Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey of inkjet-printed low-cost sensors

Abstract Inkjet-printers from the company Epson and others can be used to fabricate low-cost sensors on coated PET films. By using nanoparticle-based dispersions resistive temperature dependent sensors, strain gauges, thermocouples and pressure sensors can be fabricated. For these purposes the gauge factors, Seebeck coefficients and temperature coefficients of resistance for Ag, Carbon Black and PEDOT:PSS dispersions on Mitsubishi® and Pelikan® PET substrates are characterized. Furthermore, piezoresistive effects in transverse and longitudinal strain directions are discussed. Additionally, a printed sensor system for measuring strains within a surface is presented. Finally, an injection-moulding process and a lamination process are used to improve the mechanical scratching of those sensors.
Humanity and Environment Co-influence in the Shadow of Technological Convergence

Today one of the most important issues is the search for controlled ways of sustainable development of civilization, society, and a human. Previously, the development of society was determined by natural and evolutionary factors based on the principle of natural selection of living organisms and populations. However, as social institutions are developing, the technological factors of development, determined by the ideas about this phenomenon - the special arrangement of productive forces and the need to protect or change the environment, began to appear. The end of the 20th century was marked by the realization of a new, universal form of economic and technological synthesis - convergence, which, in the process of developing science and improving technology, became so comprehensive and widespread that today it can be called the force that transforms the environment. In this regard, it is convergent technologies that become the focus and the basis for research of the processes uniting the main spheres of human activity - science, society and technology, which will lead to a new form of interaction with the environment, conditioned not only by scientific and technological changes and social factors, but also new organizational forms of their interaction.
Bacterial Colonies on Agar Plate Counting Using Image Processing

—Many pathogenic bacteria could cause human diseases. For example, the important diseases are pneumonia and foodborne illnesses, which could be caused by Pseudomonas sp. and Salmonella sp. respectively. Direct bacteria counting methods from blood or body fluid are the best methods that could give crucial information regarding the health situation of a person, a specialist oversees these methods. For the direct counting, an error could occur from eye fatigue. In this research, image processing was applied to automatic bacteria colony counting. This method can reduce the error. In this research, MATLAB software was applied to bacterial colony counting. Salmonella enterica, Staphylococcus aureus, Escherichia coli, Serratia marcescens, and Pseudomonas aeruginosa were selected as samples. This method had an accuracy of more than 90% and could reduce human error and improve counting efficiency.
Prevalence of overweight and obesity and central adiposity indexes among school-aged children in Santa Catarina, Brazil

Abstract Objective: To estimate the prevalence of overweight and obesity in school children, their association with region, gender, age, school system, and correlation between anthropometric indexes. Method: Cross-sectional study with 4,964 children between 6 and 10 years of age of 345 elementary schools in the state of Santa Catarina. Body mass index was used to diagnose overweight and obesity according to Cole et al . (2000). Waist circumference, waist-to-hip ratio, waist-to-height ratio, triceps and subsca-pular skinfold thickness, and subscapular-to-triceps ratio were used as fat distribution indexes. We estimated the odds ratio with a 95% CI. To assess the relationship between BMI and anthropometric indexes, Pearson’s correlation was calculated. Result: 52.2% of the students were female. The prevalence of overweight and obesity were 15.4% and 6.0%. There was no association between overweight or obesity and regions, gender or age; only between overweight and the pri-vate network. Children in the private school network were 1.46 (1.22-1.74) times more likely to develop overweight. The correlation between BMI and waist circumference was the strongest (r = 0.90).
Lessons from The Gulf: Female Indigenous Emirati Students’ Persistence and Success at University

Students’ persistence and success remain significant issues for universities worldwide, but Tinto (2017a; 2017b) argued that universities need to listen to perspectives of students themselves in identifying what causes them to persist and succeed. This article reports on such perspectives of Indigenous Emirati, Muslim women at one public university in the United Arab Emirates (UAE). Data collection from original doctoral research involved an initial, customised survey completed by 22 Emirati women with subsequent interviews conducted with a further 21 female students. Data for the purpose of this article were analysed using thematic analysis. Findings are presented within Tinto’s framework: goals; sense of belonging; self-efficacy; responses to curriculum; and their impact on students’ motivation. Tinto’s framework provides a valuable insight in understanding the women’s experiences, and their statements around persistence and success have important implications for understanding women’s progression in higher education in a society where male authority remains significant.
Towards Theoretically Grounded Evolutionary Learning

Machine learning tasks are often formulated as complex optimization problems, where the objective function can be non-differentiable, non-continuous, non-unique, inaccurate, dynamic, and have many local optima, making conventional optimization algorithms fail. Evolutionary Algorithms (EAs), inspired by Darwin's theory of evolution, are general-purpose randomized heuristic optimization algorithms, mimicking variational reproduction and natural selection. EAs have yielded encouraging outcomes for solving complex optimization problems (e.g., neural architecture search) in machine learning. However, due to the heuristic nature of EAs, most outcomes to date have been empirical and lack theoretical support, encumbering their acceptance to the general machine learning community. In this paper, I will review the progress towards theoretically grounded evolutionary learning, from the aspects of analysis methodology, theoretical perspectives and learning algorithms. Due to space limit, I will include a few representative examples and highlight our contributions. I will also discuss some future challenges.
Basic Needs Insecurity and Mental Health: Community College Students’ Dual Challenges and Use of Social Support

Objective: The objective of this study is to examine the potential co-occurrence of basic needs insecurity and mental health problems among community college students. These barriers to student success are gaining significant attention from college leaders and scholars, but they are often addressed in isolation, ignoring the potential reinforcing nature of these challenges. Method: We use data from a national survey of community college students to examine the relationship between experiences of basic needs insecurity and mental health problems, and investigate the support systems that students rely on for help. Results: Findings indicate that students who experience basic needs insecurity are substantively and significantly more likely than their materially secure peers to report depression, anxiety, and suicidal ideation, planning, or attempt, even after accounting for background characteristics. Those with both food and housing insecurities are even more likely to report mental health problems and the likelihood is positively associated with severity of material hardship. Given limited institutional supports, students often rely on friends or family for emotional and mental support. Receipt of social support is higher among those with mental health challenges, but it also varies by students’ basic needs security status. This suggests that students facing the dual challenges of basic needs insecurity and mental health problems may have exhausted this important social resource. Contributions: This study raises awareness about the prevalent co-occurrence of basic needs insecurity and mental health problems, and encourages a more integrated institutionalized approach to serving students.
Molecular systematics and biogeographic insights of the Calomys callosus complex (Rodentia, Cricetidae)

The genus Calomys includes several species that have very similar external morphologies, which often leads to confusing field identification and has resulted in conflictive taxonomic histories. One of its clades involves large‐sized body forms, known as the Calomys callosus complex. In a previous study, a biogeographic hypothesis was proposed to explain a south‐to‐north colonisation of South America by species of this complex. However, other studies which not included all members of the complex' members and that used different molecular markers, did not support that biogeographic hypothesis. Here we explore the relationships among C. callosus sensu stricto (s.s.), C. fecundus, C. callidus, C. venustus, C. tocantinsi and Calomys sp. using two mitochondrial genes (Cyt‐b and Control Region) and two nuclear genes (Rhp3 and i7FGB) data. We also applied several gene trees and species delimitation methods to elucidate the taxonomy, distribution and evolutionary relationships among the analysed species. By including sequences of individuals from Argentina, Brazil, Bolivia and Paraguay, we reconstructed the possible ancestral biogeographic origin of the complex. Two main clades were recovered: the western showed the relationships (C. venustus [C. fecundus–Calomys sp.]), while the eastern (C. callidus [C. callosus s.s.–C. tocantinsi]). These results support the original biogeographic hypothesis and suggest the following scenario: in the west, from the Espinal ecoregion, first the Tucumane–Bolivian Yungas and later, the Beni savanna would have been colonised. In the east, from the Espinal, first the Chaco and later the Cerrado ecoregions may have been occupied.
Characterizing the impact of production adaptive cruise control on traffic flow: an investigation

ABSTRACT This research conducted a comprehensive study to better estimate the impact of production Adaptive Cruise Control (ACC) on uninterrupted flow facilities using the highly cited ACC car-following models in the literature. Four different car-following models were investigated, including the Autonomous Adaptive Cruise Control (AACC) model, Intelligent Driver Model (IDM), California Partners for Advanced Transit and Highways (PATH) empirical ACC model, and the Technical University of Delft empirical ACC model. Each of the four models were recalibrated using data collected by a 2013 Cadillac SRX with production ACC engaged while following a human-driven 2013 Cadillac SRX in northern Virginia. Off-the-shelf commercial microscopic simulation software VISSIM was utilized to simulate the ACC traffic flow. Sensitivity analysis was conducted on ACC market penetration and following headway. Capacity and fundamental diagrams were used to quantify the impact of the ACC on traffic flow. Results indicate that freeway capacity highly depends on the market penetration of ACC vehicles. Small number of ACC vehicles (<25%) is beneficial to roadway capacity, while wider deployment of ACC vehicles (>75%) significantly harms capacity. The results strongly suggest the urgent need for vehicle connectivity.
Propositional Gossip Protocols under Fair Schedulers

Gossip protocols are programs that can be used by a group of agents to synchronize what information they have. Namely, assuming each agent holds a secret, the goal of a protocol is to reach a situation in which all agents know all secrets. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. In this paper, we study the simplest classes of such gossip protocols: propositional gossip protocols, in which whether an agent wants to initiate a call depends only on the set of secrets that the agent currently knows. It was recently shown that such a protocol can be correct, i.e., always terminates in a state where all agents know all secrets, only when its communication graph is complete. We show here that this characterization dramatically changes when the usual fairness constraints are imposed on the call scheduler used. Finally, we establish that checking the correctness of a given propositional protocol under a fair scheduler is a coNP-complete problem.
SAXO+ upgrade: system choices and numerical simulations

SPHERE+ is a proposed upgrade of the SPHERE instrument on the ESO’s Very Large Telescope which aims at improving detection and characterization capabilities of young giant planets by means of a second-stage AO system, including dedicated wavefront sensor and deformable mirror to remove the residual wavefront errors left by the primary AO loop. This paper is focused on the numerical simulations of the second stage (SAXO+) and conclude on the impact of the main AO parameters used to build the design strategy.
Itero: An Online Iterative Voting Application

Iterative voting allows a group of agents to take a collective decision in a dynamic fashion: a series of plurality elections are staged, making the relative scores of the candidates public after each round. Voters can thus adjust their ballots at each step until the process converges (or a maximal number of steps is reached). Research in computational social choice has shown that this method has the potential of reaching good-quality decisions while at the same time being easy to explain to voters. This paper presents our implementation of iterative voting on a voting platform accessible on the web.
Smart transportation and analytics

data for predicting real-time bus arrival times. Their proposed information system is developed based on a two-way data provision framework, where the users contribute the data for bus arrival time prediction and can receive such information in return. The authors utilize multiple sources of information – GPS data contributed by passengers regarding the bus locations and speeds, road network data, and bus route data – for the estimation. Their proposed prediction approach was tested on a simulated bus network with the use of real-world traffic and bus network data and smartphone data from the participants of their study for calibration. Their simulation experiments show that crowdsourced smartphone data can predict real-time bus arrival times, but the accuracy of their approach depends on the number of contributing users and the sampling frequency of the smartphone data.
‘If a fight starts, watch the crowd’: The effect of violence on popular support for social movements

Social movements often face tactic diversification. In otherwise nonviolent movements, some groups or radical flanks may resort to violent actions such as street rioting. This article analyzes the impact that these violent episodes can have on popular support for the movement as a whole. To estimate the causal effect of violence, it exploits an unexpected riot outbreak that occurred during the fieldwork of a face-to-face survey in Barcelona in May 2016, led by a squat group linked to the anti-austerity movement known as the 15-M or indignados that emerged during the financial crisis. By comparing respondents interviewed before and after the riots, it finds that the street violence episode reduced support for the 15-M movement by 12 percentage points on average. However, the magnitude of the effect is highly conditional on the respondents’ predispositions towards the movement. Core supporters, that are expected to share the frame of the movement in justifying violent actions, are the least affected by the violent outbreak. On the other extreme, weak supporters, opposers, and non-aligned citizens reduce their support to a larger extent. Results are robust to different specifications and a wide range of robustness checks. These findings have potentially important implications for movements concerned with broadening their support base.
Design and operation of DSTATCOM for power quality improvement in distribution systems

This study provides a control scheme for distribution static compensator (DSTATCOM) which compensates three-phase, four-wire distribution system and operates the DSTATCOM in voltage control mode (VCM). This algorithm provides achievement of advantages of current control mode operation while DSTATCOM operated in VCM. Compensator injects reactive and harmonic component of load current and it provides fast voltage regulation at the load terminal with maintaining nearly unity power factor (UPF). Deadbeat voltage control law is used for generating switching pulses for insulated gate bipolar transistor (IGBT) switches and reference terminal voltage is generated by system in such a way that point of common coupling maintains nearly UPF. The proposed design is implemented to mitigate power quality issues induced by source as well as load and it is found to be efficient and robust which is validated through performance under nominal operation, unbalance and distorted source voltage, sag-swell and unbalancing in load for any types of load. Capability of this arrangement has been verified using result obtained from MATLAB–SIMULINK-based environment. Different types of loads have been considered (balanced and unbalanced, linear and non-linear) to check the performance of this arrangement.
Differential Expression of Suppressor of Cytokine Signaling and Interferon Gamma in Lean and Obese Patients with Type 2 Diabetes Mellitus

Background The model of obesity-induced insulin resistance has long been used to explain the development of type 2 diabetes mellitus (T2DM) in obese individuals (body mass index (BMI) > 25 kg/m2), but this model failed to explain the development of the disease in lean individuals (BMI < 18.5 kg/m2). Defects in the insulin signaling pathway have been postulated to play a role in these patients, particularly in suppressors of cytokine signaling (SOCS) proteins, which are involved in the downregulation of insulin transduction. The expression of SOCS is also known to be induced by cytokines such as interferon gamma (IFN-γ). It is still not clear whether these pathways operate differently in lean versus obese patients with T2DM. Therefore, this pilot study was designed to study the expression of SOCS1, SOCS3, and IFN-γ in lean and obese patients with T2DM. Objectives The levels of IFN-γ in serum and the messenger RNA (mRNA) expression of SOCS (SOCS1 and SOCS3) and IFN-γ genes in whole blood in lean and obese patients with T2DM. Methods Sixty newly diagnosed T2DM patients (not on any pharmacotherapy) were enrolled and divided into 2 groups of lean (BMI < 18.5 kg/m2) and obese (BMI > 25 kg/m2) patients (n = 30 per group). Serum IFN-γ was measured by enzyme-linked immunosorbent assay (ELISA), and mRNA expression of IFN-γ, SOCS1, and SOCS3 was measured by real-time polymerase chain reaction (PCR) using the ∆∆ Ct method. Results Serum IFN-γ levels were 10.83 ± 5.81 pg/mL in the lean group and 9.35 ± 5.14 pg/mL in the obese group (P = 0.02). Fasting serum insulin levels were 16.07 ± 8.39 µIU/mL in the lean group and 27.11 ± 4 . 91 µIU/mL in the obese group (P = 0.001). There was a 3.16-fold increase in mRNA expression of IFN-γ and a 1.3-fold increase in mRNA expression of SOCS1 in the lean group compared to the obese group. mRNA expression of SOCS3 was similar in both groups. Conclusions The level of IFN-γ increased at both transcriptional and translational levels, and mRNA expression of SOCS1 was higher in the lean group than in the obese group. The SOCS protein is a known negative regulator in insulin signaling pathways. Thus, our findings and available scientific literature suggest that IFN-γ might impair the insulin signaling pathway to a greater extent in lean patients than in obese patients via induction of SOCS1. This signaling pathway could be a major contributing factor to hyperglycemia in lean patients with T2DM compared with obese counterparts. This suggests that different therapeutic approaches to these groups might be of greater benefit in the treatment of T2DM.
Sequential Vaccine Allocation with Delayed Feedback

In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies. In particular, we show that with our allocation method, we can reduce the number of required vaccination by at least 50% in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%. We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it reduces uncertainty, hence results in better and more interpretable communication).
Attention-based Bi-directional Long and Short Term Memory Networks for Electricity Load Forecasting

With the continuous growth of power load, predicting the trend of power load changes and its efficient management have become increasingly important in recent years. Based on this research, this paper introduces a power load forecasting method based on the Bi LSTM Attention neural network. Firstly, the model only uses univariate data processing to capture short-term changes and long-term trends in the power load time series; Secondly, a deep learning model based on Bi LSTM is constructed to capture hidden features of load time series and introduce attention mechanisms to provide more accurate feature weights; Finally, an improved modified gradient descent algorithm is used for model training and parameter optimization to maximize accuracy. The experimental results show that it’s RMSE and R-squared values reach 8325.4015 and 0.9211, respectively. Compared with BP neural network and a single LSTM network, the model can achieve more accurate prediction results, thus achieving efficient power load management.
Precoding to Reduce Complexity of Factor Graph for MIMO-SCMA

Combining sparse code multiple access (SCMA) with multiple-input multiple-output (MIMO) can improve spectral efficiency of SCMA. However, in a downlink MIMO-SCMA system, each user receives signals from all transmit antennas. This increases the computational complexity of detection using the message passing algorithm (MPA) to estimate the desired symbols. To solve this problem, we propose a precoding scheme that can reduce the computational complexity of MPA without compromising the bit error rate (BER) performance. In the proposed scheme, weights are multiplied by symbols to be transmitted by the base station so that each user receives signals with a reduced number of superposed symbols. Computer simulation results show that the proposed scheme can improve the BER performance while reducing the computational complexity of MPA.