Axial loading behaviour of UHPFRC-strengthened circular CFST columns with circumferential void defects: FE modelling and design methods

Multi-purpose drilling hose for polar large-depth hot water drilling: Research on design methods and experimental validation

Machine learning-guided construction of MoS2/MoO3 heterostructures on hollow carbon shells for polysulfide mitigation in lithium-sulfur batteries.

Chiral petal honeycomb metamaterial structures: Biomimetic design and application in vascular stents.

Ecological momentary assessment (EMA) can be a powerful and flexible tool for collecting data on alcohol use, particularly to understand proximal precursors and consequences. EMA can also be leveraged to inform the development of and deploy mobile-health (mHealth) interventions. This article describes the development of an mHealth ecological momentary intervention (EMI) for young adults with high-risk alcohol use and attention-deficit/hyperactivity disorder (ADHD). This novel intervention uses EMA as an intervention component to increase self-awareness via symptom monitoring. It also incorporates additional EMI components, including personalized feedback and behavioral strategy suggestions ("tips"), which operate synergistically with EMA questions and are tailored by EMA data. The theoretical underpinnings of this intervention are described, and its distinct relevance for young adults with ADHD who engage in high-risk alcohol use is discussed. The process of developing this mHealth EMI is detailed, including examining EMA data to generate intervention content, considering participant feedback through iterative pilot testing, and applying human-centered design methods with end users and community partners. Finally, practical considerations of this intervention approach are discussed, including unique benefits, key challenges, and exciting future opportunities.

Solar water heating systems occupy a significant place among renewable energy technologies, and the complex interactions between design and operational parameters directly influence their efficiency. This study presents a comprehensive methodological framework that integrates Full Factorial Design (FFD), simulation modeling, and Response Optimization methods for the performance optimization of a solar water heating system. A detailed simulation model was developed for the climatic conditions of Phoenix, Arizona, utilizing the System Advisor Model (SAM) software, which was developed by the National Renewable Energy Laboratory (NREL). An FFD scheme was implemented to systematically examine the individual and interactive effects of two critical system performance parameters — daily hot water usage and total pipe length — on System Energy (kWh) and Capacity Factor (%). A total of 189 different design scenarios were simulated, and Analysis of Variance (ANOVA) was performed on the resulting data. The ANOVA results revealed that daily hot water usage was the most statistically dominant factor affecting system output. At the same time, the main effect of total pipe length and the interaction between these two parameters were also statistically significant. In the following phase, Response Optimization was applied to objectively determine the optimal design conditions that simultaneously maximize both performance metrics. The optimization resulted in an optimal daily hot water usage of 312.66 kg/day and an optimal total pipe length of 46.41 meters, with a combined desirability value of 0.726. This integrated approach, which offers a more efficient, reliable, and evidence-based process compared to traditional trial-and-error methods, provides engineers and decision-makers with a quantitative guide to improve design decisions and maximize system performance in solar water heating systems.

Harnessing solar energy via semiconductor-based photocatalysis offers a sustainable solution for global energy and environmental challenges. Therefore, the development of high-performance photocatalysts is a crucial strategy for mitigating the energy crisis and environmental pollution. Further, photocatalytic hydrogen production (H2) from water splitting is the most promising clean technology for renewable energy conversion. Towards this, exploring magnetic materials and their nanocomposites has gathered substantial attention for green H2 generation. This review summarizes advances in ferrite-based photocatalysts, including hematite, spinel ferrites, and magnetite, as well as their nanocomposites with carbon materials, metal oxides (MOS), conducting polymer, metal–organic frameworks (MOFs), and Maxene. Different synthesis strategies and structural modifications are discussed, highlighting their roles in enhancing charge separation, light absorption, and improving catalytic properties. Particular emphasis is given to the correlation between magnetic properties and photocatalytic performance, as well as the recyclability of these materials. Current challenges, including stability, scalability, and limited photocatalytic efficiency, are critically examined. Finally, future perspectives are presented, focusing on rational material design, multifunctional heterostructures, and scalable synthesis methods for efficient and durable hydrogen production.

ABSTRACTBackgroundIn Sub‐Saharan Africa, anemia is a significant public health issue, affecting individuals of all ages. While prevention efforts focus on infants, children, and pregnant women, adolescents are overlooked often, leading to ongoing challenges. Despite its prevalence, there is a paucity of research on anemia in adults, particularly university students in Ghana. Studying this demographic can improve understanding and inform public health interventions, addressing the unique needs of university populations and ultimately reducing the burden of anemia.AimThe aim of this research was to assess the knowledge, attitudes, and practices of newly admitted university students regarding the importance of vitamins in blood formation, renewal, and function. This study targeted newly admitted university students, as they represent a transitional group from adolescence to young adulthood, often experiencing changes in dietary patterns, living arrangements, and lifestyle that may predispose them to nutritional deficiencies and anemia.MethodologyThis study employed a cross‐sectional design, utilizing both quantitative and qualitative data collection methods. About 300 newly admitted students undergoing routine medical screening at the University Hospital were recruited. Data regarding the knowledge, attitude and practices, as well as misconceptions of participants were obtained using a structured questionnaire, whereas clinical data were obtained from their medical records.ResultsAmong the 300 participants, anemia prevalence was 40.0%, with 45.3% demonstrating good knowledge and 50.7% positive attitudes and practices. Knowledge differed significantly by program of study (p = 0.03), while misconceptions such as believing that ‘men by default have higher vitamin needs’ were strongly associated with anemia (aOR = 2.29, p = 0.022).ConclusionWhile nearly half demonstrated good knowledge and positive attitudes and practices, misconceptions, particularly gender‐based beliefs, significantly increased anemia risk. These findings emphasize the need for targeted, campus‐based nutrition education that not only improves knowledge but also actively corrects misconceptions to reduce anemia burden.

Since the emergency valve is a pneumatic brake valve used in heavy vehicles to perform sudden braking of trailers manually or semi-automatically, its dynamic performance and response time are of critical importance. In this study, relevant literature and comprehensive industrial applications were thoroughly reviewed, and the parameters which are expected to affect the braking time were investigated. The safety spring coefficient, check valve spring coefficient, and hose diameter were determined as system input parameters. Using experimental design methods, the effects of system parameters on braking response time were investigated, and a full factorial experimental design was developed. An experimental setup was created to measure the braking response time of the system, and comprehensive experimental studies were conducted. A mathematical model of the braking response time was derived through regression analysis of the obtained experimental data. To improve the system's modelling performance, studies were performed on the artificial neural network-based modelling of the experimental data. Modelling studies were carried out to train the experimental data set by determining different neuron numbers, learning algorithms, and training-test-validation percentages, and a higher accuracy model with fewer error values was investigated. The performance of the obtained mathematical and artificial neural network models in predicting the valve response time depending on the input parameters was comparatively examined, and numerical results were presented. The experimental and modelling studies have shown that the ANN model can provide more successful prediction performance compared to the mathematical model.

ABSTRACT Truss-based lattice structures provide excellent strength-to-weight performance but are particularly susceptible to elastic buckling at low relative densities. In this study, naturally-occurring lightweight truss-like structures found in the skeletal framework of the deep-sea sponge Neoaulocystis Zitelli were investigated as bioinspired models to explore their potential for improving buckling strength of truss based lattice structures. A three-dimensional lattice was reconstructed from SEM-derived dimensional data and analysed for stability using a linear buckling analysis. The resulting instability modes revealed localised structural vulnerabilities, prompting an enhancement through architectural refinement via topological simplification, yielding three derivative configurations: simple cubic, octahedral, and lantern, and through the geometric tuning of strut curvature, relative diameters, and connector aspect ratios. All designs were fabricated using the LCD-based 3D printing technique, simulated using finite element simulations, and experimentally tested under uniaxial compression at a relative density of 5%. The optimised bioinspired configuration shifted buckling from a local to a more stable global mode resulting in a 22% increase in critical buckling stress compared to the baseline simple cubic design. These findings underscore the promise offered by bioinspired, buckling-driven design methods for designing robust, lightweight lattice materials for engineering applications.

The development of nanoprobes targeting single neurons is crucial for elucidating the intrinsic mechanisms of the nervous system. However, limitations of existing nanoprobes in terms of structural design, functional expansion, and tip exposure methods significantly hinder accurate analysis and in-depth research on single-neuron behaviors. Here, we develop a nanoscale tip-processing strategy based on atmospheric plasma jet branch self-focusing (APJBSF) for achieving in situ integration of outer surface sensing, inner self-reference and inner delivery functions in the effective working area of the nanoprobe tip, enabling the three functions to collaboratively analyze single-neuron behaviors. For the outer sensing channel, protective layers with nanoscale thickness are selectively removed using the ABJBSF technique. This process allows for the controlled exposure levels of functional tip surfaces at nanoscale dimensions, while simultaneously broadening the range of applicable protective coatings for nanoprobes, which facilitates reliable recordings at intracellular specific sites. For the inner self-reference and delivery channels, intracellular delivery experiments indicate that the in situ reference enhances the accuracy and stability of intracellular recordings and the triune in situ integrated nanoprobe (TIINP) enables accurate monitoring of intracellular pH changes induced by in situ delivery. The proposed TIINP structure and the tip-processing technique for nanoprobes provide powerful tools for enhancing the functionality and reliability of single-neuron analysis.

Research findings must be representative by creating a sample of individuals, ensuring the results can be generalized and applicable to a larger population, which has historically been guided by a power analysis. However, the varied research design methods require a unique approach to sampling and a formula for recruitment and size. Therefore, the purpose of this study was to analyze historical data from published manuscripts in the Journal of Athletic Training (JAT) relative to study design and sample sizes. A secondary purpose was to further explore metrics for survey-based research. This descriptive analysis explored 1267 publications in each issue of the JAT from January 2012 (Volume 47) to December 2022 (Volume 57). We extracted publications from the JAT website. Every article was entered into a spreadsheet (year of publication, publication title) and data specific to the study design and sample size were used for analysis. For studies that were coded as survey-based research, access, response, and completion rates were completed, and topic area and use of a power analysis were extracted. Data were analyzed using measures of central tendency (mean, median, range). Of the 1267 published studies, the most frequent design was cross-sectional (394, 31.1%). In total, 1080 publications (85.2%) were not survey-based, with a median sample size of 34 participants, while 187 publications (14.8%) were survey-based, with a median sample size of 429. Among those surveys, most were cross-sectional (n = 151/187, 80.8%), with 80.7% (n = 151/187) reporting the number initially recruited and 50.8% (n = 95/187) reporting the number of surveys started. The survey publications reported recruiting an average of 4453 potential participants (median = 2500; min = 101, max = 48752), with 985 participants starting the study (median = 816, min = 57, max = 7067), and a final sample size of 819 (median = 429; min = 17, max = 13002). The grand mean access rate was 22.1%, the grand mean response rate was 18.4%, and the grand mean completion rate was 83.1%. Researchers and reviewers can use these trends to guide authorship and review processes for athletic training research. However, sampling strategies should be consistent with the research question, which may lead to deviations from these reported trends.

The aim of this study is to theoretically examine the design processes of instructional technologies and digital learning environments developed at the elementary school level within the framework of postdigital pedagogy principles. Students born in the digital age are no longer merely users of digital technologies; they participate in learning processes as natural members of digital culture. This situation necessitates a holistic and experience-based approach that goes beyond traditional instructional design methods. Learning Experience Design (LXD) provides a framework that can address this need in the design processes of instructional technologies and digital learning environments by focusing on learner-centered, affective, interactive, and contextual dimensions. Postdigital pedagogy contributes to learning experience design processes by transcending the digital-physical distinction and considering learning within the human-technology-environment continuum. In this study, the concepts of Learning Experience Design and postdigital pedagogy are conceptually examined together for the learning processes of elementary school students. Accordingly, a conceptual framework is proposed on how these two approaches can be integrated in the design of students’ learning experiences, and recommendations are developed for the design of instructional technologies and digital learning environments at the elementary level.

Background The lumbar multifidus (LM) plays a key role in static and dynamic stability; however, studies of LM motor unit behavior have yet to be extensively investigated. This study aimed to assess the test-retest reliability of motor unit behavior measurements using electromyography decomposition (dEMG) and to investigate the motor unit behavior under different speeds and loads in asymptomatic participants. Methods In this experimental repeated-measures design, 29 male and female asymptomatic participants were recruited. Motor unit behavior was measured during two sets of 60-second active trunk flexion exercises using dEMG under two speeds (15 and 25 repetitions/minute) and two loads (5% and 10% body weight). The action potential amplitude and motor unit firing rate were derived. Intraclass correlation coefficients (ICC) were used to determine within-session test-retest reliability, and a two-factor repeated-measure ANOVA was used to determine the effects of load and speed. Results Findings demonstrated acceptable within-session test-retest reliability (ICC > 0.70) for most parameters. Significantly greater peak and average amplitudes and average firing rates were seen with an increase in speed, while greater average amplitudes and firing rates were seen with an increase in load. Conclusion These findings support the use of measures of LM motor unit behavior. Exercises at greater speeds and loads increase LM firing rates and amplitudes. A better understanding of LM motor unit behavior may aid our understanding of rehabilitation protocols for low back pain.

The increasing environmental and economic drawbacks of fossil fuels have accelerated the global transition to renewable energy sources. In this context, the optimal design of hybrid renewable energy systems (HRES) that combine solar, wind, and energy storage technologies is critical for achieving sustainable and cost-effective power generation. This study addresses the problem of optimally sizing a grid-connected HRES composed of photovoltaic (PV) panels, wind turbine (WTs), batteries (BTs), and supercapacitors (SCs). A mathematical model is developed to minimize the annual cost of the system (ACS) while ensuring high renewable energy utilization and system efficiency. To solve this optimization problem, five advanced meta-heuristic algorithms-Hunger Games Search (HGS), Spider Wasp Optimizer (SWO), Kepler Optimization Algorithm (KOA), Fire Hawk Optimizer (FHO), and Coronavirus Disease Optimization Algorithm (COVIDOA)-were applied and statistically validated. The model was tested on real meteorological and load data from a university campus in Turkey. Results show that HGS achieved the most favorable performance, with an ACS of $603,538.44, a cost of energy (COE) of $0.23801/kWh, and a renewable energy fraction (REF) of 80.04%. This configuration offers significant economic advantages compared to purchasing electricity directly from the grid at $0.35/kWh. The proposed system proves commercially viable for large consumers and demonstrates the practical effectiveness of meta-heuristic methods in energy system design. MATLAB was used for simulation, while R programming was employed for statistical validation of the algorithmic performance. The study establishes a reproducible and validated framework that can guide future research and implementation in the field of hybrid energy optimization.

The aim of this study is to evaluate national and international studies on sustainable environmental education (SEE) between 2019 and 2023 using thematic analysis method. In this context, ERIC, EBSCO, ProQuest, Springer, Wiley Online Library, Science Direct, Sage, Taylor & Francis, Elsevier, Scopus, ULAKBIM and Council of Higher Education National Thesis Center databases were examined. The study included 44 theses and 94 articles. The studies were analyzed by considering parameters such as year of publication, type of study, purpose, discipline, method, design, sampling, participants, data collection and analysis methods, conclusions and recommendations. It was observed that the majority of the studies were published in 2023 and were of article type. The studies generally aim to examine the effects of educational intervention on attitudes, motivation, behavior and achievement and are concentrated in the field of science. It was determined that purposeful sampling and undergraduate students were mostly studied, qualitative method and case study were preferred. A questionnaire was used for data collection, and t-test, content analysis, and thematic analysis were used for data analysis. The results include findings on the views, awareness and experiences of the participants. In the recommendations, it is emphasized that the content of the course, curriculum and teaching program should be improved and enriched.

The function of dynamic proteins is determined by the stability of distinct conformational states and the energy barriers that separate these states. For most dynamic proteins, the molecular details of the energy barriers are not known, implying a fundamental limit to the ability of protein design methods to engineer beneficial mutations without disrupting activity. We hypothesized that designing mutations that are compatible with structurally distinct equilibrium conformations may enable a reliable stability design. We focus on periplasmic binding proteins (PBPs), a superfamily of dynamic proteins that change conformation from open to closed states in response to binding their small-molecule ligands. We find that the evolutionary constrained space of allowed mutations computed for one conformation is incompatible with that for the other. Therefore, putative conformational hinge points and interface residues were additionally constrained, and incompatible mutations were filtered out. Starting from four different PBPs, we designed a total of 16 stabilized variants with 7-28 mutations each. Our results show that design based on a single conformation with evolutionary constraints is not sufficient to maintain a wild-type-like binding affinity. Conversely, using a subset of mutations compatible with both conformations and structural constraints reliably enhances thermal stability while mitigating trade-offs in ligand binding. Our work demonstrates a straightforward method for the one-shot stabilization of dynamic proteins, which is critically required to generate robust starting points for thermostable and responsive biosensors.

In order to develop high performance electro-hydraulic proportional valve/electro-hydraulic servo proportional valve, the key objective is to develop high performance electro-mechanical converter (EMC) for driving proportional valve. (1) The structure scheme of moving coil proportional electromagnet (MCPEM) with special permanent magnet excitation is proposed. The research shows that the driving force of MCPEM based on special permanent magnet can be increased by more than 7% compared with that of cylindrical permanent magnet. MCPEM is designed as a pilot stage, and a prototype is manufactured. The dynamic and static characteristics of the prototype are tested. The experimental results verify the correctness of the design and calculation. It shows that the prototype can meet the pilot stage drive requirements of servo proportional valve with high performance and large flow rate. (2) The structure scheme of direct-drive electro-hydraulic proportional valve (DDV) composed of MCPEM is proposed. The main performance of the electro-hydraulic proportional valve with the proposed structure is analyzed. A prototype of MCPEM with large thrust and high performance is developed and tested. Experiments show that the static characteristics of the proportional electromagnet are very good. The step response time of the spool displacement of the controlled valve to 100% input signal is less than 15 ms within open-loop. The proportional valve has the advantages of high dynamic and static performance formed with this MCPEM. Several detailed design methods are provided for valve-using proportional solenoids and electro-hydraulic proportional valves, as well as the performance test methods of these solenoids and proportional valves. It has made a certain contribution to the development of electro-hydraulic proportional valves.

This study evaluates the effectiveness of the Medan Station Road Overpass in reducing traffic congestion in the urban core of Medan City (North Sumatra, Indonesia). Using a quasi-experimental Before–After design and mixed methods, we compared traffic performance before and after the overpass began operation using peak-hour surveys (morning, midday, and afternoon) and road-user perception data (n = 120) supported by stakeholder interviews. Average vehicle speed increased by 41% across vehicle classes, while traffic volume increased by 11.8%, indicating improved traffic throughput and smoother flow on the corridor. Paired t-tests on aggregated peak-period indicators suggest statistically significant improvements in speed (p < 0.001) and an increase in volume (p = 0.049). User perceptions were positive: 85% of respondents reported improved traffic flow, 82% reported improved comfort, and 79% reported improved safety. However, capacity improvements may attract additional traffic over time (induced demand); therefore, complementary traffic management measures (curbside pick-up/drop-off control, parking regulation, and signal coordination) are recommended to preserve long-term benefits. A key limitation is the absence of a control corridor, which restricts causal attribution.

The study evaluated the Ship-owner’s Demand for Port Services Relative to Changes in Port Pricing Policy Regimes in Nigeria. Its specific objectives was among other thing to estimate the coefficient of average rate of change of ship-owner’s demand for port services in Nigerian seaports relative to changes in pilotage rates charged by the Nigerian Port Authority (NPA) between 1977 and 2022. This was in realization of the fact that port charges have implications on port cost borne ship-owners and other categories of port users. It used the quantitative research design methods. It sourced secondary data from the NPA on the ship traffic call and shipping tonnages handled in the Nigerian ports over the 45 years covered in the study as proxies for ship-owners demand for port services. It also obtained time series data on pilotage rates charged by the ports between 1977 and 2022 from the NPA. The average rate of change quantitative tool and regression analysis were used to analyze the data obtained. It was found that, the average rate of change coefficient of ship traffic calls handled in the Nigerian seaports relative the trend of pilotage rates charged by the seaports over the period is 34.719. It results also show that there is significant influence of variations in pilotage rates charged by the Nigerian ports on the ship traffic calls handled in Nigerian seaports. The findings of the study further reveal that for each 1 naira variation (increase) pilotage rates charged by the NPA each year between 1977 and 2022, the GRT/shipping tonnage handled by the seaports increased by an average rate of 1713332 GRTs. Recommendations were proffered based on the study findings.