Search Results (474)

Adolescents are heavy users of digital media but often lack critical skills, increasing their vulnerability to harmful online content. The integration of game elements into learning and training offers a promising strategy to support positive behavioural change and strengthen adolescents’ skills. This paper describes the development of a conceptual framework for Dress-DIGITARIAN, a serious game aimed at improving health literacy, coping skills, and self-esteem, grounded in Self-Determination Theory (SDT). The framework was constructed to generate higher-order understanding through a multi-level process: analyzing general theory (SDT), integrating mid-range models (the Octalysis framework), and incorporating empirical insights derived from two data collection phases with the target population. This integrative approach informed and guided the game’s design through participatory methods. Developed through collaboration between schools and research institutions, this approach bridges theory and practice by aligning game mechanics with adolescents’ psychological needs. It also underscores the value of involving adolescents in research, not only to enhance scientific rigour but also to empower them as agents of change capable of contributing to health promotion policies and educational innovation. This study does not report the results of a completed intervention or outcome evaluation, which will be conducted in the sixth phase at the end of the current school year. Future research is needed to assess the model’s effectiveness and scalability and to identify areas for further refinement. Full article

Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks. Full article

Stator short-circuit faults (SSCFs) account for a significant portion of induction motor failures, yet their early detection remains a challenge in industrial environments where labeled fault data is scarce and installing additional sensors is often impractical. This paper proposes a novel, data-driven fault detection and diagnosis framework grounded in the Residual Information Value (RIV) principle to overcome reliability limitations of traditional spectral and residual energy methods. By redefining fault detection as a statistical test of independence between control inputs (voltages) and current residuals, the proposed method identifies incipient faults as model drifts without relying on prior knowledge of fault distributions. A key contribution of this work is the seamless integration of the diagnostic scheme into standard Variable Speed Drives (VSDs): the healthy nominal model (a Multilayer Perceptron) is trained exclusively using data from the drive’s existing self-commissioning routine, eliminating the need for manual data collection or complex physical parameter identification. Experimental validation on an industrial test bench demonstrates that the framework achieves superior diagnostic performance compared to traditional baselines, providing higher statistical separability and a reduced false alarm rate. The system can detect 1% incipient faults in approximately 61 ms while accurately identifying the faulty phase. The results confirm that the proposed RIV-based strategy offers a robust, non-intrusive, and industry-ready solution for predictive maintenance that effectively balances high-speed detection with enhanced statistical reliability. Full article

This study investigates how young children’s geometric reasoning develops through the act of drawing, examining how their eye movements, actions, and verbal explanations interact to reveal emerging structural awareness. Grounded in a developmental framework of structural reasoning, the study extends this model from static visual products to the dynamic processes involved in constructing geometric figures. Using an exploratory qualitative case study design, three children (ages 5.5–7.5) completed line, circle, and rectangle drawing tasks while their gaze and actions were recorded using mobile eye-tracking. Gaze data, video recordings, drawing product, and verbal responses were synchronized and analyzed frame by frame to examine gaze–action coordination. Analysis revealed a progression from partial structural awareness, where gaze remained embedded in action, to structural awareness, where gaze projected multiple steps ahead to coordinate global shape structure. Between these, an intermediate, process-oriented phase was identified, characterized by alternating gaze-in-activity and anticipatory fixations supporting local planning. These fine-grained gaze patterns reveal micro-level transitions in geometric reasoning that are not observable from final drawings alone. The study refines current models of geometric development by revealing how perceptual, representational, and embodied processes dynamically integrate during drawing, offering a more nuanced understanding of early structural reasoning and its implications for teaching geometry. Full article

In the context of three-dimensional (3D) point cloud modeling for pillar-type insulators during the “post-production–pre-use” phase, current methodologies encounter challenges in achieving a balance between cost-effectiveness, comprehensive coverage, and high precision. This study introduces a novel 3D point cloud modeling approach that utilizes multi-view two-dimensional (2D) LiDAR technology. This method employs three 2D LiDAR sensors positioned at 120° intervals to conduct layer-by-layer scanning, thereby capturing the surface point cloud data of insulators from various heights and perspectives. This approach effectively mitigates the impact of occlusion and facilitates comprehensive 360° data acquisition. Based on this foundation, the skirt structure characteristics of pillar-type insulators were extracted, and a point cloud registration and stitching algorithm, grounded in structural constraints, was developed to facilitate a high-precision 3D reconstruction. The experimental findings indicate that the proposed approach in this study demonstrates substantial improvements in modeling accuracy compared with the baseline methods. In repeated experiments, the proposed method in this study showed an average distance error with a mean (${\mu }_{\mathrm{MDE}}$) ± standard deviation ($\sigma$) of 1.15 ± 0.07, while the root mean square error had a mean (${\mu }_{\mathrm{RMS}}$) ± standard deviation ($\sigma$) of 1.26 ± 0.11. This method offers several advantages, including a straightforward structure, low system cost, and excellent point cloud continuity (1 mm). The maximum measurement error for the disc diameter was 2.986 mm, which satisfies the engineering application requirement of ±5 mm, thereby confirming the feasibility and practical utility of the method in the 3D modeling of pillar-type insulators. Full article

Current climatic conditions are drying peatland ecosystems, compromising carbon storage through increased decomposition and vegetation shifts. Large-scale monitoring is essential to quantify climate change impacts on vegetation and hydrology. PlanetScope high-resolution imagery (3 m pixel) over seven years (2017–2023) served as proof-of-concept for a central European peatland (Rzecin, Poland). The enhanced vegetation index (EVI) was selected based on ground validation (R = 0.9 vs. 0.8 for NDVI-normalised vegetation index). Phenological metrics (SOS—start of the season; EOS—end of the season; LOS—length of the season; POS—peak of the season; EVImax; amplitude; area) were derived via DATimeS from snow-free EVI time series. Trends were analysed using pixel-wise slopes, change-point detection (break ~2020–2021), paired correlations, subarea (P1–P4) behaviour, and PCA, alongside air temperature (Tair), precipitation, and water table depth (WTD). Results revealed LOS and peak EVI increased until 2020, a 2021 break, and a 2022–2023 recovery, signalling nonlinear vegetation reorganisation. Transitional mire floating mats (Sphagnum spp.–Carex spp.–Vaccinium oxycoccus) showed the longest seasons/highest greenness but weakest hydrometeorological links, implying rising internal dynamics. Phragmites mats, fern–sedge edges, and riparian willow differed in tolerance or sensitivity to WTD and precipitation oscillations. Tair dominated EVI seasonality across types, while WTD and precipitation controlled phenology and greenness in edges, showing better results with phase-aligned means. Vascular plants outpaced mosses in peak EVI and persistence, with patch-specific shifts. Full article

The current state and prospects of microreactor synthesis of functional materials in single- and two-phase flows with a liquid continuous phase are analyzed. Microreactors allow fine control over the size, composition, structure, and properties of synthesized particles in co-precipitation processes. The results obtained by various teams provide grounds to expect fairly extensive capabilities for controlling the processes of nucleation and particle growth in microreactors—by controlling the pH, reagent concentrations, micromixing quality, and residence time in each of the reactor zones—in the nucleation growth zones. The advantages of microreactor synthesis have been demonstrated with a high quality of micromixing in a volume of 0.2–0.5 mL, which ensures the production of nanoparticles without impurities, a stoichiometric ratio of atoms in the product, and limitation of agglomerate growth due to a short residence time (in the order of several milliseconds). The transition to an industrial scale is very easy due to the fairly high productivity of a single microreactor (up to 10 m³/day for suspension, up to 200–300 kg/day for solid phase). Intensive mixing in microreactors with a diameter of 2–4 mm or less, due to Taylor vortices, contributed to the use of two-phase microreactors for the synthesis of both organic and inorganic substances. Full article

Firm requirements on electromagnetic compatibility (EMC) of electronic devices demand low electromagnetic emissions (EMI) of high-speed circuits, especially in the automotive industry. To be able to apply cost-effective anti-perturbative measures that reduce noise emission, critical signal integrity and power integrity (SI/PI) tools are needed for developing high-speed printed circuit board (PCB) designs. This paper presents an efficient method for modeling and analyzing the current drawn by digital ICs based on SPICE modeling data. The profile of the current drawn by the ICs from the power supply is composed of the static supply current and the dynamic supply current. This method enables power integrity engineers, in particular, PhD students and researchers who aim to develop an intuitive understanding of PI phenomena during the pre-layout phase, to see the hidden impact of the supply current on the power rail noise through time domain simulations, using a complex simulation model that integrates the Finite-Difference Time-Domain (FDTD) method of modeling the power and ground plane, with Voltage Regulator Modules (VRMs) and decoupling capacitors. A comparison of simulation results between the proposed models and SPICE IC models is also included to validate the proposed model. Full article

Single-phase grounding is the dominant fault type in urban power distribution networks. Because the total magnetic flux would not change around the cable under a single-phase grounding fault, ferromagnetic zero-sequence current sensors cannot distinguish the faulted phase of belted cables, which are the main type in 10 kV distribution networks. To fill this gap, a two-step methodology is proposed using an annular TMR magnetic sensor to measure the magnetic field intensity at six points on the cable surface and to distinguish the faulted phase using the magnetic field intensity differences between the TMRs. The first step is calculating the rotation angles between the six magnetic sensors and the three cable cores after installation. A differential evolution algorithm is used to calculate the rotation angles in the sensing model. The second step is to detect the fault phase under a single-phase grounding fault transient, with the magnetic field intensity difference taken as the criterion. The methodology is verified through simulation and experiment. The results show that the relative errors of the rotation angles are all less than 1%. Under a single-phase grounding fault, the faulted phase can be accurately identified. The proposed method can effectively identify the faulted phase of 10 kV three-core cables under single-phase grounding and has significant engineering application value. Full article

Background: Older adult falls during step negotiation result in higher injury rates compared to level ground falls. Previous research on discrete events during step negotiations may not capture important age-related changes. Curve analysis techniques enable assessment of an entire time series and may further advance the understanding of older adult falls during step negotiation. The purpose of the current study was to investigate lower extremity kinematics during transition step negotiation in older women with fall history compared to young women using statistical parametric mapping (SPM). Methods: 15 older female adults with a fall history and 15 young female adults participated in the study. Participants performed walking trials along a 5.5 m raised walkway, descended a 17 cm step and continued walking 3 m. Data was processed from lead limb toe-off prior to the step, through lead limb weight acceptance of the transition step. SPM was used to perform independent t-test analysis of the three-dimensional lower extremity time series. Results: The older faller group showed significantly decreased lead hip abduction (9–19% of step negotiation, mean difference: 3.74°, p = 0.045), increased lead knee flexion (65–80% of step negotiation, mean difference: 5.8°, p = 0.012), and increased trail limb hip adduction (91–100% of step negotiation, mean difference: 3.92°, p = 0.046). Conclusions: The older faller group showed altered hip joint angles in the frontal plane and knee joint angles in the sagittal plane during early swing and late weight acceptance phases, which may reflect compensatory strategies for reduced strength and/or balance. Curve analysis provides additional insight into age-related kinematic changes during step negotiation that may be related to older adult fall risk. Full article

Parallel laying of high-voltage cables will generate a zero-sequence current, due to spatial electromagnetic induction, which reduces the cable’s current-carrying capacity, causing heating and corrosion of the grounding points and deteriorating grounding performance. Currently, there is a lack of effective control measures. This article establishes a calculation model for the cable sheath current under the condition of double circuit cable cross interconnection grounding, analyzes the causes of a zero-sequence grounding current in a double circuit cable sheath, and proposes an optimal phase sequence selection method, considering load changes with the goal of maximizing the probability of the cable sheath current, not exceeding the standard. The results show that when the double circuit cable is evenly distributed in the cross interconnection section, the zero-sequence grounding current will be generated on the metal sheath of the cable, causing an excessive total grounding current. By applying the proposed probability-based phase-sequence optimization, the likelihood that both circuits simultaneously satisfy the sheath-current criterion can be significantly improved; for example, under representative layouts and load distributions, the “both-within-limit” probability can reach 53.3% (horizontal layout), 76.2% (horizontal equilateral triangle layout), 90.5% (vertical layout), and 81.6% (vertical equilateral triangle layout). For different working conditions, selecting the optimal load phase sequence combination by maximizing the probability of the sheath current and not exceeding the standard within the current carrying area can help to reduce the cable sheath current. Full article

The current study investigated the effects of geopolymer composites formulated from pumice dust partially replaced by ground granulated blast furnace slag (GGBFS) and fly ash (FA) at levels of 10%, 20%, 30%, and 40% by weight. The mixtures were evaluated for flowability, compressive strength (7, 28, and 56 days), density, and water absorption (28 and 56 days) at ambient temperatures. Moreover, compressive strength, mass loss, density, and water absorption were evaluated after exposure of the mixtures to elevated temperatures (250 °C, 500 °C, and 750 °C) at 28 days. All specimens were initially cured at 60 °C for 24 h, followed by storage under ambient laboratory conditions until testing. The inclusion of GGBFS into the mixtures decreased flowability, and the inclusion of FA resulted in its improvement. At ambient temperature, GGBFS-based mixtures, which were high in calcium content, exhibited substantially superior compressive strength and reduced absorption relative to FA-based mixtures due to the development of dense C-A-S-H gel networks. However, the compressive strength of FA-based mixtures considerably increased when exposed to a temperature of 250 °C. Moreover, at 750 °C, the FA-based mixtures showed superior residual strength (up to 18.1 MPa), lower mass loss, and reduced absorption, indicating enhanced thermal stability due to the dominance of thermally resistant N-A-S-H gels. X-ray diffraction results further supported these trends by showing the rapid deterioration of calcium-rich phases under heat and the comparative stability of aluminosilicate structures in FA-based systems. Overall, the inclusion of up to 40% GGBFS is beneficial for early strength and densification, whereas the incorporation of up to 40% FA improves durability and mechanical retention under high-temperature conditions. Full article

Fruit growing is a traditional component of Bulgarian agricultural production. According to the latest statistical data, the share of areas planted with cherries is 10.5% of the total orchard area, and with apples, 7.2%, totaling 67,800 ha. This article presents the results of ground and remote (satellite) measurements and observations of cherry and apple orchards, along with the methods for their processing and interpretation, to define the current state and forecast their expected development. This research aims to combine the capabilities of the two approaches by improving and expanding observation and forecasting activities. Ground-based measurements and observations consider the dates of a permanent transition in air temperature above 5 °C and several cardinal phenological stages, based on the idea that a certain temperature sum (CU, GDH, GDD) must accumulate to move from one phenological stage to another. The obtained data were statistically analyzed, and by means of classification with the Random Forest algorithm, the dates for the occurrence of the stages of bud break, flowering, and fruit ripening in the development of cherry and apple orchards were predicted with an accuracy of −6 to +2 days. Satellite studies include creating a database of Sentinel-2 digital images across different spectral bands for the studied orchards, investigating various post-processing approaches, and deriving indicators of developmental phenostages. Ground data from the 2021–2023 experiment in Kyustendil and Plovdiv were used to determine the phases of fruit bursting, flowering, and ripening through satellite images. An assessment of the two approaches to predicting the development of the accuracy of the models was carried out by comparing their predictions for bud swelling and bursting (BBCH 57), flowering (BBCH 65), and fruit ripening (BBCH 87/89) of the observed phenological events in the two selected orchard types, representatives of stone and pome fruit species. Full article

Industrial robotic workstations contribute substantially to the total energy demand of modern manufacturing, yet most existing energy-saving approaches focus on modifying robot trajectories, motion parameters, or the position of the robot’s base. This paper proposes a novel methodology for the automatic optimization of the spatial placement of a fixed technological trajectory within the robot workspace, without altering the task itself. The method combines pre-simulation filtering of infeasible configurations, large-scale energy simulation in ABB RobotStudio, and real measurement using a dual acquisition system consisting of the robot’s controller and an external power meter. A digital twin of the workstation is used to systematically evaluate thousands of candidate positions of a standardized trajectory. Experimental validation on an ABB IRB 1600–10/1.2 confirms a 23.4% difference in total energy consumption between two workspace configurations selected from the simulation study. The non-optimal configuration exhibits higher current draw, greater power variability, and a more intensive warm-up phase, indicating increased mechanical loading arising purely from geometric placement. By providing a scalable, trajectory-preserving approach grounded in digital-twin analysis and IoT-based measurement, this work establishes a data foundation for future AI-driven predictive and adaptive energy optimization in smart manufacturing environments. Full article

Aiming to address the core issue that the current intelligence evaluation for Unmanned Ground Vehicles (UGVs) overly rely on static performance metrics and lack dynamic quantitative characterization of learning internalization capability (LIC), this study proposes a dynamic evaluation framework based on time series analysis. The framework begins by constructing a multidimensional test scenario parameter system and collecting externally observable performance sequence data. It then introduces a sliding window-based slope-standard deviation collaborative analysis technique to achieve unsupervised division of learning phases, from which five core evaluation metrics are extracted to comprehensively quantify the multidimensional dynamic characteristics of LIC in terms of efficiency, stability, and overall effectiveness. Simulation experiments were carried out using UGVs equipped with three types of path-planning algorithms in low-, medium-, and high-difficulty scenarios. Results demonstrate that the proposed algorithm can effectively distinguish multi-dimensional differences in LIC among different UGVs, exhibiting strong discriminative power and interpretability. This study provides a standardized evaluation tool for UGV intelligent selection, algorithm iteration optimization, and training strategy design, and offering significant reference value for the evaluation of the learnability of autonomous driving systems. Full article