Window Size Research Articles

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation.

Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

TPANet: A novel triple parallel attention network approach for remaining useful life prediction of lithium-ion batteries

Accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) is important for proper equipment operation. Among the numerous existing studies on battery research, data-driven approaches have gained significant attention because they obviate the need for complex chemical and physical modeling of battery processes. However, in most current data-driven methods, the sliding window size values are determined empirically, which affects both the time required for model prediction and the prediction accuracy. To address this issue, this paper proposes a two-stage prediction method for battery capacity aging trajectories. In the first stage, a false nearest neighbor (FNN) technique is utilized to infer the sliding window size of the battery, reducing the error associated with manually selecting the sliding window size. In the second stage, a novel triple parallel attention network (TPANet) based on convolutional neural networks (CNNs) and attention units is developed, which extracts the input battery capacity degradation features through a parallel mechanism. The introduction of attention units in each parallel branch allows the model to enhance the capture of capacity regeneration phenomena as well as long-term dependence on input data. The proposed approach is validated on two publicly available datasets as well as a battery developed by ourselves. Diverse simulation results demonstrate the ability of the proposed approach to accurately predict the RUL of LIBs in a short time with broad generalization capabilities.

Microporous membrane with ionized sub-nanochannels enabling highly selective monovalent and divalent anion separation

Membranes tailored for selective ion transport represent a promising avenue toward enhancing sustainability across various fields including water treatment, resource recovery, and energy conversion and storage. While nanochannels formed by polymers of intrinsic microporosity (PIM) offer a compelling solution with their uniform and durable nanometer-sized pores, their effectiveness is hindered by limited interactions between ions and nanochannel. Herein, we introduce the randomly twisted V-shaped structure of Tröger’s Base unit and quaternary ammonium groups to construct ionized sub-nanochannel with a window size of 5.89–6.54 Å between anion hydration and Stokes diameter, which enhanced the dehydrated monovalent ion transport. Combining the size sieving and electrostatic interaction effects, sub-nanochannel membranes achieved exceptional ion selectivity of 106 for Cl-/CO32- and 82 for Cl-/SO42-, significantly surpassing the state-of-the-art membranes. This work provides an efficient template for creating functionalized sub-nanometer channels in PIM membranes, and paves the way for the development of precise ion separation applications.

Digital bio-dosimeter for detection of deformed biological cells irradiated by ionizing radiations

In this paper, a digital bio-dosimeter (DBD) with a square window size of 7 × 7 pixels is proposed for the quantitative detection of the deformed biological cells irradiated by X-rays with different energies and γ-rays from 60Co. The basic idea of the DBD is to compute the energy of the bio-dosimeter window size by summing up the entire gray levels. Peripheral blood films (PBFs) from male albino rats irradiated by ionizing radiations are prepared and photographed using an Automatic Image Contour Analysis system with high magnification to produce a 2D image. The surf function in MATLAB is used to convert the 2D image to a 3D shaded surface to resample the date more finely. The proposed DBD shows that the computed energies on the boundaries of the normal RBCs are nearly the same, while the computed energies on the boundaries of the deformed cells are significantly larger.

Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Modeling and Performance Analysis of LBT-Based RF-Powered NR-U Network for IoT.

Energy harvesting combined with spectrum sharing offers a promising solution to the growing demand for spectrum while keeping energy costs low. New Radio Unlicensed (NR-U) technology enables telecom operators to utilize unlicensed spectrum in addition to the licensed spectrum already in use. Along with this, the energy demands for the Internet of Things (IoT) can be met through energy harvesting. In this regard, the ubiquity and ease of implementation make the RF-powered NR-U network a sustainable solution for cellular IoT. Using a Markov chain, we model the NR-U network with nodes powered by the base station (BS). We derive closed-form expressions for the normalized saturated throughput of nodes and the BS, along with the mean packet delay at the node. Additionally, we compute the transmit outage probability of the node. These quality of service (QoS) parameters are analyzed for different values of congestion window size, TXOP parameter, maximum energy level, and energy threshold of the node. Additionally, the effect of network density on collision, transmission, and energy harvesting probabilities is observed. We validate our model through simulations.

The anterior femoral cortical window as an alternative to an extended trochanteric osteotomy in revision hip arthroplasty surgery: the evolution of the surgical technique and outcomes in 22 consecutive cases.

The anterior femoral cortical window is an attractive alternative to the extended trochanteric osteotomy when removing femoral cement in revision hip arthroplasty. CT-based additive manufacturing technology has now permitted the creation of patient-specific instrumented (PSI) jigs to facilitate this. The jig simplifies creation of the window, potentiating medullary exposure through an optimally-sized window and therefore cement removal. Between 2006 and 2021 this technique was used in 22 cases at a regional hospital in New Zealand (mean age 74; range 44 to 89 years). 16 cases were for aseptic loosening and 6 for infection. We describe the technique and our case series. Bone incorporation for the cortical window was assessed in all cases using CT imaging. Oxford scores were obtained at a minimum of 6 months after revision surgery. Of the 6 septic cases 5 went onto successful stage-2 procedures, the other to a Girdlestone procedure. The mean rectangular shaped window size was 8 × 1.5 cm and in each case, this provided adequate intramedullary access. On average at minimum 5 months post-surgery, 84% bone incorporation of the cortical window occurred on CT (40-100%). The functional outcome Oxford hip score was 37 (range 22-48) for 10 cases. There were 2 cases with femoral component subsidence which then stabilised. This technique description and retrospective case series has shown the effectiveness of removing a distal femoral cement mantle in revision hip arthroplasty using an anterior femoral cortical window, recently optimised using a PSI jig. This technique is a straightforward alternative to a trochanteric osteotomy. Reliable bony integration of the cortical window occurred and functional outcomes were comparable with the mean score for revision hip procedures reported in the New Zealand Joint Registry.

Built-In Environmental Construction Mechanism and Sustainable Renewal Strategies of Traditional Qiang Dwellings in Western China

Indoor air quality (IAQ) has a significant impact on human health, as people spend 90% of their time in various indoor environments. Therefore, research on IAQ is extremely necessary. However, current research on traditional Qiang residences in western Sichuan mainly focuses on the indoor thermal environment and heritage protection, with relatively little attention paid to IAQ. This study investigates the IAQ of traditional Qiang residences in western Sichuan, which have open fire pits as the core of daily life, exploring the impact of passive renovation strategies on the indoor air quality. Using simulation methods, this study employs passive strategies, such as increasing the size of windward windows, changing ventilation methods, relocating the fire pit, and enlarging interior partition openings, to improve and optimize the IAQ through natural ventilation. The results show that when the windward window sizes are 0.8 m × 1.9 m and 0.7 m × 1.55 m, the reduction in the indoor CO2 concentration is the greatest, with a maximum decrease of 0.024% at the 1.5 m plane. This paper proposes passive renovation strategies to improve the indoor air quality of Qiang residences in western Sichuan. These strategies effectively enhance the indoor air quality of Qiang residences and address the research gap on indoor air quality in regional Qiang residences in western Sichuan. The insights and methods presented contribute to the improvement of the indoor air quality in traditional buildings and support the sustainable development of traditional architecture.

Beyond dopant selection: The critical grain size window as a key performance dictator for Co-free, Ni-rich cathode materials

Despite the immense potential of cobalt-free, nickel-rich cathode materials to replace their cobalt-containing counterparts in lithium-ion batteries (LIBs), their widespread adoption remains hindered by performance limitations. While extensive research has explored various doping strategies to address this challenge, the reported benefits vary significantly, suggesting an incomplete understanding of dopant influence and thus hindering the full potential of these additives. Our investigation into the effect of various dopants on Li(Ni0.95Mn0.05)O2 performance yielded a surprising discovery: the LIB performance of transition metal-doped Li(Ni0.95Mn0.05)O2 becomes remarkably consistent across different dopant types, provided the calcination conditions are optimized. Regardless of dopant type, a critical grain size window is identified as a key factor influencing the performance of the doped Li(Ni0.95Mn0.05)O2. Calcination within a specific temperature range is paramount to control this previously overlooked performance determinant, which significantly impacts grain growth, primary particle morphology, and lattice structure. This novel insight allows us to demonstrate that cost-effective dopants such as Ti can achieve performance in stabilizing Li(Ni0.95Mn0.05)O2 comparable to that of more expensive, higher-valence alternatives (e.g., Nb, Ta, W, and Mo). This discovery paves the way for developing practical material design processes that meet the stringent requirements of the LIB industry.

An IMU-based machine learning approach for daily behavior pattern recognition in dairy cows

Technological advancements have revolutionized livestock farming, notably in health monitoring. Traditional methods, which have been criticized for subjectivity and treatment delays, can be replaced with efficient health monitoring systems, thereby reducing costs and workload. Implementing cow behavior recognition allows for effective dairy cow health monitoring. In this research, we propose an integrated system using inertial measurement unit (IMU) devices and machine learning techniques for dairy cow behavior recognition. Six main dairy cow behaviors were studied: lying, standing, walking, drinking, feeding, and ruminating. All behavior types were manually labeled into the IMU data by reviewing the recorded footage. The labeled IMU data underwent four processing steps: selecting different window sizes, feature extraction, feature selection, and normalization. These processed data were then used to build the behavior recognition model. Various model structures, including SVM, Random Forest, and XGBoost, were tested. The top-performing model, XGBoost, with its proposed 58 features achieved an F1-score of 0.87, with specific scores of 0.93 for lying, 0.85 for walking, 0.94 for ruminating, 0.89 for feeding, 0.86 for standing, 0.93 for drinking, and 0.59 for other activities. During our online testing, we observed similar patterns for each healthy cow. The cumulative time spent on each behavior also matched the statistics from previous surveys. Additionally, our backend optimization approach resulted in a final overall percentage error of 1.55 % per day during online testing. In conclusion, our study presents an IMU-based system capable of accurately recognizing dairy cow behavior. Feature design and appropriate models are proposed herein. A functional optimization method was introduced indicating that our system has the potential with applications for estrus detection and other reproductive management practices in the dairy industry.

Electrochemical cell for synchrotron nuclear resonance techniques.

Developing new materials for Li-ion and Na-ion batteries is a high priority in materials science. Such development always includes performance tests and scientific research. Synchrotron radiation techniques provide unique abilities to study batteries. Electrochemical cell design should be optimized for synchrotron studies without losing electrochemical performance. Such design should also be compatible with operando measurement, which is the most appropriate approach to study batteries and provides the most reliable results. The more experimental setups a cell can be adjusted for, the easier and faster the experiments are to carry out and the more reliable the results will be. This requires optimization of window materials and sizes, cell topology, pressure distribution on electrodes etc. to reach a higher efficiency of measurement without losing stability and reproducibility in electrochemical cycling. Here, we present a cell design optimized for nuclear resonance techniques, tested using nuclear forward scattering, synchrotron Mössbauer source and nuclear inelastic scattering.

Comparative Analysis of Filtering Techniques for AGV Indoor Localization with Ultra-Wideband Technology

This paper investigates the filtering techniques to enhance the accuracy of indoor localization for Autonomous Guided Vehicles (AGVs) using Ultra-Wideband (UWB) technology. A comprehensive comparative analysis of various filtering approaches, including the Kalman Filter (KF), Moving Average Filter (MA), Savitzky-Golay Filter (SG), Weighted Average Filter (WAF), and their combinations, are conducted. The primary focus of this paper is the integration of a Moving Average-Kalman Filter (MAKF) with an extended window size of 201. Experimental findings reveal significant performance differences among these filtering techniques. The most effective approach is the MAKF technique, achieving an accuracy of 85.13% and the lowest path deviation of 0.17 meters. Conversely, the MA exhibits the lowest accuracy at 68.83%. Notably, the WAF attains an accuracy of 72.46% but exhibits a significantly higher path deviation of 2.65 meters compared to 1.45 meters of the MA filtering technique. The proposed MAKF acknowledged for its ability to effectively reduce noise with real-time responsiveness, represents a significant advancement in AGV indoor localization techniques.

Real-Time Sensing of Upper Extremity Movement Diversity Using Kurtosis Implemented on a Smartwatch.

Wearable activity sensors typically count movement quantity, such as the number of steps taken or the number of upper extremity (UE) counts achieved. However, for some applications, such as neurologic rehabilitation, it may be of interest to quantify the quality of the movement experience (QOME), defined, for example, as how diverse or how complex movement epochs are. We previously found that individuals with UE impairment after stroke exhibited differences in their distributions of forearm postures across the day and that these differences could be quantified with kurtosis-an established statistical measure of the peakedness of distributions. In this paper, we describe further progress toward the goal of providing real-time feedback to try to help people learn to modulate their movement diversity. We first asked the following: to what extent do different movement activities induce different values of kurtosis? We recruited seven unimpaired individuals and evaluated a set of 12 therapeutic activities for their forearm postural diversity using kurtosis. We found that the different activities produced a wide range of kurtosis values, with conventional rehabilitation therapy exercises creating the most spread-out distribution and cup stacking the most peaked. Thus, asking people to attempt different activities can vary movement diversity, as measured with kurtosis. Next, since kurtosis is a computationally expensive calculation, we derived a novel recursive algorithm that enables the real-time calculation of kurtosis. We show that the algorithm reduces computation time by a factor of 200 compared to an optimized kurtosis calculation available in SciPy, across window sizes. Finally, we embedded the kurtosis algorithm on a commercial smartwatch and validated its accuracy using a robotic simulator that "wore" the smartwatch, emulating movement activities with known kurtosis. This work verifies that different movement tasks produce different values of kurtosis and provides a validated algorithm for the real-time calculation of kurtosis on a smartwatch. These are needed steps toward testing QOME-focused, wearable rehabilitation.

PriorMSM: An Efficient Acceleration Architecture for Multi-Scalar Multiplication

Multi-Scalar Multiplication (MSM) is a computationally intensive task that operates on elliptic curves based on GF(P) . It is commonly used in zero-knowledge proof (ZKP), where it accounts for a significant portion of the computation time required for proof generation. In this article, we present PriorMSM, an efficient acceleration architecture for MSM. We propose a Priority-Based Scheduling Mechanism (PBSM) based on a multi-FIFO and multi-bank architecture to accelerate the implementation of MSM. By increasing the pairing success rate of internal points, PBSM reduces the number of bubbles in the pipeline of point addition (PADD), consequently improving the data throughput of the pipeline. We also introduce an advanced parallel bucket aggregation algorithm, leveraging PADD’s fully pipelined characteristics to significantly accelerate the implementation of bucket aggregation. We perform a sensitivity analysis on the crucial parameter of window size in MSM. The results indicate that the window size of the MSM significantly impacts its latency. Area-Time Product (ATP) metric is introduced to guide the selection of the optimal window size, balancing the performance and cost for practical applications of subsequent MSM implementations. PriorMSM is evaluated using the TSMC 28 nm process. It achieves a maximum speedup of 10.9× compared to the previous custom hardware implementations and a maximum speedup of 3.9× compared to the GPU implementations.

Peptide PaDBS1R6 has potent antibacterial activity on clinical bacterial isolates and integrates an immunomodulatory peptide fragment within its sequence

BackgroundResistant infectious diseases caused by gram-negative bacteria are among the most serious worldwide health problems. Antimicrobial peptides (AMPs) have been explored as promising antibacterial, antibiofilm, and anti-infective candidates to address these health challenges. Major conclusionsHere we report the potent antibacterial effect of the peptide PaDBS1R6 on clinical bacterial isolates and identify an immunomodulatory peptide fragment incorporated within it. PaDBS1R6 was evaluated against Acinetobacter baumannii and Escherichia coli clinical isolates and had minimal inhibitory concentration (MIC) values from 8 to 32 μmol L−1. It had a rapid bactericidal effect, with eradication showing within 3 min of incubation, depending on the bacterial strain tested. In addition, PaDBS1R6 inhibited biofilm formation for A. baumannii and E. coli and was non-toxic toward healthy mammalian cells. These findings are explained by the preference of PaDBS1R6 for anionic membranes over neutral membranes, as assessed by surface plasmon resonance assays and molecular dynamics simulations. Considering its potent antibacterial activity, PaDBS1R6 was used as a template for sliding-window fr agmentation studies (window size = 10 residues). Among the sliding-window fragments, PaDBS1R6F8, PaDBS1R6F9, and PaDBS1R6F10 were ineffective against any of the bacterial strains tested. Additional biological assays were conducted, including nitric oxide (NO) modulation and wound scratch assays, and the R6F8 peptide fragment was found to be active in modulating NO levels, as well as having strong wound healing properties. General significanceThis study proposes a new concept whereby peptides with different biological properties can be derived by the screening of fragments from within potent AMPs.

Dual-branch Transformer for semi-supervised medical image segmentation.

In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time-consuming and labor-intensive. Training neural networks effectively with limited labeled data is a significant challenge in medical image analysis. Secondly, convolutional neural networks commonly used for medical image segmentation research often focus on local features in images. However, the recognition of complex anatomical structures or irregular lesions often requires the assistance of both local and global information, which has led to a bottleneck in its development. Addressing these two issues, in this paper, we propose a novel network architecture. We integrate a shift window mechanism to learn more comprehensive semantic information and employ a semi-supervised learning strategy by incorporating a flexible amount of unlabeled data. Specifically, a typical U-shaped encoder-decoder structure is applied to obtain rich feature maps. Each encoder is designed as a dual-branch structure, containing Swin modules equipped with windows of different size to capture features of multiple scales. To effectively utilize unlabeled data, a level set function is introduced to establish consistency between the function regression and pixel classification. We conducted experiments on the COVID-19 CT dataset and DRIVE dataset and compared our approach with various semi-supervised and fully supervised learning models. On the COVID-19 CT dataset, we achieved a segmentation accuracy of up to 74.56%. Our segmentation accuracy on the DRIVE dataset was 79.79%. The results demonstrate the outstanding performance of our method on several commonly used evaluation metrics. The high segmentation accuracy of our model demonstrates that utilizing Swin modules with different window sizes can enhance the feature extraction capability of the model, and the level set function can enable semi-supervised models to more effectively utilize unlabeled data. This provides meaningful insights for the application of deep learning in medical image segmentation. Our code will be released once the manuscript is accepted for publication.

Auto-Correlation and Channel Attention Enhanced Deep Graph Convolution Networks for Gait Phase Prediction Based on Multi-IMU System

Gait phase prediction is important in controlling assistive robotic devices such as exoskeletons, where the control unit must differentiate between gait phases to provide the necessary assistance when the user is wearing the exoskeleton. To achieve the objective of precisely identifying the gait phase of users for the accurate control of the exoskeleton, this study proposes Auto-Correlation and Channel Attention enhanced Deep Graph Convolutional Networks (ACCA-DGCN) for gait phase prediction, and a gait phase prediction model based on multiple inertial measurement units (IMUs) and skeleton graph was established, in order to fully utilize the dependency among joints, and enhance accuracy and reliability of gait phase prediction. First, a human lower limb gait data acquisition equipment was developed, and the gait data of human walking were collected. The skeleton graph of the human lower limb was constructed through the natural connection relationship of joints in the human skeleton. After that, the ACCA-DGCN-based gait phase prediction model was constructed by using the gait data of human walking. Auto-Correlation (AC) and Efficient Channel Attention (ECA) were introduced to effectively capture periodic features of gait data and focus on the channels with high contributions to gait phase prediction. Finally, the effect of the window size on the performance of the ACCA-DGCN model was explored, and the proposed algorithm was compared with the other five deep learning algorithms: CNN, RNN, TCN, LSTM, and DGCN. The experimental results show that the average accuracy of gait phase prediction model based on ACCA-DGCN reaches up to 92.26% and 97.21% in user-independent and user-dependent experiments, respectively, which is superior to the other five algorithms. This study provides a new method for gait phase prediction, which is useful for improving the control of exoskeleton robots.

Deduplication-Aware Healthcare Data Distribution in IoMT

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization.

Mapping the wildland-urban interface at municipal level for wildfire exposure analysis in mainland Portugal

The Wildland-Urban Interface (WUI), where vegetation and built-up structures intermingle, encompasses a variety of territorial elements that interact spatially, being variable both in space and time. Mapping the WUI at finer scales is paramount to assess wildfire exposure and define tailored mitigation strategies. Our aim was to develop a semi-automated method to map the WUI at municipal level, leveraging recent advances in data and technology. We tested the procedure in four municipalities of mainland Portugal with different fire history, biophysical conditions, and sociodemographic contexts. We considered WUI as either intermix or interface. Our approach integrates both building location data and high-resolution vegetation maps, to calculate the density of buildings and forest cover proportion within different circular moving window sizes. Within each radius, we evaluated the total area and spatial distribution of the WUI types, as well as the number of buildings within WUI and within the fire perimeters recorded between the years 2000 and 2022 and analysed the differences between municipalities. We then compared the mapped WUI with previous WUI mappings for mainland Portugal, to identify common spots and potential spatial divergences. We found that the area mapped as WUI within all four municipalities ranged from about 400 km2 to 1135 km2 depending on the radius size. A distinct distribution for each type of WUI was observed as the radius size increased: the intermix WUI showed a tendency to increase, and the interface WUI increased only between the radius of 100 and 200 m, decreasing gradually in subsequent radii. Between 39.4% and 45.5% of the nearly 200,000 buildings in the study areas were within WUI, depending on radius size and a total of 5436 buildings were within the historic fire perimeter. Although the comparison with other maps showed fair agreement, due to differences in data and methodology, common areas mapped as WUI were found, which suggests that these areas should receive greater attention from decision-makers regarding fire management strategies, since their classification as WUI remains consistent across different methodologies.

Differentiating hand gestures from forearm muscle activity using machine learning

This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to 97% due to the increased temporal resolution. It was also noticed that the RF performed better with an accuracy of 85% than the NN with accuracy 80% when the temporal resolution was smaller, indicating the RF will be efficient when quick-response time is important. As the window size increases, the NN showed higher performance, suggesting that NN will be useful when higher accuracy is required. Future studies should increase the sample size, include more hand gestures, use different feature extraction methods and test different algorithms to improve the accuracy and efficiency of the system.