Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum-Energy Distributed Consensus Control of Multiagent Systems: A Network Approximation Approach

This paper investigates the problem of minimizing the global energy cost for multiagent systems to achieve consensus over undirected network topologies. Conventional optimization problems so defined require all-to-all network topologies for interagent communications, which precludes the use of distributed control or otherwise demands that the network be complete. To circumvent this difficulty, we introduce a network approximation (NA) scheme in the optimization criterion, which tends to render the optimization problem to one seemingly over a complete graph network, thus removing the all-to-all communication requirement. With the NA cost, we show that a distributed optimal consensus algorithm always exists for any given connected network topology, which can be determined by solving a single-agent-level parametric algebraic Ricatti equation (PARE). We also investigate the performance of the optimal consensus algorithm, focusing on the minimal energy cost required to achieve consensus optimally, and the speed at which consensus is achieved. Furthermore, for certain more special yet worthy cases, such as single-integrator, double-integrator, and first-order unstable agents, we derive explicit expressions for the energy cost and the consensus speed. It can be seen from these results that the energy cost can be made arbitrarily small for single-integrator and double-integrator systems under the optimal distributed control. On the other hand, for the first-order unstable agents, the energy cost increases and consensus speed decreases monotonically with the value of the agent's real unstable pole.
Matrixrobustheit Algorithmen, Komplexität Und Anwendung Zur Netzwerküberwachung Zur Erlangung Des Akademischen Grades Diplom-informatiker

For the management of a power network it is essential to know or be able to calculate the current values, e. g. active and reactive powers, voltages etc. such that, in the event of an unexpected behavior emergency actions can be initiated. But in large distribution network some values change over the time. Thus, measurements are installed to determine the required values. This diploma thesis analyzes the question whether a network remains observable, i. e. whether the required values are still calculable, even if any k measurements fail. Using a work of Jochen Alber and Markus Pöller this question leads to the problem Matrix Robustness: Does a given, so-called sensitivity matrix have full rank after dropping an arbitrary set of k rows? In this thesis divers equivalences to other problems are presented, e. g. to the well-known Minimum Distance from coding theory. Using this equivalence the NP -completeness is proven when restricting the sensitivity matrix to finite fields. The complexity for infinite fields remains open. Furthermore, algorithms solving Matrix Robustness are developed. This includes an exact polynomial-time algorithm for a special case, an exact algorithm based on mixed integer programming (MIP) and a non-exact polynomial-time heuristic. Both the MIP based algorithm and the heuristic are tested on realistic data generated from power network models and synthetic data sets. Finally, some interesting relationships to further problems are presented that may provide starting points for future research. In addition, this part of the diploma thesis presents a flaw in a proof in the literature for the case of infinite fields that could have settled the question for the complexity of Matrix Robustness in the case of infinite fields.
Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.
Joint models for a GLM-type longitudinal response and a time-to-event with smooth random effects

ABSTRACT Longitudinal studies often entail non-Gaussian primary responses. When dropout occurs, potential non-ignorability of the missingness process may occur, and a joint model for the primary response and a time-to-event may represent an appealing tool to account for dependence between the two processes. As an extension to the GLMJM, recently proposed, and based on Gaussian latent effects, we assume that the random effects follow a smooth, P-spline based density. To estimate model parameters, we adopt a two-step conditional Newton–Raphson algorithm. Since the maximization of the penalized log-likelihood requires numerical integration over the random effect, which is often cumbersome, we opt for a pseudo-adaptive Gaussian quadrature rule to approximate the model likelihood. We discuss the proposed model by analyzing an original dataset on dilated cardiomyopathies and through a simulation study.
Adaptive finite‐time neural network control for redundant parallel manipulators

This paper reports our study of an adaptive finite‐time neural network control scheme for redundant parallel manipulators. The proposed controller is based on fully tuned radial basis function neural networks (RBFNs), nonsingular fast terminal sliding mode control (NFTSMC), and nonlinearities in output feedback. RBFNs with a fully online update on the output weights and both the centers and variances of Gaussian functions are used to estimate system uncertainties and disturbances. The proposed approach possesses several advantages over other existing approaches like robustness, rapid response, nonsingularity, higher precision, finite‐time convergence, and better tracking control performance. The stability of the parallel manipulators is ensured by the Lyapunov theory. Finally, simulation results validate the effectiveness of the proposed methodology.
Lipid disorders in HIV patients: what about raised HDL-cholesterol?

Received: February 08, 2019; Accepted: February 15, 2019; Published: February 18, 2019 The lipid disorders seen in individuals with HIV infection include elevated triglycerides (TG) and total cholesterol (TC), a decrease in high-density lipoprotein cholesterol (HDL-C), and variable effects on low-density lipoprotein (LDL) cholesterol. The exact mechanism is still not clear, and the cause could be multifactorial. The individual contributions of HIV infection, specific antiretroviral agents, host genetics and changes in body composition, all should be considered. However, there has been change in the pattern of dyslipidaemia in HIV patients. This is because most of the newer antiretrovirals have better lipid profile, either they are lipid neutral or causes mild dyslipidaemia. Raised lipids have been observed less often and low HDL-C has been observed less often as well. The Multicentre AIDS Cohort Study (MACS) showed that immediately after HIV infection TC and HDL-C dropped and when treatment is started TC goes back to normal or above normal level and HDL-C comes back to normal or near normal [1]. We wonder whether HDL-C can go above normal or even abnormally high in some patients when they are on treatment for a long time. Raised HDL-C has been observed with nevirapine use but is not known to be associated with an abnormally high level. Abnormally high HDL-C has been noted in HIV patients, but the actual prevalence of abnormally high HDL-C is not reported.
Emotional Intelligence, Organizational Networks and Information Flows: Goleman and Globalization

This paper examines the concept of emotional intelligence (EI) as articulated by Goleman. In doing so, it sees his theory as a development of, and a point of departure from, the concept articulated by Mayer and Salovey. In a wider sense, while Mayer and Salovey focus on the role of emotional intelligence in expediting information processing and rational decision making, Goleman focuses on emotional and informational circuits and flows. Furthermore, Goleman sees the emotionally intelligent human subject as extremely malleable. Goleman’s EI is then put forward as a key category to facilitate a research agenda in the areas of structural holes and social networks.
Virtual titration laboratory experiment with differentiated instruction

This paper studies users' learning perception in a virtual titration experiment with differentiated instruction. We develop a virtual reality chemistry lab and use leap motion to detect users' hand gestures for operations. Users wear a head‐mounted display and use their bare hands to interact with virtual objects to perform a titration experiment. Our system implements a complete titration process and provides assistance tools for learning and operating virtual items. We report the essential ideas for building the system. We applied differentiated instruction to study the learning effectiveness of users under different learning intensities. Two groups of students with and without chemistry background participated in a user study. Our results indicated that the virtual reality chemistry lab could enhance and promote users' learning confidence under suitable learning intensities.
Evaluation of Pb and Cu contents of selected component parts of waste personal computers

Electronic waste (e-waste) is one of the fastest growing waste streams in the world. The increasing market penetration in developing countries such as Nigeria, replacement market in developed countries and high obsolescence rate, make e-waste one of the fastest growing waste streams. Thirty five (35) units of waste computer central processing unit (CPU) and 24 units of waste computer monitors of different brands, manufacturers, year of manufacture, and model were collected from different electronic repairers’ shops in Ibadan, South-western Nigeria and investigated for the lead and copper contents. The devices were disassembled and the printed wiring boards (PWB) of the CPUs and monitors; and the cathode ray tube (CRT) of the monitor were milled and representative samples digested with a mixture of HNO3-H2O2-HCl and analysed using Atomic Absorption Spectrophotometry (AAS). The results showed very high levels of Cu and Pb in the samples with Cu concentrations ranging from 83100-705300 mg/kg for PWB of CPUs, 39150-630300 mg/kg for PWB of monitors and 73.2-468 mg/kg for CRT. The Pb contents varied from 18060-400650 mg/kg for PWB of CPU, 8460-80850 mg/kg for PWB of monitor and 429-9900 mg/kg for CRT. The results exceeded the Toxicity Threshold Limit Concentration (TTLC) used in characterizing wastes as toxic. The adoption of inappropriate methods in the management of these wastes could result in environmental pollution and human exposure to toxins. Consequently, appropriate measures should be adopted to manage these wastes as hazardous wastes. © JASEM http://dx.doi. org/10.4314/jasem/v19i3.17
A CASE STUDY: WRONG PATIENT; WRONG BLOOD TRANSFUSION: IS THERE A THEORY – PRACTICE – ETHICS GAP?

This case study illustrates an ongoing therapeutic dilemma which continues to place the patient’s welfare at risk. The safety predicament is associated with the transfusion of blood or their products to the correct patient. Predictably, healthcare scholars declare that when clinical practice is ineffective, a “theory-practice gap” is typically responsible. Within this paradigm there is often a gap between theoretical knowledge and its application in clinical practice. Most of the evidence relating to the non-integration of theory and practice makes the premise that environmental factors will influence learning and practice outcomes, hence the "gap". However, it is the author's belief, that to "bridge the gap" between theory and practice an additional component called “Ethics” must be appreciated. This introduces a new concept “theory-practice-ethics gap” which must be considered when reviewing some of the unacceptable appalling outcomes in health care practice.
EFEITOS DA DIETA HIPERLIPÍDICA, CONTENDO GORDURA SATURADA, SOBRE A QUALIDADE ÓSSEA DE RATOS WISTAR MACHOS NA VIDA ADULTA

Objetivos: Avaliar a influência da ingestão de banha de porco sobre a composição óssea de ratos machos aos 120 dias de idade. Metodologia: Foram utilizados ratos Wistar, machos, provenientes do Laboratório de Nutrição Experimental da Universidade Federal Fluminense (LabNE/UFF), mantidos em Biotério com temperatura (25 a 27ºC), ciclo claro/escuro (12-12h), recebendo água ad libitum por um período de oito semanas. Os grupos, divididos em controle, que recebeu ração a base de caseína (GC, n=12) e experimental, que recebeu ração com acréscimo de banha de porco (GH, n=12), foram acompanhados até completarem 120 dias. Ao final do experimento, os animais foram anestesiados. Foi coletado o fêmur para posteriores análises. A análise estatística foi realizada através do programa GraphPadPrism (versão 5.00, 2007, San Diego, USA). Resultados: Os resultados foram expressos como média ± erro padrão da média (EPM), considerando o nível de significância de P < 0,05. O GH (6,938 ±0,030 mm) apresentou maior (p<0,05) largura da diáfise com relação ao GC (4,430±0,040 mm). No teste biomecânico, foi observado que a força máxima do GC=102,200±10,210N e GH=126,100±4,341N e o módulo elástico GC=471415±44457MPa e GH=570013±24575MPa foram maiores no grupo hiperlipídico (p < 0,05), respectivamente. A composição óssea femural, foi observado maior (p<0,05) DMO do GH (0,15±0,001g/cm²) em relação ao GC (0,144±0,002g/cm²). Discussão e Conclusões: A dieta hiperlipídica contendo gordura saturada influenciou na estrutura óssea, mas não resultou em qualidade óssea inferior.DOI: 10.12957/demetra.2019. 34223
Confirmatory Research on a Representative Method of Describing Knowledge Task Process

This paper presents research findings that knowledge task process representation can be used to enhance the efficiency of knowledge tasking. This representative knowledge tasking can simulate knowledge tasking in the same way used in artificial intelligence and knowledge engineering. It can describe the process of how to solve the Tower of Hanio in predicate logic, and analyze the problems that exist in knowledge representation. Based on cognitive science it can advance the approach to represent knowledge in Mind Operation (MO) by case study and use confirmatory factor analysis to validate the ubiquity of MO. The data supports the 9 element mind operation concept set. It further provides a basic concept and methodology to measure and optimize tasks, and explores a way of knowledge representation in AI.
Highly Sensitive and Selective Ethanol Sensor Based on ZnO Nanorod on SnO2 Thin Film Fabricated by Spray Pyrolysis

This work reports the fabrication of mixed structure of ZnO nanorod on SnO2 thin film via spray pyrolysis followed by thermal annealing and their gas sensing properties. ZnO/SnO2 nanostructures are successfully prepared on a gold interdigitated alumina substrate by spraying varying mixed precursor concentrations of zinc acetate and tin (IV) chloride pentahydrate solutions in ethanol and thermal annealing. The morphology of the nanostructures is controlled by tailoring the Zn:Sn ratio in the precursor solution mixture. Unique ZnO crystals and ZnO nanorods are observed under a field emission scanning electron microscopy (FESEM) when the Zn/Sn ratio in the precursor solution is in between 13:7 to 17:3 after thermal annealing. The fabricated nanostructures are tested for ethanol, methane and hydrogen in air ambient for various gas concentrations ranging from 25 ppm to 400 ppm and the effect of fabrication conditions on the sensitivity and selectivity are studied. Among the nanostructure sensors studied, the film fabricated with molar ratio of Zn/Sn =3:1 shows better sensitivity and selectivity to ethanol due to high sensing surface area of nanorod. The response to 25 ppm ethanol is found to be as high as 50 at an operating temperature of 400oC.
Study of technologies / research systems for big scientific data analytics

At this paper, are studied the existing technologies and the research systems, which are dedicated for the analysis of huge amounts of data, also known and often referred with the words “Big Data”. These large datasets could be, recorded data, or streaming data from Internet of Things devices that need to get analyzed with an upper purpose, such as the derivation of knowledge, in terms of learning a behavior. At first, the focus is on the everyday rapid generation of data and how it can be managed by taking advantage of the Big Data analytics systems. The Apache Hadoop Java framework is the benchmark of these systems. Afterwards, there is a review of the existing technologies that are utilized and exploited for the processing of Big Data. The most of these technologies can integrate with the Hadoop framework and produce an enriched Big Data analytics ecosystem. In the next chapter, it is described the flexibility offered by a Hadoop cluster, in terms of adding nodes, in order to empower the distributed processing. Then, it is presented the installation procedure that should be followed, so as to create a Hadoop cluster, integrated with several components/technologies. Subsequently, there are a number of experiments that examine and evaluate the performance of a Hadoop cluster and some components of the Hadoop ecosystem, according to the number of the active data nodes. Finally, all the observation, conclusions and comments are concentrated in the last chapter, with thoughts for future exploitations of Big Data analytics systems.
Fas, FasL and Foxp3 gene expression in post-liver transplant autoimmune hepatitis patients with and without acute rejection

Aim of the study In this study we investigated Fas, FasL and Foxp3 expression in relation to liver graft rejection and its severity in autoimmune hepatitis (AIH) patients. Material and methods Twenty-three AIH patients including five post-transplant patients with acute rejection (AR) and 18 patients without AR (non-AR) were studied for Fas, FasL and Foxp3 gene expression in peripheral blood mononuclear cells on days 1, 3 and 7 after transplantation by real-time PCR. The relationships between gene expression and clinical features were determined. Results Real-time PCR showed various Fas gene expression levels with no significant difference between the days in AR patients (p = 0.52). In non-AR patients, Fas level increased from 0.98 ±0.24 fold on the first day to 1.89 ±0.42 fold on day 3 after transplantation (p < 0.01). In this group of patients, we also found a significant increase in FasL expression on day 7 (29.91 ±6.89 fold) compared to day 1 (13.50 ±7.44 fold, p < 0.05). Foxp3 gene expression in both groups showed decreased levels during the first week after transplantation. The decreased Foxp3 expression in AR patients was correlated with rejection activity index (r = 0.86, p < 0.0001). Conclusions Increased Fas and FasL gene expression levels in non-AR patients and decreased Foxp3 gene expression in both groups suggested the important role of these molecules in the alloreactive response after liver transplantation in AIH patients. Foxp3 expression might be useful for monitoring rejection severity.
The Association of Serum Hepcidin Levels and Insulin Resistance in PCOS Patients: A Case-Control Study

Objective: To investigate the relationship between insulin resistance and hepcidin levels in patients with PCOS. Materials and methods: In this case–control study fifty- six patients with PCOS and forty – one healthy control subjects were included. Plasma levels of hepcidin, IL-6, Serum Insulin and ferritin using ELISA method, serum iron levels using a spectrophotometric method, and Insulin resistance by using HOMA were measured in the two groups of PCOS (case group) and healthy subjects (control group). The results were analyzed by student’s t-test, General Linear Model, Binary logistic and linear regression tests. Results: The mean hepcidin level was 1.97 ± 0.53 pg/ml and 2.40 ± 0.25pg/ml in the case and control groups, respectively. The t-test results showed a significant difference between the two groups (p = 0.0001). The mean of insulin level in case and control group was 30.65 ± 15.02g/dl and 14.71 ± 10.46g/dl, respectively. The t-test analysis indicated a significant difference between the two groups (p = 0.0001). There was an inverse relationship between the level of hepcidin with HOMA-IR (β = -0.629, p = 0.04), and IL-6 (β = -0.243, p = 0.015) in both groups. The adjusted OR proved a statistically significant association between serum hepcidin (OR = 0.063; 95 % CI: 0.01-0.385, p-value = 0. 003) and HOMA (OR = 1.569; 95 % CI: 1.254–1.964, p-value = 0.001) with PolycysticOvarian Syndrome. Conclusion: There was an inverse relationship between hepcidin levels and insulin resistance in both groups meaning decrease in hepcidin levels and increase in insulin resistance may increase the risk of PCOS.