Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful use of cidofovir to treat refractory cytomegalovirus infection in heart transplant recipient - a case report

Cytomegalovirus (CMV) infection poses a serious risk to patients after organ transplantation. It affects not only the early postoperative period, but through indirect effects contributes to shortening the long-term survival. CMV infection typically occurs within the period from the 2 nd to the 6 th month after organ transplantation. CMV infection that presents after 6 months is described as late CMV disease. Late onset CMV infection may present in an atypical way; therefore the diagnosis of CMV disease should be taken into consideration at any time after transplantation. In this article we present a clinical case of atypical CMV infection in a patient after orthotopic heart transplantation. It was unusual because of the timing and because of the lack of characteristic signs and symptoms. We also describe our experiences with cidofovir, a second-line antiviral drug, which is rarely used in patients after solid organ transplantation.
Online Trajectory Generation for Differential Drive Mobile Robots Using Multi-Dimensional Rate Limiters

This research considers rate limiters in a novel way to generate parameterized trajectories for differential drive mobile robots such that the wheel velocity and acceleration constraints are fulfilled. In order to obtain feasible wheel velocity reference signals for following a geometrical path, the classical second-order rate limiter is generalized to a two-dimensional second-order rate limiter. The target points are mapped onto a desired direction vector, which is then mapped to desired linear and angular velocities. The Kalai–Smorodinsky bargaining solution inspires the proposed mapping to a feasible solution that addresses the tradeoff between linear and angular motion while yielding a short running time.
Synchronous Distributed Greedy Weighted Graph Matching Algorithms For Wireless Sensor Networks

Wireless Sensor Networks (WSN) consist of devices that can communicate with each other without using any fixed infrastructure. These devices can gather necessary information from the environment via their sensors and share collected data with each others. WSNs can be modelled with graphs $(G(V,E))$ where $V$ is the set of vertices (nodes) and $E$ is the set of edges. Graph theoretical structures such as graph matching can be used to solve various problems such as backup assignment in WSNs. With this aim, we first design a synchronous distributed weighted graph matching algorithm based on Hoepman's algorithm. After this the synchronous algorithm is improved by using overhearing method to design ICO algorithm. Proposed ICO algorithm aims to decrease the transmitted message count for graph matching operation by applying in-network processing. These algorithms are tested on various settings having different node counts and degrees in TOSSIM simulator by comparing with each other. The results of these extensive tests reveal us that ICO is more effective in terms of energy consumption and transmitted bytes.
Food Safety Knowledge, Attitude and Practice of Food Handlers and Microbiological and Chemical Food Quality Assessment of Food for Making Merit for Monks in Ratchathewi District, Bangkok

Objective: This study was a survey to determine the food safety situation among food shops and food stalls which sell food to be given to monks for the purposes of making merit and to assess the relationship between knowledge, attitude and practice of food safety in Ratchathewi District, Bangkok. Materials and Methods: Food shops and food stalls, ready-to-eat foods, and food handlers were selected with the criteria that they were on the way of the monks path way to receive merit food in the morning. Data were collected by checklist, questionnaire and test kits, from April to June 2010. The checklist was modified inspection form from the Department of Health, Ministry of Public Health. Knowledge, attitude and practice (KAP) were tested by using KAP questionnaires. Data were analyzed by using frequency, mean, standard deviation, Chi-square and Fisher exact test Results: There were 13 food shops and 55 food stalls that were assessed for food sanitation. It was found that only 16.4% of the stalls passed the standard criteria. Ready-to-eat foods were not reheated and did not have a cover. The test results for microbiological contamination indicated total bacteria and total coliform at 15.2% and 41.3%, respectively. For the chemical contamination results, borax contamination was 3.3%, salicylic acid 14.3% and organophosphate 3.3%, while formalin and sodium hydrosulfite contamination were not found. Only 13.0% of the food handlers had good knowledge, 18.5% had a good attitude and 15.2% had good practice. Conclusion: Food safety in food shops and food stalls need continuous monitoring. Food handlers should receive suitable training in the basic principles of food safety as well as on rules of personal hygiene and approved practices in food handling.
Forensic Recognition of Narrowband AMR Signals

A vast number of audio files are submitted to forensic analysis every year, originating from both lawfully intercepted signals over communication networks and user recording devices. The forensic recognition of such signals in uncompressed format revolves around the detection of single and double adaptive multi-rate (AMR) compression traces, as well as detection of modification traces of AMR signals. Given that the high-bitrate AMR compression traces easily eschew the human sensory scrutiny, there is a large interest in exact interpretation of such traces. In this paper, we propose a method to detect and recognize different narrowband AMR single or double compression modes, using a deep neural network.
Source-side DDoS Detection on IoT-enabled 5G Environments

This paper introduces a novel approach for detecting the participation of a protected end-user or IoT device in Distributed Denial of Service attacks. With this purpose, traffic flows are inspected at source-side looking for discordant behaviors. In contrast to most previous solutions, the proposal assumes the non-stationarity and heterogeneity of the emerging communication networks, which demands a more complex analytical environment. This has led to delegate the analytic tasks to a dedicated data processing layer, where advanced feature extraction, pattern recognition, prediction and adaptive thresholding capabilities operate. The proposal relies on a sophisticated knowledge acquisition architecture enabled to operate on 5G environments, in this way supporting the leading-edge technologies it implements and being compatible with defensive self-organizing schemes. The effectiveness of the proposal has been proven by analyzing traffic from 62 network devices of different nature with different behavioral profiles, being able to accurately distinguish their normal activities from malicious traffic injections.
Scaled Conjugate Gradient Neural Network for Optimizing Indoor Positioning System

In this paper, several indoor positioning systems are reviewed and a deep neural network (DNN) algorithm based on Scaled Conjugate Gradient (SCG) algorithm is proposed. In the proposed indoor positioning system, Received Signal Strength (RSS) is used as a fingerprint to identify the indoor location in terms of Building and Floor. The performance of the system is evaluated and compared against other machine learning based positioning systems. The accuracy of the proposed DNN is 99% when tested using a standard dataset.
Finding Common Image Semantics for Urban Perceived Safety Based on Pairwise Comparisons

What influences people’s perception of safety in an urban environment? Does everyone perceive safety the same way or do different people look for different contents in an image, safety-wise? We present a user analysis on a crowd-sourced dataset that contains pairwise comparisons regarding the perceived safety of street imagery from different municipalities in the greater Lisbon area, Portugal. We use state-of-the-art semantic segmentation to extract the contents of images and cluster different people according to what they perceive as safe. Then, we study semantic classes and analyze clusters of users for semantic elements appearing in images classified as safer (or more dangerous). The results show that clusters share a lot of similarities. Our analysis evidences that, for users with more pairwise comparisons, there is only one group, while spurious groupings appear when users contribute less. This result emphasizes that a pairwise image comparison dataset potentiates agreement of users in perceptual tasks, for moderate comparison data size.
A review of various modern strategies for mitigation of cyber attacks in smart grids

Smart grids are likely to modernize the conventional power grids by commencing new sets of services and technologies which will enable the power system to be more sustainable, secure, cooperative and efficient. It is expected that the integration of more advanced communication and computing technologies will enhance the reliability and efficiency of the smart grids and eventually the overall power network. Unfortunately, one of the major issues of the smart grid is susceptibility to the cyber-attacks that eventually give rise to various problems related to control, communication, social and economic in the operations of the power network. The millions of devices in the power network are interconnected through a communication channel; thus, cyber-attack has a deep impact on the reliability of the system. In this paper, comprehensive analysis of the cyber issues of the smart grids will be discussed along with giving focus on network vulnerabilities, security requirements, techniques to detect the attack, countermeasures for attack and secure architectures and protocols of communication in smart grids. The primary purpose of this research is to develop the insight knowledge of the network vulnerabilities and possible solutions to them in smart grids.
70–90-GHz Self-Tuned Polyphase Filter for Wideband I/Q LO Generation in a 55-nm BiCMOS Transmitter

A low-power quadrature local oscillator (LO) generation scheme embedded in a direct-conversion E-band transmitter (TX) is presented. A single-stage polyphase filter is closed in a loop and continuously tuned by means of a quadrature phase detector to maintain precise I/Q signals independently from LO frequency and component variations, thus making the solution wideband and robust against process, supply, and temperature variations. The analog phase detector, realized with fully balanced analog multipliers, is critical to reach high accuracy. Careful circuit analysis and simple design solutions are proposed to avoid systematic phase errors and to maintain high detector gain despite the high operating frequency. The TX, realized in BiCMOS 55-nm technology, delivers a maximum linear output power of 20.5 dBm at 80 GHz with 14% power efficiency. The LO buffers consumes 115 mW from 2.3-V supply while the LO calibration circuits need 16 mW only and allows to maintain a remarkable image rejection ratio of 40 dB or better over 70–90 GHz.
Stock Price Prediction Using Machine Learning Techniques

In today's economy, there is a profound impact of the stock market or equity market. Prediction of stock prices is extremely complex, chaotic, and the presence of a dynamic environment makes it a great challenge. Behavioural finance suggests that decision-making process of investors is to a very great extent influenced by the emotions and sentiments in response to a particular news. Thus, to support the decisions of the investors, we have presented an approach combining two distinct fields for analysis of stock exchange. The system combines price prediction based on historical and real-time data along with news analysis. LSTM (Long Short-Term Memory) is used for predicting. It takes the latest trading information and analysis indicators as its input. For news analysis, only the relevant and live news is collected from a large set of business new. The filtered news is analyzed to predict sentiment around companies. The results of both analyses are integrated together to get a response which gives a recommendation for future increases.
Forecasting Domestic Shipping Demand of Cement: Comparison of SARIMAX, ANN and Hybrid SARIMAX-ANN

Accurate forecasting of shipping demand for cement is valuable for the ensure of proper supply and demand match. Due to the perishability nature of the shipping industry, accuracy performances of the models in the industry is particularly important. Therefore, supply and demand in the industry should match. This paper compares three methods, Seasonal Autoregressive Integrated Moving Average with Exogenous Variable (SARIMAX), Artificial Neural Network-Multilayer Perceptron (MLP), and SARIMAX-MLP hybrid model for domestic shipping demand of cement loaded at ports of Turkey. The hybrid model forecasting of the shipping demand of cement appears to be more applicable than that of the single SARIMAX. The results indicate that the prediction accuracy of SARIMAX model is higher than the MLP model and that the hybrid model is the most satisfactory of the three models.
A Demand Response Strategy in High Photovoltaic Penetration Power Systems Considering the Thermal Ramp Rate Limitation

In high photovoltaic (PV) penetration power systems, thermal units may be unable to cope with the large-scale power imbalance caused by PV reduction due to their ramp rate limitation. This paper formulates a demand response (DR) strategy to compensate for the timing imbalance between load demand and generation while avoiding high scheduling and operational expenses. First, the concept of the equivalent slope (ES) is proposed to measure the ramping speed of the net load curve and accurately quantify the DR aggregate power. Then, a DR strategy is proposed based on the ES and the thermal ramp rate limitation, in which the dynamic factor adjustment (DFA) increases the aggregate power by directly controlling the DR strategies, and the leading adjustment (LA) transfers part of the peak demand to other times when PV generation is abundant. Finally, simulation results verify the validity of the proposed strategy in maintaining the power balance while ensuring the comfort level of consumers.
Message-oriented Middleware: A Review

Message-oriented middleware (MOM) allows applications to communicate and exchange data by sending and receiving messages. With advantages of asynchronous and multi-point transmission, loosely coupling between participants etc. MOM is widely recognized as the most promising solution for communication between heterogeneous systems. This paper firstly reviews current research in MOM, then introduces main features and architecture of MOM, further elaborates on a few main functional modules, including message, queue, transmission mode and security, and finally carries out a comparative analysis of several commonly used MOM products. According to high-throughput and highly reliable data service needs for IoT and smart traffic etc. In this context, this paper analyzes opportunities and challenges to MOM when facing with new requirements and adapting to a more complicated environment.
Automatic Sprinkler System for Water Efficiency Based on LoRa Network

The agricultural sector is an important sector for economic growth in Indonesia. This was indicated by the data from the Indonesian Ministry of Agriculture that stated the Gross Domestic Product (GDP) from 2013 to 2017 in the agricultural sector grew by 9.90%. Based on this data, a research that could help farmers in terms of improving the quality of their crops by providing plants with sufficient water intake was made. With this system, farmers will be able to monitor soil moisture using a laptop or a phone. In this research we evaluate the QoS of the communication between LoRa device. This system works by using a LoRa device operating at 433 MHz paired on 2 Nodes and 1 Gateway. Each node will be given 3 soil moisture sensors that will monitor soil moisture. Then, the sensor data will be sent to the gateway via a LoRa device. In the controlling mode, LoRa average delay and data loss are 196.96 ms (node1), 207.39 ms (node2) and 4.45% (node1), 10.03% (node2). That monitoring value is good value based on ETSI standard. But, in the monitoring mode, LoRa average delay are up to 10 s and average data loss are 6.67% from node1 and 13.34 % from node2.
Joint Precoding Optimization for Secure Transmission in Downlink MISO-NOMA Networks

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. In the proposed scheme, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. Simulation results are presented to demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively.
Deep Inverse Reinforcement Learning for Sepsis Treatment

Sepsis is a leading cause of mortality in hospitals, but its optimal treatment strategy still remains unclear. Recent years have witnessed several successful applications of Reinforcement Learning (RL) approaches in sepsis treatment, achieving far more efficient strategies than those by clinicians. To ensure such applications, an explicit reward function encoding medical domain knowledge should be specified beforehand to indicate the goal of learning. However, due to the paucity of clear understanding of sepsis itself, there is still considerable inconsistency in the formulation of reward functions for sepsis treatment. In this poster, we address the reward learning problem in RL for treatment of sepsis, which has been largely neglected by previous studies. A deep inverse RL with Mini-Tree (DIRL-MT) model is proposed to infer the best reward functions from a set of presumably optimal treatment trajectories using retrospective real medical data. In the model, the MT component learns the factors that are most important in influencing the mortality during sepsis treatment, while the DIRL component infers the complete reward function in terms of weights of those factors. Our work shows that PaO2 and PT can play a vital role and should be paid more attention in the design of more efficient treatment strategies for sepsis in the future.
Au Nanoparticles-Decorated Ultraviolet Photodetector on Transparent Mica Substrate

Hydrothermally processed ZnO nanorods decorated by Au nanoparticles have been demonstrated to exhibit extraordinary optoelectronic properties. With the introduction of Au nanoparticles modification, the ultraviolet responsivity has been greatly enhanced by approximately 200 times, up to 1244 A/W, compared with that of pristine ZnO. This work will be promising for designing high responsivity ZnO photodetector.
Deep Learning Based Localization of Near-Field Sources with Exact Spherical Wavefront Model

Source localization for near-field narrowband signal is an important topic in array signal processing. Deep neural network (DNN) based methods are data-driven and free of pre-assumptions about data model and are expected to learn the intricate nonlinear structure in large data sets. This paper proposes a framework of DNN where a regression layer is utilized to address the problem of near-field source localization. Unlike previous studies in which DOA estimation is modeled as a classification problem and have a relatively low resolution, we exploit a regression model and aim to improve the estimation accuracy. In the training stage, we propose a novel form of feature representation to take full advantage of the convolution networks. In addition, the architecture of deep neural networks is well designed taking in to consideration the trade-off between the expression ability and under-training risks. The simulation results show that the proposed approach has a rather high validation accuracy with a high resolution, and also outperforms some conventional methods in adverse environments such as low signal to noise ratio (SNR) or small number of snapshots.
Double Q-learning for Demand Response of an Electric Water Heater

Increasing the share of renewable energy sources in the electrical grid requires new balancing means. Therefore, activating flexibility at the demand side with Demand Response (DR) programs has gained significant attention in recent years. In this regard, Thermostatically Controlled Loads (TCLs) are interesting appliances, due to their inherent storage capacity. In this paper, double Q-learning is applied to an Electric Water Heater (EWH) in two different DR settings. The first assumes the EWH's energy consumption is billed according to a Time-of-Use (ToU) price. The second scenario assumes households have a power consumption limit. In both cases, the control policy learned with double Q-learning is compared to a thermostat controller. With ToU pricing the EWH consumes 35% less energy during peak hours. When the EWH is charged according to a capacity tariff Q-learning reduces power consumption limit surpasses by 31%.
A Non-intrusive Opto-mechanical Technique for the Measurement of Liquid Level

In this paper, we present an opto-mechanical technique suitable for measurement of liquid level with a very high resolution. In the propose technique, the change in the liquid level in the cylindrical container, attached to the bottom of a vertically suspended helical extension spring, causes corresponding change in the elongation length of the spring. The change in the elongation length eventually changes the distance between a diverging light source and a photoresistor. The repeated experimental observations show that the proposed technique has good resolution, very small non-linearity, high repeatability and negligible hysteresis error.
A Delay-Based LO Phase-Shifting Generator for a 2-5GHz Beamsteering Receiver in 28nm CMOS

This paper proposes a wideband 2-5GHz LO phase-shifting generator based on two digitally controlled delay lines. The concept is verified on a two-channel beamsteering direct-conversion receiver prototype implemented in 28nm CMOS. The novel generator provides both tunable phase-shifting and generation of I/Q components, achieving picosecond time resolution. The generator consumes 4.5-11.2mW and occupies 0.021mm2.
Acute coronary syndrome in patients with concomitant neoplastic disease and chemotherapy. Is clinical distinctness possible

We present 2 cases of acute coronary syndrome in patients with concomitant neoplastic disease during chemotherapy. In both patients, we found unusual angiographic image which showed that despite the presence of very large thrombi, there were no or hardly any atherosclerotic lesions in the vessel occlusion site. Aspiration thrombectomy was successfully performed in both cases to restore coronary flow and in the second patient, two bare metal stents were also implanted. After the patients were discharged from hospital, we decided to add enoxaparin to the routine dual antiplatelet therapy of both of them. So far, no guidelines have been developed on how to manage patients with acute coronary syndrome and concomitant neoplastic diseases while on chemotherapy.