Search Results (413)

Digital competence refers to the integration of digital technology in teaching and learning, as outlined in the national curriculum of Estonia for upper secondary schools. This study presents original research findings on Estonian science teachers’ use of digital tools and materials, their digital competence, and the main benefits and challenges they face. The findings emphasize the need for continued professional development, accessible digital resources, and equitable digital infrastructure to maintain Estonia’s leadership in digital science education. A survey of 58 secondary school science teachers revealed that computers (desktops, laptops, and tablets) are the primary digital tools used. The article explores digital literacy advancements in secondary science education, research methodologies used to assess digital tool usage, and key findings from recent studies. However, challenges such as digital equity, technological barriers, and digital fatigue persist. Additionally, discrepancies were found in teachers’ responses regarding digital tool effectiveness, implementation strategies, and perceived barriers. While some teachers reported a successful integration and improved student outcomes, others highlighted difficulties in aligning digital resources with curriculum requirements and pedagogical approaches. Full article

Nowadays, Graphical Processor Units (GPUs) are a great technology to implement Artificial Intelligence (AI) processes; however, a challenge arises when the inclusion of a GPU is not feasible due to the cost, power consumption, or the size of the hardware. This issue is particularly relevant for portable devices, such as laptops or smartphones, where the inclusion of a dedicated GPU is not the best option. One possible solution to that problem is the use of a CPU with AI capabilities, i.e., parallelism and high performance. In particular, RISC-V architecture is considered a good open-source candidate to support such tasks. These capabilities are based on vector operations that, by definition, operate over many elements at the same time, allowing for the execution of SIMD instructions that can be used to implement typical AI routines and procedures. In this context, the main purpose of this proposal is to develop an ASIC Vector Engine RISC-V architecture compliant that implements a minimum set of the Vector Extension capable of the parallel processing of multiple data elements with a single instruction. These instructions operate on vectors and involve addition, multiplication, logical, comparison, and permutation operations. Especially, the multiplication was implemented using the Vedic multiplication algorithm. Contributions include the description of the design, synthesis, and validation processes to develop the ASIC, and a performance comparison between the FPGA implementation and the ASIC using different nanometric technologies, where the best performance of 110 MHz, and the best implementation in terms of silicon area, was achieved by 7 nm technology. Full article

We demonstrate the potential of using the convolutional equivalent layer to jointly process large gravity-gradient datasets. Based on the equivalent-layer principle, we assume a single fictitious physical property distribution on a planar layer can approximate all components of the gravity-gradient tensor. Estimating this distribution using the classical technique ensures physical consistency among components. However, the classical approach becomes computationally prohibitive for large datasets due to the need to solve a large-scale inversion with a massive sensitivity matrix. To overcome this limitation, we exploit the block-Toeplitz Toeplitz-block structure of the sensitivity matrix for data on a regular horizontal grid. This structure significantly reduces computational cost—by orders of magnitude—compared to the classical method. Applications to synthetic and real datasets show that our method offers a computationally efficient alternative for processing large gravity-gradient data from various acquisition systems (AGG and FTG), even when data are irregularly spaced or flight lines are misaligned. On a standard laptop configuration, our method processed over 290,000 AGG data points in a few tens of seconds. It also handled between 726,000 FTG and 1,250,000 AGG data points within seconds to a couple of minutes, demonstrating practical computational efficiency for large-scale datasets. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

This paper presents the conception, design and real miniature instrument implementation of a dual-purpose sensor for small satellites that can act as a star tracker and space debris detector. In the previous research work, the authors conceived, designed and implemented a breadboard consisting of a computer laptop, a camera interface and camera controller, an image sensor, an optics system, a temperature sensor and a temperature controller. It showed that the instrument was feasible. In this paper, a new real star tracker miniature instrument is designed, physically realized and tested. The implementation follows a New Space approach; it is made with Commercial Off-the-Shelf (COTS) components with space heritage. The instrument’s development, implementation and testing are presented. Full article

School-aged children are routinely exposed to additional physical stress due to carrying school backpacks. These backpacks often exceed recommended limits and can contain not only books and notebooks but also laptops, water bottles, and other personal items. The present study aimed to evaluate the impact of different backpack loads (10%, 15%, and 20% of body weight) on dynamic gait parameters in 7-year-old girls and boys. Twenty-six children (13 girls, 13 boys) participated in the study. Gait analysis was performed using the Footscan® system (RSscan International, Olen, Belgium; 2 m × 0.4 m × 0.02 m, 16,384 sensors) equipped with Footscan software version 7 (Gait 2nd generation), examining peak force (FMAX), peak pressure (PMAX), contact area (CA), and time to peak force (Time to FMAX) across five anatomical foot zones. The study revealed significant changes in all parameters, particularly at loads of 15% and 20% of body weight. Increases in plantar pressure, contact area, and asymmetry were observed, along with delays in time to peak force. These findings support the recommendation that children’s backpack loads should not exceed 10% of their body weight to prevent potential adverse effects on postural and musculoskeletal development. Full article

We propose a multiple-input multiple-output (MIMO) multi-band antenna for the WiFi-6E band. The planar size of the proposed antenna is 40 × 7.6 mm², and it is built on a FR4 substrate with a thickness of 0.8 mm. By utilizing a 3 × 3.8 mm² L-shaped parasitic element, additional resonant modes are introduced through coupling and resonance. The operating frequency bands are WLAN 2.4 GHz (2.4–2.8 GHz) and Wi-Fi 6E (5.06–7.13 GHz). The average antenna gain is 4.2 dBi, with an envelope correlation coefficient (ECC) less than 0.020 and a radiation efficiency between 70% and 92%. Full article

Today, lithium-ion batteries (LIBs) are widespread and play a vital role in advancing portable electronics (laptops and mobile phones), green energy technology (electrical vehicles), and renewable energy systems. There is about 30% off-spec scrap LIB production during manufacturing. This trend has caused the accumulation of a huge number of spent LIBs. In addition to containing chemicals that are harmful to the environment, these batteries also contain critical metals; their recycling will greatly help to maintain a green and sustainable economic transition. Therefore, this issue has forced researchers to seek cost-effective and eco-friendly strategies for recycling LIBs. The pretreatment of waste batteries is an essential part of LIB recycling. This article aims to comprehensively review the basic structure of LIBS and existing pretreatment methods in recycling critical metals from LIBs, with a special focus on recent innovations. This manuscript has been prepared to help researchers conduct cutting-edge and novel research in LIB pretreatment and recycling. This approach not only helps researchers to understand the concepts, but also helps to identify and evaluate the strengths and weaknesses of different pretreatment methods. Also, in addition to mentioning the existing research limitations, suggestions for future research perspectives and less investigated areas that need further research have been presented. Full article

This study proposes an Aspect-Enhanced Prompting (AEP) method for unsupervised Multi-Source Domain Adaptation in Aspect Sentiment Classification, where data from the target domain are completely unavailable for model training. The proposed AEP is based on two generative language models: one generates a prompt from a given review, while the other follows the prompt and classifies the sentiment of an aspect. The first model extracts Aspect-Related Features (ARFs), which are words closely related to the aspect, from the review and incorporates them into the prompt in a domain-agnostic manner, thereby directing the second model to identify the sentiment accurately. Our framework incorporates an innovative rescoring mechanism and a cluster-based prompt expansion strategy. Both are intended to enhance the robustness of the generation of the prompt and the adaptability of the model to diverse domains. The results of experiments conducted on five datasets (Restaurant, Laptop, Device, Service, and Location) demonstrate that our method outperforms the baselines, including a state-of-the-art unsupervised domain adaptation method. The effectiveness of both the rescoring mechanism and the cluster-based prompt expansion is also validated through an ablation study. Full article

This study evaluates and compares the environmental and economic implications of desktop and laptop computer systems throughout their life cycles using screening life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The functional unit was defined as the use of one computer system for fundamental home and small-business productivity tasks for over four years. The analysis considered the production, use, and end-of-life phases. The results showed the desktop system had a higher overall carbon footprint (679.1 kg CO_2eq) compared to the laptop (286.1 kg CO_2eq). For both systems, manufacturing contributed the largest share of the emissions, followed by use. Desktops exhibited significantly higher use phase emissions, due to greater energy consumption. Life cycle cost analysis revealed that laptops had slightly lower total costs (EUR 593.88) than desktops (EUR 608.40) over the 4-year period, despite higher initial investment costs. Sensitivity analysis examining different geographical scenarios highlighted the importance of considering regional factors in the LCA. Manufacturer-provided data generally showed lower carbon footprint values than the modeled scenarios. This study emphasizes the need for updated life cycle inventory data and energy efficiency improvements to reduce the environmental impacts of computer systems. Overall, laptops demonstrated environmental and economic advantages over desktops in the defined usage cases. Full article

A distance range join query (DRJQ) is a fundamental and critical operation in spatial database queries. It identifies geographic elements within specified distance ranges. This technique has a wide range of applications in multiple domains, including Geographic Information Systems (GISs), urban planning, and environmental monitoring. However, performing a DRJQ on large-scale spatial data remains a challenging problem, as the computational complexity of existing techniques escalates rapidly with increasing volumes of data. We propose PixelQuery, an efficient DRJQ method specifically optimized for visualization analysis. PixelQuery integrates spatial indexing with visualization-oriented strategies. It directly computes the display values of query results within the viewport, substantially lowering computational costs. Experiments conducted on datasets of varying scales demonstrate that this method can handle visualization queries involving tens of millions of elements on a standard laptop, with a maximum processing time of only 7.64 s. This technology provides a robust solution for rapid DRJQ processing and the visualization of large-scale vector data, offering promising potential for a diverse range of applications. Full article

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain—all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing, and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics. Full article

Phase change materials (PCMs) have the advantage of using the latent thermal heat as energy storage. Coconut oil (CO) has attracted much attention as PCM due to its high stability against oxidation. Although the viscosity of CO-based dispersions has extensively been studied, little research has been performed on time-dependent flow behaviors. In this work, the rheological properties of fumed silica dispersed in CO at fractions of 1, 2, 3, and 4 vol.% were investigated. All suspensions showed shear-thinning behavior, which became more Newtonian at temperatures above the phase change. The 3 and 4 vol.% suspensions showed gel-like microstructures. The thixotropic properties of the 3 vol.% suspension at 30 °C and 35 °C were mainly studied through stepwise changes in the shear rate. The results were consistent with thixotropic behavior, with a complete recovery of the microstructure. The sweep frequency of this sample demonstrated the dominance of the elastic modulus at both temperatures. Therefore, a thixoelastic nature of this gel could be inferred. This gel-like material flowed under high stress, providing superior thermal damping capabilities compared to conventional fluids. A reduction of 8.65 °C was confirmed after 30 min. of the laptop power supply operation. Full article

This review reveals the parameters of next-generation fuel cells for portable applications such as cellular phones, laptops, automobiles, etc. Disputes over issues such as design, fluid dynamics, channel dimensions, thermal management, and water management must be overcome for practical applications. We examine techniques such as microfabrication, material selection for membranes and electrodes, and integration challenges in small-scale devices, in addition to issues like methanol crossover, low efficiency at high methanol concentrations, thermal management, and the cost of materials. The advancements in micro-DMFC stacks and prototype developments are presented, and the challenges relating to micro-DMFCs are also identified and reviewed in detail. The challenges in the development of micro-DMFC applications are also presented, including the need for a better understanding of the anode and cathode catalyst structure and for high catalyst loadings in oxidation-and-reduction reactions. Also, a comprehensive and highly valuable database for advancing innovations and enhancing the understanding of micro-DMFCs for potential applications is provided. Full article