Miracle-dz/UniFAR-FTUs huggingface dataset:
“A lot better than it used to be”: a qualitative study of adolescents’ dynamic social recovery capital

Abstract Background Substance use recovery is a dynamic process for youth, and social networks are tied to the recovery process. The Recovery Capital for Adolescents Model (RCAM) situates the resources accessible through social networks – social recovery capital (SRC) – in a larger framework of developmentally-informed recovery resources. This study aims to investigate the social network experiences among recovering youth enrolled in a recovery high school to understand how social influences help to build, or act as barriers to building, recovery capital. Methods To gain insight into these networks, Social Identity Maps and semi-structured interviews were conducted with ten youth ages 17–19 years (80% male; 50% non-Hispanic White). Study visits were conducted virtually, recorded, transcribed, and thematically analyzed using the RCAM as an organizing framework. Results Results supported that adolescent social networks play a unique and multifaceted role in the recovery journey. Three key nuances emerged: change permeates adolescent networks throughout the treatment and recovery process; shared substance use history and non-stigmatizing attitudes play a key role in connecting with others; and SRC is interconnected with human, financial, and community recovery capital. Conclusions With adolescent recovery receiving increased attention from policy makers, practitioners, and researchers, the RCAM may be a useful way to contextualize available resources. Findings suggest SRC as a crucial, yet complex component intertwined with all other forms of recovery capital.
TRSDL: Tag-Aware Recommender System Based on Deep Learning–Intelligent Computing Systems

: In recommender systems (RS), many models are designed to predict ratings of items for the target user. To improve the performance for rating prediction, some studies have introduced tags into recommender systems. Tags beneﬁt RS considerably, however, they are also redundant and ambiguous. In this paper, we propose a hybrid deep learning model TRSDL (tag-aware recommender system based on deep learning) to improve the performance of tag-aware recommender systems (TRS). First, TRSDL uses pre-trained word embeddings to represent user-deﬁned tags, and constructs item and user proﬁles based on the items’ tags set and users’ tagging behaviors. Then, it utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of items and users, respectively. Finally, it predicts ratings from these latent features. The model not only addresses tag limitations and takes advantage of semantic tag information but also learns more advanced implicit features via deep structures. We evaluated our proposed approach and several baselines on MovieLens-20 m, and the experimental results demonstrate that TRSDL signiﬁcantly outperforms all the baselines (including the state-of-the-art models BiasedMF and I-AutoRec). In addition, we also explore the impacts of network depth and type on model performance.
Synthesis of Broadband Differential Loading Networks for High-Efficiency Power Amplifiers

A synthesis procedure of broadband loading networks for high-efficiency power amplifiers is presented. The design method utilizes the transfer function parameters from a periodic N-section network and the required impedance at the intrinsic transistor drain that produces high efficiency. The selected network topology is differential band-pass and allows the combining of two FETs to produce high power with lower device parasitics compared to a single-ended equivalent. This paper describes the synthesis procedure with a prototype design to achieve an output power of 100-W with a drain efficiency higher than 77% within the 240–380 MHz frequency range.
HEI-BCT: A Framework to Implement Blockchain-Based Self-Sovereign Identity Solution in Higher Education Institutions

Blockchain Technology, a decentralized and distributed ledger, has received extensive attention of industries to revamp operations and functionality of organizations. Implementing smart technologies such as Blockchain is an attractive solution for organizations, including the Higher Education Institutions (HEIs). Self-Sovereign Identity is one of Blockchain applications that promises to provide a more efficient management systems for organizations. This paper explains how a Self-Sovereign Identity using Blockchain Technology can be used to manage students' credentials in HEIs. It also proposes a framework to successfully implement the solution. This paper answers two questions: “How to use Self-Sovereign Identity as credentials management system” and “What are the major steps to be followed when implementing a Blockchain-based solution? ”
Network Anomaly Detection with Payload-based Analysis

Network attacks become more complicated with the improvement of technology. Traditional statistical methods may be insufficient in detecting constantly evolving network attack. For this reason, the usage of payload-based deep packet inspection methods is very significant in detecting attack flows before they damage the system. In the proposed method, features are extracted from the byte distributions in the payload and these features are provided to characterize the flows more deeply by using N-Gram analysis methods. The proposed procedure has been tested on IDS 2012 and 2017 datasets, which are widely used in the literature.
Anode Modification with Reduced Graphene Oxide–Iron Oxide Improves Electricity Generation in Microbial Fuel Cell

In recent years, much research has focused on energy recovery from biomass as an alternative to fossil fuel usage. Microbial fuel cells (MFCs), which produce electricity via microbial decomposition of organic matter, are of great interest. The performance of an MFC depends on the electrode material; most often, carbon materials with good electrical conductivity and durability are used. To increase the power output of an MFC, the anode material can be modified to reduce the internal resistance and increase the anode surface area. Therefore, this study determined how modifying a carbon felt anode with reduced graphene oxide (rGO) and a combination of rGO with iron (III) oxide (rGO-Fe) affected electricity generation in an MFC fueled with wastewater. A mixed microbial consor tium was used as the anode biocatalyst . The MFC-rGO-Fe produced significantly higher voltages than other cells (average 109.4 ± 75.1 mV in the cycle). Power density curves indicated that modifying the anode with rGO-Fe increased the power of the MFC to 4.5 mW/m 2 , 9.3- and 3.9-times higher than that of the control MFC and the MFC-rGO, respectively. Anode modification reduced the internal resistance of the cells from 1029 Ω in the control MFC to 370 and 290 Ω in the MFC-rGO and MFC-rGO-Fe, respectively. These results show that a mixture of rGO with iron (III) oxide positively affects electricity production and can be successfully used for anode modification in the MFCs fueled with wastewater.
pH and pCl Operational Parameters in Some Metallic Ions Separation with Composite Chitosan/Sulfonated Polyether Ether Ketone/Polypropylene Hollow Fibers Membranes

The development of new composite membranes is required to separate chemical species from aggressive environments without using corrective reagents. One such case is represented by the high hydrochloric acid mixture (very low pH and pCl) that contains mixed metal ions, or that of copper, cadmium, zinc and lead ions in a binary mixture (Cu–Zn and Cd–Pb) or quaternary mixture. This paper presents the obtaining of a composite membrane chitosan (Chi)–sulfonated poly (ether ether ketone) (sPEEK)–polypropylene hollow fiber (Chi/sPEEK/PPHF) and its use in the separation of binary or quaternary mixtures of copper, cadmium, zinc, and lead ions by nanofiltration and pertraction. The obtained membranes were morphologically and structurally characterized using scanning electron microscopy (SEM), high resolution SEM (HR–SEM), energy dispersive spectroscopy analysis (EDAX), Fourier Transform InfraRed (FTIR) spectroscopy, thermogravimetric analysis, and differential scanning calorimetry (TGA-DSC), but also used in preliminary separation tests. Using the ion solutions in hydrochloric acid 3 mol/L, the separation of copper and zinc or cadmium and lead ions from binary mixtures was performed. The pertraction results were superior to those obtained by nanofiltration, both in terms of extraction efficiency and because at pertraction, the separate cation was simultaneously concentrated by an order of magnitude. The mixture of the four cations was separated by nanofiltration (at 5 bars, using a membrane of a 1 m2 active area) by varying two operational parameters: pH and pCl. Cation retention could reach 95% when adequate values of operational parameters were selected. The paper makes some recommendations for the use of composite membranes, chitosan (Chi)–sulfonated poly (ether ether ketone) (sPEEK)–polypropylene hollow fiber (Chi/sPEEK/PPHF), so as to obtain the maximum possible retention of the target cation.
Energy Efficient Beamforming for Multi-Cell MISO SWIPT Systems

This paper studies beamforming design problems for multi-cell multi-user downlink networks with simultaneous wireless information and power transfer. In this system, base stations (BSs) concurrently transfer information and energy to multiple single-antenna information decoding (ID) and energy harvesting (EH) users. We aim to maximize energy harvesting efficiency (EHE), which is defined as the ratio of the harvested energy at the EH users to the amount of energy consumption at the BSs, while guaranteeing quality-of-service constraint for each ID user. First, for the centralized case where global channel state information (CSI) is available at all BSs, we propose a centralized beamforming method based on the semi-definite relaxation techniques. Next, in order to reduce the backhaul signaling overhead, a decentralized algorithm is presented where each BS computes its beamforming vector by only using local CSI. Simulation results show that the proposed algorithm offers a significant EHE performance gain over conventional schemes.
Practical applications and learning curve for EUS-guided hepaticoenterostomy: results of a large single-center US retrospective analysis

Abstract Background and study aims Endoscopic ultrasound-guided hepaticoenterostomy (EUS-HE) is an effective method of endoscopic biliary drainage in cases where endoscopic retrograde cholangiopancreatography has failed or is deemed impossible. Indications for EUS-HE have expanded, resulting in increased interest by endoscopists to learn the procedure; however, few data exist on breadth of application or experience needed to develop proficiency. We describe utilization of EUS-HE for biliary decompression at a large tertiary referral center along with procedural learning curve. Patients and methods Retrospective evaluation of 60 consecutive patients who underwent attempted EUS-HE by one endoscopist from February 2016 through June 2018. Procedures were divided into chronological and summative experience quartiles. We compared procedural success rate, procedural utilization, and procedure duration over time. Results Sixty patients underwent attempted EUS-HE during the study period: 35 with surgically altered anatomy, 23 with malignant biliary obstruction, 35 outpatients, 35 females; median age, 66 years. The procedure was technically successful in 53 patients. Success rates by summative experience quartile were 80 %, 80 %, 93.3 % and 100 % respectively. Beginning at patient number 40, the remaining cases had a success rate of 100 %. Utilization increased from eight cases in the first chronological quartile to 28 in the fourth. There was no significant reduction in procedure duration over time. Conclusion For an experienced endoscopist, EUS-HE could be performed effectively and safely after the experience of 40 cases. Limitations of this study include a single endoscopist and heterogeneous patient population with variable anatomy that may affect procedural success. Future studies should include data from multiple centers and endoscopists.
A Novel Disturbance-Rejection Control Framework for Cable-Driven Continuum Robots With Improved State Parameterizations

A soft, or continuum, robot has great potential in many practical fields with confined space, such as manufacturing system, electrical industry and other, due to its flexibility and benign bending attribute. However, its innate nonlinear dynamics and infinite degrees of freedom pose challenges both in modeling and control. How to build a proper model, which can be used to analyze the characteristics of soft robots, and how to design a robustly stabilized controller, are still open issues. In this work, we build a model for cable-driven continuum robots using a piecewise constant-curvature method. An improved state parameterization is proposed to avoid singularities, which produce pathological behaviors mainly on straight configurations of continuum robots. Then, a novel disturbance-rejection control framework is proposed with a tailored sliding mode controller to achieve stabilized control. Aiming at better robustness, we lump all system uncertainties, including unmodeled dynamics, unknown external disturbances, and unknown parameter variations, together as an extended state of the system. A linear extended state observer is then proposed to estimate the total uncertainties online, enabling compensation for their negative effects. Stability analysis and numerical simulations are carried out to show the feasibility and effectiveness of the proposed approaches.
Multi-Sensor Passive Localization Using Second Difference of Coherent Time Delays With Incomplete Measurements

The passive localization using multiple sensors is considered for emitter transmitting pulse signals with unknown start transmission time and period. Time of arrival (TOA) of a pulse is estimated at every sensor, and it is difficult for sensors to intercept all pulses during the observation time in practice, as there maybe obstacle on the line of sight or the signal is too weak to be detected, namely, incomplete measurements. In this paper, an algorithm based on a combination of a new second difference of coherent time delays (SDCTD) measurement and traditional time difference of arrival (TDOA) is proposed to improve localization performance in the presence of two kinds of TOA errors, including the independent estimation errors and common errors among multiple sensors. The Cramer–Rao lower bound for the proposed algorithm is derived, and the simulations validate that the performance of the proposed algorithm significantly outperforms existing algorithms, especially in the case of pulse loss.
Effects of Pre-crosslinking on the Aggregate Structure and Space Charge Properties of XLPE

In the extrusion process of cross-linked polyethylene (XLPE) cables, when the local extrusion temperature is higher than the decomposition temperature of the cross-linking agent, the cross-linking agent will be thermally decomposed in advance, resulting in a low degree of pre-crosslinking of the material. In order to study the effect of pre-crosslinking on the space charge characteristics of XLPE DC cable materials, this paper carried out low-level pre-crosslinking treatment at different temperatures before the high-temperature cross-linking of the material. The aggregate structure of the material was characterized by means of scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and cross-linking degree test, and the space charge characteristics at different temperatures were studied. The experimental results show that pre-crosslinking will reduce the crosslinking degree of XLPE on the one hand, and also lead to the decrease of crystallinity and the reduction of spherulite size. The changes of the aggregate structure make it easier to inject the homopolar charges in pre-crosslinked XLPE samples. Under the high temperature, the residual impurity molecules in the sample will also dissociate, generating a large number of the heteropolar charges, resulting the occurrence of severe electric field distortion.
Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

In the last decades, pervasive computing is generating a growing quantity of data. Data mining (DM) technology has become increasingly popular. However, the excessive collection and analysis of data may violate the privacy of individuals and organizations, which raises privacy concern. Therefore, a new research area known as privacy-preserving DM (PPDM) has emerged and attracted the attention of many researchers who are interested in preventing privacy disclosure during DM. In this paper, we provide a comprehensive review of studies on a specific PPDM, known as privacy-preserving association rule mining (PPARM). We present a detailed taxonomy for the existing PPARM algorithms according to multiple dimensions and then conduct a survey of the most relevant PPARM techniques from the literature. Moreover, we survey and elaborate on each type of metrics used to evaluate PPARM algorithms. Finally, we summarize some conclusions and come up with some future directions and challenges.
DNA Holliday Junction: History, Regulation and Bioactivity

DNA Holliday junction (HJ) is a four-way stranded DNA intermediate that formed in replication fork regression, homology-dependent repair and mitosis, performing a significant role in genomic stability. Failure to remove HJ can induce an acceptable replication fork stalling and DNA damage in normal cells, leading to a serious chromosomal aberration and even cell death in HJ nuclease-deficient tumor cells. Thus, HJ is becoming an attractive target in cancer therapy. However, the development of HJ-targeting ligand faces great challenges because of flexile cavities on the center of HJs. This review introduces the discovery history of HJ, elucidates the formation and dissociation procedures of HJ in corresponding bio-events, emphasizes the importance of prompt HJ-removing in genome stability, and summarizes recent advances in HJ-based ligand discovery. Our review indicate that target HJ is a promising approach in oncotherapy.
Blind Multi-User Detection of Co-Frequency Signals with Unknown Carrier Frequency Offsets

In this paper, the problem of blind multi-user detection (MUD) of co-frequency phase-shift keying (PSK) signals with unknown carrier frequency offsets (CFOs) is investigated. Firstly, a CFO estimation scheme which explores cyclic-feature of the received mixture signal to estimate CFOs of signals from different users is introduced. After that, a CFO-robust turbo MUD detector which performs blind channel estimation and symbol detection iteratively is proposed. Simulation results show that by using the proposed blind turbo MUD receiver, the symbol error rates (SERs) of the detected symbols are much lower than those achieved by using the conventional MUD receiver, and the SER obtained by the blind MUD receiver is very close to SER obtained under perfect channel state information (CSI) condition.
Flexible and Scalable Additively Manufactured Tile-Based Phased Arrays for Satellite Communication and 50 mm Wave Applications

This paper presents a novel flexible and scalable tiled phased array antenna operating in K band, with individual phased array tiles and a corporate feeding network achieved by microstrip-to-microstrip transition. The prototype fabrication utilizes additive manufacturing to achieve low-cost and flexible features. Both simulation and measurements for a 2 by 2 array demonstrate good radiation and beam steerability. The basic structure of this four-tile array can be conveniently scaled to any larger array of 2N elements or for higher frequency applications. This design can be applied to active phased-array radars and Support future demand for high volume 5G communication.
UWB MIMO Slot Antenna with Minkowski Fractal Shaped Isolators for Isolation Enhancement

A novel compact ultra-wideband (UWB) multiple input multiple output (MIMO) slot antenna with band notch characteristics is presented for portable wireless UWB applications. The antenna comprises co-planar waveguide feed (CPW) and two radiating monopoles oriented in orthogonal orientation for providing orthogonal radiation patterns. A Minkowski fractal parasitic stub along with a Minkowski fractal grounded stub has been placed at 45◦ between the monopoles to reduce the coupling between them which in turn establish high isolation between the radiators. An excellent band notch characteristic is obtained at 5.5 GHz by etching a modified E-shaped compact slot on the radiators. At the centre of notched band, the efficiency and gain of the antenna drop significantly which indicates a good interference suppression. Results show that the designed antenna meets −10 dB impedance bandwidth and −17 dB isolation throughout the entire operating band (3.1–12 GHz). Novelty of this design lies in improving isolation using compact fractal structures which occupy less space than conventional isolation mechanisms in MIMO structures. The simulated and measured results show that the proposed antenna is convenient for MIMO diversity systems.
Design and Optimization of T-Coil-Enhanced ESD Circuit with Upsampling Convolutional Neural Network

T-coils are widely used in high-speed electrostatic discharge (ESD) circuits to increase bandwidth. Like many other RF/microwave devices, T-coil modeling relies on time-consuming electromagnetic (EM) simulations, which precludes quick design space exploration and fast global optimization. In this paper, a machine learning (ML) model is presented to replace EM T-coil simulations, thereby accelerating T-coil design and optimization. Given the geometry of a T-coil layout, the ML model can infer its S-parameters from 100 MHz to 100 GHz nearly instantly. Finally, this ML model is incorporated into a genetic algorithm (GA), affording a 10× speed improvement in the optimization of a T-coil-enhanced ESD circuit in a 22nm FD-SOI CMOS process.
Delay-Optimal Scheduling for Heavy-Tailed and Light-Tailed Flows via Reinforcement Learning

We consider a delay-optimal scheduling problem in a queueing system with a mix of heavy-tailed and light-tailed flows. A light-tailed flow requires more stringent quality of services (QoSs) than a heavy-tailed flow. However, the arrival process of a heavy-tailed flow is far more bursty than that of a light-tailed flow. In addition, flows having a similar tail distribution also require distinct QoSs. This is a NP-hard problem in general. We propose a scheduling scheme that consists of two separate and parallel algorithms, including dynamic-weight-earliest-deadline-first (DWEDF) and reinforcement learning (RL), called DWEDF- RL, to address it. Specifically, we provide delay-bound-based fairness to flows having similar tail distributions in intra-queue buffering process with DWEDF. Inter-queue scheduling process further maximizes the QoS provisioning efficiency by dynamically prioritizing light-tailed flows according to network environments and QoS requirements via reinforcement learning. The effectiveness of the proposal in QoS provisioning has been demonstrated through simulation results.