Miracle-dz/UniFAR-FTUs huggingface dataset:
Propriedade Intelectual e Inovação: Um Olhar Político-Constitucional

Este estudo procura observar a Propriedade Intelectual como vetor de Inovação, sob a ótica Político-Constitucional, ou seja, enquanto projeto, cujas aspirações integram-se ao espectro de valores constitucionalmente resguardados; e enquanto ação, dada a necessária efetividade destes postulados no contexto da sociedade complexa, cujas relações de seus agentes são mediadas por Tecnologias da Informação e Comunicação (TICs). Desenvolvida a partir de uma abordagem sistêmico-dialógica, mais precisamente sob a perspectiva da complexidade abordada por Edgar Morin, a pesquisa apresenta elementos teóricos que procuram contribuir para a observação da Propriedade Intelectual como vetor estratégico de uma Política Constitucional orientada à Inovação. Estruturado em três partes, na primeira o trabalho aborda a diversidade semântica que marca o conceito de Inovação. Na segunda as categorias Criação e Autoria são evidenciadas a partir de revisão teórica que informa seus pressupostos históricos e jurídicos, especialmente em relação ao cotejo entre a legitimidade da autonomia individual e da produção coletiva. Na terceira parte são explorados os conceitos de Propriedade e Propriedade Intelectual para além da exclusividade individualista e segmentação doutrinária, costumeiramente vinculadas a estas categorias. Com especial atenção ao papel das Universidades, a conclusão aponta para o reconhecimento da Propriedade Intelectual como um vetor estratégico para realização de uma política de cariz constitucional voltada à Inovação, a partir da ação destas instituições.
Electrical Vehicle Charging Infrastructure Design and Operations

California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered charging station. The objectives are to increase customers’ satisfaction, reduce the power grid burden, and maximize the profitability of charging stations using state-of-the-art global optimization techniques, machine-learning-based solar power prediction, and model predictive control (MPC). The proposed research has broad societal impacts and significant intellectual merits. First, it meets the demand for green transportation by increasing the number of EV users and reducing the transportation sector’s impacts on climate change. Second, an optimal scheduling tool enables fast charging of EVs and thus improves the mobility of passengers. Third, the designed planning tools enable an optimal design of charging stations equipped with a solar panel and battery energy storage system (BESS) to benefit nationwide transportation system development.
Nonlocal plasmonic response of doped and optically pumped graphene, MoS2, and black phosphorus

René Petersen,1,2,* Thomas Garm Pedersen,1,2 and F. Javier García de Abajo3,4,† 1Department of Physics and Nanotechnology, Aalborg University, DK-9220 Aalborg East, Denmark 2Center for Nanostructured Graphene (CNG), DK-9220 Aalborg East, Denmark 3ICFO-Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain 4ICREA-Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys, 23, 08010 Barcelona, Spain (Received 9 August 2017; revised manuscript received 26 October 2017; published 22 November 2017)
How Does Nonpharmacological Care Affect Newborn Infants With Neonatal Opioid Withdrawal Syndrome? A Cochrane Review Summary With Commentary

This paper summarizes the published Cochrane Review, “Non-pharmacological Care for Opioid Withdrawal in Newborns,” by A. Pahl, L. Young, M. E. Buus-Frank, L. Marcellus, and R. Soll (https://doi.org/10.1002/14651858.CD013217.pub2), and discusses it through a treatment policy and practice lens. The study population in the review included infants born at term (37 weeks’ gestation or greater) and late preterm (34 weeks’ gestation to 37 weeks’ gestation) who had a known or suspected prenatal exposure to opioids or who were exhibiting symptoms consistent with opioid withdrawal in the first 7 days of life. Nonpharmacological interventions included a wide range of care and were broadly categorized as: modification of environmental stimulation, feeding practices, and support of the mother-infant dyad. The review found that randomized controlled trials, identified in searches conducted in October 2019, did not provide enough evidence regarding clinically meaningful outcomes. However, recently published trial data suggest that “Eat, Sleep, Console,” an individualized, trauma-informed, family-centered nonpharmacological approach to care, can reduce the number of days an infant experiencing symptoms consistent with opioid withdrawal requires in-hospital care.
Compressive Sensing- A Survey

Compressive sensing (CS) is a novel method of acquiring signals or images with minimum number of measurements compared to traditional method of acquiring the same which follows Nyquist/Shannon's sampling theorem. CS relies mainly on two fundamental premises which are sparsity and incoherence. Procedure of compressive sensing contains three main steps which are sparse representation, sensing using measurement matrix and reconstruction procedure. This article gives a brief idea about the various mathematical methods of compressive sensing images and reconstruction, details of various algorithms used in the field of compressive sensing.
Deterministic Construction of Compressed Sensing Matrix Based on Q-Matrix

Compressed sensing is an innovative technology, which provides a new sampling mode. The key problem in compressed sensing is the construction of sensing matrix, which has an important influence on the signal sampling and reconstruction algorithm. At present, in most cases the sensing matrix is a random structure, and is difficult to realize due to its huge storage in practical applications. In this paper, we introduce a novel deterministic construction of sensing matrix via Q -matrix, which is calculated by solving the N-queens problem. The proposed sensing matrix has good orthogonality and circularity. Using the circularity of Q -matrix, we can construct sensing matrix for compressed sensing. A large number of simulation results show that the proposed sensing matrix in this paper can obtain a better quality of the reconstructed image, and it is easily realized owing to its cyclic characteristic.
Correlate of foreign direct investment and economic development evidence from Nigeria

Foreign Direct Investment (FDI) and other macroeconomic variables such as the exchange rate, economic openness, and public sector investment are significant macroeconomic variables that drive economic growth and development. As a result, every government’s ability to sustain and maintain a balance among them is critical to long-term development. The study's goals were to establish the impact of foreign direct investment on Nigeria's economic development, as well as the impact of the exchange rate on Nigeria’s economic development. The ex-post facto research design was used in this study, as well as secondary data. The explanatory variable was Foreign Direct Investment, and the control variable was the exchange rate. The study spans the years 1981 through 2019. The explanatory variable was Gross Fixed Capital Formation (GFCF), which is a proxy for economic progress, and the model was estimated using the Auto Regressive Distributed (ARDL) Model. The data for this study came from the World Bank Data Base's World Development Indicators of 2019 and the Central Bank of Nigeria's Statistical Bulletin of 2019. According to the study, a 1.4 unit increase in foreign direct investment leads to a 1.4 unit increase in gross fixed capital creation. In addition, a unit increase in the exchange rate causes a 0.03-unit fall in gross fixed capital formation, and vice versa. According to the findings, there is a negligible positive link between FDI and GFCF, but a strong negative relationship between the exchange rates (EXR) and GFCF. As a result, the report suggests that FDI inflows be used to fund capital projects that are not for current consumption, such as good road networks, train lines across the country, and stable electricity supply. Without a doubt, this would lower the cost of doing business in Nigeria and boost profitability. According to our findings, while FDI alone cannot lead to economic growth and development, when other factors such as a favorable climate and simplified pre-investment procedures are available, more FDI will be drawn to key economic sectors, contributing to economic growth and development.
Expression of Interleukin 17A and 17B in Gingival Tissue in Patients with Periodontitis

Periodontitis (PD) is a chronic inflammatory disease that is initiated by oral microorganisms. The pathogens induce the production of cytokines, such as interleukin (IL)-17, which enhances the inflammatory response and progression of the disease. The aim of this study was to examine the expression and localization in gingival tissue of IL-17A and IL-17B in patients with periodontitis. This study included 14 patients with periodontal disease and 14 healthy subjects without periodontal disease as a control group. There were no statistically significant differences in the expression of IL-17A mRNA between patients with periodontitis and control subjects. The expression of IL-17B mRNA was statistically significantly lower in patients with periodontitis in comparison with healthy subjects (p < 0.048). The expression of IL-17A correlated significantly with the approximal plaque index. The IL-17B expression in gingival tissue correlated with the clinical attachment level. This correlation reached borderline statistical significance (p = 0.06). In immunohistochemical analysis, we have shown the highest expression of IL-17 protein in inflamed connective tissue, epithelium, and granulation tissue from gingival biopsy specimens from patients with periodontitis. In biopsy specimens from healthy individuals, no IL-17 was found in the epithelium, while an expression of IL-17 was found in the connective tissue. The results of our study confirm the involvement of IL-17 in the pathogenesis of periodontitis. Our results suggest that an increase in IL-17 protein expression in the gingival tissue of patients with periodontitis occurs at the post-translational stage.
Faint light of old neutron stars and detectability at the James Webb Space Telescope

Isolated ideal neutron stars (NS) of age $>10^9$ yrs exhaust thermal and rotational energies and cool down to temperatures below $\mathcal{O}(100)$ K. Accretion of particle dark matter (DM) by such NS can heat them up through kinetic and annihilation processes. This increases the NS surface temperature to a maximum of $\sim 2550$ K in the best case scenario. The maximum accretion rate depends on the DM ambient density and velocity dispersion, and on the NS equation of state and their velocity distributions. Upon scanning over these variables, we find that the effective surface temperature varies at most by $\sim 40\%$. Black body spectrum of such warm NS peak at near infrared wavelengths with magnitudes in the range potentially detectable by the James Webb Space Telescope (JWST). Using the JWST exposure time calculator, we demonstrate that NS with surface temperatures $\gtrsim 2400$ K, located at a distance of 10\,pc can be detected through the F150W2 (F322W2) filters of the NIRCAM instrument at SNR\,$\gtrsim 10$ (5) within 24 hours of exposure time. Independently of DM, an observation of NS with surface temperatures $\gtrsim 2500$ K will be a formative step towards testing the minimal cooling paradigm during late evolutionary stages.
Establishing a university-partnership model to #EliminateBookDeserts in urban communities

ABSTRACT The purpose of this theory-to-practice paper on community engagement within education is to focus on developing a new collaborative model within a university-school partnership to address book deserts. Drawing on the historical work of previous scholars regarding equity, policy, and community engagement, this article adds to the existing research regarding long-term initiatives to eliminate book deserts. The focus of this work includes details of an ongoing community engagement initiative with a community development literacy partnership model to build school and classroom libraries in underserved communities of color. Implications for scholars, activists, and community advocates who focus on social action surrounding community development are discussed and delineated.
A corpus study of Swahili conditionals

In this paper I analyze Swahili conditional constructions via corpus analysis. Previous works on Swahili conditional markers categorize ki as a high possibility marker and ikiwa as a low possibility marker. In this corpus based study, I show that Swahili conditional markers cannot be imbued with specific pragmatic implicatures. Further, I extend the analysis to include the relationship between the protasis and apodosis and how this relationship impacts the choice of Swahili conditional markers. Qualitative and quantitative analysis of the results indicate that both ki and ikiwa may be used in conditionals with a high as well as neutral possibility of realization. However, the conditional conjunction ikiwa significantly differs from the ki conditional morpheme in the analysis based on the relationship between the protasis and apodosis. While ki is more frequently used in content and speech act conditionals, ikiwa is more frequently used in epistemic conditionals. The study is also extended to include the Swahili subjunctive morphemes nge, used in conditionals with a low possibility of realization (hypotheticals) as well as counterfactuals, and ngeli/ngali, predominantly used in counterfactuals.
Planarity and locality in percolation theory

This thesis is part of the mathematical study of percolation theory, which includes a family of models with a phase transition. Major advances in the 2000s, including the invention of SLE and the proof of conformal invariance of critical Bernoulli percolation, provide us with a very complete picture of the Bernoulli percolation process on the triangular lattice. Fortunately, many questions remain open, and motivated our work.The first of these is the universality of planar percolation, which states that the macroscopic properties of critical planar percolation should not depend on the underlying graph. We study this question in the framework of Divide and Color percolation, and prove in this context a result that goes in the direction of universality. A weaker universality statement is given by the theory of Russo-Seymour-Welsh (RSW), which is known to hold for planar Bernoulli percolation (without dependence) on sufficiently symmetric graphs. We study new RSW-type arguments for percolation models with dependence.The second question is the absence of an infinite cluster at the critical point, an important question from a physical point of view, equivalent to the continuity of the phase transition. In two different joint works with Hugo Duminil-Copin and Vladas Sidoravicius, we show that the phase transition is continuous for Bernoulli percolation on the graph Z^2 x {0,. ,k} and for FK percolation on the square lattice with parameter q smaller than or equal to 4.Finally, the last question that guided us is the locality of the critical point: is it possible to determine with good accuracy the critical value for Bernoulli percolation on a graph if we know only the balls with large radii? Jointly with Sebastien Martineau, we answer positively to this question in the framework of Cayley graphs of abelian groups.
A Review on Modular Converter Topologies Based on WBG Semiconductor Devices in Wind Energy Conversion Systems

This paper presents a comprehensive review on the employment of wide bandgap (WBG) semiconductor power devices in wind energy conversion systems (WECSs). Silicon-carbide- (SiC) and gallium-nitride (GaN)-based power devices are highlighted and studied in this review, focusing on their application in the wind energy system. This is due to their premium characteristics such as the operation at high switching frequency, which can reduce the switching losses, and the capability to operate at high temperatures compared with silicon (Si)-based devices. These advantages promote the replacement of the conventional Si-based devices with the WBG semiconductor devices in the new modular converter topologies due to the persistent demand for a more-efficient power converter topology with lower losses and smaller sizes. The main objective of this paper was to provide a comprehensive overview of the WBG power devices commercially available on the market and employed in the modular converter topologies for renewable energy systems. The paper also provides a comparison between the WBG power technologies and the traditional ones based on the Si devices. The paper starts from the conventional modular power converter topology circuits, and then, it discusses the opportunities for integrating the SiC and WBG devices in the modular power converters to improve and enhance the system’s performance.
Altered offspring neurodevelopment in an L-NAME-induced preeclampsia rat model

Introduction To investigate the mechanism underlying the increased risk of subsequent neurodevelopmental disorders in children born to mothers with preeclampsia, we evaluated the neurodevelopment of offspring of a preeclampsia rat model induced by the administration of N-nitro-L-arginine methyl ester (L-NAME) and identified unique protein signatures in the offspring cerebrospinal fluid. Methods Pregnant rats received an intraperitoneal injection of L-NAME (250 mg/kg/day) during gestational days 15–20 to establish a preeclampsia model. Behavioral experiments (negative geotaxis, open-field, rotarod treadmill, and active avoidance tests), immunohistochemistry [anti-neuronal nuclei (NeuN) staining in the hippocampal dentate gyrus and cerebral cortex on postnatal day 70], and proteome analysis of the cerebrospinal fluid on postnatal day 5 were performed on male offspring. Results Offspring of the preeclampsia dam exhibited increased growth restriction at birth (52.5%), but showed postnatal catch-up growth on postnatal day 14. Several behavioral abnormalities including motor development and vestibular function (negative geotaxis test: p < 0.01) in the neonatal period; motor coordination and learning skills (rotarod treadmill test: p = 0.01); and memory skills (active avoidance test: p < 0.01) in the juvenile period were observed. NeuN-positive cells in preeclampsia rats were significantly reduced in both the hippocampal dentate gyrus and cerebral cortex (p < 0.01, p < 0.01, respectively). Among the 1270 proteins in the cerebrospinal fluid identified using liquid chromatography-tandem mass spectrometry, 32 were differentially expressed. Principal component analysis showed that most cerebrospinal fluid samples achieved clear separation between preeclampsia and control rats. Pathway analysis revealed that differentially expressed proteins were associated with endoplasmic reticulum translocation, Rab proteins, and ribosomal proteins, which are involved in various nervous system disorders including autism spectrum disorders, schizophrenia, and Alzheimer's disease. Conclusion The offspring of the L-NAME-induced preeclampsia model rats exhibited key features of neurodevelopmental abnormalities on behavioral and pathological examinations similar to humans. We found altered cerebrospinal fluid protein profiling in this preeclampsia rat, and the unique protein signatures related to endoplasmic reticulum translocation, Rab proteins, and ribosomal proteins may be associated with subsequent adverse neurodevelopment in the offspring.
Research on a Variable Pressure Driving Method for Soft Robots Based on the Electromagnetic Effect

This study proposes a novel variable air pressure supply structure based on the electromagnetic effect. This structure can be implemented in various soft robots driven by air pressure, including pneumatic artificial muscles, pneumatic soft grippers, and other soft robots. The structure’s main body comprises a hollow circular tube, a magnetic piston arranged in the tube, and an electromagnetic solenoid nested outside the tube. The electromagnetic solenoid is designed with special winding and power supply access modes, generating either an attractive force or a repulsive force on the magnetic piston. This solenoid conforms with the magnetic piston expectation in the tube by changing the polarity direction. The interior of the whole structure is a closed space. The gas is conveyed to the soft robot by the gas guide hoses at the two ends of the structure, and the expansion energy of the compressed gas is fully utilized. Then, the gas supply pressure is controlled to drive the robot. The mathematical model of the structure is established based on the analysis of the electromagnetic force and gas pressure on the piston. The simulation results show that the structure’s inherent vibration characteristics under various parameters align with expectations. The real-time automatic optimization of the controller parameters is realized by optimizing the incremental proportional-integral-derivative (PID) controller based on a neural network. The simulation results show that the structure can meet the application requirements. The experimental results show that the proposed gas supply structure can provide a continuous pressure supply curve with any frequency in a specific amplitude range and has an excellent tracking effect on the sinusoidal-like pressure curve.
Lecture: Aggregation of General Biased Signals

First, let us consider the case when n people make a decision between k alternatives, denoted 1, . , k, where one of the k alternatives is correct. We can make various assumptions on the type of signals individuals receive. The most general assumption is the following: there is a signal space X (e.g. X = R), and distributions P1, . , Pk on X, and if the correct alternative is i, then each individual receives independently a signal distributed according to Pi. A nice non-general assumption that we analyze in more detail is this: each individual receives independently the correct alternative with probability p > 1/k, and each other alternative with probability (1− p) / (k − 1).
An image-noise estimation approach using singular value decomposition

This paper proposes a simple and accurate estimation of the additive white Gaussian noise for the noise-contaminated digital images. One can easily estimate the noise level through singular value decomposition (SVD) to the noise-polluted image if an image is deteriorated by the additive white Gaussian noise. As described in the paper, the sum of some specific singular values has the linear relationship with the standard deviation of noise. Based on no correlation between noises, we add known noises upon a noise image. Then noise level is estimated by solving a nonlinear over-determined matrix equation. The proposed algorithm was experimentally tested by the benchmark images and outperforms estimation method of selecting weak textured patches using principal component analysis (PCA). The proposed method is more independent on the original image information and presents a higher accuracy and a stronger robustness for a range of noise level in various images.