Miracle-dz/UniFAR-FTUs huggingface dataset:
A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network

About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.
A Universality-Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error).
Back to the Future: “De-Transition” to Low-Car Cities

Current urban mobility systems in Europe, characterized by high car mobility shares, have negative environmental and health impacts but struggle to mitigate these for fear of sacrificing accessibility. Ironically, before the car mobility transition (in the 1950s and 1960s in Western countries and the 1990s in Eastern Europe), most cities were accessible by walking, cycling, public transport, and by the few cars there were. Through a longitudinal case study of a medium-sized urban area in Clermont-Ferrand, France (1950–2022), this paper explores the potential to ‘de-transition’, i.e., to reverse the urban transition process towards ‘accessible, low-car cities’ by reshaping infrastructures to constrain car use whilst accommodating walking, cycling, and public transport. We answer the following questions: To what extent can cities reverse the urban car mobility transition? How could such a process be further encouraged? Our analysis adopts a social practices perspective and uses a mixed-methods approach by combining semi-structured interviews, a survey, and a document analysis. On the one hand, our findings highlight the difficulty of an urban modality shift to car alternatives: (1) the limited reach of public transformation networks (in Clermont-Ferrand, the tramline); (2) the fact that many feel unsafe or assume they need excellent health conditions to cycle, which is associated with leisure and sports; and (3) strong convictions concerning the usefulness of vehicle ownership, which is believed to maximise comfort. On the other hand, based on a historic analysis, we offer practical recommendations to de-transition to low-car urban areas: (1) the creation of an extensive regional tramway network; (2) the development of a full cycling network; and (3) the promotion of an extensive car-free city centre.
Looking at the Expansion of Three Demersal Lessepsian Fish Immigrants in the Greek Seas: What Can We Get from Spatial Distribution Modeling?

A big number of Red Sea species have entered the Mediterranean Sea since the opening of the Suez Canal. Some of them quickly establish local populations and increase their abundance, forming a potential threat for local biodiversity and fisheries. Here, we use habitat modeling tools to study the expansion of three alien, demersal fish species that entered the Mediterranean basin at different times: Pterois miles, Siganus luridus and Siganus rivulatus. Georeferenced occurrence data from the eastern Mediterranean over the past ten years were compiled using online sources, published scientific literature and questionnaires and were correlated with environmental and topographic variables. The maximum entropy modeling approach was applied to construct habitat suitability maps for the target species over all of the Greek Seas. Results emphasized the three species’ strong coastal nature and their association with the presence of Posidonia oceanica meadows. Probability maps evidenced that for all species there is a higher likelihood of presence along the southeast and central Aegean and Ionian Sea coasts and a lower likelihood throughout the North Aegean Sea. For Siganus spp. , predictions in the Thracian Sea were highlighted as highly uncertain, as the environmental conditions in this area partly fall outside the range of values occurring in locations of their current presence.
What Mathematics Education Might Learn from the Work of Well-Respected African American Mathematics Teachers in Urban Schools

Background/Context This opening article, like the other articles in this special issue, is situated in scholarship that attempts to understand the racialized nature of mathematics education in the United States and to examine the racial identities of students and teachers in the context of school mathematics. It is designed to respond to the current (mathematics) education policy context that largely ignores teachers’ experiential and cultural knowledge while stressing the importance of teachers’ content knowledge and academic achievement. Purpose/Objective/Research Question/Focus of Study This article presents theoretical perspectives and research questions concerning the knowledge and other resources that African American teachers bring to teaching mathematics, perspectives and questions that are taken up in the five subsequent articles in this special issue. Setting The cases developed in this special issue were developed from observations of the introductory algebra classes of, and interviews with, two well-respected African American teachers in one neighborhood high school in a large urban school district that serves a predominantly African American student population. Research Design This opening article frames two case study papers and two analysis papers that report on findings from a large-scale qualitative study of the racialized identity and instructional approaches of two of the six African American mathematics teachers studied in the Mid-Atlantic Center for Mathematics Teaching and Learning Algebra 1 Case Studies Project. Conclusions/Recommendations Together with the other articles in this special issue, this work contributes to the development of more sophisticated attempts to integrate understandings of race into the work of the mathematics education community. It challenges taken-for-granted notions of the knowledge base and resources needed to be an effective mathematics teacher of African American students in underresourced large urban schools.
Analytical Models for Pose Estimate Variance of Planar Fiducial Markers for Mobile Robot Localisation

Planar fiducial markers are commonly used to estimate a pose of a camera relative to the marker. This information can be combined with other sensor data to provide a global or local position estimate of the system in the environment using a state estimator such as the Kalman filter. To achieve accurate estimates, the observation noise covariance matrix must be properly configured to reflect the sensor output’s characteristics. However, the observation noise of the pose obtained from planar fiducial markers varies across the measurement range and this fact needs to be taken into account during the sensor fusion to provide a reliable estimate. In this work, we present experimental measurements of the fiducial markers in real and simulation scenarios for 2D pose estimation. Based on these measurements, we propose analytical functions that approximate the variances of pose estimates. We demonstrate the effectiveness of our approach in a 2D robot localisation experiment, where we present a method for estimating covariance model parameters based on user measurements and a technique for fusing pose estimates from multiple markers.
Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges

Handwriting is a determining factor for academic success and autonomy for all children. Making knowledge accessible to all is a challenge in the context of inclusive education. Given the neurodevelopmental diversity within a classroom of children, ensuring that the handwriting of all pupils progresses is very demanding for education professionals. The development of tools that can take into account the variability of the profiles and learning abilities of children with handwriting difficulties offers a new potential for the development of specific and adapted remediation strategies. This narrative review aims to present and discuss the challenges of handwriting learning and the opportunities offered by new technologies involving AI for school and health professionals to successfully improve the handwriting skills of all children.
Critical-Illness: Combined Effects of Colistin and Vasoactive Drugs: A Pilot Study

Colistin is often used as a last resort for treating multidrug-resistant infections, particularly in critically ill patients in intensive care units. Nonetheless, its side effects, including myopathy, require careful monitoring. Vasoconstrictive drugs are also used in intensive care to increase blood pressure and improve blood flow to vital organs, which can be compromised in critically ill patients. The exact mechanism of colistin-induced muscle toxicity is of significant interest due to its potential intensive-care clinical implications. Colistin alone or in combination with vasoconstrictive agents was administrated in non-septic and LPS-induced septic animals for 10 days. Histopathological evaluation of the gastrocnemius muscle and dot-blot protein tissue analysis were performed. Increased intramuscular area, de-organization of the muscle fibers and signs of myopathy were observed in colistin-treated animals. This effect was ameliorated in the presence of vasoconstrictive drugs. Administration of colistin to septic animals resulted in a decrease of AMPK and cyclin-D1 levels, while it had no effect on caspase 3 levels. Vasoconstrictive drugs’ administration reversed the effects of colistin on AMPK and cyclin D1 levels. Colistin’s effects on muscle depend on septic state and vasoconstriction presence, highlighting the need to consider these factors when administering it in critically ill patients.
Comparison of Crushing Effect of Differently Shaped Crushing Rollers on Whole-Plant Silage Maize

Based on the mechanical test (shear test, compression test), the bond model of corn kernel and straw was established to explore the rolling and crushing effect of different crushing rollers. The type of crushing roller is different. The material crushing process by the force (extrusion and kneading) is different. The mechanical analysis of the crushing process reveals that the disc crushing roller (DCR) has the characteristics of large unit-length kneading area; the spiral-notched serrated crushing roller (SNSCR) has transverse shearing effect on the material; and they affect the crushing effect of the material. By means of discrete element method and simulation test, multiple regression method and variance analysis method are used to systematically analyze the data. The optimal working parameters of each roll (crushing roll speed, crushing clearance, differential ratio) were obtained. The simulation test and bench test of the crushing process of materials with different roll shapes were carried out under the optimal working parameters. The crushing effect was evaluated with a Binzhou screen and a corn silage grain-crushing score screen. The crushed materials of corn kernel can be divided into three categories according to the size (broken grains passed through 2 mm sieve; broken grains passed through 4.75 mm sieve; and broken grains that cannot pass through 4.75 mm sieve), and the crushed materials of corn stalk can be divided into four categories according to the size and thickness (broken straw through 4 mm sieve; broken straw through 8 mm sieve; broken straw through 19 mm sieve; and broken straw that cannot pass 19 mm sieve). The crushing effect and crushing classification of the simulation test and bench test were basically consistent. The results showed that the disc crushing roller group had the highest comprehensive score with straw rolling rate of 89.1% and grain crushing rate of 87.7%, which was the most suitable for harvesting whole-plant silage maize (WSM).
A Systematic Review and Meta-Analysis of the Differences in Mean Propulsive Velocity between Men and Women in Different Exercises

The purpose of this paper was to conduct a systematic review and meta-analysis of studies examining the differences in the mean propulsive velocities between men and women in the different exercises studied (squat, bench press, inclined bench press and military press). Quality Assessment and Validity Tool for Correlational Studies was used to assess the methodological quality of the included studies. Six studies of good and excellent methodological quality were included. Our meta-analysis compared men and women at the three most significant loads of the force–velocity profile (30, 70 and 90% of 1RM). A total of six studies were included in the systematic review, with a total sample of 249 participants (136 men and 113 women). The results of the main meta-analysis indicated that the mean propulsive velocity is lower in women than men in 30% of 1RM (ES = 1.30 ± 0.30; CI: 0.99–1.60; p < 0.001) and 70% of 1RM (ES = 0.92 ± 0.29; CI: 0.63, 1.21; p < 0.001). In contrast, for the 90% of the 1RM (ES = 0.27 ± 0.27; CI: 0.00, 0.55), we did not find significant differences (p = 0.05). Our results support the notion that prescription of the training load through the same velocity could cause women to receive different stimuli than men.
DoseMRT: A Software Package for Individualised Monte Carlo Dose Calculations of Synchrotron-Generated Microbeam Radiation Therapy

This work describes the creation and experimental validation of DoseMRT, a new software package, and its associated workflow for dose calculations in synchrotron-generated broad beam and microbeam radiation treatment fields. The DoseMRT software package allows users to import CT DICOM datasets into Geant4 for Monte Carlo dose calculations. It also provides basic treatment planning capabilities, simplifying the complexity of performing Geant4 simulations and making our Monte Carlo dose calculation algorithm accessible to a broader range of users. To demonstrate the new package, dose calculations are validated against experimental measurements performed in homogeneous water tank phantoms and the anatomically complex Alderson Radiotherapy Phantom for both broad-beam and microbeam configurations. Additionally, DoseMRT is successfully utilised as the primary method for patient-specific treatment prescription in an in vivo experiment involving tumour-bearing rats at the Imaging and Medical Beamline of the Australian Synchrotron.
Solution of fractional order differential equation by the Haar Wavelet method. Numerical convergence analysis for most commonly used approach

The Haar wavelet method is applied for solution of fractional order differential equations. Numerical convergence analysis is performed for most commonly used wavelet expansion. It has been observed that the order of convergence of the Haar wavelet method is equal to two if higher order derivative α in fractional differential equation exceed one (α>1). However, in the case of 0< α<1 the order of convergence of the Haar wavelet method tends to the value 1+ α.
The Impact of the Intuitionistic Fuzzy Entropy-Based Weights on the Results of Subjective Quality of Life Measurement Using Intuitionistic Fuzzy Synthetic Measure

In this paper, an extended Intuitionistic Fuzzy Synthetic Measure (IFSM) with intuitionistic fuzzy (IF) entropy-based weights is presented. This method can be implemented in a ranking problem where the assessments of the criteria are expressed in the form of intuitionistic fuzzy values and the information about the importance criteria is unknown. One example of such a problem is measuring the subjective quality of life in cities. We join the debate on the determination of weights for the analysis of the quality of life problem using multi-criteria methods. To handle this problem, four different IF entropy-based weight methods were applied. Their performances were compared and analyzed based on the questionnaires from the survey concerning the quality of life in European cities. The studies show very similar weighting systems obtained by different IF entropy-based approaches, resulting in almost the same city rankings acquired through IFSM by using those weights. The differences in rankings obtained through the IFSM measure (and only by one position) concern the six cities included in the analysis. Our results support the assumption of the equal importance of the criteria in measuring this complex phenomenon.
Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.
New Photochemical Properties of Azidoaniline and Ciprofloxacin "2279

There is a trend to find new ways of using photocatalysis in order to synthesize valuable products or to control or track live processes with special fluorescence-based molecular probes. The paper presents some results concerning new photochemical properties of azidoaniline, its 7-nitrobenzofurazan (NBD) derivative and ciprofloxacin derivatives.
Design of a DC to DC Converter for a Residential Grid Connected Solar Energy System "2279

This article presents an investigation of a PV solar system based on Maximum Power Point Tracking (MPPT) using one of the artificial intelligence control techniques. To avoid the problems of the traditional P&O method, the optimization tool of Particle Swarm Optimization (PSO) is proposed to be employed with a Perturbation & Observation (P&O) technique with added a Proportional Integral (PI) controller (PSO + PI + P&O). This proposed method achieved the optimal operating variables of a photovoltaic (PV) module in terms to mitigating the issue of partial shading weather conditions. A DC–DC boost converter is designed to be switched to obtain the desired MPP based on the corresponding duty cycle according to the parameters provided by the IV characteristics. It is observed from the results that the proposed PSO + PI + P&O showed excellent improvement. Moreover, better performance was achieved in terms of extracting the maximum unique power point between both variables of current and voltage generated from the PV. The proposed MPPT control method was implemented in MATLAB/Simulink. The testbed of the suggested method was thorough and highlighted its high efficiency compared to the conventional P&O as well as the PI controller based on P&O methods.