Search Results (184)

A design framework for developing full-textile reconfigurable dipole antennas is proposed for wearable applications. To this end, a precise embroidery process using conductive threads is applied to properly manage the antenna structure. Further, mechanical reconfiguration to enhance antenna operation by using solely clothing components is outlined. As a proof-of-concept, we present a full-textile embroidered dipole antenna with mechanical frequency reconfiguration. Specifically, reconfiguration is achieved by folding the dipole arms through a triangular formation. Conductive Velcro strips are employed to guide the necessary dipole arrangement. As shown, the proposed design methodology enables frequency tunability that ranges from 780 to 1330 MHz for UHF and L bands, with satisfactory radiation performance. The measured and simulated results are in good agreement, in terms of achieving similar frequency reconfiguration concept, as predicted by the electromagnetic simulation models. Full article

In this work, we explored the feasibility of using a transformer-based time-series forecasting architecture, known as the Temporal Fusion Transformer (TFT), for anomaly detection using threaded track data from the MITRE Corporation’s Transportation Data Platform (TDP) and digital flight data. The TFT architecture has the flexibility to include both time-varying multivariate data and categorical data from multimodal data sources and conduct single-output or multi-output predictions. For anomaly detection, rather than training a TFT model to predict the outcomes of specific aviation safety events, we train a TFT model to learn nominal behavior. Any significant deviation of the TFT model’s future horizon forecast for the output flight parameters of interest from the observed time-series data is considered an anomaly when conducting evaluations. For proof-of-concept demonstrations, we used an unstable approach (UA) as the anomaly event. This type of anomaly detection approach with nominal behavior learning can be used to develop flight analytics to identify emerging safety hazards in historical flight data and has the potential to be used as an on-board early warning system to assist pilots during flight. Full article

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user’s past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model’s success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%. Full article

The performance-degradation pattern of the planetary roller screw mechanism (PRSM) is difficult to predict and evaluate due to a variety of factors. Load-carrying capacity, transmission accuracy, and efficiency are the main indicators for evaluating the performance of the PRSM. In this paper, a testing device for the comprehensive performance of the PRSM is designed by taking into account the coupling relationships among temperature rise, vibration, speed, and load. First, the functional design and error calibration of the testing device were conducted. Secondly, the PRSM designed in the supported project was taken as the research object to conduct degradation tests on its load-bearing capacity and transmission accuracy and analyze the changes in transmission efficiency. Third, the thread profile and wear condition were scanned and inspected using a universal tool microscope and an optical microscope. Finally, based on the monitoring module of the testing device, the vibration status during the PRSM testing process was collected in real time, laying a foundation for the subsequent assessment of the changes in the performance state of the PRSM. The test results reveal the law of performance degradation of the PRSM under the coupled effects of temperature, vibration, speed, and load. Full article

Indigenous Peoples have been stewarding lands with fire for ecosystem improvement since time immemorial. These stewardship practices are part and parcel of the ways in which Indigenous Peoples have long recorded and protected knowledge through our cultural transmission practices, such as oral histories. In short, our Peoples have always been data gatherers, and as this article presents, we are also fire data gatherers and stewards. Given the growing interest in fire research with Indigenous communities, there is an opportunity for guidance on data collection conducted equitably and responsibly with Indigenous Peoples. This Special Issue of Fire presents fire research approaches and data harvesting practices with Indigenous communities as we “Reimagine the Future of Living and Working with Fire”. Specifically, the article provides future-thinking practices that can achieve equitable, sustainable, and just outcomes with and for stakeholders and rightholders (the preferred term Indigenous Peoples use in partnerships with academics, agencies, and NGOs). This research takes from the following key documents to propose an “Indigenous fire data sovereignty” (IFDS) framework: (1) Articles declared in the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) as identified by the author and specified in Indigenous-led and allied Indigenous fire research in Australia, Canada, and the U.S.; (2) recommendations specific to cultural fire policy and calls for research in the 2023 Wildland Fire Mitigation and Management Commission report; (3) research and data barriers and opportunities produced in the 2024 Good Fire II report; and threads from (4) the Indigenous Fire Management conceptual model. This paper brings together recommendations on Indigenous data sovereignty, which are principles developed by Indigenous researchers for the protection, dissemination, and stewardship of data collected from Tribal/Nation/Aboriginal/First Nations Indigenous communities. The proposed IFDS framework also identifies potential challenges to Indigenous fire data sovereignty. By doing so, the framework serves as an apparatus to deploy fire research and data harvesting practices that are culturally informed, responsible, and ethically demonstrated. The article concludes with specific calls to action for academics and researchers, allies, fire managers, policymakers, and Indigenous Peoples to consider in exercising Indigenous fire data sovereignty and applying Indigenous data sovereignty principles to fire research. Full article

Fiber-reinforced laminates with flexibility (FRLs) are becoming increasingly crucial across diverse sectors due to their adaptability and outstanding mechanical attributes. Their ability to deliver high performance relative to their weight makes them indispensable in lighter-than-air (LTA) applications, such as aerostats, inflatable antennas, surge bladders, gas storage balloons, life rafts, and other related uses. This research delved into employing woven fabrics as the reinforcement material and explored how their specific parameters, like fiber type, fabric count (warp thread density × weft thread density), fabric areal density, and fabric cover influence the bonding and mechanical properties of laminates. A thorough analysis encompassing standard T-peel (ASTM standard D1876) and a newly proposed yarn pullout in laminate test were conducted on laminates fabricated with various woven reinforcements, each with its unique specifications. The T-peel test was utilized to gauge the adhesive strength between FRL components, offering crucial insights into interfacial bonding within the laminates. Nevertheless, challenges exist with the T-peel test, including instances where the adherents lack the strength to withstand rupture, resulting in unsuccessful peel propagation and numerous outliers that necessitate costly additional trials. Thus, our research group introduced a novel yarn pullout in laminate test to accurately assess adhesion in FRLs. This study uncovered correlations between both adhesion tests (T-peel and yarn pullout in laminate), indicating that the innovative yarn pullout in laminate test could effectively substitute for characterizing adhesion in FRLs. Furthermore, the findings unveiled a complex relationship between woven fabric specifications and laminate properties. We noted that variations in fiber type, yarn linear density, and adhesive type significantly impacted adhesion strength. For instance, Kevlar exhibited markedly superior adhesion compared to Ultra-High Molecular Weight Polyethylene (UHMWPE) when paired with Thermoplastic Polyurethane (TPU) adhesive, whereas UHMWPE demonstrated better adhesion with Ethylene Vinyl Acetate (EVA). Moreover, the adhesion quality lessened as fabric count increased for the same adhesive quantity. These discoveries carry practical implications for material selection and design across industries, from automotive to aerospace, offering avenues to enhance FRL performance. Full article

Fuel cell technology is at the forefront of sustainable energy solutions, and polyvinyl alcohol (PVA) membranes play an important role in improving performance. This article thoroughly investigates the various varieties of PVA membranes, their production processes, and the numerous modification tactics used to solve inherent problems. Various methods were investigated, including chemical changes, composite blending, and the introduction of nanocomposites. The factors impacting PVA membranes, such as proton conductivity, thermal stability, and selectivity, were investigated to provide comprehensive knowledge. By combining various research threads, this review aims to completely investigate the current state of PVA membranes in fuel cell applications, providing significant insights for both academic researchers and industry practitioners interested in efficient and sustainable energy conversion technologies. The transition from traditional materials such as Nafion to PVA membranes has been prompted by limitations associated with the former, such as complex synthesis procedures, reduced ionic conductivity at elevated temperatures, and prohibitively high costs, which have hampered their widespread adoption. As a result, modern research efforts are increasingly focused on the creation of alternative membranes that can compete with conventional technical efficacy and economic viability in the context of fuel cell technologies. Full article

This paper summarizes the possibility of employing knitted textile barriers as a shield against electromagnetic fields to protect the human body from their negative impact. Ten variants of knitted fabrics made of electrically conductive yarns, steel, and copper wire that differed in stitch pattern, structural parameters, and raw material, were designed, manufactured, and tested. The knitted fabrics produced differed in structural parameters, including course and wale density, surface density, thickness, thread length in the loop, wale and course take-up, volume cover factor, and surface porosity. These parameters were examined in accordance with the research methodology used in knitting. Barrier measurements were taken in the direction of the wales and in the direction of the courses for two frequencies of electromagnetic fields: 2–4 GHz and 4–7 GHz. It was observed that the shielding effectiveness of the manufactured materials depends on the structural parameters of the fabric, the stiches applied, and the type of yarn. Full article

In this work, a GaN-on-Si quasi-vertical Schottky diode was demonstrated on a locally grown n-GaN drift layer using Selective Area Growth (SAG). The diode achieved a current density of 2.5 kA/cm², a specific on-resistance ${\mathrm{R}}_{\mathrm{O}\mathrm{N},\mathrm{s}\mathrm{p}}$ of 1.9 mΩ cm² despite the current crowding effect in quasi-vertical structures, and an on/off current ratio (I_on/I_off) of 10¹⁰. Temperature-dependent current–voltage characteristics were measured in the range of 313–433 K to investigate the mechanisms of leakage conduction in the device. At near-zero bias, thermionic emission (TE) was found to dominate. By increasing up to 10 V, electrons gained enough energy to excite into trap states, leading to the dominance of Frenkel–Poole emission (FPE). For a higher voltage range (−10 V to −40 V), the increased electric field facilitated the hopping of electrons along the continuum threading dislocations in the “bulk” GaN layers, and thus, variable range hopping became the main mechanism for the whole temperature range. This work provides an in-depth insight into the leakage conduction transport on pseudo-vertical GaN-on-Si Schottky barrier diodes (SBDs) grown by localized epitaxy. Full article

Craft and e-textile circuits are technologies that bridge the gender gap in Science, Technology, Engineering, and Mathematics (STEM) learning. Acknowledging the need to study girls’ underrepresentation in STEM, this article delves into the identity negotiations of four girls aged eleven to fourteen as they construct craft and e-textiles at a library makerspace. Qualitative analysis of their talk at the workshop found that several factors shaped the girls’ identity work, such as their awareness of their abilities and fellow participants’ projects, their understanding of parents’ expectations, and their strengths in other STEM domains. While all four girls reluctantly participated in making circuits, the reason for their reluctance varied from an interest in craft and the messiness of working with conductive thread to the preference for familiarity and complexity within other STEM domains such as programming and engineering. Further, as the girls questioned their need to engage in circuit-making, their preference for a particular identity became apparent. Overall, this study’s findings underscore the tensions in learning in technology-rich environments such as makerspaces, highlighting maker technologies’ affordances and limitations and emphasizing the need for a deeper understanding of what shapes learners’ participation and identities. Full article

We present a machine learning driven system to monitor joint flexion angles during dynamic motion, using a wearable loop-based sensor. Our approach uses wearable loops to collect transmission coefficient data and an Artificial Neural Network (ANN) with fine-tuned parameters to increase accuracy of the measured angles. We train and validate the ANN for sagittal plane flexion of a leg phantom emulating slow motion, walking, brisk walking, and jogging. We fabricate the loops on conductive threads and evaluate the effect of fabric drift via measurements in the absence and presence of fabric. In the absence of fabric, our model produced a root mean square error (RMSE) of 5.90°, 6.11°, 5.90°, and 5.44° during slow motion, walking, brisk walking, and jogging. The presence of fabric degraded the RMSE to 8.97°, 7.21°, 9.41°, and 7.79°, respectively. Without the proposed ANN method, errors exceeded 35.07° for all scenarios. Proof-of-concept results on three human subjects further validate this performance. Our approach empowers feasibility of wearable loop sensors for motion capture in dynamic, real-world environments. Increasing speed of motion and the presence of fabric degrade sensor performance due to added noise. Nevertheless, the proposed framework is generalizable and can be expanded upon in the future to improve upon the reported angular resolution. Full article

This article presents an innovative combination of textile electrical circuits with advanced capabilities of electronic RFID sensors, indicating the revolutionary nature of the development of textronics, which is used in various areas of life, from fashion to medicine. A review of the literature relating to the construction of textronic RFID identifiers and capacitive textronic sensors is performed. Various approaches to measuring capacity using RFID tags are discussed. This article focuses on presenting the concept of a capacitive sensor with an RFID interface, consisting of a microelectronic part and a textile part. The textile part is based on the WL4007 material, where antennas and capacitive sensors are embroidered using SPARKFUN DEV 11791 conductive thread. The antenna is a half-wave dipole designed to operate at a frequency of 860 MHZ. The microelectronic part is sewn to the textile part and consists of a microcontroller, an RFID-integrated circuit and a coupling loop, placed on the PCB. The embroidered antenna is coupled with a loop on the microelectronic module. This article focuses on presenting various designs of textronic electrodes, enabling various types of measurements. Article presents capacitance measurements of individual sensor electrodes, made using a measuring bridge and a built RFID tag. The sensors’ capacity measurement results are shown. Full article

In steel tied arch bridges where the hangers are made of rigid bars, the replacement of damaged hangers is rather complex. In fact, while generally the cable hangers are already prepared with anchors at the ends and their replacement traces the initial stages of construction with their prestressing, on the contrary, the rigid bars are welded to the arch and the deck, so their replacement must include the design of a new suspension system that allows the insertion of a pretension where this had never been considered. To check the reliability of this new system, a prototype of tensioner was studied for the case of a steel arch bridge in which the high level of corrosion made it necessary to replace all the original hangers with new ones. This entailed the need to test the tensioner performance with the aim of ensuring the axial force transmission between the two hanger segments without slippage in the threads, as well as to test the correct tension setting before construction and putting into service the hangers to be replaced. For this reason, a predictive experimental campaign was carried out on a prototype by means of tests for the mechanical characterization of the materials used, tensile tests of the system, and tensioning tests under load, measuring the displacements and strains of the system elements. The results of the tests, with slippage in the threads limited to the 2% of total elongation, and the turnaround-stressing curves were useful for the definition of the pieces to be assembled during on-site work and for addressing the operating procedures of the tensioning phases on-site during hanger replacement. Validation with the on-site monitoring of stressing operation was conducted at the end; the monitoring of tension through dynamic tests confirmed the agreement of on-site results with the predictive loading tests of the experimental campaign on the tensioner prototype. Full article

This paper proposes an algorithm for the conversion of raster data to hexagonal DGGSs in the GPU by redevising the encoding and decoding mechanisms. The researchers first designed a data structure based on rhombic tiles to convert the hexagonal DGGS to a texture format acceptable for GPUs, thus avoiding the irregularity of the hexagonal DGGS. Then, the encoding and decoding methods of the tile data based on space-filling curves were designed, respectively, so as to reduce the amount of data transmission from the CPU to the GPU. Finally, the researchers improved the algorithmic efficiency through thread design. To validate the above design, raster integration experiments were conducted based on the global Aster 30 m digital elevation dataDEM, and the experimental results showed that the raster integration accuracy of this algorithms was around 1 m, while its efficiency could be improved to more than 600 times that of the algorithm for integrating the raster data to the hexagonal DGGS data, executed in the CPU. Therefore, the researchers believe that this study will provide a feasible method for the efficient and stable integration of massive raster data based on a hexagonal grid, which may well support the organization of massive raster data in the field of GIS. Full article

Electro-conductive fabrics are key materials for designing and developing wearable smart textiles. The properties of textile materials depend on the production method, the technique which leads to high conductivity, and the structure. The aim of the research work was to determine the factors affecting the electrical conductivity of woven fabrics and elucidate the mechanism of electric current conduction through this complex, aperiodic textile material. The chemical composition of the material surface was identified using scanning electron microscopy energy dispersion X-ray spectroscopy. The van der Pauw method was employed for multidirectional resistance measurements. The coefficient was determined for the assessment of the electrical anisotropy of woven fabrics. X-ray micro-computed tomography was used for 3D woven structure geometry analysis. The anisotropy coefficient enabled the classification of electro-conductive fabrics in terms of isotropic or anisotropic materials. It was found that the increase in weft density results in an increase in sample anisotropy. The rise in thread width can lead to smaller electrical in-plane anisotropy. The threads are unevenly distributed in woven fabric, and their widths are not constant, which is reflected in the anisotropy coefficient values depending on the electrode arrangement. The smaller the fabric area covered by four electrodes, the fewer factors leading to structure aperiodicity. Full article