Miracle-dz/UniFAR-FTUs huggingface dataset:
RISEUP Project: An Innovative Application of Microsecond Electric Pulses for Spinal Cord Injury Regeneration

Spinal cord injury (SCI) is a disabling, devasting and irreversible condition that strongly impacts on patients' life and their families. Every year among 250.000 and 500.000 new SCI cases are diagnosed. Most of them are traumatic and the average of age at diagnosis is 20–29 years for males, while females are most at risk in adolescence (15–19), which means a tremendous socioeconomic impact on affected individuals and the health care system. Recent advances in medical management of SCI have significantly improved diagnosis, stabilization, survival rate and well-being of SCI patients. However, there has been small progress on treatment options for improving the neurological outcomes of SCI patients. This is principally due to the high complexity of spinal cord tissue and to all events that occur after its lesion. RISEUP is a H2020 FET-Open project (nr. 964562) aimed at introducing stem cells microsecond (µs) electric pulses stimulation as a strategy for guiding their differentiation towards a neuronal phenotype for SCI regeneration. Here the Authors report the first results on molecular response induced by µs pulses on mesenchymal stem cells (MSCs) and induced neuronal stem cells (iNSCs).
A Fully Integrable 915MHz Implantable Antenna System for Long-Range Telemetry in Rodents

In this paper, an implantable planar inverted-F antenna (PIFA) operating in the industrial, scientific, and medical (ISM) band (915 MHz) is proposed. It was designed and fabricated on a flex printed circuit board (PCB) platform and is capable of full integration with driving circuitry for biopotential measurements. The antenna's dimensions (26mm x 54mm x 0.2mm) were optimized for implantation and use in Norway rats to facilitate wireless biomedical testing. In a live rodent model, the antenna showed a peak S11 of -17.94 dB at its resonance frequency with a peak gain of -3.94 dBi, an efficiency of 14.86%, and a -10 dB bandwidth of 42.21 MHz. To the best of our knowledge, this paper marks the first time a 915 MHz antenna was specifically built for implantation using a flex PCB substrate and a parylene-C insulating coating.
Adaptive Gravity Compensation Framework Based on Human Upper Limb Model for Assistive Robotic Arm Extender

The Assistive Robotic Arm Extender (ARAE) is an upper limb assistive and rehabilitation robot that belongs to the end-effector type, enabling it to assist patients with upper limb movement disorders in three-dimensional space. However, the problem of gravity compensation for the human upper limb with this type of robot is crucial, which directly affects the deployment of the robot in the assistive or rehabilitation field. This paper presents an adaptive gravity compensation framework that calculates the compensated force based on the estimated human posture in 3D space. First, we estimated the human arm joint angles in real-time without any wearable sensors, such as inertial measurement unit (IMU) or magnetic sensors, only through the kinematic data of the robot and established human model. The performance of the estimation method was evaluated through a motion capture system, which validated the accuracy of joint angle estimation. Second, the estimated human joint angles were input to the rigid link model to demonstrate the support force profile generated by the robot. The force profile showed that the support force provided by the developed ARAE robot could adaptively change with human arm postures in 3D space. The adaptive gravity compensation framework can improve the usability and feasibility of the 3D end-effector rehabilitation or assistive robot.
Steered transverse oscillations for improved 3D velocity estimation

Estimating 3D blood velocities using ultrasound imaging is a current challenge in cardiac blood flow microvascular imaging. It would greatly help practitioners avoid reconstructing a 3D flow from a sequence of 2D images, as is currently done in clinical practice. To address this challenge, this paper proposes a steered 3D transverse oscillation method based on a transverse oscillation pipeline for 3D ultrasound velocity estimation of blood flow. To evaluate the accuracy of the proposed method, several simulations are performed for synthetic straight parabolic flow phantoms rotated at different angles in the xy-plane. Comparisons of the obtained 3D fields in magnitude and angle are made with the Transverse Oscillations method (3D-TOs), a 3D multi-angle cross-beam Doppler (3D-MAD) technique, and a ground truth velocity. The proposed method is more accurate than all previous techniques in mean angle and mean magnitude, and reduces the mean angle error by at least half compared to 3D-TOs.
Achieving Enhanced Performance Combining Checkpointing and Dynamic State Partitioning

Fault-tolerant systems rely on recovery techniques to enhance system resilience. In this regard, checkpointing procedures periodically take snapshots of the system state during failure-free operation, enabling recovery processes to resume from a previously saved, consistent state. Saving checkpoints, however, is costly, as it must synchronize snapshots with the processing of incoming requests to avoid inconsistency. One way to speed up checkpointing is to partition the service state, allowing a parallel checkpoint procedure to operate independently on each partition. State partitioning can also improve throughput by increasing parallelism in request processing. However, variations in the data access pattern over time can result in unbalanced partitions, posing a challenge to achieving optimal performance. In this paper, aiming to improve both checkpointing and overall system performance, we combine parallel checkpointing with a dynamic graph-based repartitioning algorithm. This work formalizes the optimization problem and presents a detailed performance assessment of the proposed approach. The experimental evaluation highlights the benefits of parallel checkpointing and emphasizes the performance gains achieved with repartitioning under realistic workloads. Comparing a cost-effective round-robin partitioning approach with our dynamic method, we examine the degree of execution parallelism achieved by checkpointing threads and the influence of repartitioning strategies on checkpoint performance. Although the rebalancing of state partitions incurs a cost, it comes for free in our technique since it takes advantage of processing idleness during the snapshot-taking process.
CMOS Adaptive Biased Second Generation Voltage Conveyor

In this paper, a new second generation voltage conveyor (VCII) topology with adaptive bias is presented. For the first time, adaptive bias has been exploited in a CMOS VCII circuit, with the goal of reducing power consumption without sacrificing transient performance, in order to design an active building block suitable for very low power sensor interface applications. Dynamic bias is achieved by means of a circuit capable of both detecting the difference between the input voltage at voltage input terminal and the voltage at output terminal, producing an additional bias current proportional to this difference. It is important to note that the same circuit can also sense an increase of the input current at the current-input node, as the current variation produces a voltage change at voltage input terminal. The new topology has been designed in $0.35\ \mu\mathrm{m}$ AMS technology with $\mathrm{a}\pm 0.9\ \mathrm{V}$ supply voltage, and Spice simulations were performed to evaluate performance in terms of slew-rate and power consumption. The same VCII topology, but without dynamic biasing, was also considered for comparison purposes: simulation results showed that the proposed topology is able to guarantee a reduction of more than 2 orders of magnitude in power consumption, with only 3 times worsening of the slew-rate.
MINDSET: A benchMarking suIte exploring seNsing Data for SElf sTates inference

Ubiquitous devices, such as smartphones and wearables, are becoming increasingly popular for monitoring user behavior, health, and well-being. Through omnipresent monitoring, they aim to raise user awareness and encourage positive health behavior change. Yet, ubiquitous technologies suffer from expectation mismatch, lack of user-centric adaptiveness, and, ultimately, high abandonment rates. This work is motivated by the vital need to tailor and personalize ubiquitous technologies while dealing with the challenges arising from the lack of user profiling and the absence of relevant, self-reported user data. To this end, we show that the automatically passively collected sensing data from the wearables can be exploited to improve personalization and infer several user states relevant to demographic, physiological, psychological, and personality aspects, complementing the need for time-consuming self-reports. To accomplish this task, and enable the reproducibility and extensibility of our work; we propose an extensive benchmark suite by exploiting sensing data harvested from ubiquitous devices.Our benchmark covers a wide range of personalization tasks, including modeling gender, age, personality states, and stress, experimenting on the publicly available, newly released LifeSnaps dataset containing over 71 million rows of data capturing the daily lives of 71 participants in their naturalistic environments. The proposed benchmarking continuum showcases the strong potential for the applicability of the presented work in critical applications, such as mental healthcare monitoring, privacy preservation, and responsible artificial intelligence (AI), by fostering fairness assessments when protected attribute knowledge is unavailable.
Enhancing the Accuracy and Explainability of Heart Disease Prediction Models through Interpretable Machine Learning Techniques

Cardiovascular disease is a significant worldwide health concern that has a devastating impact on mortality rates around the globe. Leveraging machine learning techniques for cardiovascular diseases prediction has the potential to prevent further consequences significantly. This study proposed a Grid Search Cross Validation-based model to predict heart diseases. The dataset has been utilized in this study consists of 51.3% heart disease samples and the rest of them have healthy conditions. A combination of the Pearson Correlation Coefficient and Permutation Important algorithm is used for selecting the best features for the dataset. Several Machine learning models such as Multi-layer Perceptron, Logistic Regression, Extra Trees, Decision trees, Random forests, Native Bayes, k-nearest neighbors, and C5.0 have been applied in this research. This experimental analysis is focused on different machine-learning models with default parameters which give the best outcome of 94.63% accuracy by Random Forest model. The proposed Grid Search Cross Validation model with the selected best features has been used on top of the classic machine learning approaches, giving a significant accuracy gain of 98.57% by the Extra Trees classifier. The excellent proposed result suggests that applying the Grid Search Cross Validation on the classic machine learning approach might be a promising method for cardiovascular disease detection.
Biomarkers of Peripheral Fatigue in Subjects with Vascular and Neurological Motor Disabilities During an Incremental Treadmill Test Using Near-Infrared Spectroscopy

Exercise is a recognized tool to improve or maintain physical functioning and quality of life in chronic diseases, particularly in the presence of disability. Patients affected with vascular and neurological motor disabilities, such as people with multiple sclerosis (PwMS) or peripheral arterial disease (PAD), are daily limited by fatigue. This symptom, which can occur in a variety of forms, could interfere with exercise execution and the rate of adherence to rehabilitative programs. The early detection of objective biomarkers of peripheral fatigue and their integration into specific training programs could increase the long-term engagement of these patients in exercise. In this light, the study aims to analyze the association between muscle oxygenation, cardiovascular response and perceived exertion during a treadmill incremental walking test in healthy and chronically ill subjects. After eligibility assessment, a total of 53 subjects were divided into three subgroups: 17 patients diagnosed with PAD (mean age: 71 $\pm 9$ years), 26 with MS (mean age: $45\pm 12$ years), and 10 healthy subjects (mean age: 23 $\pm 1$ years). Parameters of muscle metabolism at the gastrocnemius were collected by near-infrared spectroscopy (NIRS) during an incremental treadmill test and quantified as area-under curve for oxygenated $(\mathrm{O}_{2}\text{Hb}_{\text{AUC}})$ and deoxygenated $(\text{HHb}_{\text{AUC}})$ hemoglobin traces. Heart rate values during the test and the rate of perceived exertion by the Borg scale 0–10 were also collected. $\mathrm{O}_{2}\text{Hb}_{\text{AUC}}$ was significantly lower in the PAD group with an opposite trend for $\text{HHb}_{\text{AUC}}$, whereas MS subjects showed heterogeneous trends in both $\mathrm{O}_{2}\text{HHb}_{\text{AUC}}$ and $\text{HHb}_{\text{AUC}}$, suggesting a different but abnormal pattern associated with the perception of peripheral fatigue in the two diseases. Significant and negative correlations were indeed observed between changes in $\mathrm{O}_{2}\text{Hb}$, heart rate and perceived exertion in the whole population. At this stage of the research, the dynamic and objective measurement of oxygenation in the fatiguing muscle enabled the discrimination of different abnormal responses to exercise in different diseases and among subjects. These results in a second phase of the study will be analyzed with the purpose of identifying early objective markers of fatigue that could possibly be integrated into semiautomatic technology-assisted ergo meters to personalize training programs.
Energy-Aware Edge Server Placement in Dynamic Scenario Using Reinforcement Learning

Mobile edge computing (MEC) provides high-quality network service to mobile users. In a MEC network, the placement of edge servers not only affects the service experience of users, but also has a great influence on the energy consumption of the MEC system. In this paper, we study the edge server placement problem in dynamic MEC scenarios, to minimize the energy consumption of the MEC system in real time, so as to achieve the goal of energy saving at every time. We propose a reinforcement learning (RL) based approach called ESDR. Moreover, in order to make RL suitable for our proposed problem and enable the agent to explore better in MEC scenarios, we introduce Double Q-learning and delaying policy updates to improve the original RL framework. Furthermore, experiments are conducted using a real-world dataset. The results show that ESDR is outstanding in terms of energy saving with fewer edge servers than other approaches in dynamic MEC scenarios.
Active Wideband 55-100-GHz Downconversion Mixer in 22-nm FDSOI CMOS

This paper presents a wideband active CMOS downconversion mixer for emerging mmWave communication and sensing applications. The designed mixer utilizes transformer coupling between the transconductance stage and switching stage of a classic Gilbert cell mixer to introduce inductive peaking and decouple the stages at DC. This technique efficiently allows the improvement of Gilbert cell mixer bandwidth, gain and linearity. Additionally, the designed mixer structure comprises a transformer-loaded common-source LO buffer and a two-stage wide bandwidth IF amplifier. The complete mixer consumes 33 mW of power from a low 0.8-V supply voltage. The mixer has been implemented with a 22-nm FDSOI CMOS process and its layout area is 0.13 mm2. The design achieves a measured peak voltage conversion gain of 3.5 dB and particularly a wide 3-dB RF bandwidth from 55 to 100 GHz, covering a range of mmWave bands with a single device. The mixer also demonstrates a wide 10-GHz IF output bandwidth suitable for high channel bandwidth applications, as well as an input 1-dB gain compression point of -6 dBm.
A Low-Power Current-Reuse Self-Biased Regulated-Cascode TIA in 130nm SiGe BiCMOS for Low-Noise and High Data Rate Applications

This paper presents a 35 Gbps, current-reuse, self-biased regulated cascode (RGC) transimpedance amplifier (TIA). Thanks to an RCN-degeneration Wilson current source, the power consumption, noise, and external interference are significantly reduced. It also leads to a robust design since power-hungry bipolar transistors complicate the biasing circuit design. Additionally, inherent RC degeneration in modified Wilson current source by CN as a degree of freedom improves the bandwidth and leads to pole-zero cancelation. The proposed cascade topology leads to reduced low-frequency noise by resistance degeneration. Also, the CN reduces dominant high-frequency noise. As a result, this leads to low power, low noise, and a compact design compared to conventional RGC solutions. By consuming 5mW, the TIA in 130nm SiGe BiCMOS technology has 24 GHz BW, a transimpedance gain of 60 dBΩ and 24 pA/ √ Hz input-referred noise for a 150fF capacitance at the input.
Video-Based Jump Height Estimation in Athletic Performance Testing

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods.
Investigating the Effect of Novel Gamified Stepper on Lower Limb Biomechanics in Seated Healthy Subjects

The present study introduces a new gamified stepper device designed for bilateral lower limb rehabilitation, which is combined with a 3-D exergame. To the best of our knowledge, this is the initial study to utilize the stepping exercise for seated lower limb rehabilitation. The device comprises a stepping mechanism and a magnetic encoder. The modified stepper facilitates the bilateral training in the lower limb within its workspace. The magnetic encoder provides real-time rotational angle data during the exercise. A task-specific exergame platform was created and integrated with the device to enhance user compliance and engagement with the exercise. Experiments were conducted with ten healthy individuals with no history of lower limb injury to evaluate the system's feasibility for providing bilateral training and the effectiveness of the exergame platform. Participants were asked to perform bilateral lower limb exercise with a metronome and gamified stepper device in a seated position. Lower limb range of motion (ROM) and EMG activations were recorded during the exercises. The results indicate that the device was capable of providing cyclical ROM training with reduced muscle activation of the lower limb, and the exergame platform increased motivation to continue the exercises. This study can serve as the foundation for developing a robotic version of the proposed stepper device.
Les tortues marines à Mayotte: bilan des actions de protection et perspectives

La presence de tortues marines en phase e reproduction et d'alimentation est un atout pour l'ile de Mayotte. Un programme d'etudes et de conservation a ete mis en place des mars 1994. Les actions de protection sont menees conjointement par plusieurs organismes: ( 1 ) le Conseil General avec l'Observatoire des Tortues Marines et le Bureau de Gestion des Sites du SPN-DEDD (entites internes) pour la surveillance, la gestion des visiteurs, la sensibilisation, la recherche et le programme de conservation, (2) la Brigade Nature Mayotte (ONCFS et CG) pour des actions de sensibilisation et de police, (3) les organismes de recherches tels que Kelonia (CEDTM) de la Reunion, l'IFREMER-Reunion, le CNRS-IPHC (Strasbourg) et le CNRS-CEFE (Montpellier) pour les programmes de recherche, (4) les associations dont Oulanga Na Nyamba pour la sensibilisation et (5) la Direction de l'Environnement, de l'Amenagement et du Logement (DEAL) avec la Direction de l'Agriculture, de l'Alimentation et de la Foret (DAAF-SV) pour la reglementation, la sensibilisation et la problematique des chiens errants. Les diverses actions menees visent a reduire les principales menaces telles que les actes de braconnage, la peche accidentelle, les chiens errants, les dechets flottants ou autres pollutions et les degradations des habitats (terrestres ou marins).
SDN Multi-Domain Routing for Knowledge-Defined Networking

With the rapid rise of smart devices, such as smartphones and smart home devices, intelligent routing through Software Defined Network(SDN) has become a research focus. Recently, the concept of Knowledge-Defined Networking (KDN) has been proposed. In this paper, we propose Knowledge-Defined Networking oriented SDN multi-domain routing, and a DDQN routing algorithm based on graph neural network at the knowledge layer. The communication of SDN multi-domain architecture for Knowledge-Defined Networking and intelligent routing decision are realized. The network performance such as delay jitter, packet loss rate and throughput is improved compared to existing approaches in simulation.
Parametric Modelling of Interleaved Resonant DC — DC Converter with Common Secondary Rectifier Circuit for xEV Charging Applications

The moto of this paper is to provide a parametric modelling and step by step procedure for 3.3 kW interleaved-inductor-inductor-inductor-capacitor (iL2C) resonant full bridge converter for electric charging applications using common rectifier circuit. The interleaving technique is utilized in this paper to achieve high power density, improve the current and load sharing. To achieve the controlling aspects of converter a hybrid control technique has been designed called variable frequency+phase shift modulation (VFPSM) technique. The simulations are performed at light load condition which is 10% of full load i.e., 330 W, at variable input voltages from 300 Yin to 500 Yin and 500 Yin to 300 Yin-48 Vo and also at rated voltage of 400 Yin - 48 Y0. The steady-state and transient performances are presented with dynamic responses.
Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning

Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods.