Search Results (2,830)

Mechanical friction offers a frequent approach for sustainable energy harvesting, as it can be captured and transformed into electricity by means of the triboelectric phenomenon. Theoretically, this electricity may subsequently be employed to drive electrochemical water purification processes. Herein, the experimental results confirm that the SiC particles effectively trigger the tribocatalytic decomposition of Rhodamine B (RhB). During the tribocatalytic decomposition of dye, mechanical friction is generated at the contact surface between the tribocatalyst and a custom-fabricated polytetrafluoroethylene (PTFE) rotating disk, under varying conditions of stirring speed, temperature, and pH value. Hydroxyl radicals and superoxide radicals are confirmed as the dominant reactive species participating in tribocatalytic dye decomposition, as demonstrated by reactive species inhibition experiments. Furthermore, the SiC particles demonstrate remarkable reusability, even after being subjected to five consecutive recycling processes. The exceptional tribocatalytic performance of SiC particles makes them potentially applicable in water purification by harnessing environmental friction energy. Full article

This study investigated the effects of color shade and curing time on the degree of conversion (DC) and microhardness of colored compomers. A total of 162 samples (81 for DC, 81 for microhardness) were prepared, with nine samples per color group (gold, blackberry, green, pink, orange, lemon, blue, silver) and for the control. Samples were subdivided into three polymerization subgroups (3 s/3200 mW/cm², 10 s/1000 mW/cm², 20 s/1000 mW/cm²). The DC was analyzed via fourier transform infrared spectroscopy (FTIR) and microhardness was measured using Vickers testing. Statistical analysis included two-way ANOVA and Spearman correlation (α = 0.05). The colored compomers demonstrated a significantly lower DC compared to the control group (p ≤ 0.001). Among the tested colors, green exhibited the lowest DC (33.3%), while orange showed the highest (51.0%). A significant difference in DC was observed across curing times (p = 0.005), with the 3 s and 20 s groups exhibiting significantly higher conversion rates than the 10 s group. Microhardness values exhibited significant variation depending on the color (p < 0.001). Gold compomers demonstrated the lowest microhardness, whereas silver compomers showed comparable performance with the control group (p = 0.154). A moderate correlation between DC and microhardness was observed overall (ρ = 0.42, p = 0.003). However, the observed relationships were color-dependent: orange displayed a strong positive correlation (ρ = 0.78), whereas pink revealed no meaningful association (ρ = −0.15). Color and curing time critically influence compomer performance. High-intensity short curing is viable for lighter colors, while darker colors require extended curing. Customized protocols are essential to optimize clinical outcomes in pediatric dentistry. Full article

In football, accurately pinpointing key events like passes is vital for analyzing player and team performance. Despite continuous technological advancements, existing tracking systems still face challenges in accurately synchronizing events and positional data accurately. This is a case study that proposes a new method to synchronize events and positional data collected during football matches. Three datasets were used to perform this study: a dataset created by applying a custom algorithm that synchronizes positional and event data, referred to as the optimized synchronization dataset (OSD); a simple temporal alignment between positional and event data, referred to as the raw synchronization dataset (RSD); and a manual notational data (MND) from the match video footage, considered the ground truth observations. The timestamp of the pass in both synchronized datasets was compared to the ground truth observations (MND). Spatial differences in OSD were also compared to the RSD data and to the original data from the provider. Root mean square error (RMSE) and mean absolute error (MAE) were utilized to assess the accuracy of both procedures. More accurate results were observed for optimized dataset, with RMSE values of RSD = 75.16 ms (milliseconds) and OSD = 72.7 ms, and MAE values RSD = 60.50 ms and OSD = 59.73 ms. Spatial accuracy also improved, with OSD showing reduced deviation from RSD compared to the original event data. The mean positional deviation was reduced from 1.59 ± 0.82 m in original event data to 0.41 ± 0.75 m in RSD. In conclusion, the model offers a more accurate method for synchronizing independent datasets for event and positional data. This is particularly beneficial for applications where precise timing and spatial location of actions are critical. In contrast to previous synchronization methods, this approach simplifies the process by using an automated technique based on patterns of ball velocity. This streamlines synchronization across datasets, reduces the need for manual intervention, and makes the method more practical for routine use in applied settings. Full article

Freemium, a popular business model in the digital economy, offers a basic product for free while charging for advanced features or value-added services. This pricing strategy enables platforms to attract a broad user base and then monetize through premium offerings. Customer characteristics and service price are important factors affecting customer choice behavior in such a model. Based on consumption stickiness, we consider a monopoly that provides value-added services by incorporating a multinomial logit model into a two-stage dynamic pricing model. First, we analyze the optimal pricing of value-added services under a normal sales scenario. We then consider optimal pricing during the marketing period under two strategies—level improvement for value-added services and quality reduction for a basic product—and analyze the applicability of each. The results show that increasing the value-added service level has a positive effect on the optimal price of value-added services, whereas reducing the basic product quality has no effect on the optimal price. Furthermore, the numerical simulation shows that when the depth of consumer stickiness is low, the optimal marketing strategy reduces the quality of the basic product, the price of value-added services should be higher than that in the normal sales period but lower than the price under the level-improvement strategy for value-added services; otherwise, improving the level of the value-added services becomes the optimal approach. This study provides a theoretical basis and decision support for product quality design and service pricing that applies to freemium platforms. Full article

This paper presents a novel multi-sensor system for enhanced maneuver analysis in Olympic dinghy sailing. In the ILCA class, there is an increasing demand for precise in-field measurement and analysis of physical properties beyond well-established velocity and course metrics. The low-cost setup presented in this study consists of a combination of commercially available sensor systems, such as the AdMos sensor for IMU and GNSS measurement, in combination with custom measurement systems for rudder and mast rotations using fully waterproofed potentiometers. Data streams are synchronized using GNSS time stamping for streamlined analysis. The resulting analysis presents a selection of 12 upwind tacks, with corresponding path overlays, detailed timeseries data, and performance metrics. The system has demonstrated the value of extended data analysis of in situ data with an elite ILCA 7 sailor. The addition of rudder and mast rotations has enabled enhanced analysis of on-water maneuvers for single-handed Olympic dinghies like the ILCA 7, on a level of detail previously reserved for simulated environments. Full article

The aim of this work is to evaluate the effectiveness of antimicrobial photodynamic therapy (aPDT) against oral MDR (multi-drug-resistant) Candida spp. infections related to orthodontic treatment with palatal expanders through in vitro study. Methods: PDT protocol: Curcumin + H₂O₂ was used as a photosensitizer activated by a 460 nm diode LED lamp, with an 8 mm blunt tip for 2 min in each spot of interest. In vitro simulation: A palatal expander sterile device was inserted into a custom-designed orthodontic bioreactor, realized with 10 mL of Sabouraud dextrose broth plus 10% human saliva and infected with an MDR C. albicans clinical isolate CA95 strain to reproduce an oral palatal expander infection. After 48 h of incubation at 37 °C, the device was treated with the PDT protocol. Two samples before and 5 min after the PDT process were taken and used to contaminate a Petri dish with a Sabouraud field to evaluate Candida spp. CFUs (colony-forming units). Results: A nearly 99% reduction in C. albicans colonies in the palatal expander biofilm was found after PDT. Conclusion: The data showed the effectiveness of using aPDT to treat palatal infection; however, specific patient oral micro-environment reproduction (Ph values, salivary flow, mucosal adhesion of photosensitizer) must be further analyzed. Full article

To tackle the challenges of detecting complex cracks on large stone slabs with noisy textures, this paper presents the first domain-optimized framework for stone slab cracks, an improved semantic segmentation model (YOLOv8-Seg) synergistically integrating U-NetV2, DSConv, and DySample. The network uses the lightweight U-NetV2 backbone combined with dynamic feature recalibration and multi-scale refinement to better capture fine crack details. The dynamic up-sampling module (DySample) helps to adaptively reconstruct curved boundaries. In addition, the dynamic snake convolution head (DSConv) improves the model’s ability to follow irregular crack shapes. Experiments on the custom-built ST stone crack dataset show that YOLOv8-Seg achieves an mAP@0.5 of 0.856 and an mAP@0.5–0.95 of 0.479. The model also reaches a mean intersection over union (MIoU) of 79.17%, outperforming both baseline and mainstream segmentation models. Ablation studies confirm the value of each module. Comparative tests and industrial validation demonstrate stable performance across different stone materials and textures and a 30% false-positive reduction in real production environments. Overall, YOLOv8-Seg greatly improves segmentation accuracy and robustness in industrial crack detection on natural stone slabs, offering a strong solution for intelligent visual inspection in real-world applications. Full article

Research highlights a growing demand for active, experiential learning methods in higher education, especially in teacher education. While the benefits of parental involvement (PI) are well-documented, Hungary lacks tools to effectively prepare teacher trainees for fostering family–school cooperation. This study addresses this gap by introducing a custom-designed board game as an innovative teaching tool. The game simulates real-world challenges in PI through a cooperative, scenario-based framework. Exercises are grounded in international and national research, ensuring their relevance and evidence-based design. Tested with 110 students, the game’s educational value was assessed via post-gameplay questionnaires. Participants emphasized the strengths of its cooperative structure, realistic scenarios, and integration of humor. Many reported gaining new insights into parental roles and strategies for effective home–school partnerships. Practical applications include integrating the game into teacher education curricula and adapting it for other educational contexts. This study demonstrates how board games can bridge theory and practice, offering an engaging, effective medium to prepare future teachers for the challenges of PI. Full article

Introduction: Cystic fibrosis (CF) is a genetic condition affecting several organs and systems, including the pancreas, colon, respiratory system, and reproductive system. The detection of a growing number of CFTR variants and genotypes has contributed to an increase in the CF population which, in turn, has had an impact on the overall statistics regarding the prognosis and outcome of the condition. Given the increase in life expectancy, it is critical to better predict outcomes and prognosticate in CF. Thus, each person’s choice to aggressively treat specific disease components can be more appropriate and tailored, further increasing survival. The objective of our narrative review is to summarize the most recent information concerning the value and significance of clinical parameters in predicting outcomes, such as gender, diabetes, liver and pancreatic status, lung function, radiography, bacteriology, and blood and sputum biomarkers of inflammation and disease, and how variations in these parameters affect prognosis from the prenatal stage to maturity. Materials and methods: A methodological search of the available data was performed with regard to prognostic factors in the evolution of CF in children and young adults. We evaluated articles from the PubMed academic search engine using the following search terms: prognostic factors AND children AND cystic fibrosis OR mucoviscidosis. Results: We found that it is crucial to customize CF patients’ care based on their unique clinical and biological parameters, genetics, and related comorbidities. Conclusions: The predictive significance of more dynamic clinical condition markers provides more realistic future objectives to center treatment and targets for each patient. Over the past ten years, improvements in care, diagnostics, and treatment have impacted the prognosis for CF. Although genotyping offers a way to categorize CF to direct research and treatment, it is crucial to understand that a variety of other factors, such as epigenetics, genetic modifiers, environmental factors, and socioeconomic status, can affect CF outcomes. The long-term management of this complicated multisystem condition has been made easier for patients, their families, and physicians by earlier and more accurate identification techniques, evidence-based research, and centralized expert multidisciplinary care. Full article

Pelvic organ prolapse (POP) is a prevalent condition, affecting women all over the world, and is commonly treated through surgical interventions that present limitations such as recurrence or complications associated with synthetic meshes. In this study, biodegradable poly($ϵ$-caprolactone) (PCL) cog threads are proposed as a minimally invasive alternative for vaginal wall reinforcement. A custom cutting tool was developed to fabricate threads with varying barb angles (90°, 75°, 60°, and 45°), which were produced via Melt Electrowriting. Their mechanical behavior was assessed through uniaxial tensile tests and validated using finite element simulations. The results showed that barb orientation had minimal influence on tensile performance. In simulations of anterior vaginal wall deformation under cough pressure, all cog thread configurations significantly reduced displacement in the damaged tissue model, achieving values comparable to or even lower than those of healthy tissue. A ball burst simulation using an anatomically accurate model further demonstrated a 13% increase in reaction force with cog thread reinforcement. Despite fabrication limitations, this study supports the biomechanical potential of 3D-printed PCL cog threads for POP treatment, and lays the groundwork for future in vivo validation. Full article

This study describes and validates a novel method for assessing anterior crystalline lens curvature along vertical and horizontal meridians using radial measurements derived from Scheimpflug imaging. The aim was to evaluate whether pupil diameter (PD), anterior lens curvature, and anterior chamber depth (ACD) change during accommodation and whether these changes are age-dependent. A cross-sectional study was conducted on 104 right eyes from healthy participants aged 21–62 years. Sixteen radial images per eye were acquired using the Galilei Dual Scheimpflug Placido Disk Topographer under four accommodative demands (0, 1, 3, and 5 dioptres (D)). Custom software analysed lens curvature by calculating eccentricity in both meridians. Participants were analysed as a total group and by age subgroups. Accommodative amplitude and monocular accommodative facility were inversely correlated with age. Both PD and ACD significantly decreased with higher accommodative demands and age. Relative eccentricity decreased under accommodation, indicating increased lens curvature, especially in younger participants. Significant curvature changes were detected in the horizontal meridian only, although no statistically significant differences between meridians were found overall. The vertical meridian showed slightly higher eccentricity values, suggesting that it remained less curved. By enabling detailed, meridionally stratified in vivo assessment of anterior lens curvature, this novel method provides a valuable non-invasive approach for characterizing age-related biomechanical changes during accommodation. The resulting insights enhance our understanding of presbyopia progression, particularly regarding the spatial remodelling of the anterior lens surface. Full article

This article presents the design, development, and evaluation of an AI-based assistant tailored to support users in the application of the Specific Operations Risk Assessment (SORA) methodology for unmanned aircraft systems. Built on a customized language model, the assistant was trained using system-level instructions with the goal of translating complex regulatory concepts into clear and actionable guidance. The approach combines structured definitions, contextualized examples, constrained response behavior, and references to authoritative sources such as JARUS and EASA. Rather than substituting expert or regulatory roles, the assistant provides process-oriented support, helping users understand and complete the various stages of risk assessment. The model’s effectiveness is illustrated through practical interaction scenarios, demonstrating its value across educational, operational, and advisory use cases, and its potential to contribute to the digital transformation of safety and compliance processes in the drone ecosystem. Full article

This study dives into what drives people to use AR filters in the catering industry, focusing on the Hong Kong market. The main idea is to determine how “perceived value” shapes users’ intentions to engage with these filters. To do this, the research combines concepts from two popular models—the extended Technology Acceptance Model (TAM) and the Satisfaction–Loyalty Model (SLM)—to understand what influences perceived value. The survey data were then analyzed with Structural Equation Modeling (SEM) to see how perceived usefulness, enjoyment, satisfaction, and value connect to users’ intentions. The results showed that “perceived value” is a big deal—the main factor driving whether people want to use AR filters. Things like how useful or enjoyable the filters are and how satisfied users feel all play a role in shaping this perceived value. These findings are gold for marketing teams and AR developers, especially in the catering world. Combining TAM and the Satisfaction–Loyalty Model offers a fresh perspective on how AR technology influences consumer behavior. On top of that, it gives practical advice for businesses looking to make the most of AR filters in their marketing and customer experience strategies. Full article

Cloud computing offers a broad spectrum of services to users, particularly in multi-cloud environments where service-centric features are introduced to support users from multiple endpoints. To improve service availability and optimize the utilization of required services, cloud brokerage has been integrated into multi-cloud systems. The primary objective of a cloud broker is to ensure the quality and outcomes of services offered to customers. However, traditional cloud brokers face limitations in measuring service trust, ensuring validity, and anticipating future enhancements of services across different cloud platforms. To address these challenges, the proposed intelligent cloud broker integrates an intelligence mechanism that enhances decision-making within a multi-cloud environment. This broker performs a comprehensive validation and verification of service trustworthiness by analyzing various trust factors, including service response time, sustainability, suitability, accuracy, transparency, interoperability, availability, reliability, stability, cost, throughput, efficiency, and scalability. Customer feedback is also incorporated to assess these trust factors prior to service recommendation. The proposed model calculates service ranking (SR) values for available cloud services and dynamically includes newly introduced services during the validation process by mapping them with existing entries in the Service Collection Repository (SCR). Performance evaluation using the Google cluster-usage traces dataset demonstrates that the ICB outperforms existing approaches such as the Clustering-Based Trust Degree Computation (CBTDC) algorithm and the Service Context-Aware QoS Prediction and Recommendation (SCAQPR) model. Results confirm that the ICB significantly enhances the effectiveness and reliability of cloud service recommendations for users. Full article

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article