Search Results (509)

This study explores the valorization of agro-industrial by-products—riceberry broken rice (RBR) and soybean meal (SBM)—as cost-effective substrates for enhancing exopolysaccharide (EPS) production by Bacillus tequilensis PS21. Eight Bacillus strains were screened, and B. tequilensis PS21 demonstrated the highest EPS yield (2.54 g/100 mL DW). The EPS displayed a strong antioxidant capacity with 65.5% DPPH and 80.5% hydroxyl radical scavenging, and a FRAP value of 6.51 mg Fe²⁺/g DW. Antimicrobial testing showed inhibition zones up to 10.07 mm against Streptococcus agalactiae and 7.83 mm against Staphylococcus aureus. Optimization using central composite design (CCD) and the response surface methodology (RSM) revealed the best production at 5% (w/v) RBR, 3% (w/v) SBM, pH 6.66, and 39.51 °C, yielding 39.82 g/L EPS. This EPS is a moderate-molecular-weight (11,282 Da) homopolysaccharide with glucose monomers. X-ray diffraction (XRD) showed an amorphous pattern, favorable for solubility in biological applications. Thermogravimetric analysis (TGA) demonstrated thermal stability up to ~250 °C, supporting its suitability for high-temperature processing. EPS also exhibited anticancer activity with IC₅₀ values of 226.60 µg/mL (MCF-7) and 224.30 µg/mL (HeLa) at 72 h, reduced colony formation, inhibited cell migration, and demonstrated anti-tyrosinase, anti-collagenase, and anti-elastase effects. This study demonstrates the successful valorization of agro-industrial by-products—RBR and SBM—for the high-yield production of multifunctional EPS with potent antioxidant, antimicrobial, and anticancer properties. The findings highlight the sustainable potential of these low-cost substrates in supporting the development of green and value-added bioproducts, with promising utilizations across the food, pharmaceutical, and cosmetic sectors. Full article

Antibiotic resistance is one of the most significant public health issues today. As a consequence, there is an urgent need for novel classes of antibiotics. This necessitates the development of highly efficient screening methods for the rapid identification of antibiotic-producing bacteria. Here, we describe a new method for high-throughput screening of antimicrobial compounds (AMC) producing halophilic bacteria. Our methodology used a newly designed 3D-printed Petri plate replicator used for drop deposition and colony replication. We employed this device in combination with a modified agar overlay assay to screen more than 7400 bacterial colonies. A total of 54 potential AMC producers were discovered at a success rate of 0.7%. Although 40% of them lost their antibacterial activity during the secondary screening, 22 strains retained inhibitory activity and were able to suppress the growth of one or more safe relatives of the ESKAPE group pathogens. The ethyl acetate extract from the most potent strain, Virgibacillus salarius POTR191, demonstrated moderate antibacterial activity against Enterococcus faecalis, Acinetobacter baumanii, and Staphylococcus epidermidis with minimal inhibitory concentrations of 128 μg/mL, 128 μg/mL, and 512 μg/mL, respectively. We propose that our replica plate assay could be used for target-based antimicrobial screening of various extremophilic bacteria. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder marked by the progressive loss of dopaminergic neurons in the nigrostriatal pathway. The dopamine active transporter (DAT), a key protein involved in dopamine reuptake, serves as a selective biomarker for dopaminergic terminals in the striatum. DAT binding has been extensively studied using in vivo imaging techniques such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET). To support the design of new radiotracers targeting DAT, we employ Quantitative Structure–Activity Relationship (QSAR) analysis on a structurally diverse dataset composed of 57 compounds with known affinity constants for DAT. The best-performing QSAR model includes four molecular descriptors and demonstrates robust statistical performance: R² = 0.7554, Q²_LOO = 0.6800, and external R² = 0.7090. These values indicate strong predictive capability and model stability. The predicted compounds are evaluated using a docking methodology to check the correct coupling and interactions with the DAT. The proposed approach—combining QSAR modeling and docking—offers a valuable strategy for screening and optimizing potential PET/SPECT radiotracers, ultimately aiding in the neuroimaging and early diagnosis of Parkinson’s disease. Full article

Rapid visual screening has been used worldwide as the first approach to evaluate the seismic vulnerability of civil structures in a specific area, in order to prioritize buildings based on the need for upgrading or retrofitting. In this work, a novel mobile application tool for the rapid visual screening of reinforced concrete buildings is presented and discussed. The herein suggested “SeismicV” tool performs a pre-seismic visual screening based on the Japanese guidelines for the seismic evaluation of existing RC buildings. A preliminary seismic vulnerability assessment of a complex modern building situated in the capital of Brazil, Brasilia, was carried out using this mobile app. The data were collected from in situ and based on some data from plants and documents. The SeismicV tool consists of an effective, user-friendly, and straightforward mobile application. Since the methodology is based on a performance score that is compared to the seismic demand, this application design allows for the knowledge of intermediate indices at each step of the evaluation, including dominant variables such as structural irregularity, building age, ground index, and usage index. Although the application was conceived and applied to heritage buildings in the early stages, it can be employed for other complex structures. The findings highlight that utilizing SeismicV to assess the seismic vulnerability of complex buildings through the rapid visual screening method offers significant benefits, including faster evaluations, increased accuracy, and improved accessibility for field assessments. Full article

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article

Biophilic design has emerged as a multidimensional response to growing concerns about health, well-being, and ecological balance in the built environment. Despite its rising prominence, research on the topic remains fragmented across building typologies, user groups, and geographic contexts. This study presents a comprehensive review of the biophilic design literature, employing a hybrid methodology combining structured content analysis and bibliometric mapping. All peer-reviewed studies indexed in the Web of Science and Scopus were manually screened for architectural relevance and systematically coded. A total of 435 studies were analysed to identify key trends, thematic patterns, and research gaps in the biophilic design discipline. This review categorises the literature by methodological strategies, building typologies, spatial scales, population groups, and specific biophilic design parameters. It also examines geographic and cultural dimensions, including climate responsiveness, heritage buildings, policy frameworks, theory development, pedagogy, and COVID-19-related research. The findings show a strong emphasis on institutional contexts, particularly workplaces, schools, and healthcare, and a reliance on perception-based methods such as surveys and experiments. In contrast, advanced tools like artificial intelligence, simulation, and VR are notably underused. Few studies engage with neuroarchitecture or neuroscience-informed approaches, despite growing recognition of how spatial design can influence cognitive and emotional responses. Experimental and biometric methods remain scarce among the few relevant contributions, revealing a missed opportunity to connect biophilic strategies with empirical evidence. Regarding biophilic parameters, greenery, daylight, and sensory experience are the most studied parameters, while psychological parameters remain underexplored. Cultural and climate-specific considerations appear in relatively few studies, and many fail to define a user group or building typology. This review highlights the need for more inclusive, context-responsive, and methodologically diverse research. By bridging macro-scale bibliometric patterns with fine-grained thematic insights, this study provides a replicable review model and valuable reference for advancing biophilic design as an evidence-based, adaptable, and human-centred approach to sustainable architecture. Full article

This research presents a comprehensive methodology for calibrating Discrete Element Method (DEM) parameters governing rice grain interactions with biodegradable Polylactic Acid (PLA) components in agricultural bucket elevator systems. Rice grains, a critical global food staple requiring efficient post-harvest handling, were modeled as three-sphere clusters to accurately represent their physical dimensions (6.5 mm length), while the Hertz–Mindlin contact model provided the theoretical framework for particle interactions. The calibration process employed a multi-phase experimental design integrating Plackett–Burmann screening, steepest ascent method, and Face Central Composite Design to systematically identify and optimize critical micro-mechanical parameters for agricultural material handling. Statistical analysis revealed the coefficient of static friction between rice and PLA as the dominant factor, contributing 96.49% to system performance—significantly higher than previously recognized in conventional agricultural processing designs. Response Surface Methodology generated predictive models achieving over 90% correlation with experimental results from 3D-printed PLA shear box tests. Validation through comparative velocity profile analysis during bucket elevator discharge operations confirmed excellent agreement between simulated and experimental behavior despite a 20% discharge velocity variance that warrants further investigation into agricultural material-specific phenomena. The established parameter set enables accurate virtual prototyping of sustainable agricultural handling equipment, offering post-harvest processing engineers a powerful tool for optimizing bulk material handling systems with reduced environmental impact. This integrated approach bridges fundamental agricultural material properties with sustainable engineering design principles, providing a scalable framework applicable across multiple agricultural processing operations using biodegradable components. Full article

Background: Handheld dynamometry (HHD) is widely utilized for assessing muscle strength, particularly in the wrist. However, variability in measurement reliability due to differences in testing protocols poses a challenge for clinical and research applications. Methods: The design of this study includes a scoping review of the literature, conducted following the PRISMA-ScR checklist methodology developed by the Joanna Briggs Institute. The databases most commonly cited in review articles were consulted: EBSCO, PEDro, PubMed, Scopus, and Cochrane Library. The following MeSH terms were used: “Handheld Dynamometer”, “Wrist”, “Forearm”, “Muscle”, and “Strength”. The search strings were built using combinations of these terms. Article screening was performed by three reviewers independently, blinded to each other’s selections. Results: The review indicates that HHD can provide reliable measurements when standardized protocols are used. Most studies reported high intra-examiner reliability with Intraclass Correlation Coefficients (ICCs) between 0.71 and 0.90. However, inter-examiner reliability showed more variability, particularly when more than two examiners were involved. The review also highlights the importance of precise dynamometer placement and consistent patient positioning in order to reduce measurement variability. Conclusions: While HHD is a valuable tool for wrist strength assessment, the effectiveness of its measurements largely depends on the testing procedure’s standardization. Implementing validated standardized protocols is essential in enhancing measurement reliability and ensuring their consistent application across clinical settings. Further research is needed to firmly implement these protocols and expand their application in clinical practice. Full article

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation. Full article

Technology-based strategies for the prevention and management of suicidal behavior are widely referenced for identifying vulnerable groups and for supporting clinical reasoning, decision-making, and appropriate referrals. In this study, we estimated the interface and content validity evidence of an interactive mobile application developed for managing suicidal behavior. This methodological study employed psychometric parameters to evaluate the content and interface of the mobile application, following five action phases: analysis, design, development, implementation, and evaluation. A total of 27 healthcare professionals participated, selected by convenience sampling, all working within the Psychosocial Care Network across different regions of Brazil. Data were collected using an electronic form, the Delphi technique for evaluation rounds, and a Likert scale to achieve consensus. The validity analysis was based on a Content Validity Index (CVI) equal to or greater than 0.80. The results showed that GerenciaVida, a technology developed for healthcare workers—regardless of their level of care or professional category—can be used to screen for suicide risk in the general population and indicate preventive alternatives. The app demonstrated satisfactory indicators of content validity (0.974) and interface validity (0.963), reflecting clarity (0.925), objectivity (1.00), adequacy (0.925), coherence (0.962), accuracy (0.962), and clinical relevance (1.00). The development path of this mobile application provided scientific, technological, and operational support, establishing it as an innovative care tool. It consolidates valid evidence that supports the identification, risk classification, and prevention of suicidal behavior in various healthcare contexts. Full article

Sport injury recognition is rapidly evolving with the integration of new emerging technologies. This systematic review aims to identify and evaluate technologies capable of detecting injuries during sports participation. A comprehensive search of PUBMED, Sport Discus, Web of Science, and ScienceDirect was conducted following the PRISMA 2020 guidelines. The review was registered on PROSPERO (CRD42024608964). Inclusion criteria focused on prospective studies involving athletes of all ages, evaluating tools which are utilised to identify injuries in sports settings. The review included research between 2014 and 2024; retrospective, conceptual, and fatigue-focused studies were excluded. Risk of bias was assessed using the Critical Appraisal Skills Program (CASP) tool. Of 4283 records screened, 70 full-text articles were assessed, with 21 studies meeting the final inclusion criteria. The technologies were grouped into advanced imaging (Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging (DFI), and Quantitative Susceptibility Mapping (QSM), with biomarkers (i.e., Neurofilament Light (NfL), Tau protein, Glial Fibrillary Acidic Protein (GFAP), Salivary MicroRNAs, and Immunoglobulin A (IgA), and sideline assessments (i.e., the King–Devick test, KD-Eye Tracking, modified Balance Error Scoring System (mBESS), DETECT, ImPACT structured video analysis, and Instrumented Mouth Guards (iMGs)), which demonstrated feasibility for immediate sideline identification of injury. Future research should improve methodological rigour through larger, diverse samples and controlled designs, with real-world testing environments. Following this guidance, the application of emerging technologies may assist medical staff, coaches, and national governing bodies in identifying injuries in a sports setting, providing real-time assessment. Full article

SiC MOSFETs are becoming increasingly popular due to their superior material properties, but they lack the required reliability and ruggedness for safe applications. One of the biggest challenges in short-circuit (SC) reliability of the commercial devices and hence in the SC protection circuit design is the variability of SC withstand time (SCWT) among the devices from the same vendor, even with the same lot and batch number. In this work, a novel SC screening methodology has been presented to remove devices with lower SCWT from a pool of devices without damaging the reliable ones. The SC screening methodology has been developed using Sentaurus TCAD simulation, which is further verified using commercial devices. This work can potentially reduce field failure and, as a result, can enhance the reliability of the SiC MOSFETs in real-world applications. Full article

Despite robust evidence linking career adaptability (CA) to positive vocational and psychosocial outcomes in general populations, research on the CA among individuals with special educational needs (SEN) remains limited. Prior reviews have largely overlooked the distinct challenges faced by SEN populations. To address this gap, we conducted a systematic review across five major databases, yielding an initial pool of 81 studies. Following rigorous screening, only eight quantitative studies met the inclusion criteria, reflecting the early stage of the research in this area. The included studies span diverse SEN groups, including individuals with visual impairments, intellectual disabilities, and mental health conditions. CA was consistently found to be associated with adaptive outcomes such as self-esteem, self-efficacy, hope, and career satisfaction. However, the literature is characterized by methodological limitations, notably the predominance of cross-sectional designs, the underrepresentation of neurodevelopmental conditions (e.g., ASD, ADHD), and a lack of cross-cultural perspectives and standardized instruments specifically adapted to SEN learners. Future studies should focus on the need for longitudinal and mixed-method designs, contextually cross-cultural research, and inclusive measurement tools. Furthermore, exploring the ecological and emotional predictors of CA; expanding to underrepresented SEN subgroups; and evaluating diverse interventions beyond mentoring are essential to informing tailored educational and vocational support for individuals with SEN. Full article

To establish a fundamental property database for discrete elements targeting long-fiber materials and address the issue of response surface methodology (RSM) being prone to local optima in high-dimensional nonlinear optimization, this study conducted parameter calibration experiments and validated the calibrated parameters through a combined approach of simulation and physical testing. The Plackett–Burman design and steepest ascent test were employed to screen significant factors. Using the angle of repose (42.3°) obtained from physical experiments as the response value, response surface methodology (RSM) and a particle swarm optimization–back propagation (PSO-BP) neural network model were independently applied to optimize and compare the critical parameters. The results demonstrated that the dynamic friction coefficient between wheat straw particles, the static friction coefficient between wheat straw and steel plate, and the JKR surface energy were the most influential factors on the simulated angle of repose. The PSO-BP model exhibited superior optimization performance compared to RSM, yielding an optimal parameter combination of 0.17, 0.46, and 0.03. The simulated repose angle under these conditions was 41.67°, exhibiting a relative error of only 1.5% compared to the physical experiment. These findings provide a robust theoretical foundation for discrete element simulations of wheat straw feedstock. Full article

This study presents a systematic literature review on the mathematical modeling of resilient and viable supply chains, grounded in the PRISMA methodology and applied to a curated corpus of 235 peer-reviewed scientific articles published between 2011 and 2025. The search strategy was implemented across four major academic databases (Scopus and Web of Science) using Boolean operators to capture intersections among the core concepts of supply chains, resilience, viability, and advanced optimization techniques. The screening process involved a double manual assessment of titles, abstracts, and full texts, based on inclusion criteria centered on the presence of formal mathematical models, computational approaches, and thematic relevance. As a result of the selection process, six thematic categories were identified, clustering the literature according to their analytical objectives and methodological approaches: viability-oriented modeling, resilient supply chain optimization, agile and digitally enabled supply chains, logistics optimization and network configuration, uncertainty modeling, and immune system-inspired approaches. These categories were validated through a bibliometric analysis and a thematic map that visually represents the density and centrality of core research topics. Descriptive analysis revealed a significant increase in scientific output starting in 2020, driven by post-pandemic concerns and the accelerated digitalization of logistics operations. At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. The results confirm a paradigm shift toward integrative frameworks that combine robustness, adaptability, and Industry 4.0 technologies, as well as a growing interest in biological metaphors applied to resilient system design. Finally, the review identifies research gaps related to the formal integration of viability under disruptive scenarios, the operationalization of immune-inspired models in logistics environments, and the need for hybrid approaches that jointly address resilience, agility, and sustainability. Full article