Search Results (2,537)

Background/Objectives: The introduction of composite attachments has greatly improved orthodontic aligner therapy, through better force delivery, more predictable movements, and enhanced retention. This in vitro study aims to present and investigate an innovative digital protocol for aligner attachment fabrication incorporating the latest 3D technology used in dentistry. Methods: A virtual attachment measuring 2.5 × 2 × 2 mm was designed using computer-aided design (CAD) software (Meshmixer, Autodesk Inc., San Francisco, CA, USA) and exported as an individual STL file. The attachments were fabricated using a digital light processing (DLP) 3D printer (model: Elegoo 4 DLP, Shenzhen, China) and a dental-grade biocompatible resin. A custom 3D-printed placement guide was used to ensure precise positioning of the attachments on the printed maxillary dental models. A flowable resin was applied to secure the attachments in place. Following attachment placement, the models were scanned using a laboratory desktop scanner (Optical 3D Smart Big, Open Technologies, Milano, Italy) and three intraoral scanners: iTero Element (Align Technology, Tempe, AZ, USA), Aoral 2, and Aoral 3 (Shining 3D, Hangzhou, China). Results: Upon comparison, the scans revealed that the iTero Element exhibited the highest precision, particularly in the attachment, with an RMSE of 0.022 mm and 95.04% of measurements falling within a ±100 µm tolerance. The Aoral 2 scanner showed greater variability, with the highest RMSE (0.041 mm) in the incisor area and wider deviation margins. Despite this, all scanners produced results within clinically acceptable limits. Conclusions: In the future, custom attachments made by 3D printing could be a valid alternative to the traditional composite attachments when it comes to improving aligner attachment production. While these preliminary findings support the potential applicability of such workflows, further in vivo research is necessary to confirm clinical usability. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

Previous studies have described vehicle acceleration profiles in non-car-following scenarios; however, the underlying mechanisms governing these profiles remain incompletely understood. This study aims to enhance the understanding of these mechanisms by proposing an improved model based on an optimal control problem with two bounded conditions (OCP2B), segmenting vehicle acceleration curves into three distinct phases. Specifically, the proposed model imposes constraints on acceleration through maximum jerk and maximum acceleration functions, thereby capturing essential dynamics previously unexplained by conventional models. Our key contributions include establishing a comprehensive analytical framework for accurately describing vehicle acceleration profiles and elucidating critical characteristics overlooked in the prior literature. Our findings demonstrate that incorporating human-centric considerations, such as driving comfort, significantly enhances the model’s practical applicability. Moreover, the proposed approach provides crucial insights for designing autonomous vehicle (CAV) trajectories consistent with human driving behaviors and effectively predicts the movements of human-driven vehicles (HVs), thus facilitating smoother interactions and potentially reducing conflicts between CAVs and HVs. Full article

Research on semantic segmentation for remote sensing road scenes advanced significantly, driven by autonomous driving technology. However, motion blur from camera or subject movements hampers segmentation performance. To address this issue, we propose a knowledge distillation-based semantic segmentation network (KDS-Net) that is robust to motion blur, eliminating the need for image restoration networks. KDS-Net leverages innovative knowledge distillation techniques and edge-enhanced segmentation loss to refine edge regions and improve segmentation precision across various receptive fields. To enhance the interpretability of segmentation quality under motion blur, we incorporate fractal dimension estimation to quantify the geometric complexity of class-specific regions, allowing for a structural assessment of predictions generated by the proposed knowledge distillation framework for autonomous driving. Experiments on well-known motion-blurred remote sensing road scene datasets (CamVid and KITTI) demonstrate mean IoU scores of 72.42% and 59.29%, respectively, surpassing state-of-the-art methods. Additionally, the lightweight KDS-Net (21.44 M parameters) enables real-time edge computing, mitigating data privacy concerns and communication overheads in internet of vehicles scenarios. Full article

As a strategic replacement area for hydrocarbon exploration in the Tarim Basin, the Kuqa Depression has been the subject of relatively limited research on the sedimentary characteristics of the Triassic strata within its western Wushi Sag, which constrains exploration deployment in this region. This study focuses on the Wushi Sag, systematically analyzing the sedimentary facies types, the evolution of sedimentary systems, and the distribution patterns of the Triassic Kelamayi and Huangshanjie formations. This analysis integrates field outcrops, drilling cores, wireline logs, and 2D seismic data, employing methodologies grounded in foreland basin theory and clastic sedimentary petrology. The paleo-geomorphology preceding sedimentation was reconstructed through balanced section restoration to investigate the controlling influence of foreland tectonic movements on the distribution of sedimentary systems. By interpreting key seismic profiles and analyzing vertical facies successions, the study classifies and evaluates the petroleum accumulation elements and favorable source–reservoir-seal assemblages, culminating in the prediction of prospective exploration areas. The research shows that: (1) The Triassic in the Wushi Sag mainly develops fan-delta, braided-river-delta, and lacustrine–shallow lacustrine sedimentary systems, with strong planar distribution regularity. The exposed strata in the northern part are predominantly fan-delta and lacustrine systems, while the southern part is dominated by braided-river-delta and lacustrine systems. (2) The spatial distribution of sedimentary systems was demonstrably influenced by tectonic activity. Paleogeomorphological reconstructions indicate that fan-delta and braided-river-delta sedimentary bodies preferentially developed within zones encompassing fault-superposition belts, fault-transfer zones, and paleovalleys. Furthermore, Triassic foreland tectonic movements during its deposition significantly altered basin configuration, thereby driving lacustrine expansion. (3) The Wushi Sag exhibits favorable hydrocarbon accumulation configurations, featuring two principal source–reservoir assemblages: self-sourced structural-lithologic gas reservoirs with vertical migration pathways, and lower-source-upper-reservoir structural-lithologic gas reservoirs with lateral migration. This demonstrates substantial petroleum exploration potential. The results provide insights for identifying favorable exploration targets within the Triassic sequences of the Wushi Sag and western Kuqa Depression. Full article

Background/Objectives: Accurately predicting the arrival position of a moving target is essential in sports and daily life. While predictive saccades are known to enhance performance, the neural mechanisms underlying the timing of these strategies remain unclear. This study investigated how the timing of saccadic strategies—executed early versus late—affects cortical activity patterns, as measured by electroencephalography (EEG). Methods: Sixteen participants performed a task requiring them to predict the arrival position and timing of a parabolically moving target that became occluded midway through its trajectory. Based on eye movement behavior, participants were classified into an Early Saccade Strategy Group (SSG) or a Late SSG. EEG signals were analyzed in the low beta band (13–15 Hz) using the Hilbert transform. Group differences in eye movements and EEG activity were statistically assessed. Results: No significant group differences were observed in final position or response timing errors. However, time-series analysis showed that the Early SSG achieved earlier and more accurate eye positioning. EEG results revealed greater low beta activity in the Early SSG at electrode sites FC6 and P8, corresponding to the frontal eye field (FEF) and middle temporal (MT) visual area, respectively. Conclusions: Early execution of predictive saccades was associated with enhanced cortical activity in visuomotor and motion-sensitive regions. These findings suggest that early engagement of saccadic strategies supports more efficient visuospatial processing, with potential applications in dynamic physical tasks and digitally mediated performance domains such as eSports. Full article

Background: Distalization is a fundamental orthodontic strategy for correcting Class II and Class III malocclusions, particularly in cases where specific dental or skeletal conditions favor its application. Recent technological advances have enabled complex dental movements to be performed using clear aligners, aided by digital planning platforms such as ClinCheck^®. Methods: This retrospective study aimed to evaluate the accuracy of ClinCheck^® in predicting molar and canine distalization outcomes with the Invisalign^® system and to identify clinical factors influencing treatment predictability. Thirty patients with complete permanent dentition and at least 2 mm of programmed distalization were selected. Planned movements were extracted from the Invisalign^® Doctor Site and compared to achieved outcomes using Geomagic^® Control X™ software. Occlusal improvements were assessed using the Peer Assessment Rating (PAR) index. Results: The results revealed significant discrepancies between the programmed and achieved distalization, with mean deviations greater than 1 mm in both arches. Skeletal anchorage with mini-screws significantly improved distalization outcomes in the maxillary arch; however, no significant effect was observed in the mandibular arch. Additionally, no significant associations were found between distalization outcomes and skeletal pattern (ANB angle) or facial biotype. Conclusions: Clear aligners are effective in achieving substantial occlusal improvements, particularly when combined with personalized digital planning and supplementary strategies such as skeletal anchorage. Mandibular cases demonstrated greater reductions in PAR scores, emphasizing the potential of aligners in complex distalization treatments. Full article

In an era defined by rapid technological advancements, the intersection of artificial intelligence and industrial innovation has garnered significant attention from both academic and industry stakeholders. The emergence of Industry 4.0, characterized by the integration of cyber–physical systems, the Internet of Things, and smart manufacturing, demands the evolution of operational methodologies to ensure processes’ sustainability. One area of focus is the development of wearable systems that utilize artificial intelligence for the estimation of arm movements, which can enhance the ergonomics and efficiency of labor-intensive tasks. This study proposes a Random Forest-based regression model to estimate upper arm kinematics using only shoulder orientation data, reducing the need for multiple sensors and thereby lowering hardware complexity and energy demands. The model was trained on biomechanical data collected via a minimal three-IMU wearable configuration and demonstrated high predictive performance across all motion axes, achieving R² > 0.99 and low RMSE scores on training (1.14, 0.71, and 0.73), test (3.37, 1.97, and 2.04), and unseen datasets (2.77, 0.78, and 0.63). Statistical analysis confirmed strong biomechanical coupling between shoulder and upper arm motion, justifying the feasibility of a simplified sensor approach. The findings highlight the relevance of our method for sustainable wearable technology design and its potential applications in rehabilitation robotics, industrial exoskeletons, and human–robot collaboration systems. Full article

Addressing the stock market forecasting as a classification problem, where the model predicts the direction of stock price movement, is crucial for both traders and investors, as it can help them to allocate limited resources to the most promising investment opportunities. In this study, we propose a hybrid system that uses a Long Short-Term Memory (LSTM) network and sentiment analysis for predicting the direction of the movement of the stock price. The proposed hybrid system is fed with historical stock data and regulatory news announcements for producing more reliable responses. LSTM networks are well suited to handling time series data with long-term dependencies, while the sentiment analyser provides insights into how news impacts stock price movements by classifying business news into classes. By integrating both the LSTM network and the sentiment classifier, the proposed hybrid system delivers more accurate forecasts. Our experiments demonstrate that the proposed hybrid system outperforms other competing configurations. Full article

Dance encompasses physical, emotional, and social elements, creating a dynamic platform for the exploration of well-being. As a therapeutic approach, dance movement further applies these dimensions to enhance emotional resilience, foster mindfulness, and improve overall mental health. This study examined the relationship between self-compassion and life satisfaction among 912 recreational dancers (80% female and 20% male) in Greece. Participants completed the Self-Compassion Scale and Satisfaction with Life Scale. Confirmatory Factor Analysis validated the five-factor self-compassion model, and regression analysis identified predictors of life satisfaction. Self-kindness emerged as a strong positive predictor (β = 0.258, p < 0.001), while isolation (β = −0.307, p < 0.001) and self-judgment (β = −0.083, p = 0.029) negatively predicted life satisfaction. Common humanity (β = 0.064, p = 0.066) and mindfulness (β = 0.004, p = 0.907) showed no significant predictive effect. The model explained 21.7% of the variance in life satisfaction (R² = 0.217). Small but statistically significant differences in self-compassion dimensions were observed across dance styles. Partner-oriented dancers such as those practicing tango reported slightly higher self-kindness and mindfulness, while ballet dancers showed a small increase in self-judgment and isolation. Life satisfaction remained consistent across styles, highlighting dance’s overall contribution to well-being. These findings suggest that integrating self-compassion training into dance education and psychotherapy, particularly within a Therapeutic-Based Art Pedagogy framework, may contribute to emotional resilience, foster social connection, and promote mental health, positioning dance as a potentially transformative tool for holistic development. Full article

Strong competencies in actual motor competence (AMC) and perceived motor competence (PMC) support lifelong physical activity. However, assessing MC is time-consuming, requiring multiple AMC and PMC evaluations. Streamlining these assessments would improve efficiency at a national level. This study used machine learning (ML) classification to (1) identify AMC assessments that can be accurately predicted in an Irish context using other AMC and PMC assessments, and (2) examine prediction accuracy differences between genders. AMC was measured using the Test of Gross Motor Development (3rd Edition) and the Victorian Fundamental Motor Skills Manual, while PMC was assessed with the Pictorial Scale of Perceived Movement Skill Competence. Five ML classification models were trained and tested on an Irish MC dataset (n = 2098, mean age 9.2 ± 2.04) to predict distinct AMC assessment outcomes. The highest prediction accuracies (>85%) were found for the Catch (female and gender-combined subsets) and Bounce (male subset) AMC assessments. These assessments could potentially be removed from the current Irish testing battery for their respective gender groups. Our findings highlight the effectiveness of ML classification in optimising Irish MC assessment procedures, reducing redundancy, and enhancing efficiency. Full article

Suicide in children is a major global health crisis, with profound impacts on families, friends, and society. Understanding ways to ameliorate the rate of suicide attempt (SA) is critical given that it is a key factor in predicting future suicide risk. SA is the deliberate act of causing physical injury to oneself with the intent of death. The incidence of SA may be influenced by physical activity (PA). PA includes bodily movement via skeletal muscles that results in energy expenditure and physical fitness. While there is evidence to suggest that PA improves dysregulation of the parasympathetic nervous system which underpins the physiology of suicidal behaviour, evaluating the impact of PA on SA in children is required. Objectives: This systematic review aims to determine the relationship between PA and SA in children to inform alternative preventative and interventional strategies. Methods: This systematic review was registered with PROSPERO: CRD42023389415. Eight electronic databases were systematically searched. References were transferred to Covidence software for title and abstract screening and full text review were performed based on eligibility criteria: (1) children aged 6–18 years old; (2) participated in PA (individual, group exercise, or team sports); and (3) examined SA as a dependent variable. The JBI Checklist was used to measure the quality and level of bias of included studies. Results: Of the 2322 studies identified, 21 were included in the final analysis of the review. Twenty studies were cross-sectional in design, and one implemented a prospective study design. Thirteen studies (61.9%) yielded statistically significant results, indicating that increased PA, particularly team sport, may be associated with reduced odds of SA. There was some evidence to suggest that certain intensities and frequencies of PA may be beneficial to some and detrimental to other subgroups. Conclusions: The results suggest that PA may reduce the risk of suicide attempts. Although PA may be associated with reduced SA in children, future research is required, which (1) uses standardised outcome variables; (2) adopts longitudinal and experimental study designs; (3) explores qualitative research to determine distinctive factors that influence participation in PA not captured by quantitative research; and (4) examines different target populations such as children with a broad range of mental health issues. Full article

(1) Background: Human walking involves adapting to diverse terrains, influencing gait biomechanics. This study examined how seven outdoor surfaces—flat–even, banked-right/-left, cobblestone, grass, sloped-down, and sloped-up—affect nonlinear gait dynamics in 30 healthy adults (14 females and 15 males). (2) Methods: Trunk and shank accelerations were analyzed for movement predictability (sample entropy, SE), smoothness (log dimensionless jerk, LDLJ), symmetry (step/stride regularity), and stability (short-/long-term Lyapunov exponents, LyE_s, LyE_l). (3) Results: Surface type significantly influenced all gait metrics, regardless of sex. Banked-right and sloped-down walking reduced SE, indicating less predictable movements. All surfaces except flat–even increased LDLJ, suggesting reduced smoothness. Cobblestone and sloped-down surfaces impaired step symmetry, while banked surfaces enhanced stride symmetry. LyE_s decreased on cobblestones (lower variability), while sloped-up increased it. LyE_l rose on all surfaces except cobblestones, indicating a more chaotic gait. No significant sex differences were found, though males showed a non-significant trend toward lower LyE_s. Notably, sex–surface interactions emerged for SE and stride symmetry on banked-right surfaces, with females showing decreased SE and increased symmetry. (4) Conclusions: These findings underscore the importance of terrain and sex in gait dynamics research. Full article

Background: Prevention and clinical management of musculoskeletal injuries have historically focused on the assessment and training of modifiable physical factors, but perceptual decision-making has only recently been recognized as a potentially important capability. Immersive virtual reality (VR) systems can measure the speed, accuracy, and consistency of body movements corresponding to stimulus–response instructions for the completion of a forced-choice task. Methods: A cohort of 26 female college soccer players (age 19.5 ± 1.3 years) included 10 players who participated in a baseline assessment, 10 perceptual-response training (PRT) sessions, a post-training assessment that preceded the first soccer practice, and a post-season assessment. The remaining 16 players completed an assessment prior to the team’s first pre-season practice session, and a post-season assessment. The assessments and training sessions involved left- or right-directed neck rotation, arm reach, and step-lunge reactions to 40 presentations of different types of horizontally moving visual stimuli. The PRT program included 4 levels of difficulty created by changes in initial stimulus location, addition of distractor stimuli, and increased movement speed, with ≥90% response accuracy used as the criterion for training progression. Perceptual latency (PL) was defined as the time elapsed from stimulus appearance to initiation of neck rotation toward a peripheral virtual target. The speed–accuracy tradeoff was represented by Rate Correct per Second (RCS) of PL, and inconsistency across trials derived from their standard deviation for PL was represented by intra-individual variability (IIV). Perceptual Decision Efficiency (PDE) represented the ratio of RCS to IIV, which provided a single value representing speed, accuracy, and consistency. Statistical procedures included the bivariate correlation between RCS and IIV, dependent t-test comparisons of pre- and post-training metrics, repeated measures analysis of variance for group X session pre- to post-season comparisons, receiver operating characteristic analysis, and Kaplan–Meier time to injury event analysis. Results: Statistically significant (p < 0.05) results were found for pre- to post-training change, and pre-season to post-season group differences, for RCS, IIV, and PDE. An inverse logarithmic relationship was found between RCS and IIV (Spearman’s Rho = −0.795). The best discriminator between injured and non-injured statuses was PDE ≤ 21.6 (93% Sensitivity; 42% Specificity; OR = 9.29). Conclusions: The 10-session PRT program produced significant improvement in perceptual decision-making that appears to provide a transfer benefit, as the PDE metric provided good prospective prediction of musculoskeletal injury. Full article

Background: Clear aligner therapy (CAT) has become increasingly popular for treating mild to moderate malocclusions. However, discrepancies between predicted and achieved tooth movement remain a concern, partly due to the limited understanding of aligner material behavior under clinical conditions. Since these materials must deliver controlled and sustained forces, their flexural properties are critical for treatment efficacy. Objective: To identify and analyze in vitro studies investigating the flexural properties of thermoplastic clear aligner materials, summarize their testing methodologies, and examine the factors that may influence their clinical performance. Methods: A scoping review was conducted following the PRISMA-ScR guidelines. Three electronic databases (PubMed, Scopus, and Web of Science) were systematically searched. Studies were screened based on predefined eligibility criteria, and data extraction included testing methods, materials, and clinically relevant variables. Risk of bias was assessed using the QUIN tool. Results: Seventeen studies published between 2008 and 2024 were included. All studies used three-point bending to assess mechanical properties. Common influencing factors included thermoforming, liquid absorption, temperature changes, loading conditions, and material thickness. Most studies reported that these factors negatively affected force delivery. The most frequently tested material was Duran (PET-G). Polyurethane-based materials, such as Zendura, showed comparatively better stress relaxation properties. Conclusions: Thermoforming, intraoral temperature changes, liquid exposure, and prolonged or repeated loading can compromise the mechanical properties and force delivery capacity of aligner materials. Standardized testing methods and further investigation of newer materials are essential to enhance the predictability and performance of clear aligner therapy. Full article