Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Deep Reinforcement Learning Based Online User Association Optimization in Heterogeneous Networks

This paper studies heterogeneous networks (HetNets) where multiple wireless users are supposed to associate with an optimal base station (BS) to maximize the network utility. In order to guarantee the fairness among users and enhance the capacity of network, users should be actively associated with the BS tiers with lighter load, instead of the one with the maximum signal-to-interference-plus-noise ratio (SINR). Therefore, an optimization problem of joint user association and bandwidth allocation is formulated, which is a mixed binary integer programming and also an NP-hard problem. However, it is challenging to solve this problem by using traditional methods due to its high computational complexity and sensitivity to the change of channel parameters. This paper proposes an online deep reinforcement learning (DRL) based algorithm for HetNet, where multiple parallel deep neural networks (DNNs) can generate user association solutions. We use a shared memory structure to store the best association scheme and then use these data as training data set to train all the parallel DNNs. Numerical results show that our proposed algorithm can achieve significant performance gain in terms of the value of utility function over the max-SINR user association scheme. In addition, it also performs better than the greedy algorithm, and when the proposed algorithm are adopted with 5 DNNs, it can achieve a performance gain of up to 5% compared with the greedy algorithm.
The Effect on Outcome of Adding Magnesium Sulphate to Bupivacaine in the Ultrasound-guided Supraclavicular Brachial Plexus Block Anesthesia

This study evaluated the motor and sensory block durations and the postoperative analgesic effects of adding Magnesium Sulphate to Bupivacaine HCL for Ultrasound-guided supraclavicular brachial plexus block. Sixty ASA I-II patients subjected to elective forearm or hand surgery were randomly divided equally into 1 of 2 groups depending upon the local anesthesia (LA) solution used to receive either 23 mL of bupivacaine HCL 0.5% plus 2 mL normal saline (group B) or 23 mL of bupivacaine HCL 0.5% plus Magnesium Sulphate 100 mg diluted with 2mL normal saline (group BM). Motor and sensory block durations were evaluated until the return of normal motor and sensory function. The degree of pain was assessed during the surgery period and 24 hours postoperatively. The results of this study showed that Magnesium Sulphate provided significantly longer motor block duration (329.33 ± 86.54 min vs 237.46 ± 62.91 min, P = 0.001) and significantly longer sensory block duration (356.00 ± 117.02 min vs 278.00 ± 87.23 min, P = 0.005) when compared with bupivacaine alone. Furthermore, the patients in the Magnesium Sulphate group achieved significantly (P < 0.001) lower values of Visual Analogue Scale (VAS) of pain between the periods from 4 to 12 hours postoperatively. There was significantly less diclofenac consumed (mg) in the Magnesium Sulphate group. The patients in the Magnesium Sulphate group reported significantly higher rates of good sleep quality on the first postoperative night (75%) compared with the patients in the bupivacaine group (25%). Also, the patient satisfaction was significantly higher in the Magnesium Sulphate group compared with the bupivacaine group (75% vs 25%). We concluded that Magnesium Sulphate 100 mg to bupivacaine HCL extended the motor and sensory block durations, provided more effective postoperative analgesia with improvement in the sleep quality of the first postoperative night, and provided better patient satisfaction compared to bupivacaine HCL alone.
Ultrastructure and embryonic development of a syconoid calcareous sponge

Abstract. Recent molecular data suggest that the Porifera is paraphyletic (Calcarea+Silicea) and that the Calcarea is more closely related to the Metazoa than to other sponge groups, thereby implying that a sponge-like animal gave rise to other metazoans. One ramification of these data is that calcareous sponges could provide clues as to what features are shared among this ancestral metazoan and higher animals. Recent studies describing detailed morphology in the Calcarea are lacking. We have used a combination of microscopy techniques to study the fine structure of Syconcoactum Urban 1905, a cosmopolitan calcareous sponge. The sponge has a distinct polarity, consisting of a single tube with an apically opening osculum. Finger-like chambers, several hundred micrometers in length, form the sides of the tube. The inner and outer layers of the chamber wall are formed by epithelia characterized by apical–basal polarity and occluding junctions between cells. The outer layer—the pinacoderm—and atrial cavity are lined by plate-like cells (pinacocytes), and the inner choanoderm is lined by a continuous sheet of choanocytes. Incurrent openings of the sponge are formed by porocytes, tubular cells that join the pinacoderm to the choanoderm. Between these two layers lies a collagenous mesohyl that houses sclerocytes, spicules, amoeboid cells, and a progression of embryonic stages. The morphology of choanocytes and porocytes is plastic. Ostia were closed in sponges that were vigorously shaken and in sponges left in still water for over 30 min. Choanocytes, and in particular collar microvilli, varied in size and shape, depending on their location in the choanocyte chamber. Although some of the odd shapes of choanocytes and their collars can be explained by the development of large embryos first beneath and later on top of the choanocytes, the presence of many fused collar microvilli on choanocytes may reflect peculiarities of the hydrodynamics in large syconoid choanocyte chambers. The unusual formation of a hollow blastula larva and its inversion through the choanocyte epithelium are suggestive of epithelial rather than mesenchymal cell movements. These details illustrate that calcareous sponges have characteristics that allow comparison with other metazoans—one of the reasons they have long been the focus of studies of evolution and development.
Kernel-based system identification improving robustness to input disturbances: A preliminary study on its application to AR models

This paper discusses the kernel-based identification which takes account of robustness against input disturbances. To this end, ARX models are employed in the framework of kernel-based system identification. Since ARX models utilize the past output data, which reflects the actual input including the disturbances, as well as the input data, its output estimate is expected to be more accurate compared to conventional FIR models. We will show the Tuned and Correlated kernel, which has been widely used in FIR model identification, works well for ARX models through numerical case studies with real data. Furthermore, results show that the stability of the identified ARX models is eventually maintained and they are superior to FIR models in robustness against input disturbances.
Design of Confidence Sets for Estimation with Approximate Piecewise Linear Constraint *

Constrained state estimation has been studied in the literature usually from the point estimation perspective. That is, a random vector is designed and its covariance matrix is searched based on contextual information available in addition to the measured data. This paper deals with interval/ set estimation. A random set covering the unknown state with a predefined probability is constructed for a Gaussian estimate and an approximate piecewise linear constraint. The proposed approach combines constrained estimation with hypothesis testing and its discussion is provided.
From IoT to Cloud: An End-to-End Virtualization Approach

The integration of the Internet of Things (IoT) with the Cloud and Edge Computing infrastructures offers unprecedented opportunities for applications and services. Realizing such a holistic and large-scale platform requires a mixture of different technologies to enable the interconnection of the various functional layers of this new end-to-end (E2E) communication paradigm. Virtualization and specifically Network Function Virtualization (NFV), is the key enabling technology for creating and supporting an IoT-aware Edge-Cloud Computing infrastructure. In this tutorial session paper we study how to establish a scalable softwarized E2E solution that will gracefully combine different technologies to provide networking services with high Quality of Service (QoS) and low communication cost.
An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

In many instances, outdoor surveillance systems suffer from atrocious weather conditions such as rain, since images or videos captured by such vision systems in rainy days may undergo severe visual dilapidations. This can cause a glitch in those algorithms which are further used for object detection and tracking. Therefore, an ancillary video processing algorithm namely, video deraining is necessary prior to the implementation of object detection and tracking. This indicates the requirement of a time-consuming and complicated two-step process for the detection of moving objects in a rainy environment. This paper proposes an automated single-stage formulation for the conventional three-stage procedure such as rain steak removal, original data recovery and moving object detection as simultaneous operation in the tensor framework. The brilliance of this work is confined in the efficient formulation of an operator termed as Slice Rotational Total Variation (SRTV), to unify the different rain patterns into a common pattern so that any form of rain pattern can be effectively removed from the rainy data in a visually appealing manner by preserving the important details of the data. In this paper, SRTV regularization and tensor low-rank minimization are utilized for the effective deraining as well as efficient retrieval of clean background. Besides, $l_{1}$ norm and Tensor Total Variation (TTV) regularizers together with SRTV regularizer are employed for the faithful detection of derained moving objects. The experimental results show that the proposed method outperforms the state of the art methods in terms of deraining capability and accurate moving object detection.
Linear-time-varying feedforward control for position-dependent flexible structures

High performance motion systems in the semiconductor industry can achieve higher tracking accuracy when the structural dynamics of the system are taken into account. This work proposes an LTV feedforward control scheme which is based on the approximation of the system dynamics at low frequencies. The plant approximation contains both the low frequency contribution and resonance dynamics of the dominant modes. As an example of a flexible structure, the damped Euler-Bernoulli beam is investigated with considering the stable inversion of the approximated plant for feedforward control design. The results show a remarkable increase in positioning accuracy of the system being the consequence of the presence of feedforward control forces even when the reference setpoint accelerations equal zero.
An Improved Forward Regression Variable Selection Algorithm for High-Dimensional Linear Regression Models

Variable selection plays an important role in various fields, such as process modeling and process monitoring. It generally involves a large number of predictor variables, usually with the number of predictor variables <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> much larger than the sample size <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Therefore, how to filter useful variables and extract useful information in high-dimensional setup is a critical issue in the era of big data. This paper proposes an improved Forward Regression algorithm for variable selection under the high-dimensional setup. The proposed improved Forward Regression method demonstrates good performance in relevant-variable selection by introducing a predefined stopping rule. The stopping rule links the residual sum of squares to the noise ratio so that the relevant predictors can be distinguished from the random noises. Throughout theoretical analysis and simulations, it is confirmed that the improved Forward Regression algorithm can identify relevant predictors to ensure selection consistency in variable selection. Compared with the traditional Forward Regression method, the proposed Forward Regression algorithm can improve prediction accuracy and reduce computational cost by selecting only the relevant variables.
Low-Cost Microwave Components’ Fabrication in Hybrid Technology of Laminates and Additive Manufacturing on an Example of Miniaturized Suspended Directional Coupler

A low-cost approach for the fabrication of low-loss microwave components in hybrid technology of laminates and additive manufacturing is presented in this paper. A standard copper-cladded printed circuit board technology is used in combination with a basic stereolithographic 3D printing technology and industry-standard physical vapor deposition technology of aluminum for manufacturing one of the basic building blocks of microwave systems – a 3-dB coupled-line directional coupler in suspended technique operating at 2.0 GHz band. A thin laminate with the coupler traces’ mosaic is suspended inside a metal-coated 3D printed enclosure. The presented study explores the performance of the circuit in terms of total power loss vs. the properties of deposited metallization layer serving as a ground plane. It is shown that when the surface roughness of the 3D printed enclosure is low enough, which can be achieved with the use of a layer of lacquer primer before metal deposition, the conductor-related power losses are lowered to an acceptable level. An exemplary directional coupler was developed along with three variants of the enclosure and the reference one. The measurement results validate the potential of this hybrid technique to deliver fully valuable microwave components at a very low manufacturing cost.
A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware

The predominant Android operating system has captured enormous attention globally not only in smart phone industry but also for varied smart devices. The open architecture and application programming interfaces (APIs) while hosting third party applications has led to explosive growth of varied pervasive sophisticated Android malware production. In this study, we propose a robust, scalable and efficient Cuda-empowered multi-class malware detection technique leveraging Gated Recurrent Unit (GRU) to identify sophisticated Android malware. Experimentation of the proposed technique has been carried out using current state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Moreover, to rigorously evaluate the performance of the proposed technique, we have employed standard performance evaluation metrics (e.g., accuracy, precision, recall, F1-score etc.) and compared it with our constructed DL-driven architectures and benchmark algorithms. The GRU-based malware detection system outperforms with 98.99% detection accuracy for malware identification with a trivial trade off in speed efficiency.
E-complaints: A Semi-Structured E-forum at a University

Universities operate in a competitive market and the way a university deals with complaints will significantly contribute to its reputation and performances [1]. The University of Namibia (UNAM) has a complaint system known as a student-lecturer forum, attended in a physical venue. This forum allows UNAM management to respond to students’ complaints to improve services on campus. Currently, there is a low student and lecturer turn-up, resulting in a delay in feedback from the forum. Hence a semi-structured e-forum, an alternative to the physical forum was envisioned to accord all students an opportunity to express their complaints. The paper presents the development of the UNAM e-forum aimed to complement the existing physical forum. Furthermore, it presents and analyses results obtained from a usability study that found the developed e-forum easy to learn, efficient to use, easy to remember, has few errors and pleasing to the users based on its overall System Usability Scale (SUS) mean rating of 85.
Air Way Bacterial Colonization in COPD Patients at Stable and the Exacerbated States

Objectives: The aim of this study was to investigate the prevalence and load of airway bacteria in chronic obstructive pulmonary disease (COPD) patients and to detect the antibiotic susceptibility pattern of the isolated strains. Methods: The current study included 50 COPD patients. Paired sputum samples were taken from all patients, one at the exacerbation state and the other at the stable state. Identification of bacterial species was done using standard microbiological techniques and PCR. Results: There were 41 samples out of 50 (82%) positive for bacteria at exacerbation compared with 12 samples out of 50 (24%) positive for bacteria (either by culture or PCR or both) at stable state samples (p=0.001). The most commonly detected species was H. influenza and S. pneumoniae. There were a total of 27 bacterial species detected by PCR not detected by culture and only 4 bacterial species detected by culture not detected by PCR (p=0.001). Isolated strains show a high resistance pattern to the tested antibiotics and multi drug resistant strains were detected. Conclusions: This study has shown that airway bacterial prevalence and load increase at COPD exacerbations, and there is a significant relationship between lower airway bacterial colonization and COPD.
An Adaptive Strategy to Compensate Nonlinear Effects of Voltage Source Inverters Based on Artificial Neural Networks

In motor drives, distortions of phase voltages and currents are often caused by nonlinear effects of inverters such as dead time, turn-on delay, turn-off delay and voltage drop of power devices. To eliminate these distortions, the dead-time compensation voltage is usually investigated. Furthermore, the relationship between the dead-time compensation voltage and phase currents is nonlinear, which is related to not only the parameters mentioned above, but also the snubber and parasitic capacitance of inverters. A nonlinear function is constructed to model the nonlinear relationship in this paper. To identify the nonlinear function, a method based on artificial neural networks is proposed without inverter parameters. According to the criterion that the trajectory of voltage vector in $\alpha - \beta $ coordinate system is a circle, an adaptive law is constructed to modify the parameters of the nonlinear function. Therefore, the nonlinear dead-time compensation voltage model is obtained accurately, where the distortions of voltages and currents are reduced without any additional hardware. Applying this method to the current predictive control, the bandwidth of a current loop is increased by 500Hz. Effectiveness of the method is verified by experiments.
Time-Sensitive Multi-User Oriented Mobile Edge Computing Task Scheduling Algorithm

The Internet of Things and 5G technologies have made mobile cloud computing emerge and develop rapidly. However, the rapid development of mobile applications requires cloud computing to have lower network latency and computing energy consumption. Therefore, Mobile edge computing (MEC) which is a promising technology has received widespread attention. Nowadays, it is important to schedule resources reasonably due to the communication and computing resources shortage. In this paper, we propose a mobile edge computing task scheduling algorithm in a multi-user multi-tasking environment. The algorithm takes into account the time sensitivity of mobile applications. We optimize the traditional task scheduling algorithm with the user's minimum average execution time and minimum computing energy consumption as the goals. Extensive experiments are carried out on MATLAB. The result shows that our proposed algorithm can effectively reduce the time and energy cost of mobile edge computing.
A Prediction Model for Bus Arrival Time at Bus Stop Considering Signal Control and Surrounding Traffic Flow

The arrival time of bus at the stop is critical data in design of bus operational strategies. Especially for real-time control strategies (i.e., signal priority system), bus arrival time is usually predicted to assess the next bus operation condition in the future (i.e., bus bunching and reliability of transit service) and then can be used as a decision basis of current control actions. The signalized intersection and surrounding traffic are the key factors in bus travel time prediction, but most previous approaches focus on the impact of signal control on bus delay only. This paper proposes a prediction model for arrival time at bus stop under the influence of both upstream signalized intersection and surrounding traffic flow. Considering the affected range of signalized intersection and the dynamic variation of bus speed, bus running processes are evaluated separately (including processes from a given detection point to stop line, through intersection, and from intersection to bus stop). In the proposed models, bus speed is deduced according to the change of traffic density at different locations to reflect the micro-impact of surrounding traffic flow on bus operation. The observed bus travel time is collected from actual investigations at two bus stops incorporating signalized intersection in Jinan City and compared with the predicted travel time in the proposed model. The results show that the proposed model has a low mean relative error. In addition, through the analysis of the maximum relative error, it also can be seen that vehicle queuing with random arrival of vehicles at stop line makes a gap between the prediction and the actual situation, which will be the focus of further research.
Improved EL Model of Long Stator Linear Synchronous Motor Via Analytical Magnetic Coenergy Reconstruction Method

Electromagnetic-suspension (EMS)-type maglev, based on long stator linear synchronous motor (LSLSM), suffers from the flux linkage and thrust ripple. This is due to the non-sinusoidal airgap magnetic field distribution caused by the slot effect and end effect. In this article, it is intended to address this issue by establishing a nonlinear mathematical model of LSLSM for real-time simulation and controller design, considering the spatial harmonics and core saturation. The Euler–Lagrange (EL) model is improved by means of coenergy and then applied to obtain the general equations of LSLSM. At first, the variation in coenergy in LSLSM regarding the mover position and stator current are analyzed, in the presence of spatial harmonics and core saturation. Afterward, an analytical model of coenergy is constructed by two Fourier basis vectors of mover position and torque angle, and a coefficient matrix polynomial of stator current magnitude. Then, a new model of LSLSM is derived based on the coenergy model and EL method. The parameters of the model can be obtained from the numerical data of coenergy in all operation ranges via finite-element analysis (FEA). Finally, the new model of LSLSM and its propulsion system are integrated in MATLAB/Simulink setup. The simulation results show that the improved EL model can achieve satisfactory accuracy compared with the FEA results well, whereas the computational efficiency is improved largely.