Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Enhanced Bladder Volume Measurement with Context-Aware Bio-Impedance Sensor Fusion

Urine volume is a vital parameter of health and its continuous monitoring is necessary for managing illness in various patient populations. State-of-the-art methods include invasive catheters or sporadic measures with ultrasound. Non-invasive urine volume measurement in vivo is challenging due to the confounding effects of muscle activity. This work proposes a multi-sensor wearable system based on non-invasive tetrapolar bio-impedance (BI) measurements along with the measurement of muscle activity by including electromyography (EMG) and novel low power charge variation (QVAR) sensing integrated into inertial measurements unit, placed on the lower abdomen. A comparison of the EMG and QVAR is conducted and revealed that the QVAR sensor had a correlation coefficient of 0.93 with the EMG and consumed a sixteenth of the power of the EMG, making it more suitable for long-term, multisensor and continuous monitoring. Also, electrode placements on the lower abdomen were investigated to understand the effect of different muscle activations during bladder voiding experiments. Measurements of muscle clenching, coughing and localised movements were performed at the forearm and the lower abdomen with all three sensors. Investigations on bladder voiding experiments showed that an unexpected BI decrease at the beginning of the bladder voiding coincided with an increase in the electromyography signal over the abdominis rectus muscles and could be the cause of the unexpected BI change at the start. Therefore, a combination of low-power sensors of BI and QVAR can pave the path towards a robust sensing method to measure bladder volume changes.
Dual Contrastive Learning for Self-Supervised ECG Mapping to Emotions and Glucose Levels

We learn to map the ECG signal to emotional or physical states using a 1D-CNN followed by a Transformer. To overcome the limited number of samples, we propose a new self-supervised learning scheme that combines latent space masking with both a temporal prediction task and a matching task, both learned via a contrastive loss. Our method outperforms the existing self-supervised approaches for ECG by a wide margin.
AC-AC Converter for Wind Power Management

In this paper, a study uses an AC-AC converter to transfer active and reactive power between a wind generator and the grid. The wind energy conversion system (WECS) consists of a Permanent Magnet Synchronous Generator (PMSG) and a Direct Matrix Converter (DMC) driven by the Singular Value Decomposition (SVD) modulation method. For the current control in the dq reference frame, a linear proportional-integral (PI) compensator is employed, which operates according to the field-oriented control (FOC) technique. In an experimental wind turbine prototype, the DMC and PMSG are utilised to implement maximum power tracking (MPPT) control. Results are presented and discussed.
Gait Analysis with Trinocular Computer Vision Using Deep Learning

The recent years' progress in deep learning (DL) technology has resulted in convolutional neural networks (CNNs) capable of producing fast and accurate results, with minimal data preprocessing. Currently, gait analysis (GA) is attracting the attention of the field of deep learning due to its seamless integration applicability. Our approach focuses on CNN-based GA application on healthcare and orthopaedics. Using CNNs and visual fiducial systems for recognition and 3D mesh re-construction of the human form and the floor respectively, we can virtually recreate the human-floor interaction, which can be particularly useful in the study of gait dynamics, through the per-frame gait phase classification. However, the current state-of-the-art (SOTA) is that most CNN mesh reconstruction software produces a mesh from a monocular input. The use of photogrammetry could alternatively be implemented, but would require multiple cameras and expensive equipment. Our approach aims to create a refined mesh obtained from trinocular footage, along with an interactive and easy-to-use interface.
Compact Low Noise AC Acquisition System for Wheatstone Bridge Sensors

This paper presents a complete and compact AC acquisition system for Wheatstone bridge sensors, tested with magnetoresistance sensors in a robotics tactile application. The use of a type of digital “lock-in” amplifier, running on a mixed-signal microcontroller, instead of an analog implementation allows increased signal conditioning and acquisition flexibilty, while keeping the advantages of a lock-in amplifier. This flexibility is achieved by utilizing signal processing optimization techniques like CIC decimation filters, quadrature mixing, and embedded CORDIC devices. The acquisition system is then used in a surface roughness analysis application, achieving a 5-fold sensitivity improvement over a previous system.
Data-driven Shape Sensing of Continuum Dexterous Manipulators Using Embedded Capacitive Sensor

We propose a novel inexpensive embedded capacitive sensor (ECS) for sensing the shape of Continuum Dexterous Manipulators (CDMs). Our approach addresses some limitations associated with the prevalent Fiber Bragg Grating (FBG) sensors, such as temperature sensitivity and high production costs. ECSs are calibrated using a vision-based system. The calibration of the ECS is performed by a recurrent neural network that uses the kinematic data collected from the vision-based system along with the uncalibrated data from ECSs. We evaluated the performance on a 3D printed prototype of a cable-driven CDM with multiple markers along its length. Using data from three ECSs along the length of the CDM, we computed the angle and position of its tip with respect to its base and compared the results to the measurements of the visual-based system. We found a 6.6% tip position error normalized to the length of the CDM. The work shows the early feasibility of using ECSs for shape sensing and feedback control of CDMs and discusses potential future improvements.
Spatio-temporal Analysis of the TMI in the Major Cities of Colombia

Pavement is a crucial component of infrastructure that supports economic growth by facilitating efficient movement of goods, services, and people. To ensure the durability, load-bearing capacity, safety, drainage efficiency, and overall performance of pavements, considering climatic conditions is of utmost importance. In this study, a spatiotemporal analysis of the Thornthwaite Index (TMI) was conducted in different periods across major cities of Colombia to understand climate variations and identify significant areas. The findings revealed that cities in the Caribbean regions exhibited lower TMI values, attributed to reduced rainfall and increased temperatures. It is hypothesized that global warming may be responsible for these observed phenomena. Such insights can inform pavement design and maintenance strategies, contributing to resilient infrastructure development in the face of changing climatic conditions.
Enhancing Positioning Sensor Applications: Online Harmonic Order Determination of Atan2-Function

This study introduces novel techniques for improving error correction in actuator applications using rotary positioning sensors. It addresses challenges related to harmonic distortions and the inhomogeneous sampling of sensor signals. Our approaches include interpolation methods and Kalman filtering, capable of reducing the inhomogeneous sampling and identifying the most significant harmonic order. The developed methods demonstrate high stability with efficient sample sizes and a small distance factor. These enhancements significantly reduce shape change and offer accurate identification of the most significant harmonic order, contributing to improved sensor accuracy and actuation performance in vehicle applications.
Reinforcement Learning with UAV Assistance for Optimized Computation Offloading in Mobile Edge Computing

With the rise of computing-intensive applications like online gaming and telemedicine on user equipment (UE) and the evolution of 5G technology, there is a surge in demand for greater computing resources and power. Yet, UEs have limited resources and batteries. Mobile Cloud Computing (MCC) has emerged as a method to enhance UEs computing capabilities and conserve energy by transferring tasks to the cloud. Mobile Edge Computing (MEC) further aids by reducing delays, although it faces issues like limited resources and unpredictable network conditions. Unmanned Aerial Vehicles (UAVs) offer a remedy by serving as mobile stations for MEC, but optimal offloading decisions in UAV-assisted MEC remain intricate. Addressing this, I propose using Reinforcement Learning (RL), specifically Q-Learning, Deep Q Network (DQN), and Deep Deterministic Policy Gradient (DDPG), to enhance decision-making for offloading. Our focus is on energy efficiency and reduced service delay, and our simulations prove our method's efficacy in UAV-assisted MEC environments.
Bridging Scales With the Shift Frequency: Frequency-adaptive simulation of multiscale transients in power systems

One of the most remarkable features of power and energy systems pertains to their extremely far-reaching scales, which are unique in the field of engineering. In continental Europe, for example, the synchronous ac power system integrates capitals from Lisbon to Warsaw and from Athens to Copenhagen. Reaching farther out to the north, high-voltage dc (HVdc) links connect to Sweden and the United Kingdom. And as in other regions of the world, those grids are being further developed to integrate more renewables. This only adds to the diversity of technologies in the power grids and contributes to the wide range of timescales involved. Those range from electrothermal interactions in the range of minutes over electromechanical transients within seconds, down to microseconds or even faster for the electromagnetic transients of traveling waves.
Fabrication of Nanostructures on Optical Fibers Using Nanosphere Lithography for Biosensing

The utilization of top-down nanoparticle fabrication has been investigated to enhance the reusability, longevity, and reproducibility of bottom-up nanoparticle-dependent fiber-optic plasmonic sensors. Au nanostructures were conveniently and economically engineered using nanosphere lithography on conventional optical communication fibers. The refractive index sensing technique was employed to characterize the properties of the produced Au nanotriangles and nanoholes. The efficacy of these patterned nanostructures was demonstrated in the real-time detection of immune responses related to the carbohydrate antigen 19–9, a pancreatic cancer biomarker. Consequently, the triangle-based plasmonic nanoprobe achieved a lower detection limit of 1.06 U/mL. This advancement indicates potential commercialization prospects for reliable and cost-effective devices based on patterned nanostructures as optrodes, thereby establishing their practical utility as biosensors.
Deep Graph Representation Learning to Solve Vehicle Routing Problem

The performance of a neural network relies on the depth of a model to learn the structural correlations of the features. Nevertheless, Graph Neural Networks (GNN) tends to lose its efficiency as the depth increases. In this paper we propose a technique to alleviate this problem by building on the existing GNN architecture. In effect, we installed a gating mechanism to overcome the propagation of noise information across the layers and trained the model using a proximal policy optimization (PPO), a policy gradient-based reinforcement learning algorithm. We trained the proposed model on a capacitated vehicle routing problem (CVRP) datasets generated on the fly. We used an encoder-decoder framework where the encoder learns the representation of the graph structured CVRP instance and the decoder learns to construct an optimal route based on the reward function designed. According to experiments using randomly generated test instances, the proposed model produces better results than the current deep reinforcement learning (DRL) methods to solve CVRP. To confirm the performance of our model, we also tested using locally generated real-world data parsed from digital maps. The results affirms that our model performs well in both random instance testing and real-world instance testing.
Universal Data Acquisition System for Flexible and MEMS-Based Gas Sensors

Data acquisition system is increasingly utilized in contemporary gas monitoring applications. The complex nature of the data acquisition system, coupled with its low accuracy and lack of universal adaptability to various sensor types, has hindered its broader adoption. To address these limitations, a universal data acquisition system is proposed in this study. This novel system integrates a heating module, a temperature collection module, and a resistance measurement module onto a single printed circuit board. Accurate temperature control can be achieved by using the heating and temperature collection module. Meanwhile, the data acquisition system facilitates high-precision resistance measurement with a wide measuring range. Furthermore, the universal system is designed to accommodate both flexible and Micro-Electro-Mechanical System-based gas sensors, with the capability of cascading different boards via the RS-485 protocol.
Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.
De Nova York a Durban: o processo de institucionalização do BRICS

O objetivo e explicar o processo de institucionalizacao do BRICS, dos primeiros encontros do agrupamento em nivel ministerial a Cupula de Durban. O argumento central aponta que o desenvolvimento de instrumentos de cooperacao e colaboracao de baixas obrigacao, precisao e delegacao esta relacionado a estrutura de preferencias convergentes dos membros por maior agilidade na implementacao de compromissos, maior flexibilidade para realizacao de escolhas politicas e maior independencia em relacao a partes terceiras.
nextGSIM : Towards Simulating Network Resource Management for Beyond 5G Networks

—Researchers have already begun experimenting with next-generation cellular technologies and algorithms to enable use cases that lie beyond the scope of the current 5G standard, e.g. XR, smart factories, AI networks ops, etc. The common denominator requirement of such scenarios is the joint (coupled) operation of radio channel and edge computing resources within the core network. While there are numerous tools that allow experimenting with various aspects of radio resource management and computing resource management individually, there is a lack of solutions that enable researchers to prototype and evaluate applications and technologies dependent on both aspects simultaneously. In this work, we present nextGSIM , a 5G and beyond network simulator that realistically models the radio access network and edge network jointly to provide an end-to-end service to various user devices running microservice-based application workloads. We detail our design decisions and modular architecture of nextGSIM which resembles real-world setup of cellular networks, enabling effective and detailed simulations of resource management algorithms. We demonstrate the effectiveness and capabilities of nextGSIM through indoor factory case study wherein we evaluate widely regarded radio and edge resource management algorithms. We compare these against a joint radio-compute scheduler which emphasizes the need and benefits of joint resource allocation decision making, which is only possible through tools such as nextGSIM .
Defensive function of persecutory delusion and discrepancy between explicit and implicit self-esteem in schizophrenia: study using the Brief implicit association Test

Background: If delusions serve as a defense mechanism in schizophrenia patients with paranoia, then they should show normal or high explicit self-esteem and low implicit self-esteem. However, the results of previous studies are inconsistent. One possible explanation for this inconsistency is that there are two types of paranoia, “bad me” (self-blaming) paranoia and “poor me” (non-self-blaming) paranoia. We thus examined implicit and explicit self-esteem and self-blaming tendency in patients with schizophrenia and schizoaffective disorder. We hypothesized that patients with paranoia would show lower implicit self-esteem and only those with non-self-blaming paranoia would experience a discrepancy between explicit and implicit self-esteem. Methods: Participants consisted of patients with schizophrenia and schizoaffective disorder recruited from a day hospital (N = 71). Participants were assessed for psychotic symptoms, using the Brief Psychiatric Rating Scale (BPRS), and self-blaming tendency, using the brief COPE. We also assessed explicit self-esteem, using the Rosenberg Self-Esteem Scale (RSES), implicit self-esteem, using Brief Implicit Association Test (BIAT), and discrepancy between explicit and implicit self-esteem. Results: Contrary to our hypothesis, implicit self-esteem in paranoia and nonparanoia showed no statistical difference. As expected, only patients with non-self-blaming paranoia experienced a discrepancy between explicit and implicit self-esteem; other groups showed no such discrepancy. Conclusion: These results suggest that persecutory delusion plays a defensive role in non-self-blaming paranoia.
M OLECULAR DETERMINATION OF THE ETIOLOGICAL AGENT OF BOVINE MASTITIS FROM A NDEAN PRODUCTION UNITS

Bovine mastitis is a disease that affects the farms of small and medium producers in the cantons of Cayambe and Pedro Moncayo, Pichincha Province-Ecuador. Treating this disease is not easy due to the different microorganisms that cause it. This study focused on the molecular determination by means of polymerase chain reaction (PCR) of the etiological agents of mastitis, having multiple advantages when recognizing family, gender and species of microorganisms. It is a method capable of detecting resistance genes of antibiotics, an important analysis when diagnosing and treating diseases. The aim of this research is to identify bacteria causing bovine mastitis by using biochemical and molecular tests. Biochemical tests such as: Gram staining, Catalase, Coagulase, and Mannitol Salt Agar were efﬁcient to obtain pure strains and determine the gender of some bacteria. Speciﬁc primers (RNA16S) were used for the molecular identiﬁcation of 9 etiological agents causing the disease in the productive units. The microorganisms found were Staphylococcus pasteuri, Staphylococcus warneri, Staphylococcus sp. , Staphylococcus epidermidis, Staphylococcus aureus, Staphylococcus saprophyticus, Sphingomonas sp. , Streptococcus dysgalactiae, Streptococcus uberis , mostly present in clinic mastitis. To detect resistance genes, speciﬁc primers were used, of which 7 samples presented the gene for resistance to blaTEM ( β -lactam) and 6 samples presented the gene for resistance to tetA (tetracyclines). Multi-resistance was identiﬁed in the species Staphylococcus pasteuri, Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus uberis, Sphingomonas sp .
AL-AZHAR Assiut Dental Journal

Aim: This can be done by computed tomography (CT), three dimensional implant planning software, image guided template production techniques, and computer aided surgery. Subjects and Methods : 20 implant placed in selected adult patients of both sexes ,divided into two groups. Group I: included 10 conventional implants placed in patients. Group II: Included 10 guided implants placed in patients (by using steriolithography.). Clinical photographs were taken before ,during and after procedures . Conebeam computed tomography was performed. Surgical procedure was done and implant were positioned. Implant evaluation clinically by implant primary stability by measuring the accuracy of the surgical guide on the postoperative image after implant insertion by recorded linear deviation(coronal ,apical )angular deviation ,and measurement of deviation in the post-operative implant position from the preoperative virtual implant planning. Results: We found noticeable high significant difference in group I than Group II in Coronal ,apical ,angular direction. Conclusion: The results proved the benefit of Using surgical guide template . It enables the clinician to optimize implant position, angle, diameter and length by dictating the drilling position and angulation
Automated Data Preprocessing for Machine Learning Based Analyses

—Data preprocessing is crucial for Machine Learning (ML) analysis, as the quality of data can highly influence the model performance. In recent years, we have witnessed numerous literature works for performance enhancement, such as AutoML libraries for tabular datasets, however, the field of data preprocessing has not seen major advancement. AutoML libraries and baseline models like RandomForest are known for their easy-to-use implementation with data-cleaning and categorical encoding as the only required steps. In this paper, we investigate some advanced preprocessing steps such as feature engineering, feature selection, target discretization, and sampling for analyses on tabular datasets. Furthermore, we propose an automated pipeline for these advanced preprocessing steps, which are validated using RandomForest, as well as AutoML libraries. The proposed preprocessing pipeline can also be used for any ML-based algorithms and can be bundled into a Python package. The pipeline also includes a novel sampling method - “Bin-Based sampling” which can be used for general purpose data sampling. The validity of these preprocessing methods has been assessed on OpenML datasets using appropriate metrics such as Kullback-Leibler (KL)-divergence, accuracy-score, and r2-score. Experimental results show significant performance improvement when modeling with baseline models such as RandomForest and marginal improvements when modeling with AutoML libraries.