Low Cost Research Articles

FACTORS INFLUENCING CLOUD COMPUTING ADOPTION : TECHNOLOGY, ORGANIZATIONAL AND ENVIRONMENT (TOE) FRAMEWORK APPROACH

The latest technology that must be applied to prepare for modernity is based on information technology. This technology is not only used by large companies (enterprises) but can also be utilized by Micro, Small and Medium Enterprises (MSMEs). One of the utilization of technology is cloud computing by Micro and Medium Enterprises (MSMEs) which is increasingly widespread. This is reasonable because cloud computing technology provides benefits with a simpler process and relatively low costs. In line with this, this study aims to determine the factors that influence the adoption of cloud computing in MSMEs. An integrated model was developed based on the Technology, Organization, and Environment (TOE) framework. This research data was obtained through a structured survey questionnaire given to 150 MSME owners/managers in the Soloraya Region. The results obtained through SEM-PLS analysis show that factors such as organizational readiness and competitor pressure have a significant effect on cloud computing adoption in MSMEs in the Soloraya Region. However, factors of relative advantage, complexity, and top management support were found to have no significant effect on cloud computing adoption. This research provides a theoretical contribution to the expansion of the TOE Framework.

Design and demonstration of a simple, low-cost transparent solar absorber for glass window applications

Windows are a major source of heat loss from buildings in cold climates. Developing coatings for windows that retain high visible transparency and strongly absorbs solar energy in the near infrared region can help reduce energy consumption and cost for indoor heating. Nanophotonic structures based on metasurface and metamaterials have shown great potential for such applications. Unfortunately, most of the designs proposed so far are difficult to fabricate or expensive. In this work, we report the experimental demonstration of a low-cost alternative based on Ni/SiO2/Ni multilayer structure. The device provides a large increase in temperature under solar illumination while retaining high visible transmission. Our optical and thermal measurements reveal that the performance of the device remains stable over a long period. The combination of low cost, ease of fabrication, good optical and thermal performance, and long-term stability makes it a promising design for passive heating of windows in cold climates.

Improving the End‐to‐End Protection in E‐Voting Using BVM—Blockchain‐Based E‐Voting Mechanism

ABSTRACTVoting has always been a crucial topic of public attention for democratic reasons. The ease of use and low cost are the result of e‐voting being frequently used for such important decision outcomes. However, the tremendous authority and intervening data in current e‐voting systems make it risky and difficult to achieve correct equity and clarity in e‐voting. So, by combining e‐voting with blockchain technology, these issues can be resolved while providing reorganization and intervention‐resistant characteristics. A voter's improper manipulation, frequent voting, or non‐party voting, may also undermine fairness. A verifier is therefore required to check the e‐voting mechanism in order to ensure its effectiveness and control the process equality and fairness. In this paper, a Blockchain‐based e‐Voting Mechanism (BVM) is developed for providing the end to end security and fairness for transparent voting. This mechanism also provides a zero‐knowledge proof (ZP) based verifier to inspect the voting procedure against voter's mis‐operations and uses a novel Improved Master‐key Administration (IMA) based public key cryptography to attack prevention. The utilization of blockchain technology ensures transparency, anonymity, confidentiality, authentication, tamper resistance, and a high level of data integrity, making it a promising choice for modernizing and enhancing the electoral process. Also, the performance of BVM has been compared with similar voting mechanisms and analyzed based on time complexity, security analysis, performance factors like delay and throughput, and anti‐attack examination.

High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China

Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, APbox and APseg, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in APbox and APseg, respectively. Importantly, the APseg of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment.

Adapting and pilot testing a tool to assess the accessibility of primary health facilities for people with disabilities in Luuka District, Uganda

Abstract Background People with disabilities frequently experience barriers in seeking healthcare that lead to poorer health outcomes compared to people without disabilities. To overcome this, it is important to assess the accessibility of primary health facilities – broadly defined to include a disability-inclusive service provision – so as to document present status and identify areas for improvement. We aimed to identify, adapt and pilot test an appropriate tool to assess the accessibility of primary health facilities in Luuka District, Uganda. Methods We conducted a rapid literature review to identify appropriate tools, selecting the Disability Awareness Checklist (DAC) on account of its relative brevity and development as a sensitization and action tool. We undertook three rounds of adaptation, working together with youth researchers (aged 18–35) with disabilities who then underwent 2 days of training as DAC facilitators. The adapted tool comprised 71 indicators across four domains and 12 sub-domains. We also developed a structured feedback form for facilitators to complete with healthcare workers. We calculated median accessibility scores overall, per domain and per sub-domain, and categorised feedback form suggestions by type and presumed investment level. We pilot-tested the adapted tool in 5 primary health facilities in one sub-district of Luuka, nested within a pilot healthcare worker training on disability. Results The median overall facility accessibility score was 17.8% (range 12.3–28.8). Facility scores were highest in the universal design and accessibility domain (25.8%, 22.6–41.9), followed by reasonable accommodation (20.0%, 6.7–33.3). Median scores for capacity of facility staff (6.67%, 6.7–20.0), and linkages to other services were lower (0.0%, 0–25.0). Within the feedback forms, there were a median of 21 suggestions (range 14–26) per facility. Most commonly, these were categorised as minor structural changes (20% of suggestions), with a third categorised as no (2%) or low (33%) cost, and the majority (40%) medium cost. Conclusions Overall accessibility scores were low, with many opportunities for low-cost improvement at the facility level. We did not identify any issues with the implementation of the tool, suggesting few further adaptations are required for its future use in this setting.

Optimization of High-Speed Rail Express Transportation Scheduling Based on Spatio-Temporal-State Network Modeling

High-speed rail express is gradually gaining attention due to its low cost and high on-time performance. This paper studied the optimization of high-speed rail express transportation modes considering passenger capacity loss. A spatio-temporal network was constructed that includes the fixed passenger train schedule and the high-speed rail express transportation status. Based on this network, an optimization model for transportation schemes considering passenger capacity loss with non-splittable freight flows was developed. The model aims to minimize passenger loss and maximize net revenue from express transportation while comprehensively considering practical constraints such as transfer, loading and unloading, and carrying capacity. The Gurobi optimization solver was used to solve numerical cases, and the optimization results and solution effectiveness were comparatively analyzed under scenarios of sufficient and insufficient capacity.

Dual Ion Co-Insertion Induced Spontaneous and Reversible Phase Conversion Chemistry for Unprecedented Zn2+ Storage.

Prussian blue analogues are highly promising electrode materials due to their versatile electrochemical activity and low cost. However, they often suffer from severe structural damage caused by the Jahn-Teller distortion and dissolution of high-spin outer metal ions, resulting in poor cycle life. Material modification and electrolyte regulation have been the common approaches to address this issue, albeit with very limited success. We report here a novel and efficient strategy to preserve structural stability by co-inserting Co2+ and Zn2+ ions in KCo[Fe(CN)6]. This co-insertion induced a spontaneous and reversible phase conversion by the replacement of low-spin inner ion (Fe3+), which efficiently relieves structural damage caused by Jahn-Teller distortion and metal-ion dissolution, leading to an outstanding Zn2+ storage capacity and an exceptional improvement of cycle life with a capacity retention of 97.7 % over 4400 cycles at 40 C. We also demonstrated the enhancement of co-intercalation on ion migration using a combined approach of experimental and density functional theory (DFT) calculations. This work provides an important progress to solve the cycle stability of Prussian blue analogues towards their practical application as electrode materials for aqueous batteries.

Dual Ion Co‐Insertion Induced Spontaneous and Reversible Phase Conversion Chemistry for Unprecedented Zn2+ Storage

AbstractPrussian blue analogues are highly promising electrode materials due to their versatile electrochemical activity and low cost. However, they often suffer from severe structural damage caused by the Jahn–Teller distortion and dissolution of high‐spin outer metal ions, resulting in poor cycle life. Material modification and electrolyte regulation have been the common approaches to address this issue, albeit with very limited success. We report here a novel and efficient strategy to preserve structural stability by co‐inserting Co2+ and Zn2+ ions in KCo[Fe(CN)6]. This co‐insertion induced a spontaneous and reversible phase conversion by the replacement of low‐spin inner ion (Fe3+), which efficiently relieves structural damage caused by Jahn–Teller distortion and metal‐ion dissolution, leading to an outstanding Zn2+ storage capacity and an exceptional improvement of cycle life with a capacity retention of 97.7 % over 4400 cycles at 40 C. We also demonstrated the enhancement of co‐intercalation on ion migration using a combined approach of experimental and density functional theory (DFT) calculations. This work provides an important progress to solve the cycle stability of Prussian blue analogues towards their practical application as electrode materials for aqueous batteries.

Testing in Football: A Narrative Review

Football clubs regularly test and monitor players, with different approaches reflecting player age and competitive level. This narrative review aims to summarise justifications for testing and commonly used testing protocols. We also aim to discuss the validity and reliability of specific tests used to assess football players and provide a holistic overview of protocols currently used in football or those demonstrating potential utility. The PubMed, SportDiscus, and Google Scholar databases were screened for relevant articles from inception to September 2024. Articles that met our inclusion criteria documented tests for several purposes, including talent identification or the assessment of growth/maturation, physiological capacity, sport-specific skill, health status, monitoring fatigue/recovery, training adaptation, and injury risk factors. We provide information on specific tests of anthropometry, physical capacity, biochemical markers, psychological indices, injury risk screening, sport-specific skills, and genetic profile and highlight where certain tests may require further evidence to support their use. The available evidence suggests that test selection and implementation are influenced by financial resources, coach perceptions, and playing schedules. The ability to conduct field-based testing at low cost and to test multiple players simultaneously appear to be key drivers of test development and implementation among practitioners working in elite football environments.

Strategy for Accurate Detection of Six Tropane Alkaloids in Honey Using Lateral Flow Immunosensors

Honey, a widely consumed food, is susceptible to contamination by various toxic substances during production. Tropane alkaloids, with their potent neurotoxicity, are frequently found in honey. Hence, there is an acute need for rapid and effective detection methods to monitor these alkaloids. Lateral flow immunoassay (LFIA), known for its simple operation, low cost, and reliable results, holds great promise. In this study, we developed an efficient and user-friendly analytical method for the simultaneous detection of six tropane alkaloids (atropine, L-hyoscyamine, scopolamine, anisodamine, homatropine, and apoatropine) in honey based on an AuNPs lateral flow immunoassay (AuNPs-LFIA) with broad-spectrum antibodies. Under optimal conditions, the calculated detection limits were 0.22, 0.29, 0.51, 6.34, 0.30, and 0.94 ng/mL, respectively. By diluting the honey sample five times, the contaminants can be readily detected using LFIA. Semi-quantitative and quantitative analyses can be completed within 17 min. This innovative method fills the void in LFIA for detecting tropane alkaloids and serves as a valuable reference for LFIA detection of honey samples, providing a crucial strategy for the accurate detection of these important compounds.

A Perspective on the Use of Hydroxyapatites to Improve the Dissolution Behavior of Poorly Water-Soluble Piretanide

Background/Objectives: Interest in drug delivery systems (DDS) based on inorganic substrates has increased in parallel with the increase in the number of poorly water-soluble drugs. Hydroxyapatite is one of the ideal matrices for DDS due to its biocompatibility, low cost, and ease of preparation. Methods: We propose two doped hydroxyapatites, one with Ba on Ca sites another with Si on P sites, with the aim of improving the dissolution rate of piretanide, a diuretic, poorly water-soluble drug. The hybrids were characterized by different physical–chemical techniques, and their formation was demonstrated by infrared spectroscopy, thermal analysis, and electron microanalysis, as well as by comparing the results with those obtained on physical mixtures of HAPs and properly prepared piretanide. Results: Both the hybrids improved the piretanide dissolution rate compared with the physical mixtures and the drug alone. The dose was completely solubilized from the Si-doped hybrid in about 5 min in the three fluids considered. This remarkable improvement can be explained by an increase in the wettability and solubility of the drug loaded in the drug-carrier systems. Conclusions: Different experimental techniques, in particular spectroscopy and electronic microanalysis, proved the successful loading of piretanide onto doped HAP. Pharmaceutical measurements demonstrated rapid drug release in different fluids simulating gastrointestinal conditions after oral administration. These hybrid systems could be a very promising platform for drug delivery.

Formulation and Evaluation of Herbal Footcream

Skin diseases are numerous and a frequently occurring health problem affecting all ages from the neonates to the elderly and cause harm in number of ways. If the cracks in the heels are deep, they can be painful, hurting when a person stands up, and they may sometimes bleed. Some wild plants and their parts are frequently used to treat these diseases. Herbal crack cream contains the extracts of the plantconsists of five ingredients viz., extract of Daruharida (Berberisaristata DC.), Pashanbheda (Bergenialigulata (Wall.) Engl.), Jivanti (Leptadeniareticulata W. & A.), kampilak (Mallotusphilippensis Muell.-Arg.), Manjistha (Rubiacordifolia Linn.) were used to prepare Herbal crack cream. The main objective of the research work was to prepare and evaluate Herbal crack cream and the results showed that Herbal crack cream passeall Parameters related to cream and TLC of formulated cream and extract showed that the tannins, flavonoids and alkaloids were found to be present in mentioned medicinal plants. These days, skin conditions are a major cause of illness for people of all ages. If someone has a crack in their foot's heel, it hurts and is quite uncomfortable. Environmental factors contribute to the growth of germs and fungi on human skin. This factor is taken into account when creating HERBALFOOTCREAM. That cream's function is to hydrate the heel, and its emulsion is w/o type (cold cream). Cold cream works by producing an oily coating on the skin that aids in reducing water loss from the skin to the environment. Water serves to hydrate and refresh the skin while beeswax and cocoa butter work as emollients, which aid to soften and smooth the skin. The key ingredient in these formulations is argan oil, which has vitamin E and is healthy for the skin since it hydrates. Neem extract also has antibacterial and antifungal properties. Lemongrass essential oil is used in fragrances. This cream is homogeneous, spread sreadily, and is naturally fatty. This is W/O emulsion and with anacidic pH (within the range of skin pH). There was no phase separation in this cream. • The main aim of our research was to develop on anti-cracked heels cream formulation consisting of hedychium spicatum, aleo bar, badensis azodirachta indica for the treatment of cracked heels. Skin diseases are numerous and frequently occurring health problem affecting all age from neonate to the elderly and cause harm in number of ways. • The skin is external exposure of the body. The ageing is most visible on the skin. Nowadays, premature ageing is a big complication in the world because of the changes in an environment. Feet is important part of body; they suffer from many frictions. The lack of oil gland in the feet skin which results into very dry skin. External penetration of dirt, fungus, bacteria cause cuts and wounds. Bacterial decomposition is also occurred in foot give bad odour. For these purpose herbal foot cream is prepared. • This herbal foot cream is prepared from Argan oil (good for dry skin), Neem extract (antibacterial & antifungal effects), Aloe vera extract (for hydration) and lemongrass oil (fragrance). Other than synthetic compound, herbs have more effective, low cost, more availability, and less side effects. Here, ARGAN OIL have good property which give protection to the skin and lemongrass oil is having a good fragrance. Argan oil contains vitamin E which is known as anti-inflammatory agent in skin and several studies proves that vit E is good in seal up the cracks caused by dryness..

Temperature stability analysis of the all-fiber current sensor with a loop structure

Abstract All-fiber optical current sensor (AFOCS) is a perfect product formed by the combination of fiber-optic sensing technology and the Faraday effect. It boasts significant advantages such as resistance to electromagnetic interference, a high measurement dynamic range and precision, low power consumption, low cost, and insulation. Additionally, it enables long-distance transmission, making it one of the main devices for current monitoring in smart grids. However, the birefringence within the sensing fiber is highly susceptible to changes in temperature, causing the polarization state of light to be extremely sensitive to temperature variations. This sensitivity significantly impacts the accuracy of current measurements. A novel loop structure AFOCS with a coupled fiber polarization rotator (FPR) is introduced in this paper. The operating principle of this structure is theoretically analyzed, and the Jones matrix is used to analyze the output light intensity signal and derive the error formula. After comparing the measurement accuracy of the basic AFOCS and the loop structure AFOCS, it is demonstrated that this novel structure can improve current sensitivity by enhancing the temperature robustness of the system. Additionally, the error generated by the FPR is controlled below 1%, meeting the requirements for stable system operation. Therefore, this novel structure effectively improves the accuracy of current measurement and exhibits strong temperature stability.

Abstract B053: NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing

Abstract Introduction: Cell-free DNA (cfDNA) is emerging as a valuable cancer biomarker, enabling both the detection and epigenetic characterization of malignancies through sequencing. cfDNA sequencing can reveal cancer-specific nucleosome occupancy patterns, which provide insights into tumor type and differentiation status, which define prognosis and therapy recommendations. Low-coverage whole-genome cfDNA sequencing is a cost-efficient approach to assay multiple cfDNA samples, however, the low coverage limits its sensitivity. We developed NucAE, a denoising autoencoder model, to enhance nucleosome occupancy signals from low-coverage cfDNA-sequencing. Methods: We designed NucAE to estimate nucleosome occupancy genome- wide from low-coverage sequencing data. To train the model, we undersampled high-coverage cfDNA-sequencing data and produced high- and low-coverage sample pairs at varying coverages (1x-90x). Inputs to NucAE include cfDNA fragment data and the corresponding genomic nucleotide sequence, with the model leveraging a symmetrical architecture comprising two convolutional layers in both the encoder and decoder, connected by ReLU activations. Mean squared error relative to the high-coverage signal was employed as the loss function. To evaluate the model independently, we performed peak detection from the nucleosome occupancy signals. We conducted a grid search to optimize hyperparameters. Results: We found that NucAE enhances the signal to noise ratio by detecting periodicities in nucleosome occupancy data. Denoising was improved by supplementing the cfDNA fragment signals with the genomic nucleotide sequences as input, which demonstrates that the model learned the nucleotide- sequence dependence of nucleosome positioning. Independent validation through peak detection on previously unseen samples demonstrated a significant improvement (p<0.001) in accuracy compared to raw signals across all tested coverages (1x-45x). Remarkably, at extremely low coverages (1x-2x), denoised signals allowed for more accurate peak detection than raw signals from 10-20x deeper sequencing. Conclusions: By using advanced machine-learning, we were able to enhance low-coverage cfDNA sequencing data to assay nucleosome positions at a nucleotide resolution. NucAE achieves an order of magnitude improvement over the low- coverage signals and greatly improves the utility of cfDNA-sequencing data to identify cancer- specific nucleosome occupancy, while preserving its low cost. Our model is the first to use autoencoders for genome-wide signal enhancement with potential use cases in many other areas of genomics. Citation Format: Zsolt Balázs, Jean Radig, Noah Wolford, Manuel Schürch, Michael Krauthammer. NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B053.

Progress and Strategies of MOFs in Catalyzing Conversion Processes in Lithium‐Sulfur Batteries

AbstractLithium‐sulfur (Li−S) batteries have attracted considerable attention due to their advantages, such as high specific capacity, high energy density, environmental friendliness, and low cost. However, the severe capacity fading caused by shuttle effect of polysulfide needs to be addressed before the practical application of Li−S batteries. Crystalline porous materials including MOFs have generated great interest in energy storage fields especially batteries, because the ordered porous frameworks can offer a fast‐ionic transportation. Nevertheless, the intrinsic low conductivity of MOFs limits their rapid development in lithium‐sulfur batteries. This review mainly discusses the latest research progress on MOF main materials in Li−S batteries. The working principle of Li−S batteries and the classical “adsorption‐catalysis‐conversion” strategy are briefly introduced. Specifically, three modification methods (non‐metal atom doping, single‐atom, and dual‐atom doping modifications) applied in MOF‐based materials are analyzed and summarized, along with their respective mechanisms and advantages and disadvantages. Ligand doping is an effective strategy that can regulate the structure and properties of MOFs, thereby enhancing their catalytic activity and adsorption capacity towards polysulfides. Through ligand doping, key parameters such as the pore size, surface charge, and active site density of MOFs can be controlled, thereby influencing the adsorption and conversion of polysulfides on MOFs surfaces. Furthermore, crucial insights for the rational design of advanced MOF‐based materials for lithium‐sulfur batteries and the exploration of the main challenges and future directions for their application were also discussed.

Cost-effectiveness of diagnostic technologies for mycobacterium tuberculosis infection in India and Brazil

The economic value of new skin-based tests and blood-based interferon-γ release assays (IGRAs) for tuberculosis (TB) infection is not yet well-established. This study evaluates the cost and cost-effectiveness in two high-burden countries by comparing:(a) new skin-based tests(Diaskintest and Cy-Tb) with the purified protein derivative (PPD)-tuberculin test (TST);(b) IGRAs (Standard E TB-Feron ELISA (TBF))with approved IGRAs (QuantiFERON-TB Gold Plus (QFT-GP)and TSPOT.TB); and (c) the best performing skin-based test with the best performing IGRA) based on cost effectiveness. In this paper, we developed a decision tree model for India and Brazil from a health system perspective. To quantify the effect of parameter variability and uncertainty, we performed both univariate and probabilistic sensitivity analysis. The study findings reveal that among skin-based tests, the Diaskintest is more cost-effective compared to TST-PPD at 22.6 USD and 41.0 USD per correctly diagnosed case of TB infection for Brazil and India, respectively. For blood-based assays, TSPOT.TB outperforms QFT-GP and TBF due to its lower cost and higher effectiveness. When compared with Diaskintest, TSPOT.TB has an incremental cost of approximately 8 USD and 6 USD for India and Brazil respectively but is more effective. The incremental cost-effectiveness ratio (ICER) was 74 USD and 55 USD for India and Brazil, respectively. In summary, while Diaskintest is potentially cost-saving when compared to TSPOT.TB in these two high-burden TB countries but the TSPOT.TB demonstrates higher effectiveness.

Demonstration of a Validated Direct Current Wearable Device for Monitoring Sweat Rate in Sports

Sweat rate magnitude is a desired outcome for any wearable sensing patch dedicated to sweat analysis. Indeed, sweat rate values can be used two-fold: self-diagnosis of dehydration and correction/normalization of other physiological metrics, such as Borg scale, VO2, and different chemical species concentrations. Herein, a reliable sweat rate belt device for sweat rate monitoring was developed. The device measures sweat rates in the range from 1.0 to 5.0 µL min−1 (2 to 10 µL min−1 cm−2), which covers typical values for humans. The working mechanism is based on a new direct current (DC) step protocol activating a series of differential resistance measurements (spatially separated by 800 µm) that is gradually initiated by the action of sweat, which flows along a customized microfluidic track (~600 µm in width, 10 mm in length, and 235 µm in thickness). The device has a volumetric capacity of ~16 µL and an acquisition frequency between 0.010 and 0.043 Hz within the measured sweat rate range. Importantly, instead of using a typical and rather complex AC signal interrogation and acquisition, we put forward the DC approach, offering several benefits, such as simplified circuit design for easier fabrication and lower costs, as well as reduced power consumption and suitability for wearable applications. For the validation, either the commercial sweat collector (colorimetric) or the developed device was performed. In five on-body tests, an acceptable variation of ca. 10% was obtained. Overall, this study demonstrates the potential of the DC-based device for the monitoring of sweat rate and also its potential for implementation in any wearable sweat platform.

WKNN-Based Wi-Fi Fingerprinting with Deep Distance Metric Learning via Siamese Triplet Network for Indoor Positioning

Weighted k-nearest neighbor (WKNN)-based Wi-Fi fingerprinting is popular in indoor location-based services due to its ease of implementation and low computational cost. KNN-based methods rely on distance metrics to select the nearest neighbors. However, traditional metrics often fail to capture the complexity of indoor environments and have limitations in identifying non-linear relationships. To address these issues, we propose a novel WKNN-based Wi-Fi fingerprinting method that incorporates distance metric learning. In the offline phase, our method utilizes a Siamese network with a triplet loss function to learn a meaningful distance metric from training fingerprints (FPs). This process employs a unique triplet mining strategy to handle the inherent noise in FPs. Subsequently, in the online phase, the learned metric is used to calculate the embedding distance, followed by a signal-space distance filtering step to optimally select neighbors and estimate the user’s location. The filtering step mitigates issues from an overfitted distance metric influenced by hard triplets, which could lead to incorrect neighbor selection. We evaluate the proposed method on three benchmark datasets, UJIIndoorLoc, Tampere, and UTSIndoorLoc, and compare it with four WKNN models. The results show a mean positioning error reduction of 3.55% on UJIIndoorLoc, 16.21% on Tampere, and 16.49% on UTSIndoorLoc, demonstrating enhanced positioning accuracy.

Design and research of high voltage β-Ga2O3/4H-SiC heterojunction LDMOS

Abstract In recent years, gallium oxide (Ga2O3), one of the ultra-wide band-gap semiconductor materials, has been regarded as one of the most promising materials in the field of high-voltage and high-power electronic devices in the future because of its unique electrical properties and low preparation cost. However, it is difficult for β-Ga2O3 materials to form effective P-type doping, so the PN junction is not formed in most of its power devices, which greatly limits the improvement of its voltage resistance. A novel lateral double-diffused metal-oxide-semiconductor field-effect transistor (LDMOS) is proposed to realize a junction β-Ga2O3 power device and improve the voltage resistance performance of β-Ga2O3 power devices. In this device, a β-Ga2O3 power device with a junction is realized by using P-type 4H-SiC to form a PN junction with N-type β-Ga2O3, and the voltage resistance of the β-Ga2O3 power device is improved. Sentaurus TCAD software was used to simulate the device structure and electrical performance. The device is optimized by adjusting the length of the drift zone, drift zone concentration, SiC channel concentration, and gate oxide thickness. Optimized, the device exhibits a positive threshold voltage of 3.42 V, a breakdown voltage of 2203 V, a specific on-resistance of 7.80 mΩ·cm2, and a power figure of merit of 622 MW cm−2. The results show that the heterojunction device is significant for realizing high-performance junction β-Ga2O3 power devices.

Variable resolution machine learning optimization of antennas using global sensitivity analysis

AbstractThe significance of rigorous optimization techniques in antenna engineering has grown significantly in recent years. For many design tasks, parameter tuning must be conducted globally, presenting a challenge due to associated computational costs. The popular bio-inspired routines often necessitate thousands of merit function calls to converge, generating prohibitive expenses whenever the design process relies on electromagnetic (EM) simulation models. Surrogate-assisted methods offer acceleration, yet constructing reliable metamodels is hindered in higher-dimensional spaces and systems with highly nonlinear characteristics. This work suggests an innovative technique for global antenna optimization embedded within a machine-learning framework. It involves iteratively refined kriging surrogates and particle swarm optimization for generating infill points. The search process operates within a reduced-dimensionality region established through fast global sensitivity analysis. Domain confinement enables the creation of accurate behavioral models using limited training data, resulting in low CPU costs for optimization. Additional savings are realized by employing variable-resolution EM simulations, where low-fidelity models are utilized during the global search stage (including sensitivity analysis), and high-fidelity ones are reserved for final (gradient-based) tuning of antenna parameters. Comprehensive verification demonstrates the consistent performance of the proposed procedure, its superiority over benchmark techniques, and the relevance of the mechanisms embedded into the algorithm for enhancing search process reliability, design quality, and computational efficiency.