Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Outcomes in Low-Risk Nulliparous Women who Underwent Labor Induction versus Expectant Management: Single Institution Experience

Introduction: The ARRIVE study found that induction of labor decreased the rates for cesarean section and was not associated with adverse neonatal outcomes. However, it is unclear if their study results are generalizable. Here, we aimed to analyze the perinatal and maternal outcomes of women undergoing elective induction of labor versus expectant management at a single center tertiary hospital. Methods: We retrospectively investigated outcomes in 188 low risk nulliparous women who either underwent labor induction (n=66) or had spontaneous labor (n=122). Results: There were no statistically significant outcomes between the two groups as it relates to the mother and neonate. The rate of cesarean delivery was 20% in the induction group versus 16% in the active labor group (p = 0.713). The woman who underwent induction had a relatively higher risk for morbidity including third degree laceration (p = 0.329), hypertensive disorders of pregnancy (p = 0.246), chorioamnionitis (p = 0.828), hemorrhage (p = 0.586) and infection (p = 0.586). Women in the induction group also spent more time in the labor (p < 0.001). Neonates in the induction group did have a relatively higher risk for meconium aspiration syndrome (p = 0.246), requiring respiratory support within 72 hours (p = 0.398), hyperbilirubinemia requiring phototherapy (p = 1.00), and shoulder dystocia (p = 0.732). Conclusions: We provide evidence of higher rate of maternal and neonatal morbidity in women undergoing inductions, although not statistically significant. Thus, providers should have an informed discussion when deciding timing of delivery.
Synthesis, Characterization, and Applications of Poly(ethylene glycol)-block-poly(ether-sulfide) Dendrimers

The design and synthesis of novel copolymers consisting of a linear block of methoxypoly(ethylene glycol) (mPEG) and a dendrimeric poly(ether-sulfide) block with potential applications in drug and gene delivery are described. The copolymers bear hydroxyl or alkene functional groups at the periphery and were prepared via a divergent approach using mPEG, allyl chloride, and 3-mercapto-1,2-propanediol as starting materials. Copolymer (G3.5) with alkene chain ends was functionalized with cysteamine and its number average size in aqueous medium was determined by dynamic light scattering. The compounds prepared in this work were characterized by NMR spectroscopy and MALDI-TOF. The ability of the copolymers to solvate pyrene and form micelles was investigated by UV and fluorescence spectroscopies. The ability of G3.5-amino copolymer to condense nucleic acids was studied by gel shift. Circular dichroism was used to investigate the effect of the polymer on nucleic acid conformation. Polymer cytotoxicity was determined by MTT assay.
CONSTRUÇÃO DE UM SISTEMA ESTATÍSTICO PARA AUXILIAR O SETOR PEDAGÓGICO DO IFCE – CAMPUS CRATO NO ACOMPANHAMENTO DOS DISCENTES

Uma das atribuicoes do setor pedagogico no ensino medio e o acompanhamento do desempenho academico dos discentes a cada bimestre. Para tanto, se faz necessario agilidade e clareza na analise dos resultados emitidos pelo corpo docente em cada disciplina, para que seja possivel, quando necessario, intervir com instrumentos eficazes de recuperacao da aprendizagem dos alunos na disciplina que nao houve o exito desejado. Portanto, esse projeto tem como objetivo a construcao de um sistema computacional, sem custo financeiro para a instituicao e de facil compreensao para o usuario, que utiliza os conhecimentos estatisticos para realizar a descricao, a apresentacao e analise dos dados coletados.
Robust, Secure, and Adaptive Trust-Oriented Service Selection in IoT-Based Smart Buildings

Internet of Things (IoT) has become an integral part of a smart community that connects computing devices, smart objects, and mechanical and digital machines, having unique identifiers, to communicate with each, without human intervention. This smart connectivity within a building assists the users in real time to provide an enormous range of connected applications and facilitate the optimized use of multiple resources. These smart building applications can make our lives more comfortable and provide a more sustainable, healthy, and safe workplace. A key challenge for IoT toward smart buildings is to ensure that the service has been taken from a trustworthy service provider. It requires a reliable, robust, and adaptive context-oriented trust mechanism that conforms to the specific requirements of the end users. To enhance the security of smart buildings in IoT, this research proposes an adaptive context-based trust evaluation system for smart building (CTES-SB) applications. The trust score for service is calculated based on the client’s previous interaction and recommendation from context-similar clients. Using CTES-SB, the client selects the best service provider based on the previous and current trust scores for the next interaction. The model also helps to filter out malicious nodes through an indirect trust calculation process. This process dynamically assigns weights based on direct interactions and trustworthy recommendations for detecting and avoiding malicious interactions. We have demonstrated the effectiveness of CTES-SB by simulating multiple smart building scenarios under malicious attacks. The proposed architecture of CTES-SB has been experimentally evaluated to benchmark its performance for best service selection and resiliency against malicious nodes. The CTES-SB is proved to be efficient by having a comparison with the state-of-the-art algorithms. The comparison is in terms of filtering the malicious nodes from the network and the result shows that the trust converges quickly toward the ground-truth value.
Current perspective on bacterial pigments: emerging sustainable compounds with coloring and biological properties for the industry – an incisive evaluation

The current inclination towards exploiting bacterial pigments for various coloring functions, like food, cloth, painting, cosmetics, pharmaceuticals, plastics etc. is a well-recognized aspect. Nevertheless, the current bacterial pigment productions are not effective to meet their industrial needs. Current research going on world over on bacterial pigments signify that genetic engineering for strain improvement, optimization of bioprocess modelling and utilizing cheap agro-industrial residues as substrates are key developmental strategies to maximize pigment production from bacteria. Incidentally the superior performance characteristics of the bacteria for producing differing colouring compounds and the environmental acceptability of bacterial pigments are very encouraging factors to promote higher pigment production taking advantage of the current developmental strategies. This paper evaluates the current advances in bacterial pigment production, its recovery and wide-ranging scope of its industrial applications and commercial viability.
Dependence of Ag Deposition Methods on the Photocatalytic Activity and Surface State of TiO2 with Twistlike Helix Structure

The 1% silver clusters containing twistlike helix titania nanoparticles have been synthesized and tested in photocatalytic applications. The special twistlike helix structures were obtained by controlling the hydrolysis of titanium tetrachloride, while variety of routes were investigated to deposit the silver nanoparticles, where impregnation, chemical deposition, and photodeposition methods are adopted to fabricate the Ag/TiO2 samples. The influence of the photocatalytic activity of the mesoporous helix materials and the preparation methods on the final structure of the samples was investigated, including the surface area, pore size, silver crystallite size, silver distribution, and silver surface content. The dispersion and particle size of Ag clusters or nanoparticles on Ag/TiO2 nanocatalysts were investigated by TEM analysis. The surface Ag nanoparticles contents were determined by XPS results. Diffuse reflectance and photoluminescence analysis were used to investigate the visible light adsorption abi.
Éxito electoral y producción legislativa de asambleístas del exterior: Caso Ecuador

Esta investigacion tiene como proposito explorar la representacion especial de migrantes ecuatorianos por medio del analisis de los elementos del sistema electoral y del comportamiento legislativo desde 2007-2017. Con ello, se pretende entender a profundidad como se aplica la representacion politica de los Asambleistas del Exterior entendida como un modelo integrado compuesto por: representacion formal, descriptiva, substantiva y simbolica. El enfoque teorico empleado es el neo institucionalismo de la accion racional por su alcance y fortalezas para entender tanto el marco institucional como el comportamiento individual de los actores politicos. La estrategia de investigacion se basa en el analisis anidado (nested analysis) propuesto por Lieberman (2005), que combina elementos, tecnicas y herramientas cualitativas con cuantitativas para impulsar la complementariedad de la construccion empirica de ambos enfoques. El metodo de investigacion es el estudio de caso unico. Se selecciono a Ecuador por su aun limitado estudio y sus caracteristicas unicas en materia de representacion especial de emigrantes como la sobre representacion o la oportunidad de testear diferentes efectos de reglas electorales en un solo caso (Collyer, 2014; Dandoy & Umpierrez de Reguero, 2018). Para recolectar la informacion se utilizaron las herramientas tales como el analisis documental recopilacion de datos administrativos y entrevistas. Ademas, se genero una base de datos con los registros provistos por la biblioteca de la Asamblea nacional para documentar las votaciones en sala.
Powder X-ray diffraction data for the new polymorphic compound ω-Bi2O3

A Bi2O3 compound which is unknown to date has been synthesized at 800 °C on a BeO substrate. α-Bi2O3 at 800 °C undergoes a phase transition to a metastable new compound named ω-Bi2O3 which in turn transforms to stable δ-Bi2O3 at 900 °C. X-ray powder diffraction data were collected, indexed, and refined in order to obtain a unit cell (probable space group P1¯: a=7.2688(4), b=8.6390(6), c=11.9698(8) Å, α=87.713(6), β=93.227(6), γ =86.653(4)°. This compound is of interest for the thick films circuits and sensors technology.
Selective detection of classes of chemical compounds by gas chromatography/mass spectrometry/pattern recognition: polycyclic aromatic hydrocarbons and alkanes

A series of low-resolution mass spectra were processed by a chemometric method to determine for each spectrum whether or not the corresponding compound belongs to a certain class of chemical compounds. The graphical representation of the result of the classification is a chromatogram as it would be obtained by a selective detector. Selectivity can be changed and adjusted by computer software. Classification of mass spectra is based on the generation of new numerical features derived from spectral data. Parameters of linear, binary classifiers were computed by multivariate regression. In a synthetic mixture all 31 polycyclic aromatic hydrocarbons (PAHs) have been correctly indicated; from 39 other compounds (polycyclic aromatic heterocycles, alkanes) eight have been erroneously classified as PAHs.
Catalysis of Friedel‐Crafts Alkylation by a Montmorillonite Doped with Transition‐Metal Cations

Catalysts are obtained by exchange of the interstitial cations in the K10 montmorillonite. They are applied to Friedel-Crafts alkylations with halides, alcohols, and olefins. They are quite effective even with unactivated hydrocarbons. Isomer distribution depends little on the catalyst used. Thermodynamic equilibration does not take place, the reactions appear to be kinetically controlled. Efficiency of the catalysts bears no apparent relation to that of the corresponding Lewis acids under homogeneous conditions, and it depends on the nature of the alkylating agent. Zr(IV) and Ti(IV), in general, give the best results.
OVEREXPRESSION OF PTRLEA7, A LATE EMBRYOGENESIS ABUNDANT FAMILY GENE FROM PONCIRUS TRIFOLIATA, CONFERS ENHANCED DROUGHT TOLERANCE BY ENHANCING ANTIOXIDANT CAPACITY

GRAPHICAL ABSTRACT ABSTRACT Late embryogenesis abundant (LEA) genes encode highly hydrophilic proteins that are essential in abiotic stress responses. However, most LEA genes in higher plants have not yet been investigated. This study identi ﬁ ed an LEA family gene ( PtrLEA7 ) from Poncirus trifoliata and studied its function in drought tolerance. The full-length coding sequence of PtrLEA7 was 420 bp encoding a protein of 139 amino acids. Phylogenetic analysis shows that PtrLEA7 protein belongs to the LEA_4 subfamily. Expression pro ﬁ ling by qPCR found that PtrLEA7 was strongly induced by dehydration, cold and ABA treatments, and slightly induced by salt stress. Subcellular localization reveals that PtrLEA7 protein was located in both cytoplasm and nucleus. To investigate its function, transgenic plants of both tobacco and Poncirus trifoliata overexpressing PtrLEA7 were obtained. Stress tolerance assays show that overexpression lines had enhanced dehydration and drought tolerance compared with wild type plants, indicating that
Phytochemical Investigation of the Root Extract of Carduus Chevallieri

Plant-derived substances have recently become of great interest owing to their versatile applications. The species Carduus chevallieri (C. chevallieri) is a traditional medicinal plant used to treat abdominal disorder, wounds, diabetes and hypertension among the peoples of Southern Nations Nationalities Peoples Regional State, Ethiopia. The chemical constituents of C. chevallieri, however, have not yet investigated although it has a wide traditional use for different ailments. The aim of this study was to carry out phytochemical screening, compound isolation, and structure elucidation of compounds isolated from the root of C. chevallieri.A standard phytochemical screening method was used to investigate the presence or absence of secondary metabolites, in the crude extracts. Compound isolation was performed using chromatographic separation techniques. Spectral data for structure elucidation of the isolated compounds were obtained by using IR, 1H-NMR and 13C-NMR spectroscopic techniques. The structure elucidation of the compounds was performed by interpretation and analyzing the IR, 1H-NMR and 13C NMR spectral data and comparison with literature reports. The root of C. chevallieri was collected from Angacha town, Kembata Tembaro Zone, South Nations and Nationalities Regional State, Ethiopia. The crude extracts were obtained by maceration technique by drying the root (500 g) under shed, finely grinding, and soaking the root powder in solvents n-hexane chloroform, acetone, chloroform/methanol and methanol, sequentially. The solvent was removed by rotary evaporator and the extracts were recovered. The phytochemical screening tests of the chloroform and methanolic extracts revealed the presence of cardiac glycosides, terpenoids, steroids, tannins, alkaloids, flavonoids, anthraquinones, saponins and phenols. Syringin and stigmasterol were isolated from the methanolic extract. Other than the two isolated compounds, its richest bio-resource of several bioactive secondary metabolites that can be used as candidates in drug discovery and development programs. This makes the species a valuable medicinal plant
Using the Naive Bayes as a discriminative classifier

For classification tasks, probabilistic models can be categorized into two disjoint classes: generative or discriminative. It depends on the posterior probability computation of the label $x$ given the observation $y$, $p(x | y)$. On the one hand, generative classifiers, like the Naive Bayes or the Hidden Markov Model (HMM), need the computation of the joint probability $p(x, y)$, before using the Bayes rule to compute $p(x | y)$. On the other hand, discriminative classifiers compute $p(x | y)$ directly, regardless of the observations' law. They are intensively used nowadays, with models as Logistic Regression, Conditional Random Fields (CRF), and Artificial Neural Networks. However, the recent Entropic Forward-Backward algorithm shows that the HMM, considered as a generative model, can also match the discriminative one's definition. This example leads to question if it is the case for other generative models. In this paper, we show that the Naive Bayes classifier can also match the discriminative classifier definition, so it can be used in either a generative or a discriminative way. Moreover, this observation also discusses the notion of Generative-Discriminative pairs, linking, for example, Naive Bayes and Logistic Regression, or HMM and CRF. Related to this point, we show that the Logistic Regression can be viewed as a particular case of the Naive Bayes used in a discriminative way.
Adversarial Multi-scale Feature Learning for Person Re-identification

Person Re-identification (Person ReID) is an important topic in intelligent surveillance and computer vision. It aims to accurately measure visual similarities between person images for determining whether two images correspond to the same person. The key to accurately measure visual similarities is learning discriminative features, which not only captures clues from different spatial scales, but also jointly inferences on multiple scales, with the ability to determine reliability and ID-relativity of each clue. To achieve these goals, we propose to improve Person ReID system performance from two perspective: \textbf{1). } Multi-scale feature learning (MSFL), which consists of Cross-scale information propagation (CSIP) and Multi-scale feature fusion (MSFF), to dynamically fuse features cross different scales. \textbf{2). } Multi-scale gradient regularizor (MSGR), to emphasize ID-related factors and ignore irrelevant factors in an adversarial manner. Combining MSFL and MSGR, our method achieves the state-of-the-art performance on four commonly used person-ReID datasets with neglectable test-time computation overhead.
Doubly Stochastic Pairwise Interactions for Agreement and Alignment

Random pairwise encounters often occur in large populations, or groups of mobile agents, and various types of local interactions that happen at encounters account for emergent global phenomena. In particular, in the fields of swarm robotics, sociobiology, and social dynamics, several types of local pairwise interactions were proposed and analysed leading to spatial gathering or clustering and agreement in teams of robotic agents coordinated motion, in animal herds, or in human societies. We here propose a very simple stochastic interaction at encounters that leads to agreement or geometric alignment in swarms of simple agents, and analyse the process of converging to consensus. Consider a group of agents whose"states"evolve in time by pairwise interactions: the state of an agent is either a real value (a randomly initialised position within an interval) or a vector that is either unconstrained (e.g. the location of the agent in the plane) or constrained to have unit length (e.g. the direction of the agent's motion). The interactions are doubly stochastic, in the sense that, at discrete time steps, pairs of agents are randomly selected and their new states are independently and uniformly set at random in (local) domains or intervals defined by the states of the interacting pair. We show that such processes lead, in finite expected time (measured by the number of interactions that occurred) to agreement in case of unconstrained states and alignment when the states are unit vectors.
Modulating effect of evanescent waves on thin film growth

Rongjing Guo, Tai-Chang Chiang, 3 and Huan-hua Wang 4, ∗ Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Il 61801, U.S.A Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Il 61801, U.S.A School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100039, China (Dated: Received Dec. 28, 2020; Revised July 26, 2021)
Data augmentation and image understanding

Interdisciplinary research is often at the core of scientific progress. As an example, artificial neural networks, currently an essential tool in many applications that require learning from data, were originally inspired by insights from biological neural networks. Since its inception as a field, the progress of artificial intelligence has at times converged and at times diverged from the field of neuroscience. While occasional divergence can be fruitful, in this dissertation we will explore some advantageous synergies between machine learning, cognitive science and neuroscience. In particular, this thesis focuses on vision and images. The human visual system has been widely studied from both behavioural and neuroscientific points of view, as vision is the dominant sense of most people. In turn, machine vision has also been an active area of research, currently dominated by the use of artificial neural networks. Despite their origin and some similarities with biological networks, the recent progress in neural networks for image understanding has shown signs of divergence from neuroscience. One likely cause is the focus on benchmark performance, regardless of what the models learn. This work focuses instead on learning representations that are more aligned with visual perception and the biological vision. For that purpose, I have studied tools and aspects from cognitive science and computational neuroscience, and attempted to incorporate them into machine learning models of vision. A central subject of this dissertation is data augmentation, a commonly used technique for training artificial neural networks to augment the size of data sets through transformations of the images. Although often overlooked, data augmentation implements transformations that are perceptually plausible, since they correspond to the transformations we see in our visual world— changes in viewpoint or illumination, for instance. Furthermore, neuroscientists have found that the brain invariantly represents objects under these transformations. Throughout this dissertation, I use these insights to analyse data augmentation as a particularly useful inductive bias, a more effective regularisation method for artificial neural networks, and as the framework to analyse and improve the invariance of vision models to perceptually plausible transformations. Overall, this work aims to shed more light on the properties of data augmentation and demonstrate the potential of interdisciplinary research. The code produced in this thesis is open source and available at www.github. com/alexhernandezgarcia
Experiential Learning Approach for Software Engineering Courses

Background: Software project management activities help to introduce software process models in Software Engineering courses. However, these activities should be adequately aligned with the learning outcomes and support student's progression. Objective: Present and evaluate an approach to help students acquire theoretical and practical knowledge and experience real-world software projects' challenges. The approach combines a serious game and a design-implement task in which students develop a controlled-scale software system. Method: To evaluate our approach, we analyzed the students' perceptions collected through an online survey, their project plans, and their final reports using thematic analysis. Results: Results suggest that the approach promotes knowledge acquisition, enables students' progression, reinforces theoretical concepts, and is properly aligned with the course's learning outcomes. Conclusion: The approach seems to help to introduce software process models in Software Engineering courses. Our experience can also be inspiring for educators willing to apply our approach in similar courses.