Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Prediction of Refrigerant Amounts Based on Internet of Things

In a refrigeration unit, the amount of refrigerant has a substantial influence on the entire refrigeration system. To predict the amount of refrigerant in refrigerators with the best performance, this study used refrigerator data collected in real time via the Internet of Things, which were screened to include only the effective parameters related to the compressor and refrigeration properties (based on their practical significance and the research background) and cleaned by applying longitudinal dimensionality reduction and transverse dimensionality reduction. Then, on the basis of an idealized model for refrigerator data, a model of the relationships between refrigerant amount (the dependent variable) and temperature variation, refrigerator compartment temperature, freezer temperature, and other relevant parameters (independent variables) was established. A refrigeration model based on a neural network was then established for predicting the amount of refrigerant and was used to predict five unknown amounts of refrigerant from data sets. BP neural network and RBF neural network models were used to compare the prediction results and analyze the loss functions. From the results, it was concluded that the unknown amount of refrigerant was most likely to be 32.5 g. It is of great practical significance for refrigerator production and maintenance to study the prediction of the amount of refrigerant remaining in a refrigerator.
PORT–HAMILTONIAN GRADIENT FLOWS

In this paper we present a general framework for continuous–time gradient descent, often referred to as gradient flow. We extend Hamiltonian gradient flows, which ascribe mechanical dynamics to neural network parameters and constitute a natural continuous–time alternative to discrete momentum– based gradient descent approaches. The proposed Port-Hamiltonian Gradient Flow (PHGF) casts neural network training into a system–theoretic framework: a fictitious physical system is coupled to the neural network by setting the loss function as an energy term of the system. As autonomous port–Hamiltonian systems naturally tend to dissipate energy towards one of its minima by construction, solving the system simultaneously trains the neural network. We show that general PHGFs are compatible with both continuous–time data–stream optimization, where the optimizer processes a continuous stream of data, as well as standard fixed–step optimization. In continuous–time, PHGFs allow for the embedding of black–box adaptive–step ODE solvers and are able to stick to the energy manifold, thus avoiding divergence due to large learning rates. In fixed–step optimization, on the other hand, PGHFs open the door to novel fixed–step approaches based on symplectic discretizations of the Port–Hamiltonian with similar memory footprint and computational complexity as momentum optimizers.
Navigation for an Autonomous Helicopter using Onboard Sensors

—The localization and navigation of Unmanned Aerial Vehicles (UAVs) in indoor environments is a research topic of interest, given self-contained approaches still present limitations that require to take into consideration the surroundings, computational capabilities, and energy and weight capacities. This dissertation introduces a solution for a UAV to autonomously execute a straight path in a known indoor environment, which was implemented in a six rotor UAV. This implementation provides to an inexperienced user the ability to safely deﬁne the path of the hexacopter, without any sensor or computation external to the UAV. Our approach relies on an Inertial Measurement Unit (IMU), an ultrasonic sensor, a barometer and a laser range ﬁnder (LRF) to compute robust position and attitude estimates. Simulink control models were developed to guide the parameter tuning of the real controllers. A fault-tolerant Extended Kalman Filter (EKF) is built to estimate the vertical position and velocity of the hexacopter; the ﬁlter is robust to barometer or sonar outages and can detect ground-level changes. A cascaded EKF is used to estimate xy position and velocity, where both the EKFs perform prediction and update steps, but one focuses on computing a 2-D pose estimate using LRF measurements, whereas the other uses these pose estimates to correct the state propagated using the IMU. Although this approach does not guarantee convergence, it proved to be effective in practice. The ﬁnal system is controlled remotely by sending position and yaw setpoints; other control methods are available. The hexacopter is
Medicare and the Geography of Financial Health

We use a five percent sample of Americans’ credit bureau data to study the effects of public health insurance on the geography of consumer financial health. Exploiting the nearly universal eligibility for Medicare at age 65, we find a 30 percent reduction in the level of debts in collections with limited effects on other financial outcomes. Medicare reduces the geographic variation in collections by two-thirds at age 65 and halves the geographic correlation between collections and demographics like race and education. Areas that experienced the largest gains in financial health at age 65 had higher shares of black residents, people with disabilities, and for-profit hospitals.
Mobile Robot Localization Using On-board and Oﬀ-board Laser Range Finders and Reﬂective Markers

The application of mobile robots is a reliable solution for autonomous transportation in industry, as well as in nuclear facilities to transport radioactive loads. However, the common solutions used in industry based on on-board sensors can be compromised by the exposure to activated loads. A redundant solution based on oﬀ-board sensors can improve the reliability of the robots localisation means. The sensors can be used independently as two separate systems, one on-board and other oﬀ-board, or they can be used together as single system. The addressed sensors are the Laser Range Finders and the landmarks are reﬂective markers, which can both be installed on the environment and on the vehicle. The focus of this work are the localisation algorithms and their performance. The addressed algorithms are: the Extended Kalman Filter, the Unscented Kalman Filter and the Monte Carlo Localisation, since they are the most used and available solutions in the literature. However, the novelty of this work is to combine these algorithms with on-board and oﬀ-board sensors. The motion model used is speciﬁc for a vehicle with two driving and steerable wheels, the developed method does not lose generality for other vehicles as long as they move in a two dimensional environment. The developed methods assume marker matching without error and the detection of markers with any incident angle of the laser. Simulation results were obtained for scenarios with a layout similar to the basement level of the International Thermonuclear Experimental Reactor. Nevertheless, the proposed solution can be also applied to industrial warehouse scenarios.
Moving from conceptualization to methodology in tourism behavioural geography: the case of COP17

Conceptualizing a research study and selecting the appropriate conceptual framework and methodology in the research design is a challenging process. This paper offers some insights on formulating a conceptual framework and the associated methodologies for social science-based tourism research using an investigation on the environmentally responsible behaviour of delegates at COP17 (held in Durban, South Africa in 2011), as a case study. When using different bodies of knowledge to understand the behaviour of tourists, it is imperative that the problem under investigation be properly conceptualised. This paper explains the way in which this conceptualisation can lead to an appropriate methodology, embodying research design, philosophical approaches, methods and techniques. The paper goes on to discuss how conceptual frameworks can assist in understanding causal connections; provide theoretical clarification; provide focus on the methods and assist in evaluation and interpretation of social reality. The case study adopted a research design that used a mixed-method, cross-sectional approach, and positivistic and phenomenological philosophies to address the complex issues surrounding the behaviour of delegates (tourists). A structured questionnaire, using purposive and systematic sampling was used to obtain primary data. Various sociopsychological models, concepts and theories were then drawn on to assess the relationship between sociodemographic variables, knowledge, attitudes/ perceptions and behavioural practices of delegates at COP17. Via an appraisal of the case study the current contribution shows that even though conceptualizations and methodologies for such studies will vary across studies, the link between theory, research design and empirical data should be maintained throughout the research process.
Simulation of Conversational Behavior during Impaired Audio Transmissions

1. Motivation Measuring and predicting the quality of speech has been a research topic for a long time [1]. Subjective measurements like listening or conversation tests are used to quantify the perceived quality from the view of the users [2], while instrumental methods like the E-Model try to predict the results of those tests based on signals or parameters [3]. Recent research about influencing factors shifted towards transmission delay and packet-loss, as they are most prevalent in packet-base network switching. It has been shown that the effects of delay cannot be modeled on parameters of the transmission alone and are influence by a multitude of factors (e.g. the interactivity of the conversation) [4, 5, 6, 7]. Also with packet-loss it has been shown that the perceived quality varies depending on how much information was transmitted during the degraded part of the speech[8]. Current instrumental models either do not take the type of conversations or the pragmatics into account or try to define metrics to describe the influence of delay on the conversational behavior. In the research area of computer linguistics, a simulation of conversations (especially between humans and dialogue systems) is often used to train dialogue managers (e.g [9]). This is often done on a pragmatic level, where the dialogue system and the simulated interlocutor exchange dialogue acts. Recent research is also focusing on the prediction of end-of-turns to facilitate realistic turn-taking and making the dialogue systems more robust[10, 11, 12]. However, as far as we know this research was never used for the prediction of conversational quality. The goal of this research is to transfer the research on simulation of human-computer conversation to the prediction of conversational quality. This is done in two concrete steps: First, a human-human voice-only conversation simulation that reacts to delayed transmissions and misunderstandings due to packetloss is being created. Utilizing this simulation framework, a conversational quality estimator is being built that uses the information gained from multiple simulated conversations to give a mean opinion score estimation. Based on this goal, five research questions were formulated: 1. Simulation of conversations: How can the simulation methods of studying user behavior with spoken dialogue systems be transferred to telephone conversations between two humans? 2. Simulation of turn-taking: How can turn-taking in a telephone conversation be modeled and simulated on a pragmatic level? 3. Turn-taking during delayed transmission: How can the turn-taking models be applied to conversations under the influence of transmission delay? 4. Intelligibility: How can speech intelligibility of a specific utterance be predicted based on parameters of the transmission system? 5. Evaluation: How well can conversation parameters and the overall conversational quality be measured with this approach? 2. Related Work
A Fusion Framework of IDS Alerts and Darknet Trafﬁc for Effective Incident Monitoring and Response

: Most organizations deploy and operate intrusion detection systems (IDSs) in order to cope with cyber attacks. However, in many cases, it is very difﬁcult to not only analyze IDS alerts in real-time, but also identify real cyber attacks with a high detection accuracy because IDSs record the tremendous amount of alerts and most of them are false positives. Many approaches have been proposed to solve this issue, but there is a limitation in that they have focused on dealing with only IDS alerts. Therefore, in this paper, we propose a fusion framework of IDS alerts and darknet trafﬁc, which is aiming at improving the effectiveness of the incident monitoring and response process. The experimental results show that the proposed framework could detect real cyber attacks that were not detected by IDSs and to identify more dangerous IDS alerts related to real cyber attacks.
Numerical Simulations of Atherosclerotic Plaque Growth Using Experimental Data

Atherosclerosis is a cardiovascular disease characterized by the formation and progression of a lipid plaque inside the arterial wall innermost layer (intima). Originated by a long chronic inflammation, the lesion involves different biological agents according to stages of plaque development. For a better understanding of this pathology, the present study combines mathematical, experimental and computational approaches. Aiming at visualizing plaque growth, a novel mathematical approach is suggested, starting from previous studies to describe, through partial differential equations, cells and species which intervene in atherosclerosis. Our focus regards the way the continuous accumulation of massive cells (macrophages, foam cells and smooth muscle cells) and collagen fibres are modeled inside the intima. To describe a first phase-approach for future arterial stenosis monitorization in atherosclerotic mice, an experimental procedure was performed on healthy mice models. Intravital microscopy was used to record luminal monocytes endothelial adherence and posterior transmigration (crucial events in atherogenesis). Moreover, to evaluate intima volume growth, we resorted to an idealized rectangular geometry. The complex geometry of left main coronary artery bifurcation is also provided to study blood dynamics, specially focusing on wall shear stress role. Finally, an extensive research of model parameters origin and a sensitivity analysis to access their influence on the obtained results is also provided. Results show robust consistency with literature; however, the use of accurate human physiological parameters still remains a challenge.
Expression of gag-CA Gene of Jembrana Disease Virus with Cationic Liposomal and Chitosan Nanoparticle Delivery Systems as DNA Vaccine Candidates

One of the obstacles in increasing the population of Bali cattle is the presence of infectious diseases called Jembrana disease that attacks only Bali cattle. This disease is caused by Jembrana Disease Virus (JDV). A prevention method has been introduced; A DNA vaccine candidate was developed by inserting the gag-CA JDV gene in the pEGFP-C1 vector and a delivery system using cationic liposomes and chitosan nanoparticles, which is an important factor in the success of DNA vaccines in inducing immunity. The purpose of this study was to know the expression of the gag-CA gene with these delivery systems and the effectiveness of each delivery system. The expression of the gag-CA gene was tested using HeLa cells. The gag-CA gene JDV has been optimized using codon and inserted in pEGFP-C1. Then, the recombinant DNA was cloned using the E.coli DH5α host. The improvement result was confirmed using PCR, restriction analysis and sequencing. Recombinant DNA plasmids have been isolated and formulated with cationic liposomes and chitosan nanoparticles. The best characterization of encapsulation with gel inhibition test was chitosan nanoparticles with ratio mass of DNA with chitosan 1: 2, diameter size about 178,0 nm and zeta potential +22,5 mV from the measurement using Particle Size Analyzer, while cationic liposome complex was prepared using commercial reagents. The expression results were analyzed using fluorescence observation, Two-Step RT-PCR followed by Real-Time PCR analysis and Western Blot. The results showed the design of recombinant DNA plasmids with cationic liposomal and chitosan nanoparticle delivery systems expressing the gagCA gene JDV. This is evidenced by the proliferation of EGFP-Capsid fusion proteins in HeLa cells and Normalized Expression in Real Time PCR i.e. 5.36029 for cationic liposome conducting system and 0.00533 for chitosan nanoparticle delivery system. Also a positive band was also obtained with cationic liposomal delivery system on Western blot.
ACCRUAL BASED AND REAL EARNING MANAGEMENT ASSOCIATION WITH DIVIDENDS POLICY THE CASE OF JORDAN

This study aims to examine the association between earning management and dividend policy. Industrial companies listed in Amman Stock Exchange (ASE) are used as a sample for the years from 2010 to 2016. The dividend policy (DP) measured by dividends (DPO) payout ratio while earnings management (EM) measured by discretionary accruals (DAER) and real earning management (REM). Variables such as Firm size (SIZE), return on equity (ROE), financial leverage (LIV) and market to book value (MB) were also utilized as control variables. Ordinary least square was used to estimate the model built for the study and modified Cross Sectional models of this paper adopts the two modified Models of Kothari et al. (2005) and Roychowdhury model (2006). Our results show that dividend policy of a firm has a positive significant association with earnings management for. On the other hand the association between dividends policy and real earning management it has not been supported statistically.
WakeUpNet - Smart Pomodoro for productivity management

Performance, time management, organization and productivity are common concerns of every student and worker. Everyone has the goal to perform his own tasks the best they can in the smallest period time as possible. This document presents a proposal for a time management system and increased productivity during the study or the work of the user. The present proposal has the innovative concept of being adaptable to the user, functioning according to their brain activity, captured at all times by a non-intrusive sensor system. The solution we propose is a mobile application, which acts as a timer to account for the time spent studying / working and taking a pause individually; and suggest the user when are the best times to take their breaks according to their actual state of fatigue. After each use, the user can analyze his performance, see the evolution of their productivity results over time and in comparison with other users. The solution presented was conceived taking into account the solutions that already existed in the market, with similar functionalities and objectives. It was thus possible to develop a solution not only capable of integrating the main advantages of the existing systems, but also of solving some of its limitations. The proposed system was then implemented and evaluated, taking into account the implementation options taken.
THE INTERPLAY OF STRUCTURAL AND IDENTITY FACTORS IN MEN’S PROFESSIONAL CARE WORK

Based on individual interviews, this article aims to develop a nuanced view of the structural and identity factors that deter men from professional handson care work in childcare, eldercare and care for the disabled. It reveals that the relatively good working conditions along with the accentuation of the hegemonic aspects of masculinity attract more men to the area of care for the disabled than to other areas of care. The share of men in eldercare remains extremly small as they are deterred by both the poor working conditions and the gender connotations of intimate care and household work. In childcare, the connotations of femininity through motherhood appear to be so strong such that, despite the good working conditions, this area of care still has the smallest share of men.
Direct Marketing on a Social Network

Direct Marketing (DM) sending promotional messages to individual customers is increasingly used by marketers as a result of the explosive growth of customer databases. Most current methods to calculate optimal budgets for such DM campaigns consider customers in isolation and ignore word-of-mouth communication (WOM). When the customer base forms a network (as is the case in telecom or social network databases) ignoring WOM clearly leads to suboptimal DM budgets. This paper develops a model to help address this challenge. We assume that firms know the network structure formed by customers but do not know (or are not allowed to use) data on individuals’ connections. Under this scenario, we compare the optimal campaign of a monopolist to that of firms competing in simultaneous-move or sequential-move games. The analysis provides two key insights: (i) we show that ignoring the existence of WOM leads to significant profit loss for firms and this is more so under competition. In particular, knowing the “density” of consumer connections is crucial for the design of optimal campaigns. (ii) Competition in DM exhibits strong first-mover advantages and, even in a simultaneous-move game between identical firms, highly asymmetric outcomes are possible. Specifically, at low levels of competition, the game only has symmetric equilibria, while at high levels of competition, one of the firms tends to saturate most of the network. The paper also explores three extensions. First, we generalize our model of social influence and show that our main qualitative insights remain valid. This remains to be the case for a model where DM activity endogenously grows the customer base. Finally, we also examine a somewhat simplified model in which firms know individuals’ network positions. In this case, we find that, while targeting is more efficient, competing firms may be worse off than under “blind” targeting as they have strong incentives to target the same customers.
A Framework to build Information Systems using a Public Cloud

In the last years cloud computing has revolutionized the way organizations use computers and the internet. Most of the services oﬀered by popular cloud providers, allow organizations to migrate their information systems to the cloud based on subscription-plans regarding the resources they need. However, a few number of small organizations are still unable to support the costs associated. On the other hand, most of systems still in use reside exclusively in Microsoft Excel, with the information spread over multiple documents without any automatization leading to long and exhaustive operations. The purpose of this thesis was the development of a framework that can be adapted to diﬀerent small organizations by oﬀering a set of tools to migrate the existing systems, as also to easily create a physical database using public cloud services. Finally, considering all the research made about the capabilities and features provided by some cloud providers, extend the framework to new web applications fully integrated with the system implemented in the cloud and, most important, with no costs associated. The proposed framework was evaluated by users to obtain feedback about the framework’s usability and user experience. After a positive evaluation, some improvements were made which generated the second version of the new DB4G framework.