Investigating internal and external focus of attention strategies during return-to-sport tests post- anterior cruciate ligament reconstruction (ACLR).

We establish network predictability theory by mapping link prediction onto a spin glass model, where network partitions correspond to spin configurations, and predictability equals the system's average energy. Using the cavity method from statistical physics, we prove that global predictability decomposes into individual link contributions, enabling an efficient local sampling algorithm that reduces the computational complexity of evaluating individual link contributions from being dependent on the entire network to only its local neighborhood, scaling with the average degree. We derive exact results for canonical network models: Erdös-Rényi networks exhibit universal predictability of 0.5 regardless of algorithm choice, establishing the random baseline, while structured networks show predictability controlled by their prior parameters. We introduce the predictability index, which quantifies the maximum achievable performance without information loss and accurately predicts algorithm performance under random division. Analysis of real networks validates our framework, revealing how degree heterogeneity and structural patterns govern predictability. This physics-based approach provides both theoretical insights into link prediction limits and practical tools for assessing network reconstruction potential, with implications for applications from biological network inference to social network analysis.

An increasing amount of online news content in digital journalism leads to novel and complicated issues regarding its classification and organisation. Systems that automate operational tasks can provide numerous advantages over systems that manually classify documents. The current work attempts to solve this problem using contextual and semantic deep learning. The text's semantic meaning is captured using pre-trained word embeddings, and understanding is aided by a hybrid neural structure that incorporates local and distant text dependencies. This technique is compared against classical machine learning on two prominent news datasets. The deep learning approach yields a remarkable improvement in classification, reaching over 91% accuracy on the AG News corpus, a balanced four-class English benchmark, while its performance on the News Category Dataset V3, which is more complex and highly unbalanced, is considerably lower. These results highlight the effectiveness of contextual and semantic modelling for news categorisation, while also illustrating the impact of dataset complexity and class imbalance on achievable performance.

ABSTRACT Higher teacher expectations are generally linked to higher school engagement and achievement. This study tested whether overestimation could also have negative effects. We examined (curvi)linear associations between teacher expectations (relative to actual achievement) at the start of the school year and students’ engagement, achievement, and school performance pressure at year-end. Participants were 716 upper primary school students (Mage = 9.72, 46.5% girls) and 45 teachers. Multilevel analyses showed that teachers tended to overestimate their students’ future achievement. Overestimation was greater for boys and higher-SES students at the individual level and, at the class level, in classrooms with more students from minoritized ethnic backgrounds. We mostly found linear associations indicating that overestimation was positively related to engagement and achievement. However, a curvilinear effect emerged indicating that both under- and overestimation were associated with higher performance pressure. Overall, findings suggest that especially underestimation, but also overestimation can have adverse effects on students.

This research aimed to analyze the extent to which executive functions (EF) predict changes in Science academic performance (SAP) of primary school students, based on student self-reports and parental perceptions of their children’s executive functioning. Students attending primary school in Medina, Saudi Arabia was selected. A non-probability convenience sample was drawn. This sample consisted of 200 children from 3 schools. The present study corresponds to a non-experimental, quantitative design, with a descriptive-correlational scope. Descriptive analyses were performed, and the mean, standard deviation, and minimum and maximum values of the variables of interest were calculated. For inferential analyses, a correlation analysis was first conducted using Pearson’s correlation coefficient to examine the existence of significant correlations between the variables. The results showed that EF related to planning (PL) and working memory (WM), according to the perceptions of the students and their parents, can explain Science academic performance (SAP). The results provide evidence regarding the potential importance of using self-reports and parental reports of cognitive abilities in analyzing the relationship between executive functioning (EF) and Science academic performance (SAP), and in studying the influence of socioeconomic context on SAP in primary school students. These results regarding EF can inform interventions or policies concerning school performance in academic achievement.

The crystallographic orientation distribution function (ODF) plays a significant role in controlling the anisotropic mechanical response of aluminum alloy 6061. However, optimization of high-dimensional ODFs still remains a challenge due to the nonlinear nature of microstructure-property relationships and the computational cost of physics-based simulations. This study presents a physics-informed machine-learning framework that identifies optimal ODF configurations to maximize the stiffness–strength synergy metric F Y/E. A synthetic dataset of 150,000 microstructures was generated using Dirichlet-sampled ODFs, rotated stiffness tensors, Voigt-Reuss-Hill homogenization, and a Taylor-type crystal plasticity model. Multi-method feature ranking (Mutual Information, ANOVA, Chi-square, L1-SVC) consistently identified 20 dominant orientation components. A shallow decision tree extracted physically meaningful orientation thresholds that provides transparent processing-relevant rules. A Random Forest surrogate which was trained on scaled ODFs achieved good predictive fidelity (R 2 =0.88). It enabled rapid exploration of the ODF landscape. A multi-start surrogate-assisted optimization strategy was implemented and benchmarked against genetic algorithms and random search. It shows a 2–3% improvement in the best achievable performance. Nearest-neighbor back-projection confirmed that optimized candidates closely matched true physics-based evaluations (<3% deviation). DBSCAN clustering unveiled a unimodal distribution of optimized solutions, which means that only one strong microstructure family existed. In summary, this study opens up a clear and physically based as well as a computationally efficient route for the optimization of microstructures in high-dimensional orientation spaces. Apart from alloy 6061, the suggested framework gives a generalizable basis for the design of anisotropic materials where the mechanical performance is greatly influenced by the crystallographic texture. • Physics-based models generate high-fidelity orientation–property data for alloy 6061. • Multi-method feature ranking identifies the most influential orientation components. • Interpretable machine learning extracts clear orientation rules linked to performance. • Surrogate optimization outperforms genetic and random search in finding optimal texture. • Clustering confirms a single robust high-performance texture family in alloy 6061.

Pressure retarded osmosis (PRO) is a green technology for harvesting Gibbs free energy from mixing solutions with different salinity gradients. Although lab-synthesized membranes showed high PRO performance, there is no available flat-sheet industrial-scale PRO membrane production. Most of the previous studies have focused on enhancing the power density of the PRO process by using a hypersaline draw solution that potentially causes severe internal and external concentration polarization (ICP and ECP) and limits achievable performance. Using the most accessible resources – seawater and commercially available membranes – can be a more practical way to develop a large-scale PRO plant. However, only a limited number of studies have evaluated the PRO performance under such realistic conditions. In our study, we compared the PRO performance of some commercial FO and RO membranes. We observed that, at an elevated feed velocity and temperature, the RO membrane had a significant enhanced water flux and power density. Due to turbulent flow at a high feed velocity and low viscosity at a high feed temperature (30 °C), the RO membrane was able to perform at low concertration polarization, hence maximum power density (5.3 W/m 2 ) could be obtained at half the osmotic pressure (15 bar). • Seawater and commercial RO and FO membranes were used for PRO • An elevated feed velocity and temperature resulted in high power density • Commercial RO membranes outperformed FO membranes in the PRO tests • 5.3 W/m 2 was achieved at 15 bar using a commercial RO membrane

The achievable performance of the complementary-filter-based parallel control for a compound dual-stage nano-positioning system is limited by its model-based sequential design structure. This study proposes a frequency-response-data-based optimization approach for the simultaneous and systematic design of the complementary-filter-based dual-feedback controller. The design procedure and corresponding Nyquist stability analysis are presented in detail. By directly utilizing frequency response data, the proposed method mitigates the effects of system identification errors. The controller design objective is formulated as a constrained optimization problem to achieve a flat amplitude frequency response with high control bandwidth. Comparative experiments conducted on a dual-stage nano-positioning system verify the effectiveness of the proposed approach. The proposed method reduces the root-mean-square tracking error from 70.6nm using the baseline integral controller to 21.3nm at 50Hz sinusoidal tracking, demonstrating its clear superiority.

Abstract This study explores the association between school-based foreign language (FL) instruction and mathematical achievement among 15-year-old students, using data from the 2018 Programme for International Student Assessment (PISA). Two complementary analyses were conducted: a large-scale model ( n = 300,656) examining the relationship between time spent in FL learning and maths performance across 73 countries and a machine learning (ML) approach (random forest (RF); n = 53,459) identifying specific programme features that most strongly influence this relationship. Results show that longer exposure to FL instruction was associated with a modest but statistically robust increase in maths scores ( β = 0.08, p &lt; .001), even after controlling for socioeconomic and contextual factors. Among programme characteristics, the integration of multicultural curricula emerged as a prominent predictor of higher maths performance. These findings indicate that sustained, culturally enriched FL learning is positively associated with numeracy outcomes, with implications for equity in academic achievement and cross-disciplinary performance.

The study examined the impact of Process-Oriented Guided Inquiry Learning Strategy (POGILS) on students’ academic performance in organic chemistry among secondary school students in Kwami Local Government Area, Gombe State, Nigeria. The objectives of the study were to determine the effect of POGILS on academic performance of students with gender consideration in Organic Chemistry. A pre-test, post-test non-randomized, non-equivalent control group quasi-experimental design was employed in the study. The sample size comprised of 82 (55 male and 27 female) senior secondary school II students, selected using multi-stage sampling technique. Organic Chemistry Performance Test was used for data collection with a reliability coefficient of 0.84 established using split-half testing method and analysed using Pearson Product Moment Correlation. Data analysis involved the use of ANCOVA tested at 0.05. Finding showed that F(1,79) = 396.443, p = 0.000, implying a significant difference between the academic performance mean scores of students taught using POGILS and traditional teaching method. Also, F(1,37) = 0.101, p = 0.812 meaning no significant difference in the mean academic performance scores of male and female students taught using POGILS, implying that POGILS has a significant effect on the mean academic performance scores of male and female students taught organic chemistry concepts. The study concluded that POGILS is an effective instructional strategy and the use of POGILS enhanced academic performance of both male and female students. The study recommends that chemistry teachers should integrate POGILS into their instructional practices to boost academic achievement and close gender academic performance gaps in Chemistry.

Ice accretion on aircraft, wind-turbine blades, power networks, civil infrastructure, and exposed sensors poses severe safety risks and economic costs. Passive icephobic surfaces mitigate icing by delaying heterogeneous nucleation, altering droplet impact/solidification and wetting transitions, and/or weakening the ice–substrate bond so that accreted ice sheds under modest aerodynamic, gravitational, or vibrational loads. This review synthesizes recent progress using a unified mechanism framework linking (i) nucleation and early freezing, (ii) droplet dynamics during impact or condensation/frosting, and (iii) ice accretion and removal governed by interfacial fracture. Smooth low-surface-energy coatings, textured (superhydrophobic) surfaces, slippery liquid-infused porous surfaces (SLIPS), and low-interfacial-toughness strategies are critically compared in terms of achievable performance ranges, failure modes, durability limits, fabrication scalability, and test-method dependence. Ice-adhesion measurement approaches (push-off, pull-off/tensile, centrifugal) are assessed and a minimum reporting checklist is provided to improve comparability. Case studies across aviation, wind energy, power infrastructure, sensors, and emerging civil-engineering coatings highlight that durability and scale-dependent failure modes remain the dominant barriers to durable, energy-free icing mitigation. The review concludes with priorities for eco-friendly chemistries, self-healing or renewable layers, standardized testing/reporting, and data-driven (machine learning-assisted) optimization to accelerate translation into durable passive ice-mitigation technologies.

The soft robotics field is moving toward increasingly complex and integrated systems, which will contain interfaces between soft components and other soft, compliant, and/or rigid components. Although many soft interfaces leverage adhesion, soft robot designers currently have limited information for selecting appropriate materials and fabrication techniques. Through experimental testing, this article characterizes how the substrate material and bonding process influence the performance of soft-soft silicone [i.e., polydimethylsiloxane-based interfaces], provides a framework for approaching this analysis, and contextualizes the data to provide initial insights into material selection for soft-soft interfaces by showing how the data could be used to guide design decisions. Specifically, this article characterizes five addition-curing silicone rubbers and five bonding processes, and it defines performance using quantitative metrics relating to desirable qualitative behaviors: toughness (adhesive fracture energy), flexibility (maximum localized strain during peeling), and strength (ratio of initial-to-average force and magnitude of initial peak peel force). Together, the substrate material and bonding method jointly determine the failure behavior of soft-soft silicone interfaces, influencing both the achievable performance (toughness, strength, flexibility) and characteristic failure modes (adhesive, cohesive, mixed-mode). Understanding characteristic failure modes can inform design strategies to mitigate interfacial failure, enabling higher-capability soft robots with improved operating loads and component lifetimes.

Shipyards are transitioning toward Industry 4.0 more slowly than other industrial sectors, and this inertia often limits the adoption of reliable digital workflows for reverse engineering. Within the wider research aimed at supporting the digital transition of shipbuilding operations, this study presents a dedicated methodology for evaluating 3D scan quality by combining three complementary indicators describing geometric completeness, agreement with a reference model, and measurement accuracy and variability. A purpose-designed test sample representative of shipbuilding geometrical challenges was manufactured in metal by CNC methods and in PLA through additive manufacturing. Two scanning systems, a field-oriented portable device and a metrology-oriented fixed system, were evaluated under raw surface conditions and with tracking enhancement strategies (optical markers and scanning spray). Results show that reflective surfaces represent a critical scenario, where tracking enhancement is essential to obtain continuous reconstruction and reliable dimensional correspondence. Conversely, with low-reflectivity surfaces, high-quality reconstructions can also be achieved with portable systems, with tracking enhancements mainly improving uniformity and repeatability. Overall, the proposed workflow provides a quantitative basis to support scanner selection, which involves a compromise between portability and achievable metrological performance, for shipyards reverse engineering applications.

Real-time algorithm for table tennis with a desktop robotic arm

Introduction: Mastering kicking techniques in Pencak Silat requires integration of cognitive understanding, psychomotor execution, and high motivation. Conventional teaching often inadequately addresses all three domains simultaneously. Objective: This study examined the effectiveness of a Problem-Based Learning (PBL) model in enhancing cognitive achievement, learning motivation, and skill performance in Pencak Silat. Methodology: A quasi-experimental pretest–posttest control group design was employed. Ninety-nine undergraduates participated: 48 in the experimental group received PBL instruction, and 51 in the control group underwent conventional teaching. Instruments were validated (Aiken’s V = 0.82–0.95) and reliable (α = 0.79–0.91). Paired-sample and independent t-tests were used to analyze within- and between-group differences, with Cohen’s d assessing effect sizes. Results: The experimental group showed significant post-test improvements in cognitive achievement (t(97) = 3.416, p = 0.001, d = 0.689), learning motivation (t(97) = 12.069, p &lt; 0.001, d = 2.461), and skill performance (t(97) = 9.262, p &lt; 0.001, d = 1.851). Paired-sample tests confirmed robust within-group gains, while the control group exhibited minimal cognitive change (p = 0.211). Discussion: PBL fostered active engagement, reflective thinking, and skill mastery, consistent with constructivist and Self-Determination Theory principles. Motivation and psychomotor improvements highlight the model’s holistic effect on martial arts learning. Conclusions: PBL significantly enhances cognitive, motivational, and psychomotor outcomes, providing an effective alternative to conventional teaching. Limitations include single-site implementation, short intervention duration, and partial reliance on subjective measures; future studies should examine long-term effects and multi-institutional replication.

Construction and demolition waste (C&DW) accounts for nearly one-third of total waste generation in the European Union, representing a significant environmental challenge. Although recovery rates are high (∼89%), much of the recycled material is downcycled, hindering true circular economy goals. This study proposes an integrated analytical method combining portable X-ray fluorescence (XRF), near-infrared hyperspectral imaging (NIR-HSI), and Shallow Neural Networks (SNN) for fast, accurate classification of earthquake-related C&DW from central Italy. Thirty sample sets from the 2016-2017 earthquake zones in Abruzzo, Marche, and Emilia Romagna were analyzed using portable energy-dispersive XRF to define three recycling-oriented material classes: concrete-based (CON), ceramic-rich (CER), and natural aggregates (NAT). Statistical tests and principal component analysis (PCA) confirmed significant differences among classes. NIR-HSI spectra (1000-1700nm) were processed to train an SNN with a single hidden layer. The classifier showed excellent precision, recall, specificity, and F1-scores (≥ 0.98) across classes, with misclassifications limited to borderline cases like glazed ceramics. The goal of this work is to evaluate the best achievable performance within a controlled feasibility framework, demonstrating that the coupling of NIR-HSI with SNN provides a rapid, robust, and transferable strategy for automated C&DW classification, thereby supporting circular economy goals through improved material recovery and recycling efficiency.

The study aimed to investigate the effect of using mechanical feedback on improving the technical performance of the discus throw and to examine its impact on the values of some digital indicators of technical performance for the study sample between the pre-test and post-test measurements. The researcher hypothesized that there are statistically significant differences between the pre-test and post-test for the digital indicators of the study group. The research population consisted of second-year students, Section 1, College of Physical Education and Sports Sciences, University of Misan, for the 2024 academic year, totaling 20 students. The study recommended conducting comparative studies between different types of mechanical feedback and their impact on performance levels across different age groups and both genders. It also recommended utilizing the current study results and generalizing them, particularly for the effectiveness of discus throw performance across all age categories. The study concluded that using mechanical feedback, in both its forms, has a comparable effect in enhancing the technical performance level of the discus throw.

Determining the risk of slipping on level ice using winter footwear with varied maximum achievable angle slip-resistance performance.

This letter considers a fluid antenna system (FAS)-aided rate-splitting multiple access (RSMA) approach for downlink transmission. In particular, a base station (BS) equipped with a single traditional antenna system (TAS) uses RSMA signaling to send information to several mobile users (MUs) each equipped with FAS. To understand the achievable performance, we first present the distribution of the equivalent channel gain based on the joint multivariate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula>-distribution and then derive a compact analytical expression for the outage probability (OP). Moreover, we obtain the asymptotic OP in the high signal-to-noise ratio (SNR) regime. Numerical results show that combining FAS with RSMA significantly outperforms TAS and conventional multiple access schemes, such as non-orthogonal multiple access (NOMA), in terms of OP. The results also indicate that FAS can be the tool that greatly improves the practicality of RSMA.

In the era of globalization and rapid technological advancement, students are increasingly exposed to complex challenges that affect their personal, social, and academic development. A competitive educational environment, unrestricted access to information, and dynamic social interactions require students to develop not only cognitive and emotional intelligence, but also spiritual intelligence that provides meaning, values, and moral direction in their lives. Spiritual intelligence plays a crucial role in helping students navigate ethical dilemmas, manage inner conflicts, and develop a sense of purpose. However, previous studies indicate that contemporary education tends to prioritize intellectual achievement and academic performance. Learning processes are often dominated by cognitive demands such as memorization, conceptual understanding, and test-oriented outcomes, while the internalization of spiritual and moral values receives relatively limited attention. As a result, students may achieve academic success but exhibit weaknesses in spiritual awareness, moral sensitivity, and self-regulation. This condition highlights a gap between educational goals and the holistic development of students, particularly in the spiritual dimension. In this context, the role of Islamic Education (PAI) teachers becomes highly significant. Although earlier research has emphasized the contribution of religious education to character formation, many studies focus mainly on curriculum or learning outcomes rather than students’ lived experiences. Therefore, this study employs a qualitative approach with phenomenological methods to explore how students perceive and experience the role of PAI teachers in enhancing spiritual intelligence. The findings reveal that PAI teachers function as spiritual role models, facilitators of religious activities, and spiritual mentors. At SMAN 1 Pamanukan, spiritual development is strengthened through religious habituation, Islamic school culture, student involvement in charitable activities and ROHIS, and reflective use of Islamic holidays and moments of calamity.