a rapidly developing field that is essential for the sustainability of the solar industry. With the growth of production in the Romanian photovoltaic sector, it has been identified that the need for recycled raw materials will increase from 900 prosumers in 2019 to over 100,000 in 2024. In the future, it will be imperative to develop strategies for recovering, recycling and reintroducing materials, which will bring major benefits. This paper’s specific contributions include a bibliometric mapping of EoL-PV research trends, a technology-recycling matrix for modern cell architectures, and a perspective on the Romanian market contextualised within EU policies. Full article

Background/Objectives: Ocular trauma (OT) and traumatic dental injuries (TDI) inflicted by police officers not only cause significant physical harm, but also psychological trauma. The clinical attention given by health care teams may induce revictimization or retraumatization phenomena, which affect the psychological status of the victim. The objective of this research is to bring to light the psychological experiences related to emergency care processes and rehabilitation of people affected by OT and TDI caused by police violence. Methods: Qualitative research was conducted based on in-depth interviews with eighteen people affected by OT or TDI during the social outbreak in Chile in 2019–2020. Data analysis was based on the principles of grounded theory. Results: Three main categories emerged: quality of interpersonal relationships with health care providers, expectations of care and treatment and psychological consequences. The findings show that retraumatization and revictimization arise from clinical care in the context of state violence. Conclusions: Revictimization and retraumatization are the most characteristic phenomena occurring in the health care of people affected by OT and TDI caused by police violence. The probability of their occurrence depends mainly on the interpersonal relationships established with the health care team and the management of patient expectations regarding health care. Full article

Sustainable production also involves analyzing greenhouse gas (GHG) emissions throughout the entire cultivation and processing cycle. The emissions balance for different sugar beet varieties is a key element of environmental assessment in sustainable production systems. It is consistent with the objectives of the European Green Deal and aims to decarbonize agri-food technology. This study aims to assess and compare GHG emissions associated with the cultivation of three sugar beet variants (Viola, Jaromir, and Pulitzer) taking into account their technological and quality characteristics. The varieties were selected based on their registration in the National Register and their importance in agricultural practice in Poland, as well as their contrasting technological profiles, which allow for the assessment of the relationship between raw material quality and GHG balance. The study combines life cycle assessment (LCA) with physiological parameters such as CO₂ assimilation, sugar content, yield, fuel consumption, and fertilizer use. The aim is to identify the correlation between the technological value of a variety and its environmental impact. It has been shown that genotypic characteristics have a significant impact on both yield and emissions. The Viola and Jaromir varieties showed a favorable balance between photosynthetic efficiency and greenhouse gas emissions, while the Pulitzer variety, despite low emissions per kilogram of product, showed poorer yield performance. The importance of using integrated assessment methods combining production efficiency, environmental efficiency, and crop quality was emphasized. Such an approach is essential for the development of sustainable agricultural practices in line with the EU’s climate neutrality goals. Further research is needed to optimize agrotechnical strategies tailored to the requirements of individual varieties, contributing to climate-resilient and environmentally friendly crop production. Full article

This study investigated the drying–grinding–extrusion processing of camel compound feeds enriched with locally available botanicals. A 2 × 2 × 3 full factorial design was applied to evaluate the effects of infrared drying temperature (two levels), grinding time (two levels), and extrusion screw speed (three levels) on process efficiency and product quality. Moisture calibration was performed using gravimetric reference values. Drying kinetics were modeled with Page and Midilli equations, while specific energy consumption (SEC) and specific moisture extraction rate (SMER) were calculated. Particle-size distribution, extrusion parameters, and extrudate properties (expansion ratio, bulk density, water absorption index (WAI), water solubility index (WSI), hardness, and color) were analyzed. Infrared drying resulted in faster moisture removal and greater energy efficiency compared with convective drying. The Midilli model provided the best fit to drying kinetics data. The results indicate that optimized combinations of drying, grinding, and extrusion conditions can enhance the technological and nutritional potential of camel compound feeds; however, biological validation is required. Limitations: These findings are limited to processing and compositional outcomes; biological validation in camels (in vivo or in vitro) remains necessary to confirm effects on digestibility, health, or performance. Full article

Public service provision is still limited to document-centric procedures that require citizens to submit data and information needed for the execution of a service via documents. This, amongst others, is time-consuming, error-prone and hinders progress towards data-centricity. This study proposes a data-centric framework for a public service recommender system that combines knowledge graphs (KGs) and large language models (LLMs) in a neuro-symbolic AI architecture. The framework expresses public service preconditions as machine-readable rules based on data standards and provides dynamic recommendations for public services based on citizens’ profiles through automated reasoning. LLMs are utilized to extract preconditions from unstructured textual regulations and create RDF-based evidence models, while KGs provide validation of preconditions through SHACL rules and explainable reasoning towards semantic interoperability. A prototype use case on students applying for housing allowance showcases the feasibility of the proposed framework. The analysis indicates that combining KGs with LLMs for identifying relevant public services for different citizens’ profiles can improve the quality of public services and reduce administrative burdens. This work contributes and promotes the proactive “No-Stop Government” model, where services are recommended to users without explicit requests. The findings highlight the promising potential of employing neuro-symbolic AI to transform e-government processes, while also addressing challenges related to legal complexity, privacy and data fragmentation for large-scale adoption. Full article

Objective: Medical Visual Question Answering (Med-VQA), a key technology that integrates computer vision and natural language processing to assist in clinical diagnosis, possesses significant potential for enhancing diagnostic efficiency and accuracy. However, its development is constrained by two major bottlenecks: weak few-shot generalization capability stemming from the scarcity of high-quality annotated data and the problem of catastrophic forgetting when continually learning new knowledge. Existing research has largely addressed these two challenges in isolation, lacking a unified framework. Methods: To bridge this gap, this paper proposes a novel Evolvable Clinical-Semantic Alignment (ECSA) framework, designed to synergistically solve these two challenges within a single architecture. ECSA is built upon powerful pre-trained vision (BiomedCLIP) and language (Flan-T5) models, with two innovative modules at its core. First, we design a Clinical-Semantic Disambiguation Module (CSDM), which employs a novel debiased hard negative mining strategy for contrastive learning. This enables the precise discrimination of “hard negatives” that are visually similar but clinically distinct, thereby significantly enhancing the model’s representation ability in few-shot and long-tail scenarios. Second, we introduce a Prompt-based Knowledge Consolidation Module (PKC), which acts as a rehearsal-free non-parametric knowledge store. It consolidates historical knowledge by dynamically accumulating and retrieving task-specific “soft prompts,” thus effectively circumventing catastrophic forgetting without relying on past data. Results: Extensive experimental results on four public benchmark datasets, VQA-RAD, SLAKE, PathVQA, and VQA-Med-2019, demonstrate ECSA’s state-of-the-art or highly competitive performance. Specifically, ECSA achieves excellent overall accuracies of 80.15% on VQA-RAD and 85.10% on SLAKE, while also showing strong generalization with 64.57% on PathVQA and 82.23% on VQA-Med-2019. More critically, in continual learning scenarios, the framework achieves a low forgetting rate of just 13.50%, showcasing its significant advantages in knowledge retention. Conclusions: These findings validate the framework’s substantial potential for building robust and evolvable clinical decision support systems. Full article

Coal is commonly used as both fuel and reducing agent in producing synthetic rutile from ilmenite (FeTiO₃) via the Becher process, which upgrades ilmenite to high-purity TiO₂ (>88%). However, coal-based reduction generates significant carbon waste. This study investigated the effect of adding 1–5% w/w potassium hydroxide (KOH), sodium hydroxide (NaOH), and sodium tetraborate (borax) to coal during ilmenite reduction to improve metallisation and reduce carbon burn-off. Results showed that 1% w/w additives significantly increased metallisation to 96% (KOH), 95% (NaOH), and 93% (borax), compared to 80% without additives, while higher concentrations (3–5% w/w) decreased metallisation. Scanning electron microscopy (SEM)analysis showed cleaner particle surfaces and optimal metallisation at 1% w/w, whereas higher additive levels caused agglomeration or sintering due to elevated silica and alumina activity. Additive type also influenced TiO₂ quality, with KOH enhancing TiO₂ at low concentrations but causing negative effects at higher levels, while NaOH and borax reduced TiO₂ quality via sodium-based compound formation. All additives reduced carbon burn-off, with KOH producing the greatest reduction. The iodine number of the carbon residue increased with higher additive concentrations, with KOH achieving 710 mg/g at 1% w/w and 900 mg/g at 5% w/w, making the residue suitable for water treatment. Overall, KOH is the most effective additive for producing high-quality synthetic rutile while minimising carbon waste. Full article

Background: Broccoli contains diverse phytochemicals, including glucosinolates and their hydrolysis products, with potential nutritional and bioactive properties. Accurate metabolite profiling requires optimized sample preparation and comprehensive databases. Methods: A rapid enzymatic deactivation method with 70% methanol, implemented prior to cryogrinding, was evaluated for processing freeze-dried and fresh broccoli florets, which were compared as plant materials. A widely targeted, organ-resolved metabolite database was constructed by integrating over 612 reported phytochemicals with glucosinolate degradation products. LC-HRMS combined with MS-DIAL and GNPS was employed for metabolite detection and annotation. Results: Freeze-dried samples yielded nearly twice the number of glucosinolates, isothiocyanates, and nitriles compared with standard-processed fresh tissue. Methanol pre-treatment preserved metabolite integrity in fresh samples, achieving comparable sensitivity to freeze-dried material. Using the integrated database, 998 metabolites were identified or tentatively characterized, including amino acids, carboxylic acids, phenolics, alkaloids, terpenoids, and glucosinolate derivatives. Cross-platform reproducibility was improved and false positives reduced. Conclusions: Optimized sample preparation combined with a curated metabolite database enables high-confidence, comprehensive profiling of broccoli florets phytochemicals. The resulting dataset provides a valuable reference for studies on genotype–environment interactions, nutritional quality, and functional bioactivity of cruciferous vegetables. Full article

Fulfilling the demand for functional food with cost safety and environmental sustainability, our novel anthocyanin-enriched functional drink containing the purple waxy corn cob-derived functional ingredient “MP1” showed cognitive enhancing effects with safety in bilaterally ovariectomized rats, a validated model of menopause. Since no clinical evidence that confirms the mentioned effect was available until now, we conducted a two-arm, randomized, double-blind, placebo-controlled, crossover study to confirm the benefits mentioned above. A total of 32 menopausal participants were divided into placebo and MP1 (400 mg) groups, and were subject to a 2-month study period. Safety parameters, working memory and brain components, especially N100 and P300, the negative and positive potentials derived from the event-related potential (ERP) which indicated attention and cognitive processing, together with oxidative stress markers acetylcholinesterase (AChE) and monoamine oxidase (MAO), were assessed at baseline and every month. No serious side effects or toxicity signs were observed. Subjects who consumed MP1 also had decreased N100 and P300 latency, improved working memory and decreased oxidative stress status. Therefore, a byproduct of purple corn can successfully serve as a novel functional ingredient for developing a cognitive enhancer drink with the qualities of safety, cost reduction, and environmental sustainability promotion. Full article

Background/Objectives: Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods: Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results: According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions: Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs. Full article

The urgent need to transform initial teacher education (ITE) in Australia has reached a critical juncture, as the Quality Initial Teacher Education (QITE) Review reveals concerning attrition rates with nearly 40% of ITE students sleaving within six years and approximately one in five beginning teachers exiting within their first three years. Traditional approaches to teacher preparation are failing to adequately equip educators for contemporary classrooms, particularly in developing the cultural responsiveness needed to serve Australia’s diverse student populations. This paper presents a case for reconceptualising ITE through pedagogical features that underpin dance education as a transformative vehicle for reform. In this context, dance education is defined as structured movement-based learning that integrates physical expression, cognitive development, cultural understanding, and pedagogical skills through embodied practices. Through a critical discourse analysis of recent Australian policy documents including the Teacher Education Expert Panel (TEEP) Report and Quality Initial Teacher Education (QITE) Review, alongside systematic examination of international empirical research on dance education, this study reveals how dance education’s inherent integration of physical, cognitive, social-emotional, and cultural learning uniquely addresses persistent challenges in teacher education. This article suggests that embedding dance education principles throughout ITE programs could revolutionise teacher preparation by providing embodied understanding of learning processes while developing practical teaching skills. This innovative approach holds particular promise in developing teachers who are not only technically skilled but also emotionally intelligent and culturally responsive, with implications extending beyond Australia to teacher preparation programs internationally. Full article

This study provides a comprehensive review of ultrasonic welding research in molecular dynamics simulations, encompassing the latest advancements by scholars worldwide. Compared to traditional welding methods, ultrasonic welding offers advantages such as faster processing speed, higher mechanical strength, and environmentally friendly characteristics. However, its process parameters are subject to multiple influencing factors. Molecular dynamics simulations enable the detailed visualization of material interactions and structural changes at atomic/molecular levels during ultrasonic welding. These simulations not only predict how different process parameters affect weld quality but also facilitate the rapid identification of viable solutions, thereby reducing experimental iterations and lowering R&D costs. This review delves into the core theoretical issues pertaining to ultrasonic welding, providing robust support for practical applications. Additionally, specific optimization strategies are proposed to enhance welding performance and efficiency, promoting sustainable development in related industries. Future research could focus on exploring ultrasonic welding mechanisms under complex structures and multi-component systems. Full article

Objective: The purpose of this study was to explore the impact of disability on dyadic quality of life (QoL) among stroke survivors and to examine the mediating role of social support in this process. Methods: Outcome measures were collected at four time points: baseline, 1 month, 3 months, and 6 months post-discharge. The Actor–Partner Interdependence Mediation Model was used to analyze the dyadic data. Results: A significant association was observed between a higher degree of disability and more severe depressive symptoms in stroke survivors (β = 0.626) and their spouses (β = 0.426). Survivors’ disability had a negative impact on their own physical health (β = −3.731) and indirectly affected the physical health of the spouse caregiver through the spouse caregiver’s depression (β = −0.198). In addition, disability affects the survivor’s own mental health through depression and social support (β = −0.231) and indirectly through the spouse caregiver’s depression and their own social support (β = −0.156). Conclusions: Survivor disability has a major impact on depression and QoL in couples with stroke. It is recommended that healthcare providers should identify disability early in stroke survivors and then target interventions to improve the QoL of couples affected by stroke who are at high risk of negative emotions. Full article