Miracle-dz/UniFAR-FTUs huggingface dataset:
Fetal Biometry Assessment of Femur Length for Pregnant Women in Dammam, Saudi Arabia

This study was conducted to establish a new reference chart and equation for femur length (FL) based on Saudi pregnant women in Dammam, and to compare it with references from different populations. This cross-sectional study involved 400 women with singleton pregnancies who attended the Maternal and Children Hospital in Dammam in 2018, which confirmed their gestational age (GA). For this study, each woman was scanned once only, between 14th to 40th completed weeks ofgestation using Ultrasound machine Voluson E10 (GE Healthcare, Austria) with a 2–5 MHz convex abdominal probe. The SPSS software version 24 (descriptive, regression analysis, and paired t-test) was used for analysis. The mean maternal age was 29 ± 5.92 years. The mean value of FL in second trimesters was 33.75 mm while it reaches to 62.80 mm in third trimesters. At 14th and 40th weeks, mean values (±SD) for FL were 14.85 (±2.58) mm and 78.75 (±1.97) mm. The quadratic regression models were fitted to estimate the mean and separate linear regression to estimate the standard deviation at each GA. The comparison of the FL values of Saudi chart shows a significant difference with the common references used. The FL values in this study were significantly higher than the corresponding values of USA, French, UK, German, Japan, Korea, Malaysia, Iran, Bangladesh, Egypt, and Thailand but lower than China and Nigerian. Thus, these new FL values are highly recommended to be used by the medical practitioner as the reference range of fetus biometry for Saudi Arabia population regarding providing better healthcare and wellbeing of the maternal and fetuses.
Vibration Control for Electric Vehicles With In-Wheel Switched Reluctance Motor Drive System

The in-wheel switched reluctance motor (IWSRM) has significant potential for utilization in electric vehicle (EV) because of its inherent advantages such as low cost, robustness, wide speed range, and direct drive mode. However, the unbalanced radial force caused by eccentricity of IWSRM results in increasing vehicle vibration and decreasing riding comfort. In this paper, various vertical vibration control strategies for IWSRM are proposed and compared to improve the vibratory behavior of vehicle. First, effects of eccentricity ratio and eccentric angle on radial force, torque, and inductance are investigated by numerical analysis method for IWSRM. Then, IWSRM model, vehicle model, and suspension model are developed, based on which two common control algorithms (i.e. CCC and PWM) are provided and used as benchmarks to compare with the newly proposed two (i.e. CCC-F and PWM-F). They are designed by introducing the vehicle vertical acceleration as an extra control objective. Finally, the dynamic response of four control strategies are analyzed and the comparison results show that each of them performs differently under different driving conditions. As a result, a switchable controller based on the proposed control algorithms is presented to improve the overall performance of the IWSRM for EVs under various driving modes.
Continuation-Based Metric Semantics for Concurrency

By using the mathematical methodology of metric semantics and continuation semantics for concurrency (CSC) we design semantic models for a concurrent language extended with multiparty synchronization based on Hoare’s CSP. We present a new kind of domain for CSC, where the structure of continuations is expressed by using functions which map computations to computations. We study the abstractness of CSC, by using a new optimality criterion specific to continuation semantics, that we call k weak abstractness - a weaker version of Milner’s full abstractness criterion.
Evaluation of the Energy Consumption Model Performance for Electric Vehicles in SUMO

In recent years, the diffusion of electric vehicles(EVs) is of great importance in the road transport and automotive sectors. Reducing electricity consumption and increasing battery autonomy are actually motivating vehicle manufacturers to focus their attention on the EV concept. The evaluation of EV performance through realistic scenarios in traffic simulations is still a key issue. In fact, the road traffic Simulator Simulation of Urban Mobility (SUMO) integrates an energy model for calculating the EV energy consumption. However, this model underestimates the energy consumption values in the real world. In this paper, we intend to investigate the energy consumption models existing in the literature. Then, we assess the performance of the existing energy model in SUMO through a set of simulation scenarios. Finally, we present the main requirements for a realistic, accurate and scalable energy model to estimate the instantaneous energy consumption for EVs.
Combining Acoustic and Semantic Similarity for Acoustic Scene Retrieval

Automatic retrieval of acoustic scenes in large audio collections is a challenging task due to the complex structures of these sounds. A robust and flexible retrieval system should address both the acoustic-and semantic aspects of these sounds and how to combine them. In this study, we introduce an acoustic scene retrieval system that uses a combined acoustic-and semantic-similarity method. To address the acoustic aspects of sound scenes, we use a cascaded convolutional neural network (CNN) with a gated recurrent unit (GRU). The acoustic similarity is calculated in feature space using the Euclidean distance and the semantic similarity is obtained using the Path Similarity method of the WordNet. Two performance datasets from the TAU Urban Acoustic Scenes 2019 and the TUT Urban Acoustic Scenes 2018 are used to compare the performance of the proposed retrieval system with the literature and the developed baseline. Results show that the semantic similarity improves the mAP and P@k scores.
Fast Amplitude Estimation for Low-Voltage Ride-Through Operation of Single-Phase Systems

Estimating the amplitude of the grid voltage is important for low-voltage ride-through (LVRT) operation in single-phase systems. It requires fast dynamics and is typically achieved through a phase-locked loop (PLL). This paper proposes a fast amplitude estimation (FAE) method without a PLL structure. The grid voltage is formulated as a vector sum of sine and cosine functions, resulting in an algebraic model with two unknown parameters. The proposed FAE identifies the two parameters, and thus achieving a fast fault detection (i.e., the amplitude estimation). Experimental results verify the performance of the proposed method in terms of fast dynamics.
A Wideband Dual-Polarized Antenna and its Array With Electrically Downtilt Function for 5G Sub-6 GHz Communication Applications

A wideband dual-polarized antenna is proposed for 4G and 5G communication applications. The antenna element consists of a pair of opened-loop dipoles for dual-polarization and its three inherent resonant modes are shifted closer to form a wide bandwidth. With additional U-shaped slots etched around the feed point, the input impedance of the antenna element is matched to 50 Ohm. The fabricated antenna element operates from 1.8 to 4.0 GHz, having 75.9% (VSWR ≤ 2) impedance bandwidth and high port-to-port isolation (>25 dB). It also achieves a 67±1° beamwidth in H-plane and 68.7 ± 3.3° in V-plane with 8.5 ± 1 dBi gain across the supported bands. By using six antenna elements and designing RF phase shifting module (RFPSM) based on vector modulators, a six-element dual-polarized array with electrically downtilt is also fabricated and measured, realizing a peak gain of 16.8 dBi, a similar beamwidth in H-plane as single antenna element and an electrically downtilt in V-plane from 0° to 12° which is preferred in communication applications. This antenna array could be used for future 5G communication and other applications.
Support vector machine for seagrass percent cover mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara

Seagrass meadows have many ecosystem services to coastal areas and adjacent ecosystems, these services include nursery area for marine organisms, sea turtle feeding ground, and blue carbon sequestration. Therefore, it is important to protect seagrass in order to preserve their functions. Seagrass percent cover is one of the parameters to asses seagrass condition. Several approaches have been developed to map seagrass in optically shallow waters and one of them is by using remote sensing. This approach is more effective and efficient compared to field survey alone. The aim of this study is to produce seagrass spatial distribution and percent cover map using high resolution image. In this research, Support Vector Machine (SVM) classification and regression, one of the machine learning algorithms, was used to classify PlanetScope image using field data as training area to map seagrass spatial distribution and percent cover. The result show that SVM produced 73.98% overall accuracy for benthic mapping, with seagrass class producer’s accuracy and user’s accuracy is 93.71% and 85.35% respectively. Meanwhile, for seagrass percent cover, the SVM algorithm produced map with 26.48% standard error.
Mapping Robotic Affordances with Pre-requisite Learning Interventions for Children with Autism Spectrum Disorder

For children with Autism Spectrum Disease (ASD), pre-requisite learning (PRL) skills are particularly important because they form the basis for acquiring other advanced cognitive skills. Globally, researchers have shown that robot-assisted therapy (RAT) can have several positive effects on children with ASD. However, previous researches have failed in clearly mapping the PRL skill training tasks and strategies to robot affordances. In this research, we foster a better understanding of the objectives of the PRL skills required for children with ASD and provide a mapping with robot affordances to execute PRL training activities. In-depth interviews and focus group discussions (N=25) with paediatricians, ASD therapists, and educators from three nonprofit organisations were conducted to understand the clinical practices for teaching PRL skills among children with ASD. Naturalistic observations were used to understand the exercise and training protocols implemented for improving PRL skills among children with ASD. Finally, clinical literature on robotic therapy and technical documents provided by the manufacturers were analysed for identifying commercially available robots and evaluating their features and affordances. Our analysis revealed that affordances offered by several commercially available robots could be effectively leveraged to develop Robot-Assisted Therapies (RATs) to improve PRL skills in children with ASD. Strategies and implications for developing RATs to improve PRL skills among children with ASD are also discussed.
BID: An Effective Blind Image Deblurring Scheme to Estimate the Blur Kernel for Various Scenarios

In recent years, image deblurring has been widely investigated. In order to solve this ill-posed problem, a variety of prior models have been proposed successively. The two-tone prior has been successfully applied to text images deblurring and achieved significant results. However, the natural image with rich color clusters does not meet the two-tone prior, which requires only two color clusters in the image. In this paper, a local two-tone prior is proposed for images with complex color clusters, which decomposes the image with complex color clusters into patches with simple color clusters. We also find that the process of image blurring is a weighted average of pixel values, which will lead to an increase of intermediate pixel values. Therefore, a new measure of the dynamic range of the image is proposed, which indicates the difference of the color cluster using the average value of the color cluster, and it is rigorously proved in mathematics. Besides, we analyze the effectiveness of the proposed prior in image deblurring in detail. Experimental results on the widely used datasets show that the proposed method performs favorably against the state-of-the-art algorithms, both qualitatively and quantitatively.
Ustilago Rabenhorstiana—An Alternative Natural Itaconic Acid Producer

Itaconic acid is an industrial produced chemical by the sensitive filamentous fungus Aspergillus terreus and can replace petrochemical-based monomers for polymer industry. To produce itaconic acid with alternative renewable substrates, such as lignocellulosic based hydrolysates, a robust microorganism is needed due to varying compositions and impurities. Itaconic acid producing basidiomycetous yeasts of the family Ustilaginaceae provide this required characteristic and the species Ustilago rabenhorstiana was examined in this study. By an optimization of media components, process parameters, and a fed-batch mode with glucose the final titer increased from maximum 33.3 g·L−1 in shake flasks to 50.3 g·L−1 in a bioreactor. Moreover, itaconic acid was produced from different sugar monomers based on renewable feedstocks by U. rabenhorstiana and the robustness against weak acids as sugar degradation products was confirmed. Based on these findings, U. rabenhorstiana has a high potential as alternative natural itaconic acid producer besides the well-known U. maydis and A. terreus.
Spatial Interpolation with Message Passing Framework

Spatial interpolation is the task to predict a measurement for any location in a given geographical region. To train a prediction model, we assume to have point-wise measurements for various locations in the region. In addition, it is often beneficial to consider historic measurements for these locations when training an interpolation model. Typical use cases are the interpolation of weather, pollution or traffic information. In this paper, we introduce a new type of model with strong relational inductive bias based on Message Passing Networks. In addition, we extend our new model to take geomorphological characteristics into account to improve the prediciton quality. We provide an extensive evaluation based on a large real-world weather dataset and compare our new approach with classical statistical interpolation techniques and Neural Networks without inductive bias.
Automating Non-Blocking Synchronization In Concurrent Data Abstractions

This paper investigates using compiler technology to automatically convert sequential C++ data abstractions, e.g., queues, stacks, maps, and trees, to concurrent lock-free implementations. By automatically tailoring a number of state-of-the-practice synchronization methods to the underlying sequential implementations of different data structures, our automatically synchronized code can attain performance competitive to that of manually-written concurrent data structures by experts and much better performance than heavier-weight support by software transactional memory (STM).
Sitting Posture Prediction and Correction System using Arduino-Based Chair and Deep Learning Model

In this paper, we propose a system that applies deep learning to classify different postures of the users of our specially designed chair composed of Arduino hardware. The system provides users with information of their real-time posture and analytics for a given period of time, hence users can figure out their sitting habits. Moreover, the system suggests guidance videos of stretching body parts that help the users to correct their sitting postures.
Perovskites As Surface-Assisted Room Temperature Protonic Conductor Humidity Sensor

This paper shows a correlation between surface effective porosity due to various sintering regimes and humidity sensitive electrical properties of the perovskite-based bulk type humidity sensors. Furthermore, room temperature humidity transduction mechanism of the thick film type humidity sensors was studied in details through electrochemical impedance spectroscopy (EIS) and major transmissive components were detected by fitting to the equivalent circuits. The materials were synthesized employing solid state reaction and bulk and film type devices were fabricated by hydraulic pressing and screen printing techniques, respectively. The morphological and elemental characterizations were carried on using FESEM, EFTEM, and EDX. Physical properties including open porosity/bulk density were investigated through ASTM methods. An innovative self-designed material test fixture with ceramic supports was fabricated for high S/N ratio electrical measurement of the bulk samples. All the sensors were set up at 20-95% RH. The morphological, physical, and electrical results of bulk pellets indicate direct correlation of the open cavities and AC conduction. Presence of the ionic transport is clearly observed from the frequency-conductance spectra at room temperature. Noise-free detected behavior via EIS proves that proton transfer mechanism is a dominant responsible.
UAV mapping for Mangrove ecosystem management in the coastal area of special region Yogyakarta

The coastal area of Special Region of Yogyakarta (DIY) will experience rapid regional development after the functioning of the Southern Cross Roadway (JJLS) and New Yogyakarta International Airport (NYIA). In order to increase Regional Original Income (PAD) from the tourism sector, these developments must be welcomed with developments in the field of coastal tourism, especially in the form of ecotourism. The process of developing mangrove ecosystems for ecotourism in DIY must be synchronized with applicable government policies, especially policies that regulate spatial planning and development policies in the tourism sector. This study aims to analyze the existing conditions of mangrove ecosystems in the coastal areas of the Special Region of Yogyakarta. Mangrove mapping was carried out in four different locations, namely at Baros Beach, Kretek, Bantul Regency and Jangkaran Mangrove Forest Area, Kulonprogo Regency. Fieldwork consists of small format aerial photo data acquisition, Ground Control Point (GCP) and Independent Check Point (ICP) collection using Geodetic GPS, along with observations of mangrove conditions in the field. Post-field stages consist of various types of processes regarding processing field data and analysis, including orthophoto mosaics, accuracy calculations, and acquisitions of mangrove information such as area and distribution. The main benefit of this research is the availability of basic data on the existing conditions of mangrove areas in the Special Province of Yogyakarta. This basic data will then be used as a reference for managing the coastal environment and improving the economic conditions of the community through the development of mangrove ecotourism-based tourism. it also supports disaster risk reduction efforts through the development of mangrove ecosystems.
Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System

Users’ feedback information as the ground-truth has attracted a lot of attention in recommender systems. However, the feedback that could be contaminated by users’ misoperations or malicious operations is probably not true in real scenarios. This work aims to develop a technique based on an improved Bayesian personalized ranking (BPR), called adversarial training-based mean Bayesian personalized ranking (AT-MBPR). In this method, we divide the feedback information into three categories based on the mean Bayesian personalized ranking (MBPR), then gain the implicit feedback from the mean and non-observed items of each user, following which, adversarial perturbations are added on the embedding vectors of the users and items by playing a minimax game to reduce the noise. The experiments demonstrate in five datasets that our approach outperforms the traditional BPR methods and state-of-the-art methods used for the recommendation. Our implementation is available at: https://github.com/HanXia001/Adversarial-Training-based-Mean-BPR-for-Recommender.
Developing a Large-Scale Microscopic Model of Electric Public Bus Operation and Charging

The pace of electric bus deployment is increasing rapidly worldwide. Transportation agencies and bus operators are faced with the challenges of re-evaluating the dimensioning of their bus fleet and the associated charging infrastructure with the introduction of electric buses. This paper introduces a new agent-based public transport simulation model currently being developed and based on a general-purpose microscopic traffic simulation platform. It enables users to conduct city-scale studies and analysis of the day-to-day operation of a public bus fleet of traditional and electric buses, including detailed modelling of charging stations, termini and depots. Results are presented in a case study to electrify the entire bus fleet of Singapore.
Comparative Study of Deep Learning-Based Sentiment Classification

The purpose of sentiment classification is to determine whether a particular document has a positive or negative nuance. Sentiment classification is extensively used in many business domains to improve products or services by understanding the opinions of customers regarding these products. Deep learning achieves state-of-the-art results in various challenging domains. With the success of deep learning, many studies have proposed deep-learning-based sentiment classification models and achieved better performances compared with conventional machine learning models. However, one practical issue occurring in deep-learning-based sentiment classification is that the best model structure depends on the characteristics of the dataset on which the deep learning model is trained; moreover, it is manually determined based on the domain knowledge of an expert or selected from a grid search of possible candidates. Herein, we present a comparative study of different deep-learning-based sentiment classification model structures to derive meaningful implications for building sentiment classification models. Specifically, eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets, and the classification performances are discussed under different perspectives.
The Impact of Anthropometric Measurements on Lipid Profile in Normal, Overweight and Obese Adults with Hypertension

Abstract Background and Aims: We investigated the association of anthropometric measurements with lipid profile in adult hypertensive patients with normal weight and overweight including obesity. Materials and Method: This was a randomized study performed in Tiaret, Algeria, on 150 hypertensive patients aged 33-91 years. Patients were divided according to their body weight (normal weight vs. overweight and obesity) into two groups. Weight, height and body mass index (BMI) were measured to estimate the various categories of body weight. The blood pressure (systolic and diastolic) and lipid profile (total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c) and triglycerides (TG) were analyzed. Results: We found significant relations between body weight and blood pressure. Patients with normal weight had a normal blood pressure. However, a significant increase in blood pressure was observed in patients with overweight and obesity. Numerous significant relationship were disclosed for TC and LDL-c. Conclusion: Lipid profile is related with weight status in hypertensive overweight/obese patients.
Towards Balancing the Complexity of Convolutional Neural Network with the Role of Optical Coherence Tomography in Retinal Conditions

Convolutional neural networks have shown impressive performance in the medical image domain, but medical experts are somewhat skeptical in their predictions since the features are not directly graspable. We are looking into one of the technical challenges, namely the explainability of the results, to try to find out some demonstration of the regions deemed abnormal by deep learning. Following the trend of heatmaps indicating which local morphology changes would modify the predictions, we are trying to verify the facilitation of the clinical understanding in the eyes of the ophthalmologist.