Search Results (32,964)

This study examines the diversity of terminologies associated with assistive technology (AT), a crucial field that promotes autonomy and inclusion for people with disabilities. Although the wide use of assistive technology is observed in the literature, a variety of terms are often used interchangeably, which hinders research, technological development, and the formulation of public policies. In this sense, this systematic review aimed to identify, categorise, and analyse the diversity of terms used to describe AT in the scientific literature, contributing to greater conceptual clarity and supporting structured and interdisciplinary development in the field. A comprehensive search was conducted in July 2024 across the Scopus, Web of Science, and PubMed databases, covering publications from 1989 to 2024. Eligible studies were peer-reviewed journal articles in English that conceptually defined at least one AT-related term. The selection process followed the PRISMA 2020 guidelines and included studies from Q1 and Q2 journals to ensure academic rigour. A total of 117 studies were included out of 11,941 initial records. Sixteen distinct terms were identified and grouped into five clusters based on semantic and functional similarities: Cluster 1—Technologies for assistance and inclusion. Cluster 2—Functional assistive devices. Cluster 3—Assistive interaction interfaces. Cluster 4—Assistive environmental technologies. Cluster 5—Assistive systems. A complementary meta-analysis revealed geographic and temporal trends, indicating that terms such as “assistive technology” and “assistive device” are globally dominant. In contrast, others, like “enabling technology,” are more context-specific and emerging. The findings contribute theoretically by providing a structured framework for understanding AT terminology and practically by supporting the design of public policy and interdisciplinary communication. Full article

Coxiella burnetii, the etiological agent of Q fever, is a globally distributed zoonotic pathogen affecting both animals and humans. Despite its known endemicity in various Mediterranean regions, data on human seroprevalence in Sardinia are still lacking. This study aimed to assess seroprevalence in patients and to analyze the annual positivity rate related to the serum samples collected in Sardinia over a ten-year period (2015–2024). For this purpose, a total of 1792 patients were involved in the survey, and 4310 serum samples were analyzed using indirect immunofluorescence assay (IFI) to detect IgM and IgG antibodies against C. burnetii. The global seroprevalence rates relating to all the patients over a ten-year period were determined along with the annual positivity rate and trends from all sera. An overall seroprevalence of 27.0% and an average of annual positivity rate of 16.0% were determined, with the IFI detecting IgG antibodies in 15.2% of positive samples and IgM antibodies in 0.9%, suggesting significant prior exposure of the population evaluated. Annual positivity rates ranged from 24.8% in 2016 to 8.0% in 2020. These results confirmed the endemic circulation of C. burnetii in Sardinia and the ongoing risk of human exposure. A GIS-based map was built to evidence the spatial distribution of Q fever in Sardinia. Interestingly, areas with higher seroprevalence appear to coincide with the distribution of sheep and goat farms, indicating a link between livestock and human exposure. These findings confirm the circulation of C. burnetii in Sardinia and underscore the importance of epidemiological monitoring, public health interventions, and educational efforts in populations at increased risk. Full article

In this study, GH19 chitinase (Chi) gene family was systematically identified and characterized using genomic assemblies from four cotton species: Gossypium barbadense, G. hirsutum, G. arboreum, and G. raimondii. A suite of analyses was performed, including genome-wide gene identification, physicochemical property characterization of the encoded proteins, subcellular localization prediction, phylogenetic reconstruction, chromosomal mapping, promoter cis-element analysis, and comprehensive expression profiling using transcriptomic data and qRT-PCR (including tissue-specific expression, hormone treatments, and Fusarium oxysporum infection assays). A total of 107 GH19 genes were identified across the four species (35 in G. barbadense, 37 in G. hirsutum, 19 in G. arboreum, and 16 in G. raimondii). The molecular weights of GH19 proteins ranged from 9.9 to 97.3 kDa, and they were predominantly predicted to localize to the extracellular space. Phylogenetic analysis revealed three well-conserved clades within this family. In tetraploid cotton, GH19 genes were unevenly distributed across 12 chromosomes, often clustering in certain regions, whereas in diploid species, they were confined to five chromosomes. Promoter analysis indicated that GH19 gene promoters contain numerous stress- and hormone-responsive motifs, including those for abscisic acid (ABA), ethylene (ET), and gibberellin (GA), as well as abundant light-responsive elements. The expression patterns of GH19 genes were largely tissue-specific; for instance, GbChi23 was predominantly expressed in the calyx, whereas GbChi19/21/22 were primarily expressed in the roots and stems. Overall, this study provides the first comprehensive genomic and functional characterization of the GH19 family in G. barbadense, laying a foundation for understanding its role in disease resistance mechanisms and aiding in the identification of candidate genes to enhance plant defense against biotic stress. Full article

Meteorological reanalyses are one of the means to assess the solar irradiance reaching the ground. This paper deals with estimates of the hourly means of irradiance in clear-sky conditions provided by the ERA5, JRA-3Q, and MERRA-2 reanalyses. They are compared to coincident ground-based measurements from 28 BSRN stations located worldwide, selected by a new algorithm for detecting cloud-free instants. Although ERA5 most often underestimates measurements, it is quite reliable over time because it captures the temporal variability of measurements well and provides a constant level of uncertainty. JRA-3Q offers a complex pattern with negative and positive biases depending on station and season. It captures well the temporal variability but, as a whole, is not reliable over time. None of the three reanalyses is reliable in space. Because of its use of the mean solar time instead of the true solar time, MERRA-2 suffers many drawbacks over intraday scales. Its statistical indicators exhibit marked patterns depending on the season and station. Its assimilation of aerosol properties offers advantages when compared to the climatologies used in ERA5 and JRA-3Q. This work exposes the strengths and weaknesses of each reanalysis in clear-sky conditions and formulates suggestions to providers for further improvements. Full article

Plastic waste, particularly poly(ethylene terephthalate) (PET), negatively impacts the environment and human health. Biotechnology could become an alternative to managing PET waste if enzymes ensure the recovery of terephthalic acid with efficiencies comparable to those of chemical treatments. Recent research has highlighted the potential of fungal cutinases, such as wild-type ANCUT1 (ANCUT1^wt) from Aspergillus nidulans, in achieving PET depolymerization. Fungal cutinases’ structures differ from those of bacterial cutinases, while their PET depolymerization mechanism has not been well studied. Here, a reliable model of the ANCUT1^wt was obtained using AlphaFold 2.0. Computational chemistry revealed potential cation-binding sites, which had not been described regarding enzymatic activation in fungal cutinases. Moreover, it allowed the prediction of residues with the ability to interact with a PET trimer that were mutation candidates to engineer the substrate binding cleft, seeking enhancements of PET hydrolysis. Enzyme kinetics revealed that both ANCUT1^wt and ANCUT1^N73V/L171Q (DM) were activated by MgCl₂, increasing the dissociation constant of the substrate and maximal reaction rate. We found that in the presence of MgCl₂, DM hydrolyzed different PET samples and released 9.1-fold more products than ANCUT1^wt. Scanning Electron Microscopy revealed a different hydrolysis mode of these enzymes, influenced by the polymer’s crystallinity and structure. Full article

Background/Objectives: The pain associated with invasive procedures is one of the most common experiences in the pediatric population. Its management remains insufficient due to gaps in healthcare training and knowledge. The aim of this study was to analyze the attitudes, beliefs, care practices, and training of healthcare professionals in relation to pediatric pain, through the development and application of the questionnaire ‘Humanisation of Pediatric Care in Pain Management with a Non-Pharmacological Approach (HUPEDCARE-Q)’. Methods: A cross-sectional, observational, and descriptive study with a quantitative approach was conducted to validate a questionnaire. The process was carried out in three phases: (1) design and initial development of the instrument; (2) evaluation of content validity through expert judgment, using the Content Validity Coefficient (CVC); (3) administration of the questionnaire to a large sample of healthcare professionals to assess its internal consistency and psychometric structure. Results: The evaluation involved five experts, and the items were assessed using the Content Validity Coefficient (CVC), with the overall CVC of the questionnaire exceeding 0.80. The average item scores given by the experts ranged from 0.88 to 0.95, indicating a high level of agreement in their evaluations. The results showed statistically significant positive correlations among most items (p < 0.001), indicating adequate internal consistency. Conclusions: The content validation and pilot study confirmed the theoretical relevance and appropriateness of the HUPEDCARE-Q questionnaire items in the Spanish context. The results support its usefulness as a valid and reliable tool to identify attitudes, beliefs, knowledge, and training needs in the humanized management of pediatric pain. Full article

RNA-binding proteins (RBPs), particularly IGF2BP3, play critical but underexplored roles in lung adenocarcinoma (LUAD). This study investigated IGF2BP3′s clinical and functional significance using single-cell/RNA sequencing, validated by qPCR, Western blot, and immunohistochemistry. The results show IGF2BP3 was significantly upregulated in LUAD tissues and associated with advanced-stage, larger tumors, lymph node metastasis, and poor prognosis. A prognostic nomogram confirmed its independent predictive value. Functionally, IGF2BP3 knockdown suppressed proliferation, and induced G2/M arrest and apoptosis. GSEA linked high IGF2BP3 to cell cycle activation and low expression to metabolic pathways. Notably, high IGF2BP3 correlated with immune evasion markers (downregulated CD4+ effector T cells, upregulated Th2 cells), while TIDE analysis suggested a better immunotherapy response in low-expressing patients. Drug screening identified BI-2536 as a potential therapy for low-IGF2BP3 cases, supported by strong molecular docking affinity (−7.55 kcal/mol). These findings establish IGF2BP3 as a key driver of LUAD progression and a promising target for immunotherapy and precision medicine. Full article

This work investigates the application of artificial intelligence techniques for optimizing photovoltaic systems using maximum power point tracking (MPPT) algorithms. Simulation models were developed in MATLAB/Simulink (Version 2024), incorporating conventional and intelligent control strategies such as fuzzy logic, genetic algorithms, neural networks, and Deep Reinforcement Learning. A DC/DC buck converter was designed and tested under various irradiance and temperature profiles, including scenarios with partial shading conditions. The performance of the implemented MPPT algorithms was evaluated using such metrics as Mean Absolute Error (MAE), Integral Absolute Error (IAE), mean squared error (MSE), Integral Squared Error (ISE), efficiency, and convergence time. The results highlight that AI-based methods, particularly neural networks and Deep Q-Network agents, outperform traditional approaches, especially in non-uniform operating conditions. These findings demonstrate the potential of intelligent controllers to enhance the energy harvesting capability of photovoltaic systems. Full article

Background: In this study, the preparation of pea glycopeptides based on the Maillard glycosylation pathway (PPH-M) and its antagonistic mechanism against alcoholic liver injury in zebrafish were studied. Results: The results showed that the conjugation of D-xylose significantly improved the antioxidant activity of pea protein hydrolysates (PPHs). The structural characterization indicated that PPH was successfully covalent binding to D-xylose, which was mainly manifested as a stretching vibration change in Fourier transform infrared spectroscopy (FTIR) and molecular size increase. Scanning electron microscopy (SEM) and zeta potential also confirmed the covalently bound of the two. In addition, a model of alcohol-induced liver injury in zebrafish was established. Through the intervention of different doses of PPH-M, it was found that the intervention of PPH-M could significantly increase superoxide dismutase (SOD) activity, reduce malondialdehyde (MDA) content, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) activity, and significantly improve alcohol-induced liver injury in zebrafish. The protective effect of PPH-M was also confirmed by liver pathology and fluorescence microscopy. Finally, reverse transcription-polymerase chain reaction (qRT-PCR) results indicated that PPH-M could significantly regulate the expression level of antioxidant-related mRNA. PPH-M could also regulate the expression of the Keap1/Nrf2 signaling pathway and up-regulated glutathione synthesis signaling pathway to antagonize alcohol-induced liver injury in zebrafish. Conclusion: This study revealed the mechanism of PPH-M antagonized alcoholic liver injury and laid a theoretical foundation for its development as functional foods. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

With the rapid development of unmanned aerial vehicle (UAV) technology and the rise of the low-altitude economy, the accurate tracking of UAVs has become a critical challenge. This paper considers a deep learning-based localization scheme that combines microphone arrays for audio source reception. The microphone array is utilized to capture sound source reception from various angles. The proposed TSNetIQ combines elaborately designed Transformer and convolutional neural networks (CNN) modules, and the raw in-phase (I) and quadrature (Q) components of the audio signals are used as input data. Hence, the direction of arrival (DOA) estimation is treated as a regression problem. Experiments are conducted to evaluate the proposed method under different signal-to-noise ratios (SNRs), sampling frequencies, and array configurations. The results demonstrate that TSNetIQ can effectively estimate the direction of the sound source, outperforming conventional architectures trained with the same dataset. This study offers superior accuracy and robustness for real-time sound source localization in UAV applications under dynamic scenarios. Full article

This paper examines the impact of agent banking activities, a recent FinTech development, influencing the profitability and financial outcomes of commercial banks operating in Bangladesh, as agent banking has been receiving significant global attention due to its technology-driven approach, cost-effectiveness and easy accessibility, and broader coverage of the unbanked population. Through the application of penal data regression methods, the study estimates a random-effect model using panel data comprising quarterly observations from nine Bangladeshi commercial banks that maintained uninterrupted agent banking activities, covering both deposit mobilization and lending during the period from 2018Q1 to 2024Q4. The empirical findings indicate that credit disbursement by agent banks has a positive and statistically significant impact on bank profitability measures, return on assets (ROA), and return on equity (ROE). Similarly, the expansion of agent banking outlets positively and significantly influences ROA. Therefore, an appropriate agent banking policy aimed at increasing agent banking outlets using digital platforms based on FinTech is vital for ensuring positive growth in credit disbursement to achieve improved financial outcomes for the banking sector in a developing country like Bangladesh. Full article

The representation and assessment of static liquefaction in mine tailings is a significant challenge due to the severe environmental and social damage it can cause. This phenomenon, known for its catastrophic nature, is triggered when the undrained shear strength is exceeded by a static loading stress. In this study, the constitutive models HSS and NS were evaluated to calibrate the experimental curves from an isotropically consolidated undrained (CIU) triaxial test on a low-plasticity silt derived from mine tailings. An axisymmetric model was developed in Plaxis 2D for calibration, followed by a sensitivity analysis of the parameters of both constitutive models, using the RMSE to validate their accuracy. The results indicate that the proposed methodology adequately simulates the experimental curves, achieving an RMSE of 8%. After calibration, a numerical model was implemented to evaluate the propagation of the PFS of a mine tailings storage facility using both models, in terms of excess pore pressures, shear strains, and p’-q diagrams at three control points. The results show that both models are capable of representing the PFS; however, the HSS model reproduces the experimental curves more accurately, establishing itself as an ideal tool for simulating undrained behavior and, consequently, the phenomenon of static liquefaction in mine tailings. Full article

Sex-based immunological differences significantly influence the outcome of vaccination, yet the molecular mediators underpinning these differences remain largely elusive. MicroRNAs (miRNAs), key post-transcriptional regulators of gene expression, have emerged as critical modulators of innate and adaptive immune responses. In this study, we investigated the expression profile of selected circulating miRNAs as potential biomarkers of sex-specific humoral responses to the mRNA COVID-19 vaccine in a cohort of health care workers. Plasma samples were collected longitudinally at a defined time point (average 71 days) post-vaccination and analyzed using RT-qPCR to quantify a panel of immune-relevant miRNAs. Anti-spike (anti-S) IgG titers were measured by chemiluminescent immunoassays. Our results revealed sex-dependent differences in miRNA expression dynamics, with miR-221-3p and miR-148a-3p significantly overexpressed in vaccinated female HCWs and miR-155-5p overexpressed in vaccinated males. MiR-148a-3p showed a significant association with anti-S/RBD (RBD: receptor binding domain) IgG levels in a sex-specific manner. Bioinformatic analysis for miRNA targets indicated distinct regulatory networks and pathways involved in innate and adaptive immune responses, potentially underlying the differential immune activation observed between males and females. These findings support the utility of circulating miRNAs as minimally invasive biomarkers for monitoring and predicting sex-specific vaccine-induced immune responses and provide mechanistic insights that may inform tailored vaccination strategies. Full article

This study used the Q methodology to examine how Korean science, technology, engineering, and mathematics (STEM) students perceive the experience of reading classical texts and how such experiences relate to their overall well-being. We developed 31 statements for the Q-sorting process and collected data from 39 undergraduate students majoring in science, technology, engineering, and mathematics (STEM). The analysis identified three distinct perception types: type 1—exploratory type, which broadens thinking through diverse perspectives, type 2—experience type, which shares achievement and enjoyment through reading together, and type 3—insight type, which seeks universal values and truth. These findings suggest that, for science and engineering students, reading classics offers a multidimensional experience—encompassing intellectual expansion, relational engagement, and philosophical reflection—beyond conventional academic activities. In particular, the therapeutic dimension of reading, as discussed in bibliotherapy, has emerged as a mechanism that supports self-reflection and emotional resilience. Although each type approached classical reading differently, the participants demonstrated varied perceptions that reflect dimensions of well-being, such as emotional awareness, relational connection, and self-reflection, as expressed through the Q-sorting of pre-defined statements. Based on these results, this study concludes that classical reading can function as a significant mechanism for promoting well-being, offering new directions and practical implications for classical reading education. Full article