Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Glyphaea brevis twig extract on nucleus, tight junctions and expression of inhibin-β, stem cell factor, and androgen binding protein in TM4 Sertoli cells

Objective: To examine the influence of Glyphaea (G.) brevis twig extract on the mitochondrial dehydrogenase activity, integrity of the tight junctions between adjacent cells, mitochondria, apoptosis, nucleus and expression of inhibin-ß, stem cell factor, and androgen binding protein in TM4 Sertoli cells. Methods: TM4 cell line was used in this study as it exhibited properties similar to the Sertoli cells. TM4 Sertoli cells were exposed to G. brevis twig extract (0.1, 1.0, 10.0, 100.0, or 1 000.0μg/mL) for 24, 48 and 72 h. Parameters studied included cell viability [3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay], mitochondrial membrane potential (tetra methyl rhodamine ethyl ester dye), transepithelial electrical resistance, apoptosis (Annexin V Alexa Fluor®488/propidium iodide assay) and mRNA expression (quantitative reverse transcription polymerase chain reaction). Results: G. brevis twig extract had no cytotoxic impact on cell viability, thus, considerably increasing the activity of mitochondrial dehydrogenase enzyme after 24 and 72 h exposure. Transepithelial electrical resistance values revealed substantial (P<0.05) rise in treated groups, especially after 72 h of treatment. Moreover, there was a significant decrease in mitochondrial depolarization of TM4 Sertoli cells exposed to G. brevis twig extract when compared to controls. In addition, G. brevis twig extract significantly reduced necrosis and apoptosis of TM4 Sertoli cells when compared to control. Nevertheless, fluorescence microscopy revealed that the nuclei were egg-shaped and marked uniformly with consistent cell shape at the middle of the TM4 Sertoli cells. Significant stimulatory effects were observed on mRNA levels of inhibin-β , androgen binding protein and stem cell factor. Conclusions: G. brevis twig extract may increase the secretory roles of TM4 Sertoli cells, cells proliferation, as well as cell-cell tight junction integrity. Thus, G. brevis twig may enhance spermatogenesis.
BIER Use Cases

Bit Index Explicit Replication (BIER) is an architecture that provides optimal multicast forwarding through a "BIER domain" without requiring intermediate routers to maintain any multicast related per- flow state. BIER also does not require any explicit tree-building protocol for its operation. A multicast data packet enters a BIER domain at a "Bit-Forwarding Ingress Router" (BFIR), and leaves the BIER domain at one or more "Bit-Forwarding Egress Routers" (BFERs). The BFIR router adds a BIER header to the packet. The BIER header contains a bit- string in which each bit represents exactly one BFER to forward the packet to. The set of BFERs to which the multicast packet needs to be forwarded is expressed by setting the bits that correspond to those routers in the BIER header. This document describes some of the use- cases for BIER.
Type-Spread Molecular Communications: Principles and Inter-Symbol Interference Mitigation

Diffusion-based Molecular Communication (DMC) is a feasible method for information transmission in some nano-networks operated in gas or liquid environments. In this paper, we first propose an information modulation scheme for DMC, which is referred to as the Type-Spread Molecular Shift Keying (TS-MoSK). Considering that DMC signals usually experience severe inter-symbol interference (ISI), our TS-MoSK is characterized by introducing extra types of molecules for ISI mitigation (ISIM). Furthermore, we introduce two ISIM methods to the TS-MoSK modulated DMC systems, which are the active ISIM and passive ISIM. We detail their operation principles, and investigate as well as compare their achievable performance. Our studies show that, aided by the extra types of molecules, TS-MoSK outperforms the MoSK without spreading. Both ISIM approaches are effective for further improving the performance of TS-MoSK.
Diversity Framed Slotted Aloha with Interference Cancellation for Maritime Satellite Communications

Providing reliable and efficient connectivity for maritime systems is a key objective to enable new services and to offer anytime-anywhere communication solutions to vessels operating in remote areas (e.g. oceans). Such a task has recently become quite compelling because of the increasing maritime traffic, which should be supported by an information distribution infrastructure in order to guarantee efficient data communication among vessels and control centres on land. To this end, satellites are the perfect candidates for achieving such an objective. This paper focuses on the case of messaging from moving vessels to fixed control centres on land by means of advanced channel random access schemes exploiting time diversity. In more detail, the paper extends an existing theoretical framework to evaluate the packet loss probability in Framed Slotted ALOHA systems by delving into the probability distribution of both colliding users within a frame and their replicas in time slots. The theoretical framework is validated through simulations campaigns, showing a good match between analytical and simulated results.
A General Rate Splitting Scheme for Hybrid Precoding in mmWave Systems

To reduce the high hardware cost in millimeter wave (mmWave) systems, hybrid analog/digital precoding structure is employed. Combination of hybrid precoding and massive-element antennas enables mmWave communication to provide a significant throughput gain. However, multiuser interference remains a bottleneck for performance improvement in mmWave systems. In this paper, in order to tackle the multiuser interference, we first introduce a general rate splitting (RS) strategy for mmWave downlink transmission. Then, we analyze the achievable rate regions under joint decoding and successive decoding schemes, respectively. After that, a novel hybrid precoding scheme that aims at maximizing the achievable sum rate under successive decoding is investigated. The proposed algorithm formulates the precoder design problem into a standard semidefinite programming (SDP), which can be solved iteratively with sub-gradient search. Finally, simulation results show that the proposed RS scheme and the hybrid precoding design bring considerable improvements over existing algorithms in rich scattering environments.
SmartWriting: Pen-Holding Gesture Recognition with Smartwatch

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, we present the design, implementation and evaluation of SmartWriting, which utilizes the smartwatch for pen-holding gesture recognition for both Chinese and English writing. SmartWriting can automatically identify whether the user is writing Chinese or English, based on which two classifiers are built to detect pen-holding gestures by strokes in Chinese characters or English letters. In particular, we propose to leverage the combined signal of one vertical stroke and one horizontal stroke for efficient pen-holding gesture identification in Chinese writing, and we are able to infer pen-holding gesture according to any letter in English writing. We build a fully functional prototype of SmartWriting, and extensive experiments confirm its effectiveness and robustness. The detection accuracy is 9. % and 9 % for Chinese and English respectively. SmartWriting provides a natural, convenient and inexpensive way to improve users' writing habits.
Cooperative Beamforming for Multi-Cell Full Dimensional Massive MIMO Networks

In this paper, we study the cooperative beamforming schemes for multi-cell multi-user full-dimensional (FD) massive multiple-input multiple-out (MIMO) networks. In the considered network, base stations (BSs) work together to direct their beams to user equipments (UEs) such that inter-cell interference is minimized and each UE is assigned a beam from one BS by user association. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and the user association factors (UAFs), which is further transformed into an optimization on UAFs only by maximizing a lower bound of the signal-to-leakage-and-noise ratio (SLNR). To solve the optimization on UAFs, we propose a belief propagation (BP) based algorithm to obtain the UAFs at the UE level in a parallel manner. Simulation results show that the proposed cooperative beamforming method significantly outperforms the benchmarks in the literature.
Physical-Layer Analysis of IEEE 802.11ay Based on a Fading Channel Model from Mobile Measurements

In this paper, we analyze the physical layer of IEEE 802.11ay, the new standard for next-generation Wi-Fi operating in the unlicensed 60-GHz band, expected for release in 2019. Realistic physical-layer analysis is based on a multipath fading channel model reduced from mobile measurements, however most millimeter-wave channel sounders to date can only characterize static environments because sweeping the angular space of a channel typically takes hours due to the slow mechanical rotation of single directional antennas. Our state-of-the-art 60-GHz channel sounder, rather, employs arrays of electronically switched antennas so that a full channel sweep can be taken in fractions of a millisecond. This enabled us to conduct an extensive channel measurement campaign in an indoor environment with pedestrian motion. The fading model reduced from the measurements was used to feed our 802.11ay transceiver implementation, an amendment to its 802.11ad predecessor incorporating 8×8 single-user MIMO. The analysis resulted in bit-error-rate curves for various transceiver parameters, namely phased-array-antenna dimension, number of RF chains, and modulation and coding scheme.
Power-Delay Trade-off for Heterogenous Cloud Enabled Multi-UAV Systems

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. However, some of them are incompetent in tackling with computation-intensive tasks due to limited processing capability and battery life. In this paper, we combine the mobile edge computing and traditional cloud computing techniques for offloading the tasks from multi-UAV systems. Specifically, we jointly optimize the task scheduling and resource allocation in the heterogeneous cloud architecture, where we strike a power-delay trade-off of the system relying on the queue theory and Lyapunov optimization, followed by its optimal strategy analysis in each time slot. Moreover, we conceive an iterative algorithm with a closed-form solution at each iteration round in order to reduce the computational complexity. Finally, numerical results demonstrate both the feasibility and effectiveness of our proposed scheme. This paper validates that the heterogeneous cloud structure can be the beneficial for improving quality-of-service performance of multi-UAV systems.
Belief Propagation Based Distributed Content Delivery Scheme in Caching-Enabled D2D Networks

Content delivery in wireless caching networks via device-to-device (D2D) communications can effectively offload the traffic burden and reduce the delivery delay and the energy consumption. This paper develops a distributed content delivery scheme to leverage D2D communications for content sharing by using belief propagation (BP) approach. With the target of minimizing average energy consumption for content delivery, the optimization of content delivery is formulated as a fractional programming which is NP-hard. By jointly considering channel conditions, cache status, and asynchronous demands, a distributed energy-balancing algorithm based on BP framework is proposed to facilitate the content requesters (CRs) to select the best content helpers (CHs). Furthermore, an implementation method is designed to motivate the practical deployment of the distributed scheme in caching-enabled D2D networks.
A Cloud-Fog Based Architecture for IoT Applications Dedicated to Healthcare

Cloud-Fog computing architectures are new paradigms designed to add advantages to the existing architectures for Internet of Things (IoT). This paper proposes an inter-operable cloud-fog based IoT architecture for healthcare. It describes its architecture, environmental context, and user context. The proposed architecture supports the mobility of the patients as well as the diversity of the medical cases. The features of the individual modules are discussed and the interconnection between the different underlying modules and tiers is explained. Task scheduling and allocation approach is proposed to effectively balance healthcare tasks distribution. The performance evaluation of the proposed approach is presented with different number of tasks and cloud nodes. The simulation results show acceptable results in terms of miss ratio, cost, and latency.
Dynamic Program File Placement Strategies for Machine-to-Machine Service Network Platform

The machine-to-machine (M2M) service network platform that accommodates and controls various types of Internet of Things (IoT) devices has been presented. This paper investigates the program file placement strategies for the M2M service network platform which achieve low blocking ratio of new task requests and accommodate as many tasks as possible in the dynamic scenario. We present four strategies to arrange the program file placement, which differ in the objective function, the computation method, and the timing of rearrangement of whole program file placement. The simulation results show that a strategy based on solving a mixed-integer linear programming (MILP) model achieves the lowest blocking ratio and the highest number of completed tasks within a certain time period. In the case that the MILP-based strategies are intractable due to the computation time, a heuristic algorithm-based strategy which basically determines the program file placement for only a newly requested task achieves low blocking ratio.
A Survey on Data Mining Methods and Techniques for Diabetes Mellitus

Detection of knowledge patterns in clinical data through data mining. Data mining algorithms can be trained from past examples in clinical data and model the frequent times non-linear relationships between the independent and dependent variables. The consequential model represents formal knowledge, which can often make available a good analytic judgment. Classification is the generally used technique in medical data mining. This paper presents results comparison of five supervised data mining algorithms. We evaluate the performance for C4.5, SVM, k-NN, Naive Bayes and Apriori then Comparison a performance of data mining algorithms based on accuracy. The study describes algorithmic discussion of the dataset for the disease acquired from on line repository of large datasets.
Pseudo Whitening of Intercarrier Interference in the Asynchronous URLLC Downlink

Applying the concept of virtual cell together with open-loop communications and proactive network association has recently been shown to achieve ultra-reliable and low-latency communication (uRLLC) in vehicular networks. Nevertheless, losing perfect synchronization creates difficulty in tackling multiple access interference (MAI). Multiple carrier frequency offsets (CFOs) due to different oscillators at different access points (APs) incur serious intercarrier interference (ICI) to complicate downlink MAI, from which a more challenging scenario emerges. Asynchronous multiuser detection (MUD) with ICI-Whitening leads to satisfactory performance, but the whitening scheme needs the covariance matrix of ICI that is practically hard to obtain for downlink receivers. We therefore propose Pseudo-ICI-Whitening, which does not rely on the estimation of ICI covariance, is suitable for asynchronous downlink. In terms of post-processing signal-to-interference-plus-noise ratio (SINR) and bit-error rate (BER), our proposed mechanism can approach ICI-Whitening as a practical receiver design.
Deep Reinforcement Learning for the Coexistence of LAA-LTE and WiFi Systems

As a promising technique to handle the conflict between the explosive mobile traffic and scare spectrum resource, license-assisted access (LAA) has been proposed to operate the LTE network on the unlicensed band. This paper considers the LAA-LTE system coexisting with an unsaturated WiFi system. Specifically, deep reinforcement learning (DRL) is adopted to enable the LAA-LTE system to learn the traffic pattern of the WiFi system and adaptively optimize its transmission time in each frame. Different from conventional coexistence schemes, which require massive signaling exchanges between the two systems to achieve fairness, the proposed DRL-based algorithm can maximize the spectrum usage while protecting the WiFi system without such signaling requirements. Simulation results demonstrate that the proposed scheme can achieve almost the same LAA-LTE throughput and protection to the WiFi system of the genie-aided exhaustive search algorithm, which has high complexity and requires to know the WiFi information perfectly.
A D2D-Assisted MEC Computation Offloading in the Blockchain-Based Framework for UDNs

The past few years have witnessed the explosive growth of mobile user equipment (UEs) and the popularity of computation-intensive applications, leading to a mobile edge computing paradigm for Ultra-dense wireless networks (UDNs). Since numerous UEs need to offload a large amount of computation tasks to edge servers/UEs, it is very challenging to coordinate computation offloading among UEs and edge servers in UDNs. To address this issue, we propose a decentralized computation offloading coordination platform that is based on the blockchain. Specifically, we first establish a blockchain platform for announcing computation offloading requests and coordinate computation offloading among UEs and edge servers. Then, we develop a modified GS-based user matching algorithm to find the matching relationship between offloading requester's computation tasks and the edge server/UEs. In particular, user matching is based on task execution time and energy consumption. We conduct simulations and provide extensive simulation results to show the significant performance improvement achieved by the proposed offloading scheme.
Prediction-Based Switch Migration Scheduling for SDN Load Balancing

Distributed architectures of the SDN control plane require a careful design for balancing the load among controllers. Solutions proposed for SDN load balancing usually use switch migration operations. However, an efficient switch migration means triggering the operation at the right moment, and judiciously choosing the migrated switch and the destination controller. Here, we propose a switch migration scheduling algorithm to improve the migration efficiency, and ensure load balancing between controllers. Our algorithm uses a multi-step ARIMA forecasting model to predict the long-term controllers load. When an overload is predicted, a switch migration operation is scheduled in advance. After validating the accuracy of the ARIMA forecasting model, we evaluated the performance of the algorithm by analyzing the response time of controllers. Numerical results confirm the performance of the proposed algorithm.
Latency in Downlink Cellular Networks with Random Scheduling

The characterization of the latency is essential for operation of 5G and beyond 5G cellular networks. This paper develops a spatiotemporal model to characterize latency in the downlink of large-scale cellular networks with random scheduling. In particular, the framework integrates stochastic geometry and queueing theory, to capture the interwoven interactions between the microscopic behavior of each wireless link and the macroscopic mutual interference between all links in the network. The developed framework enables a traffic-aware characterization of the transmission success probability and of the latency across the network.
Nash Bargaining Based Economic Analysis of Cognitive Cellular Networks

In this paper, we consider an opportunistic cognitive radio (CR) network comprising of a primary base station (PBS) and a secondary base station (SBS), both associated with multiple users. SBS obtains the right to use the licensed spectrum of PBS by paying a remuneration. Moreover, both PBS and SBS provide service to their corresponding users in exchange of an economic price. PBS, being a profit maximizer, optimally balances between the revenue directly coming from its own users and the monetary reward provided by SBS. On the other hand, SBS must maintain a positive gain to support its infrastructure. Thus, a two-person non-cooperative bargaining game is formulated where both PBS and SBS concur at the values of two parameters, i.e., the activity factor of PBS and the payment made by SBS for opportunistically accessing the licensed spectrum. PBS has knowledge of detection and false-alarm probabilities of the spectrum sensing by SBS and hence can infer how the interference created by missed detection can degrade its achievable capacity. In this framework, we utilize Nash solution to determine a mutually beneficial outcome of the bargaining process. The solution leads to a mathematical relation dictating the viability of the network in the long-run. This result holds for arbitrary continuous user demand functions. For linear user demand, we obtain closed-form expression of the bargaining solution that enables us to analyze the influence of different key system parameters on the economic outcomes.
Statistics of Terrestrial Fixed Service Interference in the Aeronautical SATCOM Channel

The increasing demand for in-flight connectivity via satellite communications and the use of unmanned aircraft beyond line of sight pose the requirement for additional allocations in the non-exclusive Ka band satellite spectrum. This is to be decided by the International Telecommunications Union at the World Radio Conference 2019. With the terrestrial fixed service being the dominant interference source of the incumbent radio services in the Ka band satellite downlink, we simulate radio frequency interference due to emissions from multiple sources into an aeronautical receiver. Based on a specified reference scenario, we provide first order interference statistics for different flight altitudes. To this end, we approximate the sample probability density distributions of exhaustive interference scenario simulations using a log skew normal distribution to provide an analytical expression of probabilities for frequency coordination and system design.
Dynamic Slide Window-Based Feature Scoring and Extraction for On-Line Rumor Detection with CNN

User-generated content on social media platforms are major forces in the shaping and diffusion of popular topics. Online rumors among regular user-generated content have increased considerablely, and stimulate the diffusion of fake popular topics in the social network or even panic among people. The existing researches on rumor detection, such as the detection mechanisms based on Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM), suffer from their rough feature extraction processes, and thus need to be improved in terms of contextual feature extraction. This paper proposes a dynamic slide-window based text feature scoring and extraction mechanism, which facilitates accurate semantic structure representation. In addition, we design an effective rumor detection scheme by incorporating the proposed feature scoring and extraction with CNN. Extensive experiments on real-world datasets demonstrate that the proposed feature scoring and extraction mechanism can extract the important text features by considering their roles in on-line texts and the relations of these features, and thus the detection scheme can distinguish rumors from regular messages more accurately.