Search Results (11,826)

The Sustainable Development Goals (SDGs), launched by the United Nations in 2015, outline 17 interconnected objectives designed to promote human well-being and sustainable development worldwide. Education is recognized by the United Nations as a key factor in promoting sustainable development. To cultivate students with both global perspectives and local engagement, it is essential to integrate sustainability education into elementary curricula. Accordingly, this study aimed to enhance elementary school students’ understanding of the SDGs by designing a structured instructional activity and developing a hybrid digital-physical board game. The game was implemented as a supplementary review tool to traditional classroom teaching, leveraging the motivational and knowledge-retention benefits of physical board games while incorporating digital features to support learning process monitoring. To address the limitations of conventional review approaches—such as reduced student engagement and increased cognitive load—the instructional model incorporated the board game during review sessions following formal instruction. This was intended to maintain student attention and reduce unnecessary cognitive effort, thereby supporting learning in sustainability-related content. A quasi-experimental design was employed to evaluate the effectiveness of the instructional intervention and the board game system, focusing on three outcome variables: learning motivation, cognitive load, and learning achievement. The results indicated that students in the game-based Sustainable Development Goals group achieved significantly higher delayed posttest scores (M = 72.91, SD = 15.17) than the traditional review group (M = 61.30, SD = 22.82; p < 0.05). In addition, they reported significantly higher learning motivation (M = 4.40, SD = 0.64) compared to the traditional group (M = 3.99, SD = 0.69; p < 0.05) and lower cognitive load (M = 1.84, SD = 1.39) compared to the traditional group (M = 2.66, SD = 1.30; p < 0.05), suggesting that the proposed approach effectively supported student learning in sustainability education at the elementary level. Full article

Sign language recognition plays a crucial role in enabling communication for deaf individuals, yet current methods face limitations such as sensitivity to lighting conditions, occlusions, and lack of adaptability in diverse environments. This study presents a wearable multi-channel tactile sensing system based on smart textiles, designed to capture subtle wrist and finger motions for static sign language recognition. The system leverages triboelectric yarns sewn into gloves and sleeves to construct a skin-conformal tactile sensor array, capable of detecting biomechanical interactions through contact and deformation. Unlike vision-based approaches, the proposed sensor platform operates independently of environmental lighting or occlusions, offering reliable performance in diverse conditions. Experimental validation on American Sign Language letter gestures demonstrates that the proposed system achieves high signal clarity after customized filtering, leading to a classification accuracy of 94.66%. Experimental results show effective recognition of complex gestures, highlighting the system’s potential for broader applications in human-computer interaction. Full article

Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp generation. In this paper, we propose a reasonably improved generation scheme, called Diffangle-Grasp, consisting of two parts: contact map generation based on a conditional variational autoencoder (CVAE), sharing the potential space with the diffusion model, and optimized grasping generation, conforming to the physical laws and the natural pose. The experimental findings demonstrate that the proposed method effectively reduces the loss in contact map reconstruction by 9.59% in comparison with the base model. Additionally, it enhances the naturalness by 2.15%, elevates the success rate of grasping by 3.27%, reduces the penetration volume by 11.06%, and maintains the grasping simulation displacement. The comprehensive comparison and qualitative analysis with mainstream schemes also corroborate the rationality of the improvement. In this paper, we provide a comprehensive account of our contributions to enhancing the accuracy of contact maps and the naturalness of grasping gestures. We also offer a detailed technical feasibility analysis for robotic human grasping. Full article

The scientific literature currently lacks studies that evaluate the nutritional composition of the tissues of cattle raised in different systems, so that the nutritional effects can be known and used to enhance consumption and use in the diet. The aim was therefore to assess whether the mineral content of muscle tissue (longissimus lumborum) in cattle finished during the rainy season in the Eastern Amazon is influenced by different farming systems. The treatments consisted of four systems (three pasture production systems and one feedlot system). 1. native wetland pasture in Santa Cruz do Arari (Mesoregion of Marajó); 2. native wetland pasture in Monte Alegre (Mesoregion of Baixo Amazonas); 3. cultivated dryland pasture in São Miguel do Guamá (Mesoregion of Nordeste Paraense); and 4. Confinement in Santa Izabel do Pará (Metropolitan Region of Belém). The analyses were carried out on samples of the longissimus lumborum muscle tissue of 48 male, castrated, crossbred Nelore cattle, twelve per breeding system, from commercial farms, destined for meat production, finished during the rainiest period of the year (between January and June). In systems 1 and 2, the animals were slaughtered in licensed slaughterhouses; the animals in systems 3 and 4 were slaughtered in commercial slaughterhouses. Food sampling and chemical analysis, soil sample collection and analysis, longissimus lumborum muscle tissue collection, sample preparation and digestion, and inductively coupled plasma optical emission spectrometry were evaluated. The experimental design was completely randomized in a linear model with four rearing systems and one period (rainy). The data was compared using the Statistical Analysis Systems (SAS) program. All analyses were carried out considering a significance level of 0.05. Samples of the diets offered (pasture and concentrate) were also collected. The Amazon systems influenced the macro- and micromineral content in the muscles of cattle (p < 0.05). The interaction between pasture systems vs. confinement showed differences in the minerals calcium (Ca), magnesium (Mg), phosphorus (P), copper (Cu), zinc (Zn), iron (Fe), and manganese (Mn) (p < 0.05). However, there was no difference in the values of sodium (Na), potassium (K), and sulfur (S) between the rearing systems (p > 0.05). By contrast, the cultivated pasture system vs. extensive pasture showed differences in all the elements evaluated (p < 0.05). The rearing systems of the Eastern Amazon influenced the mineral content of beef, which continues to be an excellent source of macro- and microminerals and can compose the human diet. Full article

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition remains underexplored due to subtle inter-class CSI variations and significant intra-class variance. These challenges make it difficult for existing deep learning models typically relying on fully connected MLPs to accurately recognize keypad inputs. To address the issue, we propose a novel approach that combines a discriminative feature extractor with a Kolmogorov–Arnold Network (KAN)-based classifier. The combined model is trained to reduce intra-class variability by clustering features around class-specific centers. The KAN classifier learns nonlinear spline functions to efficiently delineate the complex decision boundaries between different keypad inputs with fewer parameters. To validate our method, we collect a CSI dataset with low-cost Wi-Fi devices (ESP8266 and Raspberry Pi 4) in a real-world keypad sensing environment. Experimental results verify the effectiveness and practicality of our method for keypad input sensing applications in that it outperforms existing approaches in sensing accuracy while requiring fewer parameters. Full article

Introduction: The gut microbiota is vital for human health, and its modulation through dietary and pharmaceutical compounds has gained increasing attention. Among gut microbes, Akkermansia muciniphila has been extensively researched due to its role in maintaining intestinal barrier integrity, regulating energy metabolism, and influencing inflammatory responses. Subject: To analyze and synthesize the available scientific evidence on the influence of various bioactive compounds, including prebiotics, polyphenols, antioxidants, and pharmaceutical agents, on the abundance and activity of A. muciniphila, considering underlying mechanisms, microbial context, and its therapeutic potential for improving metabolic and intestinal health. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. Databases such as PubMed, ScienceDirect, Scopus, Web of Science, SciFinder-n, and Google Scholar were searched for publications from 2004 to 2025. Experimental studies in animal models or humans that evaluated the impact of bioactive compounds on the abundance or activity of A. muciniphila were prioritized. The selection process was managed using the Covidence platform. Results: A total of 78 studies were included in the qualitative synthesis. This review compiles and analyzes experimental evidence on the interaction between A. muciniphila and various bioactive compounds, including prebiotics, antioxidants, flavonoids, and selected pharmaceutical agents. Factors such as the chemical structure of the compounds, microbial environment, underlying mechanisms, production of short-chain fatty acids (SCFAs), and mucin interactions were considered. Compounds such as resistant starch type 2, GOS, 2′-fucosyllactose, quercetin, resveratrol, metformin, and dapagliflozin showed beneficial effects on A. muciniphila through direct or indirect pathways. Discussion: Variability across studies reflects the influence of multiple variables, including compound type, dose, intervention duration, experimental models, and analytical methods. These differences emphasize the need for a contextualized approach when designing microbiota-based interventions. Conclusions: A. muciniphila emerges as a promising therapeutic target for managing metabolic and inflammatory diseases. Further mechanistic and clinical studies are necessary to validate its role and to support the development of personalized microbiota-based treatment interventions. Full article

Objectives: Sialic acid (SA), a naturally occurring compound abundantly found in birds’ nests, holds immense promise for skincare applications owing to its remarkable biological properties. However, its low bioavailability, poor stability, and limited skin permeability have constrained its widespread application. Methods: To overcome these challenges, SA was encapsulated within nanoliposomes (NLPs) by the high-pressure homogenization technique to develop an advanced and efficient transdermal drug delivery system. The skincare capabilities of this novel system were comprehensively evaluated across multiple experimental platforms, including in vitro cell assays, 3D skin models, in vivo zebrafish studies, and clinical human trials. Results: The SA-loaded NLPs (SA-NLPs) substantially improved the transdermal penetration and retention of SA, facilitating enhanced cellular uptake and cell proliferation. Compared to free SA, SA-NLPs demonstrated a 246.98% increase in skin retention and 1.8-fold greater cellular uptake in HDF cells. Moreover, SA-NLPs protected cells from oxidative stress-induced damage, stimulated collagen synthesis, and effectively suppressed the secretion of matrix metalloproteinases, tyrosinase activity, and melanin production. Additionally, zebrafish-based assays provided in vivo evidence of the skincare efficacy of SA-NLPs. Notably, clinical evaluations demonstrated that a 56-day application of the SA-NLPs-containing cream resulted in a 4.20% increase in L*, 7.87% decrease in b*, 8.45% decrease in TEWL, and 4.01% reduction in wrinkle length, indicating its superior brightening, barrier-repair, and anti-aging effects. Conclusions: This multi-level, systematic investigation strongly suggests that SA-NLPs represent a highly promising transdermal delivery strategy, capable of significantly enhancing the anti-aging, barrier-repair, and skin-brightening properties of SA, thus opening new avenues for its application in the fields of dermatology and cosmeceuticals. Full article

Background: A critical review of the literature demonstrates that masticatory apparatus with an artificial oral environment is of interest in the fields including (i) dental science; (ii) food science; (iii) the pharmaceutical industries for drug release. However, apparatus that closely mimics human chewing and oral conditions has yet to be realised. This study investigates the vital role of dental morphology and form–function connections using two-bite test parameters for effective drug release from medicated chewing gum (MCG) and compares them to human chewing efficiency with the aid of a humanoid chewing robot and a bionics product lifecycle management (PLM) framework with built-in reverse biomimetics—both developed by the first author. Methods: A novel, bio-engineered two-bite testbed is created for two testing machines with compression and torsion capabilities to conduct two-bite tests for evaluating the mechanical properties of MCGs. Results: Experimental studies are conducted to investigate the relationship between biting force and crushing/shearing and understand chewing efficiency and effective mastication. This is with respect to mechanochemistry and power stroke for disrupting mechanical bonds releasing the active pharmaceutical ingredients (APIs) of MCGs. The manuscript discusses the effect and the critical role that jaw physiology, dental morphology, the Bennett angle of mandible (BA) and the Frankfort-mandibular plane angle (FMA) on two-bite test parameters when FMA = 0, 25 or 29.1 and BA = 0 or 8. Conclusions: The impact on other scientific fields is also explored. Full article

The mechanical stability and deformability of erythrocytes are vital for their function as they traverse capillaries, where shear stress can reach up to 10 Pa under physiological conditions. Human serum albumin (HSA) is known to help maintain erythrocyte stability by influencing cell shape, membrane integrity, and resistance to hemolysis. However, the precise mechanisms by which albumin exerts these effects remain debated, with some studies indicating a stabilizing role and others suggesting the opposite. This review highlights that under high shear rates, albumin molecules may undergo unfolding due to normal stress differences. Such structural changes can significantly alter albumin’s interactions with the erythrocyte membrane, thereby affecting cell mechanical stability. We discuss two potential scenarios explaining how albumin influences erythrocyte mechanics under shear stress, considering both the viscoelastic properties of blood and those of the erythrocyte membrane. Based on theoretical analyses and experimental evidence from the literature, we propose that albumin’s effect on erythrocyte mechanical stability depends on (i) the transition between unfolded and folded states of the protein and (ii) the impact of shear stress on the erythrocyte membrane’s ζ-potential. Understanding these factors is essential for elucidating the complex relationship between albumin and erythrocyte mechanics in physiological and pathological conditions. Full article

Background: Neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), are increasingly recognized as conditions arising from multifaceted interactions among genetic predisposition, environmental exposures, and epigenetic modifications. Among epigenetic mechanisms, non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and PIWI-interacting RNAs (piRNAs), have gained attention as pivotal regulators of gene expression during neurodevelopment. These RNA species do not encode proteins but modulate gene expression at transcriptional and post-transcriptional levels, thereby influencing neuronal differentiation, synaptogenesis, and plasticity. Objectives: This systematic review critically examines and synthesizes the most recent findings, particularly in the post-COVID transcriptomic research era, regarding the role of ncRNAs in the pathogenesis, diagnosis, and potential treatment of neurodevelopmental disorders. Methods: A comprehensive literature search was conducted to identify studies reporting on the expression profiles, functional implications, and clinical relevance of ncRNAs in neurodevelopmental disorders, across both human and animal models. Results: Here, we highlight that multiple classes of ncRNAs are differentially expressed in individuals with ASD and ADHD. Notably, specific miRNAs and lncRNAs demonstrate potential as diagnostic biomarkers with high sensitivity and specificity. Functional studies further reveal that ncRNAs actively contribute to pathogenic mechanisms by modulating neuronal gene networks. Conclusions: Emerging experimental data indicate that the exogenous administration of certain ncRNAs may reverse molecular and behavioral phenotypes, supporting their therapeutic promise. These findings broaden our understanding of neurodevelopmental regulation and open new avenues for personalized diagnostics and targeted interventions in clinical neuropsychiatry. Full article

Emotion detection using computer vision has advanced significantly in recent years, achieving remarkable performance that, in some cases, surpasses that of humans. Convolutional neural networks (CNNs) excel in this task by capturing facial features that allow for effective emotion classification. However, most research focuses on basic emotions, such as happiness, anger, or sadness, neglecting more complex emotions, like frustration. People set expectations or goals to meet; if they do not happen, frustration arises, generating reactions such as annoyance, anger, and disappointment, which can harm confidence and motivation. These aspects make it especially relevant in mental health and educational contexts, where detecting it could help mitigate its adverse effects. In this research, we developed a CNN-based approach to detect frustration through facial expressions. The scarcity of specific datasets for this task led us to create an experimental protocol to generate our dataset. This classification task presents a high degree of difficulty due to the variability in facial expressions among different participants when feeling frustrated. Despite this, our new model achieved an F1-score of $0.8080$, thus obtaining an adequate baseline model. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

Current mainstream classification methods predominantly employ end-to-end multi-class frameworks. These approaches encounter inherent challenges including high-dimensional feature space complexity, decision boundary ambiguity that escalates with increasing class cardinality, sensitivity to label noise, and limited adaptability to dynamic model expansion. However, human beings may avoid these mistakes naturally. Research indicates that humans subconsciously employ a decision-making process favoring binary outcomes, particularly when responding to questions requiring nuanced differentiation. Intuitively, responding to binary inquiries such as “yes/no” often proves easier for humans than addressing queries of “what/which”. Inspired by the human decision-making hypothesis, we proposes a decision paradigm named the evolutionary binary decision framework (EBDF) centered around binary classification, evolving from traditional multi-classifiers in deep learning. To facilitate this evolution, we leverage the top-N outputs from the traditional multi-class classifier to dynamically steer subsequent binary classifiers, thereby constructing a cascaded decision-making framework that emulates the hierarchical reasoning of a binary decision tree. Theoretically, we demonstrate mathematical proof that by surpassing a certain threshold of the performance of binary classifiers, our framework may outperform traditional multi-classification framework. Furthermore, we conduct experiments utilizing several prominent deep learning models across various image classification datasets. The experimental results indicate significant potential for our strategy to surpass the ceiling in multi-classification performance. Full article

Porcine endogenous retroviruses (PERVs) are integrated into the genome of all pigs. As they can be released as infectious virus particles capable of infecting human cells in vitro, they pose a potential risk for xenotransplantation involving pig cells or organs. To assess whether pigs produce infectious human-tropic viruses, infection assays with human cells are required. There are three main types of assays. First is the incubation of human target cells with gamma-irradiated pig cells. This method ensures that viral transmission is assessed in the absence of replicating pig cells. However, gamma irradiation may alter gene expression in pig cells, potentially affecting the results. Second is the co-culture in a double-chamber system in which pig and human cells are separated by a porous membrane, preventing direct cell-to-cell contact. While this method allows for the detection of infection by free virus particles, it does not account for infection via cell-to-cell transmission, which is a common mode of retroviral infection. And third is the co-culture of pig cells with human cells expressing a resistance gene. The resistance gene allows selective elimination of pig cells upon the addition of a selection medium. This assay enables both free virus and cell-to-cell transmission as well as complete removal of pig cells, which may not be fully achieved in the first type of assay. The third assay best simulates the conditions of in vivo xenotransplantation. However, in all cases the selection of donor and recipient cells is crucial to the experimental outcome. Results only indicate whether a specific pig cell type releases PERVs and whether a specific human cell type is susceptible to infection. A negative infection result does not necessarily reflect the in vivo situation, in which a transplanted organ consists of multiple pig cell types interacting with a diverse range of human cells within a living organism. Knowledge of these limitations is important for authorities regulating clinical applications for xenotransplantation. Full article