Miracle-dz/UniFAR-FTUs huggingface dataset:
A Network Simulator to Develop Wireless Networking Protocols Suited for Vital Information Collection from a Group of Exercisers

To develop a wireless networking protocol suited for real-time vital information collection from a group of exercisers, it is mandatory to compare the performances among different protocol candidates by experiments using a group of subjects as a matter of course. However, fair comparison is almost impossible because the experiments have no repeatability. In this paper, we propose a software network simulator to fairly compare the performances of different networking protocols. The simulator is composed of two unique functions such as mobility model and channel model, both of which were developed through real experiments using a group of subjects, The mobility model outputs data sets for the time-series of locations of persons during different kinds of sports and exercises, and the channel model determines the channel characteristics between persons taking into consideration the transmission power, antenna pattern and blocking by other persons. We demonstrate by the simulator the performance of a wireless networking protocol using different types of antennas for the case of football game.
275–500-GHz Wideband Waveguide SIS Mixers

We have successfully demonstrated low-noise and wideband SIS mixers for the 275–500-GHz radio-frequency (RF) band, aiming to cover Atacama Large Millimeter/submillimeter Array band 7 (275–373 GHz) and band 8 (385–500 GHz) simultaneously. The waveguide SIS mixers integrate a multisection impedance transformer with a parallel-connected-twin-junction or single-junction circuits based on Nb/AlN/Nb trilayers with a critical current density of 31 kA/cm2. In order to achieve the targeted broadband performance, we designed an asymmetric one-side waveguide probe placed across a reduced height waveguide with dimensions 100 μm × 580 μm. The shape of the waveguide probe was optimized to have nearly constant impedance at around 35 Ω in the desired RF range. For evaluation of the receiver performance, we have established a dual-band noise temperature measurement system. The system implements a broadband corrugated horn antenna and a 15-dB RF/local oscillator (LO) waveguide coupler based on WR-2.3 (290 μm × 580 μm) waveguide size. The target bandwidth is too wide for typical LO sources at these high frequencies, and, therefore, we applied a dual-band LO injection system utilizing two different LO sources and an LO diplexer. The IF chain uses a cryogenic isolator and a low-noise amplifier with a typical noise temperature of 2.1 K over the 4–8-GHz range. The measured double sideband receiver noise temperature is two to three times the quantum noise at all LO frequencies for both the twin-junction and the single-junction circuits.
Beneficial effects of maternal swimming during pregnancy on offspring metabolism when the father is obese

Abstract We aimed to evaluate the impact of maternal exercise training on the offspring metabolism and body size caused by father obesity. C57BL/6 male 4-week-old mice were fed a high-fat diet (HF father) or control diet (C father), while equal age female mice were fed only a C diet and were separated into two groups: trained (T mother) and non-trained (NT mother), and at 12 weeks of age mice were mated. A continuous swimming protocol was applied for 10 weeks (before and during gestation), and offspring were followed since weaning until sacrifice (at 12 weeks of age). HF father, compared to C father, showed obesity, elevated total cholesterol (TC) and triglycerides (TG), and glucose intolerance. Both sexes HF/NT offspring showed hyperglycemia, glucose intolerance and high levels of TC and TG, without obesity. However, HF/T offspring showed data close to C/NT, demonstrating the beneficial effect of maternal exercise in the offspring of obese fathers.
Kinematic Data Clustering for Healthy Knee Gait Characterization

The purpose of this study is to investigate data clustering to determine representative patterns in three-dimensional (3D) knee kinematic data measurements. Kinematic data are high-dimensional vectors to describe the temporal variations of the three fundamental angles of knee rotation during a walking cycle, namely the abduction/adduction angle, with respect to the frontal plane, the flexion/extension angle, with respect to the sagittal plane, and internal/external angle, with respect to the transverse plane. To offset the curse of dimensionality, inherent to high dimensional data pattern analysis, the method reduces dimensionality by isometric mapping without affecting information content. The data thus simplified is then clustered by the DBSCAN algorithm. The method has been tested on a large database of 165 healthy knee kinematic data measurements. Clusters are validated in terms of the silhouette index, the Dunn index, and connectivity. Results show that a two-cluster characterization of the kinematic knee data in each plane is quite effective. A further clinical investigation shows that the men and women knee patterns are balanced between the two clusters and, for 80% of participants, the right and left knees are in the same cluster.
Predicting Academic Performance Based on Learner Traces in a Social Learning Environment

Predictive modeling is an important part of learning analytics, whose main objective is to estimate student success, in terms of performance, knowledge, score, or grade. The data used for the predictive model can be either state-based data (e.g., demographics, psychological traits, and past performance) or event-driven data (i.e., based on student activity). The latter can be derived from students’ interactions with educational systems and resources; learning management systems are a widely analyzed data source, while social media-based learning environments are scarcely explored. In this paper, our objective is to predict students’ performance based on their social media traces. Data is collected from a Web Applications Design course, in which students use wiki, blog, and microblogging tools, for communication and collaboration activities in a project-based learning scenario. A total of 343 students, from six consecutive course installments, are included in the study. In addition to the novel settings and performance indicators, an innovative regression algorithm is used for grade prediction. Very good correlation coefficients are obtained and 85% of predictions are within one point of the actual grade, outperforming classic regression algorithms. From a pedagogical perspective, results indicate that, as a general rule, a higher engagement with social media tools correlates with a higher final grade.
Laser-Assisted Additive Manufacturing of mm-Wave Lumped Passive Elements

The performance of additively manufactured microwave passive elements and interconnects continues to improve as better materials, and process techniques are developed. In this paper, laser-enhanced direct print additive manufacturing (LE-DPAM) is used to fabricate capacitors and inductors for coplanar waveguide (CPW) circuits. Acrylonitrile butadiene styrene is used as the dielectric that is printed using fused deposition modeling, and DuPont CB028 conductive paste is deposited using microdispensing. These two techniques are combined with picosecond-pulsed laser machining to achieve <inline-formula> <tex-math notation="LaTeX">$\simeq 12$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> slots on printed conductors, producing aspect ratios greater than 2:1. The same laser is used to fabricate vertical interconnections that allow for the fabrication of multilayer inductors. Inductances in the range of 0.4–3 nH are achieved, with a maximum quality factor of 21, selfresonance frequencies up to 88 GHz, and an inductance per unit of area of 5.3 nH/mm<sup>2</sup>. Interdigital capacitors in the range of 0.05–0.5 pF are fabricated, having a maximum quality factor of 1000, a capacitance per unit area of 1 pF/mm<sup>2</sup>, and selfresonances up to 120 GHz. All the components are made on the center line of a CPW that is 836-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> wide. The results show that LE-DPAM enables the fabrication of compact passive circuits that can be easily interconnected with MMIC dies, which at the same time can be manufactured as part of a larger component. This enables the fabrication of structural electronics that are functional into the millimeter-wave frequency range. Bounds on the inductances and capacitances per unit area are related to material and geometry limitations.
Radar Cross Section Based Passive Wireless Sensor for Volatile Sensing

This paper presents a radar cross section-based passive, wireless acidic/basic volatile sensor with a built-in reference enabling differential sensing capabilities. The concept of a two-electrode gas sensing scheme coupled with indirect capacitive transduction is shown. The low cost, passive nature and volatile sensing ability makes the sensor suitable for food quality monitoring applications. Simulation results and preliminary measurements of the proposed sensing element are presented. Simulations indicate a sensitivity of 188MHz change in resonant frequency for 1pF change in capacitance.
Mesoporous Silica Materials as Drug Delivery: “The Nightmare” of Bacterial Infection

Mesoporous silica materials (MSM) have a great surface area and a high pore volume, meaning that they consequently have a large loading capacity, and have been demonstrated to be unique candidates for the treatment of different pathologies, including bacterial infection. In this text, we review the multiple ways of action in which MSM can be used to fight bacterial infection, including early detection, drug release, targeting bacteria or biofilm, antifouling surfaces, and adjuvant capacity. This review focus mainly on those that act as a drug delivery system, and therefore that have an essential characteristic, which is their great loading capacity. Since MSM have advantages in all stages of combatting bacterial infection; its prevention, detection and finally in its treatment, we can venture to talk about them as the “nightmare of bacteria”.
Agatston score of the descending aorta is independently associated with coronary events in a low-risk population

Objectives A standard coronary artery calcium scan includes part of the aorta. This additional information is often not included in routine analyses. We aimed to determine the feasibility of assessing the Agatston score of the descending aorta calcification (DAC) on standard coronary calcium scans and the association of this score with coronary events in a low-risk study population. Methods Between January 2008 and March 2011, 390 consecutive patients who were referred for cardiac CT as part of work-up for pulmonary vein isolation (n=115) or assessment of presence of coronary artery disease (n=275) were included. At baseline, all patients were free of a history of cardiovascular disease. Two independent observers determined the Agatston score of the ascending aorta and descending aorta. Results A total of 16 patients (4.1%) developed coronary events (acute coronary syndrome (n=6) and symptomatic significant coronary artery disease requiring treatment (n=10)) during a follow-up of 67±12 months, with more events in patients with calcifications in the descending aorta than in those without (8.4% vs 3.7 %; p=0.08). Multivariable Cox regression, corrected for Framingham Risk Score (FRS) and coronary Agatston score (CAC), revealed that DAC was independently associated with coronary events (per 100 units; HR: 1.06, 95% CI 1.02 to 1.09; p=0.001). DAC furthermore increased the identification of patients that will experience a coronary event (area under the curve: 0.68 for FRS only, 0.75 for FRS+CAC and 0.78 for FRS+CAC+DAC). Conclusions The Agatston score of the descending aorta could be included in the standard analysis of cardiac CT scans of low-risk patients since it holds valuable information for the prediction of coronary events.
Heavy Metal Accumulation in Common Aquatic Plants in Rivers and Lakes in the Taihu Basin

We investigated the concentrations of 10 heavy metals in Potamogeton malaianus, Nymphoides peltata, Eichhornia crassipes, and Hydrilla verticillata to evaluate their potential to bioaccumulate heavy metals and related influencing factors in Taihu Lake. Enrichment factor (EF) values of Cu, Cr, Mn, Ni, Zn, Co, Pb, and V were above 2.0, indicating moderate to significant contamination in sediment. Most of Ti, V, Cr, Mn, and Ni in P. malaianus, E. crassipes, and H. verticillata and V in N. peltata were within excess/toxic level in plants, but higher than normal level. Even though no aquatic plants in this study were identified as a hyperaccumulator, relatively higher concentrations in aquatic plants were found in Taihu Lake than have been found in other previous studies. Heavy metal in submerged plants, especially in their stems, seemed to be more closely related to metals in water and sediment than those in floating-leaf plants. Ratios of metals in stem versus leaves in all plants ranged from 0.2 to 25.8, indicating various accumulation capabilities of plant organs. These findings contribute to the application of submerged aquatic plants to heavy metal removal from moderately contaminated lakes.
Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications

Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produced by these tested protocols. The insufficient portions of a sinogram were inpainted by applying a context encoder (CE), a type of generative adversarial network, to the LAIR process. The context image was passed through an encoder to identify features that were connected to the decoder using a channel-wise fully-connected layer. Our results evidence the excellent performance of this novel approach. Even when we reduce the radiation dose by 1/4, the iterative-based LAIR improved the full-width half-maximum, contrast-to-noise and signal-to-noise ratios by 20% to 40% compared to a fully-sampled FDK-based reconstruction. Our data support that this CE-based sinogram completion method enhances the efficacy and efficiency of LAIR and that would allow feasibility of limited angle reconstruction.
COO-MAC: A Novel Cooperative MAC Protocol for Wireless Sensor Networks

To improve transmission performance of wireless sensor networks (WSN), we design a media access control layer protocol in this article. A novel channel allocation scheme for nodes is proposed in the protocol based on maximization channel gain. Detail steps of the proposed protocol and nodes access assignment based on channel gains are described. To reduce transmission collision and solve disconnection problem, a retransmission scheme is presented according to carrier sensing multiple access with collision avoid scheme. Furthermore, we extend the protocol application from single transmission environment networks to different transmission environment networks. The numerical analysis and simulation results clearly show that the proposed protocol has a better transmission probability both analytically and via simulation in improving accessing fairness. These results also show that the proposed protocol can improve the performance of WSN efficiently on throughput and transmission fairness.
Visual Servoing and Compliant Motion Control of a Continuum Robot

This paper presents a visual servoing and compliant motion control design of a continuum robot. Two endoscopic cameras were installed at the tip of the continuum robot to build a stereo camera system. The depth information is obtained by using semi-global block-matching (SGBM) algorithm. The visual servoing system proposed in this work includes the feature point extraction by using speeded-up robust features (SURF), followed by feature-point matching using nearest neighbor distance ratio (NNDR), and tracking of feature points by using mean shift algorithm. Through the pressure sensor installed around the circumference of the continuum robot, a compliant motion control design is proposed to make the robot to move in a constrained environment while executing the visual servoing task. Practical experiments show that the proposed method can effectively track a target and generate the adaptive motion when the robot encounters an obstacle during visual servoing.
Self-learning agent for battery energy management in a residential microgrid

This paper presents a data-driven control approach for battery energy management in a residential microgrid. The objective is to develop a battery operation mode scheduling strategy that maximizes self-consumption of local PhotoVoltaic (PV) production. A model-free reinforcement learning technique, policy iteration, is used to solve the underlying sequential decision-making problem. The proposed algorithm learns the stochastic load of the consumer, the PV production, and the dynamics of the microgrid to construct a control policy. A case study of a residential microgrid is used to evaluate the performance of the policy iteration algorithm. Simulation results using data from Belgian residential consumers show a 12% increase in the PV captured by the battery, leading to a 30% decrease in electricity costs compared to the fitted Q-iteration algorithm. These results show the feasibility and effectiveness of the proposed algorithm.
A class of robust consensus algorithms with predefined‐time convergence under switching topologies

This paper addresses the robust consensus problem under switching topologies. Contrary to existing methods, the proposed approach provides decentralized protocols that achieve consensus for networked multiagent systems in a predefined time. Namely, the protocol design provides a tuning parameter that allows setting the convergence time of the agents to a consensus state. An appropriate Lyapunov analysis exposes the capability of the current proposal to achieve predefined‐time consensus over switching topologies despite the presence of bounded perturbations. Finally, this paper presents a comparison showing that the suggested approach subsumes existing fixed‐time consensus algorithms, which allows to provide extra degrees of freedom to obtain predefined‐time consensus protocols with improved convergence characteristics, for instance, to reduce the slack between the true convergence time and the predefined upper bound. Numerical results are given to illustrate the effectiveness and advantages of the proposed method.
A Path/Surface Following Control Approach to Generate Virtual Fixtures

The workspace of a robot can be restricted by virtual fixtures to assist an operator in physical human–robot interaction tasks. This paper introduces a combination of surface following control (SFC) with compliance control and presents a path/SFC approach to systematically generate virtual fixtures. This approach allows implementation of numerous types of constraints like guidance and forbidden region virtual fixtures, hard and soft constraints, as well as static and dynamic virtual fixtures, and their combinations. Additionally, closed-loop stability proofs of the proposed control concepts are given. The flexibility of the presented approach is demonstrated by a series of measurement results from an industrial robot.
Two-Scale Envelope-Domain Analysis of Injected Chirped Oscillators

The response of chirped oscillators under the injection of independent signals, for spectrum sensing in cognitive radio, and under self-injection, for radio frequency identification, is analyzed in detail. The investigation is performed by means of a semianalytical formulation, based on a realistic modeling of the free-running oscillator, extracted from harmonic-balance simulations or from experimental measurements, through a new characterization technique. In the new formulation, the oscillator is linearized about a free-running solution that varies with the control voltage. This enables its application to oscillators having a frequency characteristic that deviates from the linear one. In the case of injection by independent signals, the two-scale envelope-domain formulation will enable an efficient handling of the difference between the slow chirp frequency and the beat frequency. The input carriers can be detected from their dynamic synchronization intervals or, at lower input-power levels, from the dynamics of the beat frequency. Noise perturbations are introduced into the formulation, which enables an estimation of the minimum detectable signal. In the case of a self-injected oscillator for radio frequency identification, an insightful formulation is derived to predict the propagation and tag-resonance effects on the instantaneous oscillation frequency. The tag-resonance signature gives rise to a distinct modulation of the oscillation frequency during the chirp period, which can be detected from the variation of the oscillator bias current. The analysis methods are illustrated through their application to a chirped oscillator, operating in the band 2–3 GHz.
A Nanosurface Microfluidic Device for Capture and Detection of Bacteria

Here we report on design, fabrication and implementation of a nanosurfac microfluidic device for efficient bacteria capture and optical detection. The device features simple design and ease of implementation. The principal of operation depends on the self-assembly of microparticles (polystyrene particles) at a pillar array region to form a Nano-filter for subsequent bacteria capture on gold nano/micro islands. The design was optimized using 2D COMSOL simulation. The device was fabricated using a single UV lithography step followed by electrodeposition of the gold structures and a subsequent step of polydimethylsiloxane (PDMS) bonding for device sealing. Lastly, the device was experimentally implemented using Escherichia coli (E.coli) bacteria showing efficient bacteria capturing performance.
Analysis and Optimization of Probabilistic Caching in Micro/Millimeter Wave Hybrid Networks With Dual Connectivity

Dual connectivity (DC) is a promising solution to boost system capacity and enhance mobility robustness in the fifth generation networks, allowing mobile users (MUs) to aggregate radio-frequency resources simultaneously from at least two kinds of serving base stations. In this paper, we focus on a novel cache-enabled heterogeneous network, where MUs can flexibly schedule its traffic between traditional sub-6 GHz (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave) macro cells and dense millimeter-wave (mmWave) small cells. We propose a comprehensive framework of mathematical models for the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave and mmWave hybrid caching network based on stochastic geometry, and then derive general and tractable expressions of the average successful delivery probability (SDP) for different networks with reasonable approximations. Furthermore, we optimize the caching probabilities for maximizing the SDP in the hybrid network with DC. The problem is non-convex and we solve it numerically using simulated annealing particle swarm optimization algorithm. At last, the simulation results show the effectiveness of our analytical and optimal conclusions.