Miracle-dz/UniFAR-FTUs huggingface dataset:
AB1318 Practical aspects of biological-drug monitoring in rheumatoid arthritis and spondyloarthritis

Objectives To prepare recommendations on practical aspects of biological-drug monitoring that may be useful for rheumatologist Methods A systematic review of the literature was carried out to determine in which patients and in what clinical circumstances may be most beneficial to monitor the levels of biological drugs and their antibodies in patients suffering Rheumatoid Arthritis (RA) and/or Spondyloarthritis (SpA). With the results of the review, a group of experts produced a series of clinical questions, to be answered through the available scientific evidence, and then, subsequently, to make the recommendations and clinical algorithms for instructing the decision-making process. Results Results from the systematic review showed that the biological-drug monitoring might be especially useful in two clinical situations; in case of a treatment failure (primary or secondary) and in sustained remission (see figure 1). It was also reviewed which laboratory technique might be better and the optimal time for blood withdrawal. Specific recommendations are given on the interpretation of drug levels and on factors that should be taken into account are given (i.e. body mass index and concomitant medication). Abstract AB1318 – Figure 1 Conclusions Informed algorithms have been developed regarding the optimal time for requesting drug levels and anti- drug antibodies in patients with RA and SpA. This recommendations may help in the decision-making process and could be useful as part of future guidelines in the field. Acknowledgements This study was funded by Grifols through the Asociación para la Investigación en Reumatología de la Marina Baixa (AIRE-MB). The content is solely the responsibility of the authors and does not necessarily represent the official views of Grifols. Disclosure of Interest None declared
Dyme: Dynamic Microservice Scheduling in Edge Computing Enabled IoT

In recent years, the rapid development of mobile edge computing (MEC) provides an efficient execution platform at the edge for Internet-of-Things (IoT) applications. Nevertheless, the MEC also provides optimal resources to different microservices, however, underlying network conditions and infrastructures inherently affect the execution process in MEC. Therefore, in the presence of varying network conditions, it is necessary to optimally execute the available task of end users while maximizing the energy efficiency in edge platform and we also need to provide fair Quality-of-Service (QoS). On the other hand, it is necessary to schedule the microservices dynamically to minimize the total network delay and network price. Thus, in this article, unlike most of the existing works, we propose a dynamic microservice scheduling scheme for MEC. We design the microservice scheduling framework mathematically and also discuss the computational complexity of the scheduling algorithm. Extensive simulation results show that the microservice scheduling framework significantly improves the performance metrics in terms of total network delay, average price, satisfaction level, energy consumption rate (ECR), failure rate, and network throughput over other existing baselines.
Genetic variation for nitrogen use efficiency in a set of recombinant maize inbred lines. I. Agrophysiological results.

To avoid pollution by nitrates and to maintain a sufficient net income, the farmer must optimize the use of nitrogen fertilizer Using varieties with a better nitrogen use efficiency (NUE) is a way to reach sucl an objective. To study the genetic variability and genetic basis ot NUE in maize(Zea mays L.) a set of 99 recombinant inbred lines crossed to a tester was studied for grain yield and other traits including N content in comparison to commercial varieties at low input (N - ) and high input (N + ), during two years in one location. From N + to N - grain yield was reduced by 38% kernel number by 32% and kernel weight by 9%. Vegetative development was reduced by 14% whereas the growth after anthesis was reduced by 29%. N uptake efficiency (NUpE) was reduced by 35% at harvest whereas N utilization efficiency (NUtE) was increased by 27% Genotype x nitrogen (G x N) interaction variance was significant for yield and kernel number, but not for kernel weight. G x N interaction for kernel number was highly correlated to the interaction observed for yield Responsiveness for yield or kernel number was negatively correlated to yield in N - . Heritability was decreased in N Differences in grain yield were easier to explain in N - than in N + . Vegetative development was favourable in both N conditions. In N - , N uptake and nitrogen nutrition index at silking were positively correlated to grain yield whereas leaf senescence and anther-silking interval were negatively correlated. NUE was explained both by NUpE and NUtE. However NUpE was more variable in N + than in N and the reverse for NUtE. Limiting steps in N metabolism appears to be different at low and high levels of N fertilizer.
Complications after Displaced Talar Neck Fracture: Results from a Case Series and a Critical Review of Literature

Talar fractures are uncommon injuries, whose outcome is often characterized by the onset of serious complications. The aims of the present study are: 1) to review the available evidence concerning the outcome and complications of talar neck fractures; 2) to describe the clinical results and the rate of post-op complications in a series of patients treated surgically after talar fracture-dislocations. The review of the literature was performed on the Pubmed, Embase and Web of Science databases and aimed at identifying clinical trials with at least 10 patients and dealing with surgical management of talar fracture-dislocation (Hawkins grade II to IV). All the papers were analyzed to extract data concerning common complications such as non-union, mal-union, infection, osteonecrosis and osteoarthtritis. The clinical arm of this paper included 26 patients (19 men and 7 women), who underwent fixation of displaced talar fracture by cannulated screws and were retrospectively evaluated, both clinically and radiographically, at a mean 51.2 ± 23.4 months of follow-up. The evaluation through the AOFAS score revelead a loss of functional performance with respect to the pre-injury status. Four patients were re-operated within final follow up, whereas the remaining 22 presented excellent results in 2 cases, good results in 4 cases, fair results in 11 cases and poor results in 5 cases. Osteoarthritis and osteonecrosis incidence were 51.9% and 25.9% respectively. Sixteen studies were included in the review and it was confirmed that the most frequent complication was post-traumatic OA, followed by osteonecrosis and mal-union. A variable range in percentage of complications described should be ascribed to the low quality of trials currently available. Displaced talar fracture are challenging to treat and the outcomes at middle-term are often modest. Looking at complications, the present case series and the review of literature revealed that the most common one is peritalar OA. Osteonecrosis, traditionally regarded as the most fearsome adverse event, is relevant but less frequent than OA.
Performance Evaluation & Analysis of Direction of Arrival Estimation Algorithms using ULA

In Array signal processing field, the problem of finding the direction of signal sources at the receiving antenna is one of the most important and challenging tasks in many of the applications such as radar, wireless communications, sonar, radio astronomy and navigation. Direction of Arrival (DOA) estimation can be achieved by the antenna array at the receiver side. The resolution of a DOA estimation algorithm depends on the antenna array structures and these algorithms also have the capability to identify the number of signal sources and their directions (multiple signal sources). This paper presents the simulation and performance comparison of all the low and high-resolution DOA estimation algorithms namely Beamforming, Capon/MVDR, MUSIC, Improved-MUSIC, Root-MUSIC and ESPRIT. These algorithms are simulated and investigated using Uniform Linear Array (ULA) in presence of White noise and interference noise. The simulation results shown in this paper investigates that the performance of any DOA estimation algorithm depends on features such as number of array elements, number of signal sources, correlation between the signal sources, Signal-to-noise ratio, array element spacing, number of snapshots and the angle separation between adjacent signal sources.
Multitask-Based Temporal-Channelwise CNN for Parameter Prediction of Two-Phase Flows

Gas–liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas–liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas–liquid two-phase flow. We conduct the vertical upward gas–liquid two-phase flow experiments to measure the flow signals by using the four-sector distributed conductance sensor. Then, we design a novel multitask-based temporal-channelwise convolutional neural network (MTCCNN) to predict the gas void fraction. In MTCCNN, we first utilize the decomposed convolutional block to extract temporal dependence and channel connection from fluid data. After further fusion by the dense layer, we apply multitask learning to make full use of the extracted features through both classification branch and gas void fraction prediction branch. We compare our MTCCNN with its variations to demonstrate the proposed improvements. We also present other competitive methods for comparisons, which shows that our MTCCNN presents a better performance in gas void fraction prediction.
3D-ReG

Deep neural network (DNN) models are being expanded to a broader range of applications. The computational capability of traditional hardware platforms cannot accommodate the growth of model complexity. Among recent technologies to accelerate DNN, resistive memory (ReRAM)-based processing-in-memory (PIM) emerged as a promising solution for DNN inference due to its high efficiency for matrix-based computation. We face two major technical challenges in extending the use of ReRAM-based accelerators for training: (1) full-precision data is essential in back-propagation; (2) the need to support both feed-forward and back-propagation aggravates the data-movement burden. We propose a heterogeneous architecture named as 3D-ReG, which leverages full-precision GPU to ensure training accuracy and low-overhead 3D integration to provide low-cost data movements. Moreover, we introduce conservative and aggressive task-mapping schemes, which partition the computation phases in different ways to balance execution efficiency and training accuracy. We evaluate 3D-ReG implemented with two 3D integration technologies, through-silicon vias (TSVs) and monolithic inter-tier vias (MIVs), and compare them with GPU-only and PIM-only counterparts. Various GPU-only platforms using two main-memory technologies (DRAM, ReRAM) and three interconnect technologies (2D, TSV, MIV) are evaluated as well. Experimental results show that 3D-ReG can achieve on average 5.64× training speedup and 3.56× higher energy efficiency compared with the GPU with DRAM as main memory, at the cost of 0.05%–3.39% accuracy drop. We define a new metric, gain-loss ratio (GLR), which quantitatively evaluates the capability of a DNN training hardware in terms of the model accuracy and hardware efficiency. The results of our comparison show that the aggressive task-mapping scheme on MIV-based 3D-ReG outperforms the other methods.
Embedding With Feature Selection and Emojis Detection in Sentiment Analysis.

We propose learning particular word embeddings along with Feature selection and Emotion Detection in the paper. Existing word installing learning calculations commonly just utilize the settings of words however overlook the notion of writings. It is unsafe for estimation examination in light of the way that the words with similar settings yet converse supposition furthest point, for instance. By combining setting and estimation level evidences, the nearest neighbours in evaluation embeddings space are semantically tantamount and it favours words with a comparable inclination furthest point. Remembering the true objective to learn estimation embeddings effectively, we develop different neural frameworks with fitting disaster limits, and assemble tremendous messages normally with supposition signals like emoticons as the planning data.
Nutrient optimization of polyhydroxyalkanoate production from palm oil fiber by Ralstonia eutropha TISTR 1095 using response surface methodology.

Polyhydroxyalkanoate (PHA) is polyesters formed in several bacteria as a carbon or energy storage under some nutrient limitation. Ralstonia eutropha has potential to produce PHA from various substrates, including carboxylic acids. In this research, the nutrient optimum to produce PHA from carboxylic acid was studied by using response surface methodology. The carboxylic acids were produced by anaerobic palm oil fiber fermentation for 7 days. Then Ralstonia eutropha TISTR 1095 was cultured in fermented broth containing 840 mg/l of acids. The interaction of two factors at the same time was investigated. The effects of propionic acid, butyric acid, (NH4)2SO4 and K2HPO4 addition were examined. The result showed that the nutrient optimum for PHA production was fermented broth with nutrient addition (2.50 g/l propionic acid, 6.53 g/l butyric acid, 1.53 g/l, (NH4)2SO4 and 0.03 g/l K2HPO4). The cell concentration, PHA concentration, and PHA content were 1.53 g/l, 0.70 g/ l and 46.5%, respectively.
A Model for Developing Computational Thinking Skills

Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increas-ing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation.
Correct-by-Construction Typechecking with Scope Graphs

Dependently-typed languages are well-known for the ability to enforce program invariants through type signatures, and previous work establishes the effectiveness of this style of program verification in the implementation of type-safe interpreters for a wide class of languages with a variety of interesting scoping semantics, offering an account of dynamic semantics. This thesis covers the complementary topic of static semantics, in the form of a pattern for constructing verified typechecking procedures in a dependentlytyped setting. Implementations are given for simply-typed lambda calculus and a small procedural language as well as a module system with unrestricted cyclic module dependency semantics that are traditionally hard to formalize, parameterized over the choice of base language. A library of finite graphs and decision procedures for path search queries is presented and used in the construction of the example language implementations to resolve variable references. The resulting development is suitable as a static analysis phase (“middle end”) in a hypothetical end-to-end verified interpreter developed in a dependently-typed setting.
Do Not Let the Robot Get too Close: Investigating the Shape and Size of Shared Interaction Space for Two People in a Conversation

Robotic and other autonomous systems that need to operate in environments with people should respect social rules. One important aspect of this is personal space, which is the space surrounding a person. When two people are in a conversation, they position themselves such that a so-called shared interaction space is created in the middle of them. The aim of the current research was to experimentally investigate the shape and size of this shared interaction space in different formations. In three experiments, we had a robot approaching two people who were having a conversation from 5 different directions, and those people indicated what would be a comfortable distance for the robot to stop. We expected that people would take the personal space of their conversation partner into account when stopping the robot. Findings of the three studies mostly confirm this expectation. Apart from some exceptions, people tend to stop the robot at a similar distance from their conversation partner as from themselves. If these findings are applied in the behavior of robotic and other autonomous systems, people would be more likely to trust and later accept robots in their physical space, ultimately creating natural social interactions between humans and robots.
Analysis and Measurement Technique for 1- dB Compression Point of Single Balanced RF Mixer

The present paper discusses the basic working and operation of RF Mixers being used broadly for communication purposes. In the present paper we have discussed various important parameters of Mixer and elaborately described the instrument setup and measurement techniques for measuring the most vital parameter for analyzing performance of mixer i.e., 1-dB Compression Point (P1dB). 1- dB compression point is very significant parameter as it indicates the power level that causes the gain to drop by 1 dB from its small signal value. In this paper we have described test set up for measuring this important parameter with various measuring instruments and has obtained good agreement between the predictions and experimental data. Index Terms - Single diode mixers, 1 dB Compression Point (P1dB), Down Converter, VM-3 Receiver, Power meter, Attenuator
Emerging & Unconventional Malware Detection Using a Hybrid Approach

Advancement in computing technologies made malware development easier for malware authors. Unconventional computing paradigms such as cloud computing, the internet of things, In-memory computing, etc. introduced new ways to develop more complex and effective malware. To demonstrate this, we designed and implemented a fileless malware that could infect any device that supports JavaScript and HTML5. In addition, another proof-of-concept is implemented that signifies the security threat of in-memory malware for in-memory data storage and computing platforms. Furthermore, a detailed analysis of unconventional malware has been performed using current state-of-the-art malware analysis and detection techniques. Our analysis shows that, by utilizing the unique characteristics of emerging technologies, malware attacks could easily deceive the anti-malware tools and evade themselves from detection. This clearly demonstrates the need for an innovative and effective detection mechanism. Because of the limitations of existing techniques, we propose a hybrid approach using specification-based and behavioral analysis techniques together as an effective solution against unconventional and emerging malware instances. Our approach begins with the specification development where we present the way of writing it in a succinct manner to describe the expected behavior of the application. Moreover, the behavior monitoring component of our approach makes the detection mechanism effective enough by matching the actual behavior with pre-defined specifications at run-time and alarms the system if any action violates the expected behavior. We demonstrate the effectiveness of the proposed approach by applying it for the detection of in-memory malware that threatens the HazelCast in-memory data grid platform. In our experiments, we evaluated the performance and effectiveness of the approach by considering the possible use cases where in-memory malware could affect the data present in the storage space of HazelCast In Memory Data Grid (IMDG).
Towards efficient support for massive Internet of Things over cellular networks

The usage of Internet of Things (IoT) devices over cellular networks is seeing tremendous growth in recent years, and that growth in only expected to increase in the near future. While existing 4G and 5G cellular networks offer several desirable features for this type of applications, their design has historically focused on accommodating traditional mobile devices (e.g. smartphones). As IoT devices have very different characteristics and use cases, they create a range of problems to current networks which often struggle to accommodate them at scale. Although newer cellular network technologies, such as Narrowband-IoT (NB-IoT), were designed to focus on the IoT characteristics, they were extensively based on 4G and 5G networks to preserve interoperability, and decrease their deployment cost. As such, several inefficiencies of 4G/5G were also carried over to the newer technologies. This thesis focuses on identifying the core issues that hinder the large scale deployment of IoT over cellular networks, and proposes novel protocols to largely alleviate them. We find that the most significant challenges arise mainly in three distinct areas: connection establishment, network resource utilisation and device energy efficiency. Specifically, we make the following contributions. First, we focus on the connection establishment process and argue that the current procedures, when used by IoT devices, result in increased numbers of collisions, network outages and a signalling overhead that is disproportionate to the size of the data transmitted, and the connection duration of IoT devices. Therefore, we propose two mechanisms to alleviate these inefficiencies. Our first mechanism, named ASPIS, focuses on both the number of collisions and the signalling overhead simultaneously, and provides enhancements to increase the number of successful IoT connections, without disrupting existing background traffic. Our second mechanism focuses specifically on the collisions at the connection establishment process, and used a novel approach with Reinforcement Learning, to decrease their number and allow a larger number of IoT devices to access the network with fewer attempts. Second, we propose a new multicasting mechanism to reduce network resource utilisation in NB-IoT networks, by delivering common content (e.g. firmware updates) to multiple similar devices simultaneously. Notably, our mechanism is both more efficient during multicast data transmission, but also frees up resources that would otherwise be perpetually reserved for multicast signalling under the existing scheme. Finally, we focus on energy efficiency and propose novel protocols that are designed for the unique usage characteristics of NB-IoT devices, in order to reduce the
Enhancing Collaborative Variational Autoencoder with Tag and Citation Information for Scientific Article Recommendation

Hybrid methods such as collaborative deep learning (CDL) and collaborative variational autoencoder (CVAE) have become state-ofthe-art methods in recommender systems for scientific articles. However, they typically use only information from titles and abstracts of articles, and ignore potentially useful information in the tags and citations. Therefore, they may miss articles that contain vastly different content from other articles, although those articles present the same topic. We addressed this problem by developing the CiT-CVAE model that considers tag and citation information when providing recommendations. Our experimental results indicate that the proposed model achieves consistent improvement compared with CDL and CVAE.