Search Results (14,222)

In this paper, an integrated geometric and thermal-induced errors prediction approach for active error compensation in machine tools is proposed and evaluated. The proposed approach is based on a hybrid of physical and neural network predictive modeling to drive an adaptive position controller for real-time error compensation including geometric and thermal-induced errors. Error components are formulated as a three-dimensional error field in the time-space domain. This approach involves four key steps for its development and implementation: (i) simplified experimental procedure combining a multicomponent laser interferometer measurement system and sixteen thermal sensors for error components measurement, (ii) artificial neural network-based predictive modeling of both position-dependent and position-independent error components, (iii) tridimensional volumetric error mapping using rigid body kinematics, and finally (iv) implementation of the real-time error compensation. Assessed on a turning center, the proposed approach conducts a significant improvement of the machine accuracy. The maximum error is reduced from 30 µm to less than 3 µm under thermally varying conditions. Full article

Proteins elicit various metabolic and physiological functions that are related to physical performance. Due to increased need in athletes, protein supplementation has been widely used to support recovery and performance. However, the extent to which acute gains in muscle protein synthesis translate into measurable performance remains debated. This narrative review synthesizes evidence from trials on supplemental proteins across resistance, endurance, and mixed-modality training, comparing sources (whey, casein, soy, pea, and blends). Moreover, this review summarizes dosing and timing strategies, with notes for master, diabetic, and female athletes. It is well-established that supplemental protein enhances fat-free mass and, to a lesser extent, strength when baseline dietary protein is suboptimal. However, the effects are smaller when habitual intake already meets athletic targets. Whey, as a rapid protein and rich in leucine, reliably elicits an acute anabolic response, while casein provides prolonged elevated aminoacidemia. When total intake and leucine thresholds are matched, plant proteins and blends can yield comparable long-term adaptations. In addition, studies showed that the distribution and strategic timing around exercise (post-exercise first, with optional pre-sleep casein or blends) support recovery during high-frequency training or energy deficit. Protein co-ingested with carbohydrate in endurance and high-intensity functional training (HIFT) can also help glycogen restoration and attenuate muscle-damage markers, though effects on sport outcomes are inconsistent. The evidence in diabetic athletes is limited; guidance extrapolates from diabetes and athlete studies, with benefits apparent when intake, quality, or distribution are limited. Furthermore, evidence indicates that anabolic resistance in master athletes requires higher per-meal doses and distribution, with post-exercise and pre-sleep feedings valuable. Consistently, female athletes partaking in aerobic and resistance training while supplementing with protein demonstrate desired body composition adaptations. Overall, although supplemental protein helps close gaps between intake and physiological demand, various factors may influence its regimen. Protein source may help the kinetics balance, amino-acid profile, and dietary preferences. Alternatively, timing may influence the protein effects on training and recovery. Full article

Real-time monitoring of atmospheric emissions is critical for ensuring transparency, compliance, and rapid response to environmental risks. However, traditional systems often suffer from latency and a lack of verifiable data integrity. This paper presents AABF+, an adaptive packetization and microblock model built on a permissioned blockchain that supports intelligent emissions monitoring. The proposed system dynamically groups sensor readings into microblocks and commits them using Byzantine Fault Tolerant (BFT) consensus, enabling both high throughput and verifiable traceability. Unlike fixed-window blockchains, AABF+ adapts the microblock size and time window based on incoming data rates, balancing responsiveness and reliability. The model was implemented and experimentally evaluated in an edge-class 1 GbE testbed under real MRV (Measurement–Reporting–Verification) conditions. Results show that AABF+ achieves a median end-to-end latency of 0.96 s for single-record transactions and 3.07 s for 1000-record batches, while maintaining strong cryptographic verification of all entries. These findings demonstrate that AABF+ provides second-level data freshness with verifiable provenance, offering a practical foundation for digital environmental governance and regulatory compliance in Industry 4.0 ecosystems. Full article

This article presents a system for automatically updating a digital twin model, used for automated path planning of an industrial robot. The digital twin needs to be accurately calibrated in relation to the resource locations due to the physical limitations of placing resources out precisely. The process considered is a surface roughness measurement of aerospace metal parts that requires high positional accuracy. The scenario takes place in a robot cell that is a Plug & Produce system, where resources can be added and removed in minutes, allowing fast reconfiguration of the production resources. This means that an automated path planner is required for the robot to adapt to new locations of these resources automatically. A digital twin is proposed, consisting of a robot path planner and a simulation model that is updated when resources are added to the system. The resources should automatically appear in the simulation and be placed at an accurate location. The purpose of automating these steps is to make the update of the digital twin faster during production and remove the requirement for expert knowledge. Full article

Accurate remaining useful life (RUL) prediction of tantalum capacitors is essential for enhancing the reliability and maintainability of power electronic systems. However, online RUL prediction remains a challenging task due to the difficulty of accessing internal degradation states and the non-stationarity of operating conditions. This paper presents a novel CNN-LSTM-Attention-based deep learning framework for accurate online RUL prediction of tantalum capacitors, leveraging infrared surface temperature measurements and ambient thermal compensation. The proposed framework initiates with the collection of degradation temperature data under controlled accelerated aging experiments, where true degradation indicators are extracted by eliminating ambient temperature interference through dual-sensor compensation. The resulting preprocessed data are used to train a hybrid deep neural network model that integrates convolutional layers for local feature extraction, long short-term memory (LSTM) units for sequential dependency modeling, and a soft attention mechanism to selectively focus on the critical degradation patterns. A channel attention module is further embedded to adaptively optimize the importance of different feature channels. Experimental validation using three groups of aging data demonstrates the effectiveness and superiority of the proposed method over conventional LSTM and CNN-LSTM baselines. The CNN-LSTM-Attention model achieves a substantial improvement in prediction accuracy, with mean absolute percentage error (MAPE) reductions of up to 60.97%, root mean squared error (RMSE) reductions of up to 65.63%, and coefficient of determination (R²) increases of up to 68.67%. The results confirm the ability to deliver precise and robust online RUL predictions for tantalum capacitors under complex operational conditions. Full article

Background/Objectives: The marginal adaptation of CAD/CAM restorations remains a key determinant of long-term clinical success, particularly in minimally invasive preparations. This in vitro study evaluated and compared the marginal gap of three CAD/CAM restorative materials—Cerasmart, G-CAM, and IPS Empress CAD—using standardized preparation and SEM measurement protocols. Methods: A total of 18 crowns were fabricated, of which 9 presented margins sufficiently interpretable under SEM and were included in the pooled quantitative analysis (n = 362 measurement points). Marginal gaps were recorded at 45×, 100× and 450× magnification using a Jeol JSM 25S scanning electron microscope. Normality and variance homogeneity were verified prior to parametric testing. Results: When pooled per material group, the mean ± SD marginal gap values were 18.53 ± 14.15 µm for Cerasmart, 21.60 ± 14.89 µm for G-CAM, and 47.09 ± 16.93 µm for IPS Empress CAD. All values fell below the contemporary clinical threshold of <70 µm for adhesive cementation. Pairwise comparison showed a large difference between IPS Empress CAD and the two resin-based materials, whereas the difference between Cerasmart and G-CAM was small. Conclusions: Hybrid and resin nano-ceramic CAD/CAM materials demonstrated narrower marginal gaps compared with the glass ceramic tested, likely due to their lower elastic modulus and greater seating accommodation during cementation. Within the limits of this in vitro design, all materials exhibited marginal adaptation consistent with current clinical acceptability criteria. Full article

As the transformative influence of novel technologies sweeps across industries, organisations are called upon to position their staff in the equally dynamic operational environment, which includes embedding technical and legal communication skills in their training programs. For many organisations, internal and external communication of data modelling and related concepts, reporting, and monitoring still pose major challenges. The aim of this research is to develop an effective data training framework for learners with or without mathematical or computational maturity. It also addresses subtle aspects such as the legal and ethical implications of dealing with organisational data. Data was collected from a training course in Python, delivered to government employees in different departments in the United Arab Emirates (UAE). A structured questionnaire was designed to measure the effectiveness of the training program using Python, from the employees’ perspective, based on three key attributes: their personal characteristics, professional characteristics, and technical knowledge. A descriptive analysis of aggregations, deviations, and proportions was used to describe the data attributes gathered for the study. The main findings revealed a huge knowledge gap across disciplines regarding the core skills of big data analytics. In addition, the findings highlighted that previous knowledge about statistical methods of data analysis along with prior programming knowledge made it easier for employees to gain skills in data analytics. While the results of this study showed that their training program was beneficial for the vast majority of participants, responses from the survey indicate that providing a solid knowledge of technical communication, legal and ethical aspects would offer significant insights into the big data analytics field. Based on the findings, we make recommendations for adapting conventional data analytics approaches to align with the complexity or the attainment of the non-orthogonal United Nations Sustainable Development Goals (SDG). Associations of selected responses from the survey with some of the key data attributes indicate that the research highlights vital roles that technology and data-driven skills will play in ensuring a more prosperous and sustainable future for all. Full article

This paper proposes a novel convention-based subgrid scale (SGS) model for large eddy simulation (LES) by using the Liutex concept. Conventional SGS models typically rely on the eddy viscosity assumption, which uses the linear eddy viscosity terms to approximate the nonlinear effects of unresolved turbulent eddies, that should be measured by unresolved Liutex. However, the eddy viscosity assumption is empirical but lacks a scientific foundation, which limits its predictive accuracy. The proposed model in this paper directly models the convective terms and demonstrates several key advantages: (1) the new model gets rid of isotropic assumption for the unresolved SGS eddies which are, in general, anisotropic, (2) the new model contains no empirical coefficients which need to be adjusted case by case, (3) the new model explicitly captures nonlinear convective effects by the SGS eddies and (4) the new model is consistent with the physics for boundary layer as the model becomes zero in the laminar sublayer, where Liutex becomes zero automatically. This new model has been applied in the flat plate boundary transition flow, and the results show that it outperforms the popular and widely adopted wall-adapting local eddy (WALE) model. This new model is a conceptual breakthrough in SGS modeling and has the potential to open a new direction for more accurate SGS models and future LES applications. Full article

We propose a novel target detection algorithm that addresses the issues of ignoring shape attributes in regression loss and the inability of the high-parameter PAFPN to jointly perceive scale–space–task information. Specifically, we construct a Lightweight Path Aggregation Feature Pyramid Network (LPAFPN) to reduce model parameters by shuffling and fusing features across channels. To further enhance its perception ability, we augment LPAFPN with a scale–space–task joint-perception enhancement module, terming the resulting network ALPAFPN, which can adaptively process joint information of scale, space, and task. Finally, we introduce a shape-scale bounding box regression loss method that focuses on the target’s intrinsic attributes to optimize the regression measurement, thereby boosting the detection accuracy. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of F1 score, Precision, and Mean Average Precision (mAP) on the PASCAL VOC and VisDrone2019-DET datasets. Full article

Monitoring electrodermal activity (EDA) and sweat rate (SR) and volume hold promise for yielding neurological health insights about individuals. A combination of standard EDA monitoring with the quantitative analysis of perspired sweat volume, rate, and composition represents a promising advancement for improving the understanding and reliability of EDA signals. In this picture, exploiting printed electronics to face challenges related to bulky gold-standard setups and to achieve integration in fully wearable devices represents one of the most interesting approaches addressed by recent research. In this review, we present an overview of the principal techniques, materials, and measurement methods reported for fabricating EDA and sweat monitoring electrodes. We highlight the increasing effect of printing technologies as a key enabler for scalable, low-cost, and customizable fabrication of flexible sensors suited for on-skin applications. These approaches not only support mass production but also enhance adaptability and comfort in wearable formats. Overall, the review emphasizes how printed technologies significantly improve physiological signal quality and open new opportunities for continuous, non-invasive, and personalized health monitoring. Full article

Cardiopulmonary exercise testing (CPET) is a gold standard to assess cardiorespiratory fitness (CRF). CRF varied through the lifespan, increasing in children until early adulthood and then gradually declining. Reference values for CPET are used to check whether the child’s CRF falls within the normal range. The differences between directly measured and normative age-adjusted exercise data may suggest pathology and are helpful during the diagnostic process. Deriving reference values for children is particularly challenging. Moreover, many children participate in sports, some at an advanced/elite level, which translates into specific adaptations in CPET. The ATS/ACCP statement on CPET presented a checklist that should be followed when reporting reference values. However, the checklist originally focused on adults. This aggravates the quality of reporting pediatric reference values for CPET, making between-studies comparisons difficult. This review (1) presents a step-by-step protocol to fulfill all requirements from the ATS/ACCP statement in the pediatric population, and (2) summarizes the key challenges in deriving reference values for CPET in children, especially among pediatric athletes. Additional recommendations to enrich the quality of reporting reference values for CPET in pediatric athletes were also discussed. Full article

As the volume of reinforced concrete structures continues to grow, it is important to determine the quality of concrete in the shortest time possible. Therefore, the development and validation of methods for non-destructive testing (NDT) of concrete structures are becoming increasingly important. However, some factors may affect the accuracy of the measurement results obtained as concrete is often exposed to a moist environment, e.g., in marine structures. Ignoring these factors may lead to an inaccurate interpretation of measurements. Therefore, in this research, the water saturation factor of concrete was investigated in response to various NDT methods. C25/30 and C40/50 MPa concrete were evaluated using ultrasonic pulse velocity and rebound hardness devices, and for the first time, a drilling resistance (DR) method was systematically adapted and validated for moisture-affected concrete testing. Unlike conventional approaches that only consider surface effects, the DR method introduced here provides in-depth profiling of concrete, revealing variations in resistance with depth and identifying zones influenced by internal moisture distribution. This study demonstrates that the DR method can complement traditional NDT techniques, providing a more reliable evaluation of moisture-induced variations in concrete properties. Moreover, with the novel DR method, changes in the mechanical response with depth have been quantified, offering new insight into internal moisture effects that are not accessible by conventional NDT methods. Full article

Developmental language disorder (DLD) is associated with persistent language difficulties that may impact social competence. The aim of this study is to describe the relationship between language, pragmatics, and social competence in French-speaking preschoolers and to identify the specific social competence difficulties observed in children at risk of DLD at this age. The sample included 63 children aged between 36 and 59 months, 12 of whom were at risk of having DLD. Children were assessed using measures of vocabulary, morphosyntax, pragmatic skills, and narrative abilities, while childcare educators completed a questionnaire evaluating social competence. Results revealed that children at risk for DLD exhibited more characteristics related to dependence on adults compared to their peers without DLD. No significant group differences were observed for the other components of social competence. The findings also identified a relationship between pragmatic and personal narrative skills, and social adjustment. These findings support the social adaptation model, suggesting that functional social impacts in children with DLD may arise from limited language abilities rather than an intrinsic socio-emotional disorder. This study highlights the importance of early pragmatic and narrative development in supporting social competence from the preschool age. Full article

Problematic Internet use is an emerging concern in adolescent mental health and is closely linked to psychological distress and emotional regulation. This cross-sectional study analyzed self-reported data from 1550 adolescents aged 11–19 years in South Tyrol, a linguistically and culturally diverse region in Northern Italy. Problematic Internet use was measured using the validated Generalized Problematic Internet Use Scale 2 (GPIUS-2), along with standardized instruments for depressive symptoms (PHQ-2) and anxiety (SCARED-GAD). Multivariable regression analysis revealed that depression and anxiety symptoms were the strongest independent predictors of higher GPIUS-2 scores. In contrast, demographic factors such as gender, family language, urbanization, migration background, and parental education were not significantly associated with PIU. Modest associations were observed between GPIUS-2 scores and both perceived economic burden and parental use of digital control tools. Perceived family support showed a small protective effect. These findings underscore the central role of emotional vulnerability in adolescent PIU and suggest that interventions should focus on supporting mental health and adaptive coping rather than solely targeting screen time or structural family characteristics. Full article

Artificial intelligence (AI) is generating a profound and quick transformation in several areas of knowledge, as well as in industry and society on a global scale, and is considered one of the most significant technological advances of the present era. Understanding citizens’ attitudes toward AI is essential forguiding its development and implementation. To achieve this, valid and reliable instruments are needed to assess attitudesin different sociocultural contexts. With this objective, the General Attitudes towards Artificial Intelligence Scale (GAAIS) was adapted to Spanish. The sample comprised 644 participants: 327 men and 316 women, aged between 18 and 78 years (M = 33.06, SD = 14.91). The original two-factor structure (Positive GAAIS and Negative GAAIS) was validated using Confirmatory Factor Analysis (CFA). Both the fit indices and the internal consistency of the scale were adequate. Furthermore, the validity of the measure (i.e., convergent and discriminant) and the invariance of the model were confirmed. The analyses performed support the adequacy of the model and, therefore, the usefulness of the instrument, considering the ambivalence that people often experience regarding AI. The limitations of the study and the implications for the design of public policies and intervention strategies that promote the ethical, equitable, and socially responsible use of AI are discussed in this study. Full article