Search Results (9,910)

This study establishes an efficient and eco-friendly ultrasound-assisted extraction (UAE) method for total flavonoids present in Cercis chinensis seeds using natural deep eutectic solvents (NADES). Among nine NADES formulations screened, choline chloride–levulinic acid (ChCl–Lev, 1:2) demonstrated optimal performance, yielding 112.1 mg/g total flavonoids. Through Response Surface Methodology (RSM), the ultrasound-assisted extraction (UAE) parameters were explored. Under the optimized conditions (water content of 30%, time of 28 min, temperature of 60 °C, and solvent-to-solid ratio of 1:25 g/mL), the total flavonoid yield reached 128.5 mg/g, representing a 195% improvement compared to conventional ethanol extraction. The recyclability of NADES was successfully achieved via AB-8 macroporous resin, retaining 80.89% efficiency after three cycles. Extraction kinetics, modeled using Fick’s second law, confirmed that the rate constant (k) increased with temperature, highlighting temperature-dependent diffusivity as a key driver of efficiency. The extracted flavonoids exhibited potent antioxidant activity, with IC₅₀ values of 0.86 mg/mL (ABTS•⁺) and 0.69 mg/mL (PTIO•). This work presents a sustainable NADES-UAE platform for flavonoid recovery and offers comprehensive mechanistic and practical insights for green extraction of plant bioactives. Full article

This study presents the design, development, and experimental validation of a novel cable-driven shoulder exosuit (CDSE) for upper limb rehabilitation and assistance. Unlike existing exoskeletons, which are often bulky, limited in degrees of freedom (DOFs), or impractical for home use, the proposed DSE offers a lightweight (≈2 kg), portable, and wearable solution capable of supporting three shoulder movements: abduction, flexion, and horizontal adduction. The system employs a bioinspired tendon-driven mechanism using Bowden cables, transferring actuation forces from a backpack to the arm, thereby reducing user load and improving comfort. Mathematical models and inverse kinematics were derived to determine cable length variations for targeted motions, while control strategies were implemented using a PID-based approach in MATLAB Simscape-Multibody simulations. The prototype was fabricated in three iterations using PLA, aluminum, and carbon fiber—culminating in a durable and ergonomic final version. Experimental evaluations on a healthy subject demonstrated high accuracy in position tracking (<5% error) and torque profiles consistent with simulation outcomes, validating system robustness. The CDSE successfully supported loads up to 4 kg during rehabilitation tasks, highlighting its potential for clinical and at-home applications. This research contributes to advancing wearable robotics by addressing portability, biomechanical alignment, and multi-DOF functionality in upper limb exosuits. Full article

Amazon River dolphins often form multi-species aggregations at water confluences. This study used a multi-year data set to examine dolphins, fish, and geomorphology at dolphin aggregations. Methods included dolphin transect surveys, dolphin point counts, net and line fish captures, side-scan sonar, and eDNA analyses at five dolphin aggregations and two control sites. Amazon River dolphins (Inia geoffrensis and Sotalia fluviatlis) are typically found at aggregation sites that occur at water confluences that have greater dolphin numbers than control sites. The confluences had riverbed depressions averaging six metres in depth where fish were concentrated. Pink river dolphins preferred to form aggregations in flooded forest tributaries and large rivers, while grey river dolphins preferred the larger rivers. There were eighty-nine fish species at the confluences within the size of fish consumed by dolphins, and a higher abundance of fish occurred in and around the aggregation sites compared to control sites. The number of dolphins present at the aggregation sites correlated with fish abundance. Dolphin life history, such as fishing, resting, raising calves, and social interactions, occur at the aggregation sites. The aggregation sites are important conservation areas of the endangered pink and grey river dolphins, and through their folklore, Indigenous people living at confluence sites assist in the conservation of the aggregations and have lived with dolphins at confluences for thousands of years, contributing to their survival. Full article

Trust plays a pivotal role in individuals’ interactions with technological systems, and those incorporating artificial intelligence present significantly greater challenges than traditional systems. The current landscape of higher education is increasingly shaped by the integration of AI assistants into students’ classroom experiences. Their appropriate use is closely tied to the level of trust placed in these tools, as well as the strategies adopted to critically assess the accuracy of AI-generated content. However, scholarly attention to this dimension remains limited. To explore these dynamics, this study applied the POTDAI evaluation framework to a sample of 132 engineering and social sciences students at Camilo José Cela University in Madrid, Spain. The findings reveal a general lack of trust in AI assistants despite their extensive use, common reliance on inadequate verification methods, and a notable skepticism regarding professors’ ability to detect AI-related errors. Additionally, students demonstrated a concerning misperception of the capabilities of different AI models, often favoring less advanced or less appropriate tools. These results underscore the urgent need to establish a reliable verification protocol accessible to both students and faculty, and to further investigate the reasons why students opt for limited tools over the more powerful alternatives made available to them. Full article

Artificial intelligence tools are quickly being applied in many areas of science, including learning sciences. Learning requires various types of thinking, sustained by distinct sets of neural networks in the brain. Labelling these systems gives us tools to manage them. This paper presents a pilot application of Large Language Models (LLMs) to physics textbook analysis, grounded in a well-developed neural network theory known as the Five Pillars of the Mind. The domain-specific networks, innate sense, and the five pillars provide a framework with which to examine how physics is learnt. For example, one can identify which pillars are active when discussing a physics concept. Identifying which pillars belong to which physics concept may be significantly influenced by the bias of the author and could be too time-consuming for longer, more complex texts involving physics concepts. Therefore, using LLMs to identify pillars could enhance the application of this framework to physics education. This article presents a case study in which we used selected Large Language Models to identify pillars within eight pages of learning material concerning forces aimed at 12- to 14-year-old pupils. We used GPT-4o and o4-mini, as well as MAXQDA AI Assist. Results from these models were compared with the authors’ manual analysis. Precision, recall, and F1-Score were used to evaluate the results quantitatively. MAXQDA AI Assist obtained the best results with 1.00 precision, 0.67 recall, and an F1-Score of 0.80. Both products by OpenAI hallucinated and falsely identified several concepts, resulting in low precision and, consequently, low F1-Score. As predicted, ChatGPT o4-mini scored twice as high as ChatGPT 4o. The method proved to be promising, and its future development has the potential to provide research teams with analysis not only of written learning material, but also of pupils’ written work and their video-recorded activities. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds, $t_k^{\mathrm{*}}$ at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse. Full article

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning. Full article

Five-membered nitrogen heterocycles exhibit a diverse range of applications across various fields, including medicine, agrochemicals, and materials science. Worldwide industries have exploited hazardous organic solvents and catalysts to afford key bioactive heterocycles, which in turn have a devastating impact on the aqueous environment. The tremendous rise in environmental contamination has shifted the focus of the scientific community towards sustainable alternatives. In line with this, ionic liquids have received the attention of investigators and are widely preferred in organic transformations as catalysts, solvents, ligands, and co-catalysts. Ionic liquids exhibit superior physicochemical properties, such as non-volatility, excellent conductivity, low vapour pressure, non-flammability, and electrochemical and thermal stability, thereby emerging as a clean and efficient alternative to the hazardous volatile organic solvents. The ionic-liquid-assisted synthetic approach has become a popular, greener method owing to high efficiency and product yield with notable purity. Thus, the present article aimed at highlighting catalytic applications of ionic liquids in the synthesis of aromatic five-membered nitrogen heterocycles such as pyrrole, pyrazole, imidazole, 1,2,3-triazole, 1,2,4-triazole, and tetrazole. This article will provide an insight into ionic liquids for their further exploration in organic transformations and related applications. Full article

As stroke is becoming more frequent nowadays, cutting edge rehabilitation approaches are required to recover upper limb functionalities and to support patients during daily activities. Recently, focus has moved to robotic rehabilitation; however, therapeutic devices are still highly expensive, making rehabilitation not easily affordable. Moreover, devices are not easily accepted by patients, who can refuse to use them due to not feeling comfortable. The presented work proposes the exploitation of a virtual prototype of the human–robot ecosystem for the study and analysis of patient–robot interactions, enabling their simulation-based investigation in multiple scenarios. For the accomplishment of this task, the Dynamics of Multi-physical Systems platform, previously presented by the authors, is further developed to enable the integration of biomechanical models of the human body with mechatronics models of robotic devices for motion assistance, as well as with PID-based control strategies. The work begins with (1) a description of the background; hence, the current state of the art and purpose of the study; (2) the platform is then presented and the system is formalized, first from a general side and then (3) in the application-specific scenario. (4) The use case is described, presenting a controlled gym weightlifting exercise supported by an exoskeleton and the results are analyzed in a final paragraph (5). Full article

This study investigates the challenges of promoting accessibility for deaf teachers and students in higher education, focusing on the development of inclusive teaching materials. A qualitative case study was conducted in ten teacher training programmes at the Federal University of Alagoas (Brazil), including nine distance learning courses and one face-to-face LIBRAS programme. Analysis of the Virtual Learning Environment revealed a predominance of text-based content, with limited use of Libras videos, visual resources, or assistive technologies. The integration of Brazilian Sign Language into teaching practices was minimal, and digital translation tools were rarely used or contextually appropriate. Educators reported limited training, technical support, and institutional guidance for the creation of accessible materials. Time constraints and resource scarcity further hampered inclusive practices. The results highlight the urgent need for institutional policies, continuous teacher training, multidisciplinary support teams, and the strategic use of digital technologies and Artificial Intelligence (AI). Compared with previous studies, significant progress has been made. The present study highlights the establishment of an Accessibility Centre (NAC) and an Accessibility Laboratory (LAB) at the university. These facilities are designed to support the development of policies for the inclusion of people with disabilities, including deaf students, and to assist teachers in designing educational resources, which is essential for enhancing accessibility and learning outcomes. Artificial intelligence tools—such as sign language translators including Hand Talk, VLibras, SignSpeak, Glove-Based Systems, the LIBRAS Online Dictionary, and the Spreadthesign Dictionary—can serve as valuable resources in the teaching and learning process. Full article

The pursuit of a comprehensive understanding of human consciousness, which includes the subjective experience of perception, is a long-standing endeavor. A multitude of disciplines have sought to elucidate and define consciousness, with a particular emphasis on its etiology. What is the cause of consciousness? One particularly eye-opening idea is that humans attempt to identify the source of consciousness by leveraging their own consciousness, as if something is attempting to elucidate itself. Strikingly, the results of brain-imaging experiments indicate that the brain processes a considerable amount of information outside conscious awareness of the organism in question. Perhaps, the vast majority of decision making, thinking, and planning processes originate from non-conscious brain processes. Nevertheless, consciousness is a fascinating phenomenon, and its intrinsic nature is both intriguing and challenging to ascertain. In the end, it is not necessarily given that consciousness, in particular the phenomenon of perception as the subjective experience it is, is a tangible function or process in the first place. This is why it must be acknowledged that this theoretical paper is not in a position to offer a definitive solution. However, it does present an interesting new concept that may at least assist future research and potential investigations in achieving a greater degree of elucidation. The concept is founded upon a physical (mathematical) phenomenon known as stochastic resonance. Without delving into the specifics, it is relatively straightforward to grasp one of its implications, which is employed here to introduce a novel direction regarding the potential for non-conscious information within the human brain to become conscious through the introduction of noise. It is noteworthy that this phenomenon can be visualized through a relatively simple approach that is provided in the frame of this paper. It is demonstrated that a completely white image is transformed into an image depicting clearly recognizable content by the introduction of noise. Similarly, information in the human brain that is processed below the threshold of consciousness could become conscious within a neural network by the introduction of noise. Thereby, the noise (neurophysiological energy) could originate from one or more of the well-known activating neural networks, with their nuclei being located in the brainstem and their axons connecting to various cortical regions. Even though stochastic resonance has already been introduced to neuroscience, the innovative nature of this paper is a formal introduction of this concept within the framework of consciousness, including higher-order perception phenomena. As such, it may assist in exploring novel avenues in the search for the origins of consciousness and perception in particular. Full article

Objective: In the isokinetic literature, relatively limited attention has been paid to muscles of the wrist. Therefore, the objective of this study was to present an isokinetic profile of these muscles comprising the flexors (F); extensors (E); and ulnar (U) and radial (R) deviators. Method: The dominant-side F, E, U and R in 40 healthy participants (20 women and 20 men) were tested concentrically (Con) and eccentrically (Ecc) using a single speed of 90°/s. Results: Men were significantly stronger than women in both the Con and Ecc tests, as indicated by both the absolute (Nm) and the bodyweight-normalized (Nm/kgbw) representations. However, the bodyweight-normalized women/men strength ratio (78.6 ± 8.0%) was significantly higher than the absolute strength ratio (64.1 ± 6.6%). For both the Con and Ecc tests, and irrespective of the representation (absolute or normalized), the U was the strongest muscle group, followed successively by the F, R and E. This rank order was highly significant statistically. The eccentric/concentric strength ratios, E/C_F and E/C_U, were significantly higher in men than in women, with no remarkable inter-sex differences for E/C_E and for E/C_R. A correlational analysis, which included all pairs of basic isokinetic outcome parameters (e.g., the PM of F_con), was performed with respect to ‘sex’ using a nonparametric bootstrap procedure, revealing that men had significantly higher overall correlation coefficients compared to women. Conclusions: The consistency of the main findings with respect to both the sex of the participants and the various strength ratios supports the use of the current protocol. The observed strength order (U > F > R > E) may assist clinicians in setting preliminary return-to-function targets after wrist rehabilitation. Full article

At present, artificial intelligence (AI) has shown significant potential in digestive endoscopy image analysis, serving as a powerful auxiliary tool for the accurate diagnosis and treatment of gastrointestinal diseases. However, mainstream models represented by deep learning are often characterized as complex “black boxes,” with decision-making processes that are difficult for humans to interpret. The lack of interpretability undermines physicians’ trust in model results and hinders the broader use of models in clinical practice. To address this core challenge, Explainable AI (XAI) has emerged to enhance the transparency of decision-making, thereby establishing a foundation of trust for human–machine collaboration. The review systematically reviews 34 articles (7 articles in esophagogastroduodenoscopy, 13 articles in colonoscopy, 9 articles in endoscopic ultrasonography, and 5 articles in wireless capsule endoscopy), focusing on the research progress and applications of XAI in the field of digestive endoscopic image analysis, with particular emphasis on the visual explanation-based methods. We first clarify the definition and mainstream classification of XAI, then introduce the principles and characteristics of key XAI methods based on visual explanation. Subsequently, we review the applications of these methods in digestive endoscopy image analysis. Lastly, we explore the obstacles presently faced in this domain and the future directions. This study provides a theoretical basis for constructing a trustworthy and transparent AI-assisted digestive endoscopy diagnosis and treatment system and promotes the implementation and application of XAI in clinical practice. Full article

Foodborne diseases (FBDs) represent significant public health concerns as they are conditions associated with deficient manufacturing practices. They comprise important diseases with acute or chronic courses, frequently occurring in outbreak form and associated with significant gastrointestinal disorders. FBDs are related to infrastructure and organizational issues in urban centers, such that contamination in food processing facilities, lack of access to basic sanitation, and social and financial vulnerability are some of the factors that favor their occurrence and the demand for health services. Among the agents associated with FBDs is Listeria sp., especially Listeria monocytogenes (L. monocytogenes). The objective of this article is to characterize L. monocytogenes and its potential impact on One Health, given its importance as a significant foodborne pathogen. A thorough scientific literature search was conducted to obtain information on the subject, aiming to assist in the verification and presentation of evidence. L. monocytogenes is a pathogen with specific characteristics that ensure its adhesion, adaptation, growth, and survival on various surfaces, such as biofilm formation ability and thermotolerance. Several diagnostic methods are available for detection of the agent, including enrichment media, molecular techniques, and subtyping evaluation. Its control represents a significant challenge, with critical implications due to bacterial perpetuation characteristics and the implementation/monitoring of sanitization programs and commercialization of animal-derived products (POAO). Thus, vulnerable and susceptible populations are more exposed to foodborne pathogens due to health-related determinants, such as inadequate sanitation, poor food safety control, and insufficient personal hygiene. The pathogen’s persistence and difficulty of control represent a significant public One Health threat. Full article