Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19: Local/regional inequalities and impacts over critical healthcare infrastructure in Brazil

This article addresses regional inequalities about critical healthcare infrastructure for coping with the COVID-19, based on geospatial analysis and compartmental modeling (SEIR). The study sites were the Sao Paulo Macro Metropolis (MMP) and the Metropolitan Region of Vale do Paraiba/Litoral Norte (RMVPLN). The results indicated the absence of ICU beds in more than 2/3 of the municipalities, in addition to the concentration in regional hubs, for which the migration of COVID-19 cases from neighboring municipalities should be considered. The analysis with the SEIR model, performed for scenarios with and without social isolation, showed that the isolation adopted in the regions was able to postpone the collapse of the health infrastructure, reducing the reproduction rate of the disease. The simulations indicated a reproduction rate of 0.95 in order to avoid collapse in the health infrastructure for RMVPLN in the analyzed period. In addition, for future resilience, it is essential to address inequalities in terms of health infrastructure available for public and private access.
Responsible innovation in molecular robotics in Japan

Over the last decade Japanese researchers have taken the lead in the emerging discipline of molecular robotics. This new technology aims to produce artificial molecular systems that can adapt to changes in the environment, self-organize and evolve. This paper explores the question of how to stimulate responsible research and innovation in the field of molecular robotics technologies. For this, we first draw lessons from earlier societal responses in Japan to emerging technologies, such as genetic engineering, nanotechnology, synthetic biology and genomic research. Next we describe various real-time technology assessment (TA) activities on molecular robotics in Japan to depict the state-of-the-art of the academic and public debate on the social aspects of molecular robotics. Lessons from earlier societal responses to emerging technologies demonstrated three potential challenges: finding and involving the ‘right’ experts and stakeholders, keeping regulations up to date, and getting scientists and citizens involved in science communication. A literature review, ‘future workshop’ and scenario workshop raised a number of ethical, social, political and cultural issues, and addressed desirable and undesirable scenarios for the next few decades. Twitter text mining analysis indicates that the level of attention, knowledge and awareness about molecular robots among a broader audience is still very limited. In conclusion, we identify four activities crucial to enable responsible innovation in molecular robotics—getting to grips with the speed of the development of molecular robotics, monitoring related technical trends, the establishment of a more stable TA knowledge base, and a sustained interaction between molecular roboticists and social scientists.
Multilevel Optimization Design for a Flux-Concentrating Permanent-Magnet Brushless Machine Considering PM Demagnetization Limitation

The flux-concentrating permanent-magnet (FCPM) machine with two V-shape magnet pieces in one stator pole has attracted considerable attention due to its excellent torque density and permanent magnet (PM) usage efficiency, whereas the torque ripple of the FCPM machine is usually high. This article aims to make a multiobjective optimization design for the FCPM machine. The optimization process is divided into three steps for different optimization objectives on two sets of geometric variables at different operation conditions. The simulated results show that the output torque of the optimized FCPM machine is increased significantly while the torque ripple has been decreased greatly. Finally, the open-circuit field distributions, the output torque, the torque-speed characteristics, and PM demagnetization of the optimized FCPM machine are investigated considering different PM shapes and PM temperature rise, which indicates that the optimized FCPM machine has favorable torque characteristics, speed range, and antidemagnetization abilities.
Velocity Constraint on Double-Integrator Dynamics Subject to Antagonistic Information

This paper is dedicated to the double-integrator dynamics subject to antagonistic interaction among participating individuals, apart from the unavailability of the velocity information on the interacting individuals. Unlike the dependence of the signed graph theory and structural balanced theory, eventually positive matrix is used to quantify antagonistic information among participating individuals, upon which the hypothesis of digon sign-symmetry imposed on the signed graph as broadly used in the literature can be entirely got rid of. Moreover, as an application, the “opinion” evolution of the agents is analyzed, and some criteria assuring all agents to share some steady “opinions” are presented. Simulation results are presented to well support the proposed setup and the derived theoretical results.
Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

Unit commitment is an intractable issue aiming to reduce the overall economic cost of power system operation while maintaining the system constraints. Due to the emerging scenario of global warming, many countries are vigorously developing renewable energy to replace the traditional fossil power plant, in order to reduce the environmental and carbon emission. The increasing penetration of renewable generation significantly challenge the economic and security of power system operation. In this paper, a low carbon multi-objective objective unit commitment model considering economic cost, environmental cost and, more importantly, the carbon emission is established, integrating wind and solar power and therefore generating a multi-objective, high-dimensional, strong non-linear, multi-constraint and mixed integer optimization problem. The non-dominated sorting genetic algorithm-III is tailored and adopted for solving the proposed challenging task, where the decision-making scheme is designed according to the normalization method and weighted sum function. Numerical results show that the proposed complex many-objective low carbon unit commitment model can be successfully solved by the proposed algorithm and the carbon emission is effectively reduced by the integration of renewable generations.
Subjective Beliefs in International Agreements

In this paper, we study the impact of countries’ subjective beliefs, i.e., pessimism and optimism, on international agreements to provide global public goods. Under symmetric beliefs, we find that while pessimism (optimism) could decrease (increase) signatories’ efforts, it can also increase (decrease) the coalition size. Under asymmetric beliefs, optimists entering the coalition may crowd out the more pessimistic countries. If a stable coalition exists, then while optimists are willing to participate even at rather small coalition sizes, the participation of more pessimistic types requires the coalition size to be larger. A stable coalition would consist of the most optimists and possibly some other types.
Reinforcement Learning In DASH

---------------------------------------------------------------------- ABSTRACT ------------------------------------------------------------ Reinforcement Learning is an important class of optimization which has recently been used in the area of dynamic adaptive streaming over HTTP (DASH). Though DASH is very popular method of video delivery in recent years it is plagued with problems when multiple players share a bottleneck link. Thus, this area has become a very active area of research. Two works which implement Reinforcement Learning in DASH are selected and their performances compared against the Conventional DASH player. It is shown that SDP works well for various network conditions.
Using Web 2.0 Tools in and beyond the University Classrooms: A Case Study of Edmodo.

New internet technologies have not only transformed communication but also revolutionized teaching and learning. One of the greatest steps forward in this area was Web 2.0 technology. In addition, new technologies such as podcasts, social networking, and online learning communities started to shape communication between teachers and students, and these became tools for sharing educational content. This paper examines the use of Web 2.0 tools in higher education, specifically in a case study of Edmodo. The first section of the paper provides information about the relationship between Web technologies and education. The second section focuses on a case study using Edmodo as a Web 2.0 tool to teach classical Turkish literature to third-year undergraduate students. The main purpose of the case study is to identify and classify the purposes for which the students and teacher preferred to use Edmodo for communication and in which ways they communicated with each other. A mixed research methodology with a combination of qualitative and quantitative methods was used to collect the data. An open-ended survey was also conducted via SurveyMonkey (www.surveymonkey.com) in regard to students’ perceptions of Edmodo. The result of the study showed that student-initiated communications via Edmodo were much fewer than teacher-initiated communications.
NRF2-dependent metabolic reprogramming is required for tumor recurrence following oncogene inhibition

Oncogenic signaling pathways both directly and indirectly regulate anabolic metabolism, and this is required for tumor growth. Targeted therapies that inhibit oncogenic signaling have dramatic impacts on cellular metabolism. However, it is not known whether the acquisition of resistance to these therapies is associated with – or driven by – alterations in cellular metabolism. To address this, we used a conditional mouse model of Her2-driven breast cancer to study metabolic adaptations following Her2 inhibition, during residual disease, and after tumor recurrence. We found that Her2 downregulation caused widespread changes in cellular metabolism, culminating in oxidative stress. Tumor cells adapted to this metabolic stress by upregulation of the antioxidant transcription factor, NRF2. Constitutive NRF2 expression persisted during residual disease and tumor recurrence, and NRF2 was both sufficient to promote tumor recurrence, and necessary for recurrent tumor growth. These results are supported by clinical data showing that the NRF2 transcriptional program is activated in recurrent breast tumors, and that NRF2 is associated with poor prognosis in patients with breast cancer. Mechanistically, NRF2 signaling in recurrent tumors induced metabolic reprogramming to re-establish redox homeostasis and upregulate de novo nucleotide synthesis. Finally, this NRF2-driven metabolic state rendered recurrent tumor cells sensitive to glutaminase inhibition, suggesting that NRF2-high recurrent tumors can be therapeutically targeted. Together, these data provide evidence that NRF2-driven metabolic reprogramming is required for breast cancer recurrence following oncogene inhibition. Significance Although tumor recurrence is the leading cause of mortality in breast cancer, the cellular properties that allow tumor cells to evade therapy and form recurrent tumors remain largely uncharacterized. Similarly, very little is known about how tumor metabolism changes following therapy, or whether alterations in cellular metabolism drive tumor recurrence. In this study, we identify the antioxidant transcription factor NRF2 as a critical positive regulator of breast cancer recurrence. We find that NRF2-dependent metabolic reprogramming is both sufficient and required to promote tumor recurrence. Additionally, we demonstrate that the NRF2-driven metabolic state renders recurrent tumors sensitive to glutaminase inhibitors, suggesting a novel therapeutic approach for the treatment of recurrent breast cancer.
Analysis of Worst-Case Data Dependent Temporal Approximation in Floating Point Units

In this brief, we study the impact of input data distribution on temporal approximation (TA) in floating point units (FPUs). In TA, rather than performing computations, prior computed results are used as output to introduce approximation. Thus, temporal locality of inputs plays an important role in TA. We show that efficacy of TA is strongly dependent on the input data distribution. While in prior works, uniform random input data distribution is used to perform the worst case analysis in approximate FPUs, it fails to capture the worst case for TA. We show that contrary to conventional idea, input data samples from normal distribution with mean (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>) equal to zero captures the worst case for TA irrespective of the FP operations. We evaluated TA in FP multipliers and FP dividers by studying four different data distributions: a) Normal Distribution (<inline-formula> <tex-math notation="LaTeX">$\mu =0$ </tex-math></inline-formula>) b) Normal Distribution (<inline-formula> <tex-math notation="LaTeX">$\mu =1$ </tex-math></inline-formula>) c) Uniform Distribution d) Power Law Distribution. The inputs generated by sampling from these distributions were applied to algorithms– dot product, principal component analysis, page rank, vector normalizations, and dot divide. On average, normal distribution (<inline-formula> <tex-math notation="LaTeX">$\mu =0$ </tex-math></inline-formula>) is more efficient in capturing the worst case as compared to the widely used uniform distribution by 8% and 12% in for FP multiplier and FP dividers respectively. We also highlight that prior knowledge of input data distribution can be exploited to reduce power delay product.
Convolution Neural Network for Image Processing of Friction Stir Welded and Conventional Welded Joints Texture

Image processing becomes powerful if deep learning algorithms like Convolution Neural Networks are used. This research paper deals with the classification of types of welding into Conventional welding and Friction stir Welding. Friction Stir Welding (FSW) method is an innovative solid-state welding technique used for joining the alloys which are difficult to weld. We tried implementing a convolution neural network that can classify them.
Disease Gene Knowledge Map Construction Technology

Based on the research of domestic and international generic and bio-industry-specific knowledge maps, we use the fusion of multiple disease-related gene banks (structured data) to explore the automated construction methods and standardization processes of disease genes and explore the relevance of pathogenic gene knowledge maps. Applications include knowledge-based gene search and prediction of pathogenic genes.
The Prognostic Role of Aortic Stiffness in Patients Hospitalized for an Acute Heart Failure Syndrome

Background: Although impaired arterial function has been associated with adverse prognosis in chronic Heart Failure (HF), its role in Acute HF Syndromes (AHFS) has been little studied. We prospectively investigated the prognostic role of arterial function on mortality and HF Hospitalizations (HHF) in patients with AHFS. Design and Methods: A thorough assessment of arterial function was performed in patients hospitalized for AHFS 24–48 h before discharge and followed-up for 6 months for all-cause death and HHF. MAGGIC risk score was used to evaluate the additive predictive value of vascular biomarkers for clinical events. Results: One-hundred patients were studied; aged 70 ± 11 years, 78% males, 61% had left ventricular ejection fraction ≤40% and 24% ≥50%. Mean aortic Pulse Wave Velocity (PWV) was 11.2 m/s, mean augmentation index 21% and median brachial flow-mediated dilation 3.14%. Higher PWV was associated with all-cause mortality (Hazard Ratio [HR] 1.32 per 1 m/s, p < 0.001) and the combined clinical event of mortality and HHF (HR 1.12 per 1 m/s, p = 0.012) even after adjustment for MAGGIC score. MAGGIC score predicted mortality (HR 3.40 per group increase, Area under Curve [AUC] = 0.741, p = 0.017) in our population; addition of PWV to MAGGIC score increased the predictive accuracy (AUC = 0.911, C-statistic p < 0.01 vs. MAGGIC score alone) for mortality. Conclusion: In these AHFS patients, increased aortic stiffness was independently associated with mortality and further improved the predictive accuracy of an established risk model. Further research is needed to show whether a comprehensive assessment of AHFS patients focusing both on cardiac and vascular function, may improve management and ameliorate prognosis following an AHF hospitalization.
Probing the Pharmacological Parameters, Molecular Docking and Quantum Computations of Plant Derived Compounds Exhibiting Strong Inhibitory Potential Against NS5 from Zika Virus

Zika virus (ZIKV) is known for microcephaly and neurological disease in humans and the nonstructural proteins of ZIKV play a fundamental role in the viral replication. Among the seven nonstructural proteins, NS5 is the most conserved and largest protein. Two major functional domains of NS5 i.e. methyltransferase (MTase) and RNAdependent RNA polymerase (RdRp) are imperative for the virus life cycle and survival. The present study explicates the inhibitory action of phytochemicals from medicinal plants against NS5 from ZIKV, leading to the identification of potential inhibitors. The crystal structure of the protein is retrieved from RCSB protein data bank. A total of 2035 phytochemicals from 505 various medicinal plants are analysed for their pharmacological properties and pharmacokinetics. Compounds having effective drug-likeness are docked against the protein and further analysed using density functional theory approach. Among the 2035 phytochemicals, 13 are selected as potential inhibitors against MTase having high binding affinities and 17 compounds are selected for RdRp. HOMO and LUMO energies are calculated for the docked compounds within and outside binding pockets of MTase and RdRp, adapting the B3LYP hybrid exchange-correlation functional with def2-SV(P) basis set. Physicochemical properties such as ionization energy, electronic chemical potential, electronegativity, electron affinity, molecular softness, molecular hardness and electrophilicity index have also been analysed for selected phytochemicals. Based upon the results, it is concluded that the selected phytochemicals are highly competent to impede the replication of the virus by inhibiting the ZIKV-NS5.
Security analysis of robotic vehicles protected by control-based techniques

Robotic vehicles (RV) are increasing in adoption in many industrial sectors (e.g., agriculture, surveillance, package delivery, warehouse management, cinematography, etc). RVs use auto-pilot software for perception and navigation, and rely on sensors and actuators for operating autonomously in the physical world. As RVs rely on sensor measurements for actuation, a common way of triggering attacks against RVs is through sensor tampering or spoofing. Such attacks cannot be prevented through traditional software security methods (e.g., cryptography, memory isolation, etc). Real-time invariant analysis has been proved effective in detecting sensor tampering attacks against CPS such as smart meters, water treatment plants, and smart grids. Because RVs inherently use control algorithms for minimizing sensor or actuator faults and for trajectory planning, control-based invariant analysis techniques have been proposed to detect attacks against RVs. In this thesis, we evaluate the efficacy of control-based intrusion detection techniques, and propose three kinds of stealthy attacks that evade detection and disrupt RV missions. By design, control-based techniques perform threshold analysis to tolerate environmental noise e.g., wind, friction, etc. Our main insight is that due to model inaccuracies, control-based intrusion detection techniques have a high detection threshold to avoid false positives. We propose automated process by which an attacker can learn the thresholds, and consequently perform targeted attacks against the RV. We also present algorithms for performing the attacks without requiring the attacker to expend significant effort or know specific details of the RV, making the attacks applicable to a wide range of RVs. We demonstrate the attacks on eight RV systems including three real vehicles, in the presence of an Intrusion Detection System (IDS) using control-based techniques to monitor RV’s runtime
GUI2WiRe: Rapid Wireframing with a Mined and Large-Scale GUI Repository using Natural Language Requirements

High-fidelity Graphical User Interface (GUI) prototyping is a well-established and suitable method for enabling fruitful discussions, clarification and refinement of requirements formulated by customers. GUI prototypes can help to reduce misunderstandings between customers and developers, which may occur due to the ambiguity comprised in informal Natural Language (NL). However, a disadvantage of employing high-fidelity GUI prototypes is their time-consuming and expensive development. Common GUI prototyping tools are based on combining individual GUI components or manually crafted templates. In this work, we present GUI2WiRe, a tool that enables users to retrieve GUI prototypes from a semiautomatically created large-scale GUI repository for mobile applications matching user requirements specified in Natural Language (NLR). We extract multiple text segments from the GUI hierarchy data and employ various Information Retrieval (IR) models and Automatic Query Expansion (AQE) techniques to achieve ad-hoc GUI retrieval from NLR. Retrieved GUI prototypes mined from applications can be inserted in the graphical editor of GUI2WiRe to rapidly create wireframes. GUI components are extracted automatically from the GUI screenshots and basic editing functionality is provided to the user. Finally, a preview of the application is created from the wireframe to allow interactive exploration of the current design. We evaluated the applied IR and AQE approaches for their effectiveness in terms of GUI retrieval relevance on a manually annotated collection of NLR and discuss our planned user studies. Video presentation of GUI2WiRe: https://youtu.be/2nN-Xr2Hk7I
First Isolation of Rotavirus Associated with Neonatal Diarrhoea in Guanacos (Lama guanicoe) in the Argentinean Patagonia Region

Group A rotavirus (RV) and coronavirus (CV) are common viral pathogens associated with neonatal diarrhoea in numerous animal species. The purpose of this work was to investigate the presence of these viral agents in two farm populations of captured guanacos (Lama guanicoe) in the Argentinean Patagonia region, that developed severe diarrhoea outbreaks. Stool and serum samples were analysed for RV and bovine CV antigen and antibody by enzyme‐linked immunosorbent assay. Rotavirus was detected in faeces from two new‐born guanacos with acute diarrhoea, one in each farm. After electrophoretic analysis, each isolated strain, showed a distinctive long dsRNA electropherotype characteristic of group A rotaviruses (4:2:3:2). In addition, 95% (38 of 40) of the sampled animals were positive for RV antibodies, suggesting a high prevalence of RV infection in the populations tested. No evidence of CV circulation by antigen or antibody analysis was observed. To our knowledge, this is the first report of the detection and isolation of RV associated with neonatal diarrhoea in Lama guanicoe.