Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressive Sensing Based Nuclear Norm Minimization Method for Massive MU-MIMO Channel Estimation

We address the problem of uplink channel estimation in TDD massive multiuser multi-input-multi-output (MU-MIMO) systems, when the uplink training duration is limited. Based on the concept of compressive sensing (CS), the uplink channel could be estimated with limited training duration if the channel can be sparsely represented. In this paper, a low-rank matrix approximation (LRMA) based on CS technique is proposed for the massive MU-MIMO channel estimation problem. As such, the channel estimation problem was formulated as a quadratic nuclear norm optimization problem with linear constraint. Consequently, the regularization parameter, which minimizes the error between a data fidelity and convex penalty function, is selected based on cross-validation (CV) curve method. The simulation results demonstrate that the proposed method outperforms the LS method in terms of the estimator performance. In addition, the proposed method reduces the pilot length and the computational cost as well.
A Secure Inductive Power Transfer Using $2\times 2$ MIMO

This paper proposes a $2\times 2$ MIMO deployment for realization of secure inductive power transfer (IPT). The design concept is to optimize the configuration such that the two output/input currents going in opposite directions to create a loop magnetic field confined in the space among the coils. EM simulations show that the system effectively prevents energy from leaking to an unauthorized user even it is placed on top of the authorized user.
Touchless Haptic Feedback for VR Rhythm Games

Haptics is an important part of the VR space as seen by the plethora of haptic controllers available today. Recent advancements have enabled touchless haptic feedback through the use of focused ultrasound thereby removing the need for a controller. Here, we present the world's first mid-air haptic rhythm game in VR and describe the reasoning behind its interface and gameplay, and in particular, how these were enabled by the effective use of state-of-the-art ultrasonic haptic technology.
Clove attenuates UVB-induced photodamage and repairs skin barrier function in hairless mice.

Syzygium aromaticum L. , commonly named clove, is widely used in the food industry due to its antioxidant and antibacterial capabilities. However, little information is available regarding its role in resisting skin photoaging. This study investigated 50% ethanol extract of Syzygium aromaticum L. (SA) and eugenol (EO) for anti-aging effects in UVB-irradiated normal human dermal fibroblasts (NHDFs) and hairless mice. In vitro, SA and EO suppressed matrix metalloproteinase-1, 3 (MMP-1 and MMP-3) secretion as well as the activator protein 1 (AP-1) phosphorylation. SA and EO also activated nuclear erythroid 2-related factor/antioxidant-response element (Nrf2/ARE) signaling which improves the antioxidant activity and inhibited nuclear factor-κB (NF-κB) and interleukin-6 (IL-6) expression, pro-inflammatory factors. Furthermore, SA and EO suppressed the nuclear factor of activated T cells c1 (NFATc1) which is a known activator of MMPs, cooperator transforming growth factor beta (TGF-β) and NF-κB in Ca2+/calcineurin-regulated transcription. In vivo, SA significantly improved the levels of procollagen type I and elastin through TGF/Smad signaling. The histopathological studies found that SA reduced wrinkles. SA also increased filament aggregating protein (filaggrin), which repairs the skin barrier function and improved the skin's hydration. Altogether, SA effectively ameliorated UVB-induced photoaging. It is expected to become a promising natural product.
Signal Transmission Utilizing Compensation Inductor for Capacitive Power Transfer System

This paper proposes a novel wireless power and signal transfer method utilizing the compensation inductors for CPT systems. In this method, the power is transferred via a capacitive channel, while the signal is transferred in an inductive channel which is constructed by the compensation inductors of the CPT system. No additional coils or metal plates are needed in this system, and the power interference on the signal channel is low. The mathematical model of the system is established, and the operation principle of power and signal channels is analyzed. The gains of the power channel and the signal channel, as well as the power interference on the signal channel are provided. On the basis of analysis, a design method to determine the parameters of power and signal channels is proposed. This method can ensure a low power interference on the signal channel and maintain the power transfer capability of the CPT system with signal branches. By the proposed parameter design method, a 50.7W experimental setup is built and the signal transmission rate is 200 kbps. Both the simulation and experimental results have demonstrated that the system can realize a reliable wireless power and signal transfer, and verified the effectiveness of the proposed parameter design method.
Immersive Visualization of Abstract Information: An Evaluation on Dimensionally-Reduced Data Scatterplots

The use of novel displays and interaction resources to support immersive data visualization and improve analytical reasoning is a research trend in the information visualization community. In this work, we evaluate the use of an HMD-based environment for the exploration of multidimensional data, represented in 3D scatterplots as a result of dimensionality reduction (DR). We present a new modeling for this problem, accounting for the two factors whose interplay determine the impact on the overall task performance: the difference in errors introduced by performing dimensionality reduction to 2D or 3D, and the difference in human perception errors under different visualization conditions. This two-step framework offers a simple approach to estimate the benefits of using an immersive 3D setup for a particular dataset. Here, the DR errors for a series of roll call voting datasets when using two or three dimensions are evaluated through an empirical task-based approach. The perception error and overall task performance, on the other hand, are assessed through a comparative user study with 30 participants. Results indicated that perception errors were low and similar in all approaches, resulting in overall performance benefits in both desktop and HMD-based 3D techniques. The immersive condition, however, was found to require less effort to find information and less navigation, besides providing much larger subjective perception of accuracy and engagement.
Neurometabolite Levels in Alcohol Use Disorder Patients During Baclofen Treatment and Prediction of Relapse to Heavy Drinking

Background and Aims: Baclofen, a GABAB agonist, is used as a treatment for alcohol dependence. We aimed to examine brain metabolites following administration of baclofen or placebo in alcohol dependent individuals enrolled in a randomized placebo-controlled trial. Methods: Participants included 31 alcohol dependent individuals (recent drinking: N = 16; and abstinent: N = 15) who had received daily baclofen (BAC 30–75 mg = 20) or placebo (PL = 11) for at least 2 weeks (average 17 days). Using in vivo proton magnetic resonance spectroscopy (1H-MRS), spectra from the right parietal lobe were analyzed to obtain measures of GABA, Glutamate (Glu), Glutathione (GSH) and N-Acetyl Apartate (NAA) 120 min following administration of PL or BAC. Results: When weighting alcohol dependent participants according to recent alcohol consumption (within 24 h), there were significant differences between BAC and PL on parietal concentrations of GSH (p < 0.01) and NAA (p < 0.05). Multiple linear regression revealed a significant predictive effect of GSH on heavy drinking days at 12 weeks follow-up (Model: F = 14.28, R2 = 0.85; GSH: B = −1.22, p = 0.01) and also percentage days abstinent at 12 weeks follow-up (Model: F = 6.50, R2 = 0.72; GSH: B = 0.99, p = 0.06). Conclusion: Our data provide preliminary evidence that the effect of baclofen may be mediated by increased parietal concentrations of the antioxidant GSH and NAA in recently drinking alcohol dependent patients. GSH/Cr levels were also predictive of improved drinking outcomes in the trial and suggests a role for neural oxidative stress in alcohol use disorder.
Three Main Inducers of Alphacoronavirus Infection of Enterocytes: Sialic Acid, Proteases, and Low pH

Transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhea virus (PEDV) are similar coronaviruses, causing diseases characterized by vomiting, diarrhea, and death from severe dehydration in piglets. Thus, they have caused huge losses to the swine-breeding industry worldwide. Nowadays, they are easily transmitted among the continents via vehicles, equipment, and cargo. Both viruses establish an infection in porcine enterocytes in the small intestine, and their spike (S) proteins play a key role in the virus-cell binding process under unfavorable conditions when the intestine with a low pH is filled with a thick layer of mucus and proteases. Sialic acid, proteases, and low pH are three main inducers of coronavirus infection. However, the details of how sialic acid and low pH affect virus binding to the host cell are not determined, and the functions of the proteases are unknown. This review emphasizes the role of three factors in the invasion of TGEV and PEDV into porcine enterocytes and offers more insights into Alphacoronavirus infection in the intestinal environment.
A Novel RTRLNN Model for Passive Intermodulation Cancellation in Satellite Communications

Passive intermodulation (PIM) often limits the performance of satellite communication systems with multi-carriers. The PIM interference has a peculiarity of time-varying and non-Markov. While the existing digital signal processing methods have limited effects, we propose a novel adaptive real-time recursion learning neural network (RTRLNN) which is suitable for dynamic PIM in satellite communications. The proposed novel RTRLNN method has fast convergence with its adaptive learning rate and accurate approximation ability. In this paper, a cancellation system with RTRLNN algorithm is designed for the PIM interference in satellite communications. The system has two processing sections which are pilot slot and data transmission slot. The system extracts the feature of PIM interference and trains itself in the pilot slot while achieves cancellation of PIM interference in the data transmission slot. Simulation results are presented on time and frequency domain illustrating the effectiveness of PIM interference cancellation. Compared with the modified least mean square(LMS) method, the proposed novel RTRLNN method significantly decreases the bit error rate(BER) of PIM interference. Furthermore, the proposed novel RTRLNN method shows an enhancement of $10\mathrm {d}\mathrm {B}$ in SIR gain while the $\mathrm {E}\mathrm {b}/\mathrm {N}0$ fixed $10\mathrm {d}\mathrm {B}. $
Multi-Objective Virtual Machine Placement Algorithm Based on Particle Swarm Optimization

According to the cloud computing paradigm, cloud services make use of Virtual Machines (VMs) that are running on Physical Machines (PMs). A user consumes VMs that are hosted on PMs located at one or several data centers. Energy consumption of a data center is highly correlated with the number of active PMs. A data center relies on a placement algorithm to assign the required VMs on a minimum number of PMs in order to minimize energy consumption. In this paper, we propose a VM placement algorithm based on Particle Swarm optimization (PSO) meta-heuristics. As opposed to reported VM placements, which all seek to minimize either energy consumption or resource wastage, our proposed PSO algorithm seeks to maximize packing efficiency while minimizing energy consumption. Extensive simulation results show that our algorithm significantly produces better results when compared to suggested work. Indeed, the solutions obtained with our algorithm require a lower number of PMs, lower energy consumption and a better balanced utilization of PMs.
Optimal Design of Redundant Structures by Incorporating Various Costs

Redundant systems, which usually consist of a number of same/similar components (or modules), have been used in various critical infrastructures to ensure the system's normal function. Usually, system reliability can be improved with the adoption of additional components or redundancies. Typically, two types are mainly included, i.e., majority voters and standby redundancies (referred to as SPARE gates for simplicity). Nevertheless, with the increment of redundancies, the consumed cost or space requirement also grows. This study considers a tradeoff between cost and reliability in order to pursue the cost-effective optimal design. The relationships between the total cost and corresponding parameters are discussed thoroughly. Besides, in order to determine the cost-effective design at the expense of a unit of cost, a revised evaluation standard is proposed (referred to as ${\rm{R}\_\rm{per}\_\rm{Cost}}$). In this paper, we perform a cost-effective analysis of majority voters with different implementations; and we also perform the analyses of SPARE gates (here, a warm spare gate and a cold spare gate are mainly focused). For deriving corresponding total cost, algorithms are presented to predict the necessary failure time of components. In this line, cost-effective analyses of several case studies are performed.
MtMR: Ensuring MapReduce Computation Integrity with Merkle Tree-Based Verifications

Big data applications have made significant impacts in recent years thanks to the fast growth of cloud computing and big data infrastructures. However, public cloud is still not widely accepted to perform big data computing, due to the concern with the public cloud's security. Result integrity is one of the most significant security problems that exists in the cloud-based big data computing scenario. In this paper, we propose MtMR, a Merkle tree-based verification method that assures high result integrity of MapReduce jobs. MtMR overlays MapReduce on a hybrid cloud environment and applies two rounds of Merkle tree-based verifications on the pre-reduce phase (i.e., the map phase and the shuffle phase) and the reduce phase, respectively. In each round, MtMR samples a small portion of reduce task input/output records on the private cloud and performs Merkle tree-based verification on all the task input/output records. Based on the design of MtMR, we perform a series of theoretical studies to analyze its security and performance overhead. Our results indicate that MtMR is a promising method in terms of high result integrity and low performance overhead. For example, by setting the sampled record ratio as an optimal value, MtMR can guarantee no more than 10 incorrect records in each reduce task by sampling only 4 percent of records in that task.
The Value of Providing Smokers with Free E-Cigarettes: Smoking Reduction and Cessation Associated with the Three-Month Provision to Smokers of a Refillable Tank-Style E-Cigarette

Despite the uptake of tobacco smoking declining in the United Kingdom (UK), smoking is still the leading cause of preventable poor health and premature death. While improved approaches to smoking cessation are necessary, encouraging and assisting smokers to switch by using substantially less toxic non-tobacco nicotine products may be a possible option. To date, few studies have investigated the rates of smoking cessation and smoking reduction that are associated with the provision of free electronic-cigarettes (e-cigarettes) to smokers. In this exploratory study, the Blu Pro e-cigarette was given to a convenience sample of adult smokers (n = 72) to assist them in reducing and quitting over a 90-day period. The rates of smoking abstinence and daily smoking patterns were assessed at baseline, 30 days, 60 days, and 90 days. The response rate was 87%. After 90 days, the complete abstinence rate was 36.5%, up from 0% at baseline. The frequency of daily smoking reduced from 88.7% to 17.5% (p < 0.001), and the median consumption of cigarettes/day reduced from 15 to five (p < 0.001). The median number of days per month that participants smoked also reduced from 30 to 13 after 90 days (p < 0.001). On the basis of these results, there may be value in smoking cessation services and other services ensuring that smokers are provided with e-cigarettes at zero or minimal costs for at least a short period of time.
Towards Zero Re-Training for Long-Term Hand Gesture Recognition via Ultrasound Sensing

While myoelectric pattern recognition is a prevailing way for gesture recognition, the inherent nonstationarity of electromyography signals hinders its long-term application. This study aims to prove a hypothesis that morphological information of muscle contraction detected by ultrasound image is potentially suitable for long-term use. A set of ultrasound-based algorithms are proposed to realize robust hand gesture recognition over multiple days, with user training only at the first day. A markerless calibration algorithm is first presented to position the ultrasound probe during donning and doffing; an algorithm combining speeded-up robust features and bag-of-features model being immune to ultrasound probe shift and rotation is then introduced; a self-enhancing classification method is next adopted to update classification model automatically by incorporating useful knowledge from testing data; finally the performance of long-term hand gesture recognition with zero re-training is validated by a six-day experiment of six healthy subjects, whose outcomes strongly support the hypothesis with about 94% of gesture recognition accuracy for each testing day. This study confirms the feasibility of adoption of ultrasound sensing for long-term musculature related applications.
Spatial Updating and Simulator Sickness During Steering and Jumping in Immersive Virtual Environments

Many recent head-mounted display applications and games implement a range-restricted variant of teleportation for exploring virtual environments. This travel metaphor referred to as jumping only allows to teleport to locations in the currently visible part of the scene. In this paper, we present a formal description and classification scheme for teleportation techniques and its application to the classification of jumping. Furthermore, we present the results of a user study (N=24) that compared jumping to the more conventional steering with respect to spatial updating and simulator sickness. Our results show that despite significantly faster travel times during jumping, a majority of participants (75%) achieved similar spatial updating accuracies in both conditions (mean difference 0.02°, =5.05°. In addition, jumping induced significantly less simulator sickness, which altogether justifies it as an alternative to steering for the exploration of immersive virtual environments. However, application developers should be aware that spatial updating during jumping may be impaired for individuals.
Using an Adaptive Filter to Remove ECG Motion Artifact Interference

Motion artifact removal is one of the most important issues in portable ECG (electrocardiogram) monitoring. Motion artifacts are usually accompanied by significant baseline drift. Adaptive filtering has been used to filter motion artifacts and baseline drift. The 3-axis acceleration signal is selected as the noise reference signal. Results show that motion artifacts and baseline drift have been removed in filtered ECG, and the QRS complex of the filtered ECG has clearly appeared.
Lymphoma survivors’ experiences at the end of treatment

AIMS AND OBJECTIVES To explore lymphoma survivors' experiences on their end of treatment and follow-up care at a large urban haematology centre in Ireland. METHODS This was a qualitative study using semistructured interviews with lymphoma patients post-treatment (n = 14). Thematic analysis guided the analysis of interview data. The study adhered to the consolidated criteria for reporting qualitative research (COREQ) guidelines. FINDINGS Five main themes were identified following analysis of the interview data: dealing with uncertainty, changed relationships, returning to work, extended recovery time and concerns for the future. Findings of note were that some participants were unaware that their treatment had ended, many experienced recurrent infections which prolonged recovery time, and many had no recall of discussions on healthy lifestyle behaviours or recommended screening programmes at their follow-up visits. CONCLUSIONS The findings suggest that the period of transition from active treatment to survivorship can be challenging for lymphoma survivors, and they experience ongoing needs. While the challenges of survivorship in lymphoma mirror those of other cancers, this cohort of patients require focused preparation for the end of active treatment and the transition to follow-up care. Moreover, this patient group requires repeated specific education on late effects and second cancers, education with regards to identifying the signs of cancer recurrence and promotion of healthy lifestyle practices. RELEVANCE TO CLINICAL PRACTICE This study highlights the importance of a dedicated end of treatment visit with the clinical nurse specialist to confirm the completion of active treatment with lymphoma patients and focus on health promotion.
An Optimization-based mTSP Clustering Algorithm for Wireless Sensor Networks

In this paper, we investigates the problem of computing the optimal trajectories of multiple mobile elements (e.g. robots, vehicles, aircrafts, etc.) to minimize energy consumption in the Wireless Sensor Networks (WSNs). We present a clustering algorithm, EmCA (An Effective mTSP based Clustering Algorithm), based on the multiple Traveling Salesman Problem (mTSP). It is designed to solve mTSP as the first step, which can transform mTSP to multiple standard TSP. Numerical simulation indicates that the algorithm can obtain a serial of uniform cluster. For each cluster, the density of sensor nodes has been minimized, and then the total travel distance has been further minimized.
A Novel Collaborative Task Offloading Scheme for Secure and Sustainable Mobile Cloudlet Networks

With the advancement of wireless networking technologies and communication infrastructures, mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity-limited mobile devices. More specifically, at the network edge, the resource-rich and trusted cloudlet system can provide in-proximity computing services by executing the workloads for nearby devices. Nevertheless, there are chances for malicious users to generate distributed denial-of-service (DDoS) flooding tasks to overwhelm cloudlet servers and block computing services from legitimate users. Load balancing is one of the most effective methods to solve DDoS attacks in distributed networks. However, existing solutions require overall load information to achieve load balancing in cloudlet networks, making it costly in both communication and computation. To achieve more efficient and low-cost load balancing, we propose CTOM, a novel collaborative task offloading scheme to avoid DDoS attacks for secure and sustainable mobile cloudlet networks. The proposed solution is based on the balls-and-bins theory and it can balance the task loads with extremely limited information. The CTOM reduces the number of overloaded cloudlets smoothly, thus handling the potential DDoS attacks in mobile cloudlet networks. Extensive simulations and evaluation demonstrate that, the proposed CTOM outperforms the conventional random and proportional allocation schemes in reducing the task gaps between maximum load and minimum load among mobile cloudlets by 65% and 55%, respectively.