Search Results (22,292)

In this work, we analyze the arithmetic building blocks of DNA basecalling to motivate runtime approximate computing in bio systems-on-chip (BioSoCs). We propose and characterize a reconfigurable compressor-tree multiplier whose operating mode can be selected at runtime to trade energy for controlled arithmetic error. Using a 45 nm CMOS evaluation flow, the proposed design demonstrates a clear power–accuracy trade-off across 64 operating modes, achieving about a 58–61% reduction in multiplier power (per multiply under fixed V/f) relative to an accurate Wallace baseline, with mean relative error distance (MRED) in the 1.05–2.88% range. At the application level, we outline a first-order noise-propagation model and, consistent with prior approximate-inference studies, note that task-level quality loss is often within a few percent (up to 5%), motivating end-to-end basecalling evaluation. Application-level evaluation on a TinyX3 DNA basecaller—a compact Bonito-based model—shows that the proposed multiplier with measured REV = 0.012 and MRED = 1.98% preserves near-baseline performance, with negligible degradation in sequence identity and relative length at low perturbation levels and only gradual accuracy decline (confirming ≤ 5% accuracy drop) emerging as perturbations increase into the moderate regime. Finally, a processor-level case study using convolution microbenchmarks (kernel footprints 9–49 weights per output) shows an 11% improvement in energy per instruction and a 12% reduction in energy per MAC when integrating the proposed multiplier into an embedded RISC-V execution engine. Full article

Background: Adult circumcision is an effective surgical treatment for symptomatic phimosis, but its effects on sexual function and psychosocial outcomes remain incompletely characterized. This study aimed to assess patient-reported motivations for circumcision due to phimosis and to evaluate changes in sexual function, genital self-image, and depressive symptoms after surgery. Methods: We performed a prospective, single-center observational pre–post cohort study of adult men who underwent circumcision for symptomatic phimosis at a tertiary academic center in Poland between January 2023 and April 2025. Participants completed an author-designed questionnaire and validated patient-reported outcome measures before surgery and at six months after circumcision. The primary endpoint was the change in sexual function measured by the International Index of Erectile Function (IIEF-15). Secondary endpoints included changes in genital self-image assessed using the Male Genital Self-Image Scale (MGSIS-7), depressive symptoms measured with the Beck Depression Inventory (BDI-II), and patient-reported motivations for surgery. Paired comparisons were performed using the Wilcoxon signed-rank test. Results: A total of 148 men were included in the study. The most common reasons for surgery were pain/discomfort (58/148, 39.2%) and impaired sexual activity (51/148, 34.5%). The total IIEF-15 score increased from 46.4 ± 20.4 to 57.5 ± 21.0 (p < 0.001), with significant improvements across all domains (all p < 0.001). The total MGSIS-7 score improved from 19.9 ± 4.5 to 23.1 ± 4.0 (p < 0.001). Paired BDI-II data were available for 77 men; the total BDI-II score decreased from 1.2 ± 3.8 to 0.3 ± 1.6 (p = 0.004). Most men reported improved sexual life (127/148, 85.8%) and cosmetic satisfaction (124/148, 83.8%), and 119/148 (80.4%) reported no complications. Conclusions: Circumcision for symptomatic phimosis was associated with improved sexual function and genital self-image, alongside a statistically significant but clinically negligible decrease in subclinical depressive symptoms. Full article

It is widely acknowledged that academic writing constitutes a challenge for EFL learners. This is why areas like the use of writing strategies, writing motivation and writing anxiety have attracted extensive attention in the literature. However, the interplay between these three areas has not been researched sufficiently, particularly among Arab learners. The present study bridges this gap by examining the use of writing strategies by Saudi EFL learners and the relationship between the learners’ use of these strategies and their writing motivation and writing anxiety. A total of 206 Saudi EFL learners responded to a questionnaire that assessed how they used writing strategies, as well as their writing motivation and anxiety. Using descriptive statistics, the results showed that the Saudi EFL learners use most writing strategies moderately or to a high extent. It was also found that students generally experience a high degree of motivation and a moderate degree of anxiety while writing. The Pearson Correlation analysis indicated that students’ use of writing strategies is somewhat positively correlated with their writing motivation and negatively correlated with their writing anxiety. Additionally, multiple regressions revealed that the use of writing strategies was predicted by both writing motivation and writing anxiety, although prediction by the writing motivation was more prominent. The results are discussed in reference to the relevant theories and pedagogical implications and future research directions are proposed. Full article

Effective teaching and learning in classrooms are achievable only through sound classroom management. While positive attitudes and behaviors exhibited by faculty members enhance instructional quality, undesirable behaviors may impede and negatively influence the teaching–learning process. The purpose of this study is to examine the undesirable behaviors displayed by faculty members in classroom settings based on the perspectives of pre-service teachers. The study adopted a phenomenological design and was conducted with 95 pre-service teachers enrolled in the Classroom Management course at the Faculty of Education, Çanakkale Onsekiz Mart University. Data were collected through a semi-structured interview form and analyzed using content analysis. The findings revealed that undesirable faculty members’ behaviors were characterized as actions stemming from instructors’ inadequacies that negatively affect students and the overall educational process. These behaviors were categorized under four themes: instructional management, time management, communication management, and behavior management. The results indicated that undesirable behaviors predominantly originate from instructor-related factors. Pre-service teachers reported experiencing such behaviors most frequently within the theme of behavior management. These behaviors primarily diminish their motivation and negatively influence their participation and academic performance. Pre-service teachers emphasized the need for both institutional and individual measures to prevent undesirable faculty members’ behaviors. Full article

Writing-intensive courses help undergraduate students develop disciplinary knowledge and communication skills, yet many students, particularly first-generation college students and those writing in a second language, enter these courses with low confidence and high writing anxiety. Writing self-efficacy, or students’ beliefs about their ability to succeed as writers, is associated with motivation and academic success, but less is known about how instructional practices shape its development. This qualitative study examined how students experienced instructional practices in a writing-intensive public health course and how these experiences influenced writing self-efficacy. Data were collected through a focus group with six undergraduate students and analyzed using a deductive thematic approach guided by Bandura’s four sources of self-efficacy. Students identified scaffolded assignments, opportunities for revision, and explanatory feedback as key facilitators of writing self-efficacy. Supportive classroom relationships, including proactive instructor outreach and consistent feedback, also appeared to foster confidence. Barriers included linguistic challenges, limited academic role models, and negative experiences with writing support services. These findings suggest writing self-efficacy may develop through the interaction of structured instructional practices and supportive classroom environments. Full article

Cryptocurrency adoption remains difficult to explain when treated as a single decision or static outcome. Addressing this limitation, the present study develops a qualitative, process-oriented account of cryptocurrency adoption among users in Central and Eastern Europe, with particular attention to how engagement emerges, changes, and stabilizes over time. Semi-structured individual in-depth interviews were conducted with 25 cryptocurrency users, and the material was analyzed using reflexive thematic analysis within an interpretivist framework. The findings show that adoption unfolds as a multi-phase process embedded in users’ biographies, financial practices, and socio-technical environments. Across accounts, cryptocurrencies were described not only as speculative assets but also as tools of financial autonomy, learning, and optionality under conditions of institutional uncertainty and constrained access to conventional financial pathways, making the CEE context particularly revealing for a process-oriented understanding of adoption. The analysis identified six interrelated themes: adoption as a project of financial autonomy; the “conscious investor” identity; the market as a school of cost and irreversibility; platforms and communities as adoption infrastructures; the relational politics of visibility; and practice stabilization. Together, these themes show that factors already highlighted in prior adoption research—such as trust, risk, autonomy, and knowledge—do not function as stable predictors, but change their meaning across different phases of engagement. The study contributes to FinTech adoption research by proposing a processual model that reconceptualizes cryptocurrency adoption as a phased, experience-dependent pattern of participation rather than a static outcome of parallel determinants. In doing so, it extends existing variable-centered frameworks toward a more dynamic and interpretive understanding of financial technology use. Full article

Courses in electromagnetism and related technical subjects are often dominated by lecture-heavy instruction and complex mathematical concepts, which can make it difficult for students to stay engaged. This is particularly problematic in today’s hyper-digitalized society, where constant screen exposure and shortened attention spans challenge traditional learning methods. While computer-based tools and hands-on laboratories offer some pedagogical improvements, they often fall short in terms of interactivity, dynamism, adaptiveness, and student engagement. In an effort to enrich the learning experience and boost student motivation, we have created a gamified learning activity for the undergraduate course “Radiocommunications”—commonly referred to as Antennas and Propagation in other institutions— implemented in the form of a question-based board game. The activity, carried out over three academic years, is fully aligned with the course syllabus and encourages active learning, healthy competition, and collaborative problem-solving. Custom-made materials—including a game board, 270 question cards, wildcards, and incentive-based rewards—were developed specifically for this purpose. The qualitative results from a student survey, together with statistical evidence from hypothesis testing, suggest that the activity enhances conceptual understanding, helps students connect ideas across related subjects, and contributes to a more motivating and enjoyable learning experience. Full article

Multidimensional line spectral estimation plays a fundamental role in communication and sensing systems, where it is often used for estimating channel parameters such as angles of arrival and time delays. Existing channel parameter estimation methods often suffer from limited resolution, high computational complexity, or strong sensitivity to noise, and the multidimensional variational line spectral estimation (MDVALSE) algorithm, although effective in off-grid estimation, degrades significantly under low signal-to-noise ratio (SNR) conditions. Recently, generative models, especially diffusion models, have demonstrated strong capabilities in prior-guided denoising and reconstruction of noise-contaminated signals by effectively learning the underlying data structure. Motivated by this, we propose a diffusion-enhanced multidimensional variational line spectral estimation algorithm for channel parameter extraction. Specifically, a diffusion model is first employed to denoise the estimated channel response and improve the observation quality. Then, considering that the residual error after diffusion-based denoising is generally colored rather than white, a colored-noise extension of MDVALSE, termed C-MDVALSE, is derived to better match the statistical structure of the denoised observations. Simulation results in various scenarios show that the proposed algorithm achieves more accurate channel reconstruction and channel parameter estimation than MDVALSE and other existing methods, with particularly significant improvements in low-SNR regimes. Full article

Multi-modal Magnetic Resonance Imaging (MRI) provides complementary information for clinical diagnosis, yet its large-scale storage, privacy sensitivity, and annotation cost pose significant challenges. Inspired by biological vision systems, which integrate multi-sensory inputs and compress experiences into compact memory representations, we propose a bio-inspired framework termed Contrast-Guided Multi-modal Dataset Distillation (CGMDD). In biological perception, different sensory channels observe the same environment from complementary perspectives, while hierarchical neural processing ensures perceptual consistency across modalities. Meanwhile, memory systems such as the associated medial temporal lobe structures consolidate redundant experiences into efficient representations for long-term storage. Motivated by these principles, CGMDD treats multi-modal MRI as multi-view perceptual signals and introduces a hierarchical cross-modal contrastive learning mechanism that enforces perceptual alignment across modalities, analogous to multi-level processing in the visual cortex. Furthermore, we design a dynamic dataset distillation strategy that mimics memory consolidation by compressing large-scale data into compact, informative synthetic representations through gradient-based optimization. The proposed framework jointly optimizes perceptual alignment and memory compression in an end-to-end manner, achieving a biologically plausible integration of perception and learning. Experimental results on two MRI datasets demonstrate that CGMDD can compress the original dataset to 5% of its size while maintaining competitive performance, even with only 30% of the labels. These findings highlight the effectiveness of bio-inspired mechanisms in building efficient, robust, and privacy-preserving computer vision systems. Full article

Digital games have become increasingly prominent in adolescents’ daily lives. Although controlled use may provide cognitive and recreational benefits, excessive engagement can increase addiction risk and negatively affect physical activity levels. However, the relationship between digital game addiction and motivation to participate in physical activity remains unclear. Therefore, the present study examines the association between digital game addiction and motivation to participate in physical activity among adolescents. The study employed a quantitative correlational design and included 682 high school students (females 54.55%; males 45.45%). Data were collected using the Digital Game Addiction Scale (DGAS-7) and the Motivation Scale for Participation in Physical Activity (MSPPA). Independent samples t-tests, one-way ANOVA, and Pearson correlation analyses were conducted. The findings reveal that male students demonstrated higher levels of both digital game addiction and motivation for physical activity compared to female students (p < 0.05). Students engaged in sports reported higher digital game addiction and motivation levels than those who did not. Digital game addiction was significantly higher among students who used digital devices for more than seven hours per day compared to those who used them for 1–3 h (p < 0.05). However, usage duration was not significantly associated with motivation (p > 0.05). A weak but statistically significant positive association was identified between digital game addiction and motivation to participate in physical activity (r = 0.171; p < 0.01). The findings suggest that digital and physical activities among adolescents may not be mutually exclusive but could be complementary. This pattern may be associated with shared motivational dynamics such as competition and achievement. Full article

This study investigates the interaction between a manufacturer’s blockchain adoption strategy and a retailer’s demand information sharing strategy in a green supply chain. For four strategy combinations, we establish a multi-stage game-theoretical model of a green supply chain consisting of a single manufacturer and a single retailer. We first derive the optimal pricing, greenness, service level, and profits, followed by sensitivity and comparative analyses. Next, by examining how consumer price sensitivity and the unit adoption cost of blockchain technology interact, we identify equilibrium strategy combinations. Finally, we validate the relevant findings through numerical analysis. The results demonstrate that adopting blockchain can mitigate the double marginalization effect when consumer price sensitivity is moderate, and can enhance product greenness and service level when the adoption cost remains low. Interestingly, the manufacturer is inclined to adopt blockchain irrespective of the degree of consumer skepticism. Meanwhile, the implementation of blockchain may motivate the retailer to share information when price sensitivity falls within a moderate range. These findings present actionable guidance for green supply chains regarding blockchain and information-sharing strategies. Full article

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

Environmental pollution constitutes an increasingly complex global challenge, largely driven by industrial expansion and the consequent release of toxic species such as Cd²⁺, Pb²⁺, Cu²⁺, Hg²⁺, Fe³⁺, As³⁺, and Rh³⁺ into natural ecosystems. These contaminants pose significant risks to environmental integrity and public health, motivating the development of analytical technologies capable of sensitive, selective, and reliable detection. In this context, graphene-based electrochemical sensors have emerged as versatile platforms for monitoring a broad range of analytes, particularly in environmental applications involving heavy-metal detection. The intrinsic physicochemical properties of graphene derivatives have enabled low detection limits, rapid response times, and tunable selectivity. Despite analytical advances, critical challenges persist regarding operational stability in complex matrices, inter-batch reproducibility, and robustness to interfering species, which continue to hinder large-scale deployment and real-world applicability. However, challenges remain regarding stability and performance in complex arrays, reproducibility, and resistance to interference, necessitating innovative strategies for functionalization and molecular recognition. This review article establishes a comparative framework based on functionalization strategies (covalent, non-covalent, and hybrid), the chemical nature of graphene (GO, rGO, and doping), and various types of polymers (conductors and insulators), using statistical metrics such as the limit of detection (LOD), linear range, working potential, stability, and interferences, employing a bibliometric analysis using the PRISMA 2020 methodology. This comparative framework enables analysis and explanation of performance trends, and the generation of design and functionalization recommendations for versatile applications, including criteria for reproducibility and sustainability. Full article

Background/Objectives: Nowadays, social media is increasingly utilized in the field of orthodontics for information sharing and promotion, yet its influence on patients’ willingness to initiate orthodontic treatment remains insufficiently defined. This systematic review aims to synthesize the available evidence on the impact of orthodontic-related social media content on patients’ willingness to seek orthodontic treatment. Methods: An extensive literature search was performed across five electronic databases up to August 2025, complemented by manual screening of reference lists. Randomized and non-randomized studies evaluating orthodontic-related social media exposure and reported treatment-related willingness or motivation outcomes were considered for inclusion. Results: A total of 1243 records were identified, and eight studies met the inclusion criteria, including six cross-sectional studies, one randomized controlled trial, and one qualitative study. Given the diversity of study designs and assessment methods, the results were synthesized narratively. Visually oriented orthodontic-related social media posts, particularly outcome-focused imagery such as before–after photographs, were more frequently associated with increased willingness to seek orthodontic treatment compared with technical content. Gender-related differences were reported, with female participants appearing more responsive to orthodontic-related social media exposure. Across the included studies, Instagram was identified as the platform exerting the strongest influence. Conclusions: The findings of this systematic review indicate that visually oriented orthodontic-related social media content, particularly outcome-focused imagery such as before–after photographs, shows more consistent associations with willingness to seek orthodontic treatment, alongside gender-related differences and platform-specific effects. Full article

The cathodic oxygen reduction reaction (ORR) remains a major kinetic barrier to high-efficiency proton exchange membrane fuel cells (PEMFCs), motivating the search for electrocatalysts that combine high activity, low metal usage, and long-term durability. This review examines single-atom catalysts (SACs) as an emerging platform for fuel-cell cathodes with particular emphasis on how atomic-level design, ORR mechanism, and practical deployment barriers are interrelated. The review discusses the key ORR pathways, intermediate binding principles, and scaling constraints that govern cathodic performance, and examines how metal-center selection, coordination-environment engineering, support regulation, synergistic multi-site construction, and morphology-controlled synthesis can be used to tune intrinsic activity and stabilize isolated active sites. It further highlights mechanistic insights from theoretical and operando studies, with emphasis on structure–activity relationships, dynamic active-site evolution, and approaches to mitigate scaling limitations. Major barriers to practical deployment, including carbon corrosion, demetalization, agglomeration, peroxide/reactive oxygen species attack, and the persistent gap between half-cell metrics and membrane electrode assembly performance, are also critically assessed. Rather than treating these topics separately, this review discusses them as connected factors that together determine the viability of SAC-based fuel-cell cathodes. Full article