Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation & Analysis of Movie Aspects: Based on Sentiment Analysis

The objective of this project is to address the research question “What factor determines the success of an action movie? ”, where the success factors are quantified as plot, music, plot, and director. Based on the research question, the author conducts a literature review of the development of the UK film industry, the impact of social media and film criticism on the film industry. To evaluate film reviews, sentiment analysis, which can efficiently convert text into structured data, is utilized in the project. In the methodology and the case study, the method of constructing the VADER model and the SVM model with the process including data processing, modelling, result correctness verification, and model evaluation are expounded and shown. Finally, the correctness of the two models is verified and the model output is visualized for analysis. The conclusion of the project is as follows: (1) Responding to the inquiry: In action films, the plot is the aspect that the audience pays the most attention to, but first the actors and music, and finally the director. (2) When compared to the experimental findings of Wang et al. (2020a), the project came to different conclusions, presumably as a result of the project’s limited access to movies and the change in the data processing approach. (3) Given that the two models employed in this research did well in the evaluation, they can be effectively applied to help production firms raise the caliber of action movies. (4) The models in the research can be easily developed and used to other domains because film review analysis can be compared with film reviews from other industries. However, some restrictions are also brought up, which offers suggestions for further work.
A New Theoretical Approach of Studying Resonances in Single Finline Transitions

- In this article an innovative method of studying and removing the resonances, inherently exhibit by some waveguide to microstrip transitions, is presented. By modeling an equivalent circuit, this new approach allows to obtain the constructive parameters of a finline to microstrip transition, only using the values of the resistance and capacitance components of the equivalent circuit. This procedure will allow small microwave design Companies to realize these transitions only implementing circuit analysis software, and not having to afford electromagnetic analysis software, which are very expensive and time-consuming. A full 3D electromagnetic analysis confirms that the simulation results are in excellent agreement with the results obtained by the new equations discussed in this work.
TESTE DE PROGENIES DE POLINIZAÇÃO LIVRE DE Pinus tecunumaníi (Eq. et Per.) Styles DE SAN RAFAEL DEL NORTE, NA REGIÃO DE SÃO SIMÃO, SP

Um teste de 20 progênies de polinização livre de Pinus tecunumanii (Eq. et Per.) Styles, de origem San Rafael dei Norte, Nicarágua, foi implantado na Estação Experimental de Bento Quirino, SP, do Instituto Florestal de São Paulo, no ano de 1986, em blocos casualizados, com 6 repetições, 25 plantas por parcela, 3 linhas de bordadura externa e espaçamento 3 x 2 metros, objetivando o estudo do comportamento genético da espécie, análise de seu potencial no local de ensaio, estimativa dos componentes de variância, e parâmetros genéticos, visando o melhoramento da espécie. O incremento médio anual (IMA) em volume real do P. tecunumanii, relativamente ao P. caribaea Mor. var. hondurensi.s Bar. et Gol. e o P. caribaea Mor. var. caribaea Bar. et Gol. , apresentouase bem superior, mostrando seu potencial adaptativo na região de experimentação. As análises de variâncias revelaram variações genéticas entre progênies (Ô\) significativas para as características DAP aos 2 anos de idade, altura para todas as idades estudadas e forma do tronco. Por sua vez os coeficientes de herdabilidades, ao nível de médiás de famílias (h.2 m), indicaram que a melhor estratégia de seleção, seria entre famílias de meios-irmãos. As correlações entre as idades nas características DAP e altura, mostraram valores altos, positivos e significativos, revelando um comportamento relativamente homogêneo das progênies nas idades estudadas, portanto, potencialmente aptas à seleção em idades precoces. Da mesma forma, as correlações entre características aos 7 anos de idade, revelaram-se altas, indicando a possibilidade de avanços genéticos simultâneos para mais de uma característica, a partir da seleção de um caráter.
Dopamine D3 Receptor Modulates Akt/mTOR and ERK1/2 Pathways Differently during the Reinstatement of Cocaine-Seeking Behavior Induced by Psychological versus Physiological Stress

Stress triggers relapses in cocaine use that engage the activity of memory-related nuclei, such as the basolateral amygdala (BLA) and dentate gyrus (DG). Preclinical research suggests that D3 receptor (D3R) antagonists may be a promising means to attenuate cocaine reward and relapse. As D3R regulates the activity of the Akt/mTOR and MEK/ERK1/2 pathways, we assessed the effects of SB-277011-A, a D3R antagonist, on the activity of these kinases during the reinstatement of cocaine-induced conditioned place preference (CPP) induced by psychological (restraint) and physiological (tail pinch) stress. Both stimuli reactivated an extinguished cocaine-CPP, but only restrained animals decreased their locomotor activity during reinstatement. Cocaine-seeking behavior reactivation was correlated with decreased p-Akt, p-mTOR, and p-ERK1/2 activation in both nuclei of restrained animals. While a D3R blockade prevented stress-induced CPP reinstatement and plasma corticosterone enhancement, SB-277011-A distinctly modulated Akt, mTOR, and ERK1/2 activation depending on the stressor and the dose used. Our data support the involvement of corticosterone in the SB-277011-A effects in restrained animals. Additionally, the ratios p-mTOR/mTOR and/or p-ERK1/2 /ERK1/2 in the BLA during stress-induced relapse seem to be related to the locomotor activity of animals receiving 48 mg/kg of the antagonist. Hence, our study indicates the D3R antagonist’s efficacy to prevent stress-induced relapses in drug use through distinct modulation of Akt/mTOR and MEK/ERK1/2 pathways in memory-processing nuclei.
Randomization inference for stepped-wedge cluster-randomized trials: An application to community-based health insurance

National health insurance schemes are generally impractical in low-income countries due to limited resources and low organizational capacity. In response to such obstacles, community-based health insurance (CBHI) schemes have emerged over the past 20 years. CBHIs are designed to reduce the ﬁnancial burden generated by unanticipated treatment cost among individuals falling sick, and thus are expected to make health care more affordable. In this paper, we investigate whether CBHI schemes effectively protect individuals against large ﬁnancial shocks using a stepped-wedge cluster-randomized design on data from a CBHI program rolled out in rural Burkina Faso. We investigate statistical properties of the stepped-wedge design following the parametric mixed model approach proposed by Hussey and Hughes in 2007. We ﬁnd that testing for the treatment effect is generally sensitive to speciﬁcation of the parametric model. For instance, if we fail to account for cluster-by-time interactions present in the data, the Type I error rate is severely inﬂated. We develop a more robust and efﬁcient strategy— randomization inference. We demonstrate how to apply randomization inference to test for constant treatment effects and discuss test statistics suitable for the stepped-wedge design. Randomization inference guarantees a valid Type I error rate; simulation studies show that randomization inference test statistics also have power that is comparable to the currently used procedures that do not guarantee a valid Type I error rate. Finally, we apply our proposed method to the Burkina Faso CBHI dataset. We conclude that the insurance had limited effects on reducing the likelihood of low to moderate levels of catastrophic health expenditure, but substantially reduced the likelihood of extremely high health expenditure that exceeds half of a person’s monthly income.
RECENT TRENDS IN DIFFERENT TYPES OF SYNTHETIC HYDROPHILIC POLYMER NANOPARTICLES, METHODS OF SYNTHESIS & THEIR APPLICATIONS

Numerous types of hydrophilic polymer nanoparticles (NPs) have recently become research hotspots because of their ability to dissolve in water and can be adapted with respect to physical, chemical, and biological properties to meet the requirements of different applications. Synthetic hydrophilic polymeric NPs had successfully gained much attention because of their unique physicochemical properties, such as low toxicity, biodegradability, bioavailability, and support material for extensive swelling in water. These synthetic hydrophilic polymers NPs create new opportunities to produce water-soluble polymer types that would be able to imitate the structure and function of biological polymers. Several synthetic hydrophilic polymer NPs that gain high interest recently including poly(N-isopropyl acrylamide) (PNIPAM), poly(ethylene glycol) (PEG), poly(vinyl alcohol) (PVA) and poly(N-(2-hydroxypropyl) methacrylamide (PHPMA) are reviewed in this paper. Furthermore, various synthesis methods to produce synthetic hydrophilic polymer NPs for instance emulsion polymerization, microemulsion polymerization and inverse miniemulsion polymerization are highlighted, and a brief overview on their recent applications especially in medical applications are also be discussed thoroughly in this review.
Integrated bioinformatic analysis and experimental validation for exploring the key molecular of brain inflammaging

Aims Integrating bioinformatics and experimental validation to explore the mechanisms of inflammaging in the Brain. Method After dividing the GSE11882 dataset into aged and young groups, we identified co-expressed differentially expressed genes (DEGs) in different brain regions. Enrichment analysis revealed that the co-expressed DEGs were mainly associated with inflammatory responses. Subsequently, we identified 12 DEGs that were related to the inflammatory response and used the DGIdb website for drug prediction. By using both the least absolute shrinkage and selection operator (LASSO) and random forest (RF), four biomarkers were screened and an artificial neural network (ANN) was developed for diagnosis. Subsequently, the biomarkers were validated through animal studies. Then we utilized AgeAnno to investigate the roles of biomarkers at the single cell level. Next, a consensus clustering approach was used to classify the aging samples and perform differential analysis to identify inflammatory response-related genes. After conducting a weighted gene co-expression network analysis (WGCNA), we identified the genes that are correlated with both four brain regions and aging. Wayne diagrams were used to identify seven inflammaging-related genes in different brain regions. Finally, we performed immuno-infiltration analysis and identified macrophage module genes. Key findings Inflammaging may be a major mechanism of brain aging, and the regulation of macrophages by CX3CL1 may play a role in the development of inflammaging. Significance In summary, targeting CX3CL1 can potentially delay inflammaging and immunosenescence in the brain.
Statistical issues in binding site identification through CLIP-seq

With the advent and development of CLIP-seq technologies, a growing number of CLIP-seq experiments are being performed to identify the targets of RNA-binding proteins and understand the regulation mechanism of these proteins. Although broad similarities exist between CLIPseq and ChIP-seq, statistical methods developed to identify binding sites from ChIP-seq data are not directly applicable to CLIP-seq data because of some differences between the two technologies. First, transcript abundance has a large impact on CLIP-seq results, and needs to be accounted for when analyzing CLIP-seq data. Second, mutations near the binding sites from CLIP-seq data offer valuable information that can be incorporated in analysis. Other differences arise from the ability of RNA to form complex secondary structures and from many other technical aspects of the two purification protocols. To date, no systematic studies have been conducted to investigate the general statistical properties of CLIP-seq data, the merits of including RNA-seq as a matching control, and the performance of different binding site identification methods for CLIP-seq data. In this study, we performed a comprehensive evaluation of various statistical issues in using CLIP-seq data to identify RNA-protein binding sites. We demonstrate the value of RNA-seq data in background estimation and peak calling. We show that the large dispersion in CLIP-seq data compared to ChIPseq data is the main reason for the difficulty in peak calling in the former. Using both real and simulated data, we also show the importance of biological/technical replicates and of combining mutation and peak analysis to accurately identify binding sites from CLIP-seq data.
Modeling and Analysis of Aberrations in Electron Beam Melting (EBM) Systems

We present a modeling framework which can be used to study aberrations in an Electron Beam Melting (EBM) system. This is achieved by using COMSOL Multiphysics R software to model the magnetic fields and relativistic charged particle trajectories of a model EBM system. This involves defining a model for the magnetic lenses that handle the functions of focusing, deflecting and correcting the electron beam. Simulations of the magnetic fields have been made for multipole fields up to 24 poles. Methods have been developed for characterization and quantification of the beam in the model. This is done in terms of deflection angles, focusing power and aberration spectra. A realization of aberration correction using a superposition of a Quadrupole and a Hexapole in a single lens is also presented along with aberration coefficients. We conclude with a discussion of the practical implementation of the beam sensing and control in EBM and future use of the modeling tools built for this thesis.
Many-Objective Real-World Engineering Problems: A Comparative Study of State-of-the-Art Algorithms

Many-objective optimization has recently gained popularity as it poses significant challenges for the existing algorithms. Therefore, numerous optimization algorithms have been developed to handle many-objective optimization in the literature. In addition, several studies have conducted experimental comparisons to assess the performance of optimization algorithms. Nevertheless, existing empirical studies have analyzed the performance of optimization algorithms on well-defined test problems, but it remains unclear whether the results translate to real-world scenarios. Furthermore, empirical studies on validating the performance of algorithms on real-world many-objective problems are intriguing but not yet fully explored. Therefore, in this article, we present a comprehensive comparative study evaluating the performance of 15 state-of-the-art algorithms on ten real-world many-objective applications with four to ten objectives from various domains. Further, these ten applications exhibit various mathematically challenging properties, including stochastic objectives, complex Pareto frontiers, and strong nonlinearity. In addition, four performance metrics are employed to visualize the performance of MOEAs in experimental settings. Based on comparative results, the performance of state-of-the-art algorithms with respect to different problems is evaluated herein.
Image-based metal artifact reduction in x-ray computed tomography utilizing local anatomical similarity

X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifactfree image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.
Shape constancy in human beings and computers based on a perspective invariant

This dissertation studied shape constancy in human beings and computers. Shape constancy is useful to human beings, and potentially also useful for computer vision systems, because it permits veridical perception of shape. Specifically, the percept of an object's shape agrees with the actual shape of the object despite changes of the shape of the retinal image produced by changes of the object's orientation relative to the observer. The currently accepted explanation of shape constancy was proposed by the Gestalt psychologists, who assumed that shape constancy is derived from two perceptual variables; the shape of the proximal stimulus and the perceived orientation of the object. I point out that this explanation can be shown to be wrong by an examination of prior publications. Specifically, it rests on a misunderstanding of projective geometry by Thouless (1931a, b) in his often-cited studies and Stavrianos' (1945) misinterpretation of her data. An alternative explanation, based on a new perspective invariant, is proposed. This explanation is based on a single variable. It does not assume that shape constancy requires knowledge of an object's orientation relative to the observer. It uses properties of the shape, itself, that are invariant under perspective transformation. This new explanation takes the form of a computer algorithm that solves the shape constancy problem, i.e., shape can be recognized on the basis of a single perspective image taken from an unknown viewing direction. This algorithm provided a model for the study of shape processing by human beings. Some of its specific properties are: processing time is not sensitive to the slant of an object, it is sensitive to tilt and it is not sensitive to the size of the shape. This last property is accomplished by an exponential pyramid that processes size. These properties of the algorithm were examined in a series of psychophysical experiments on both shape and size perception. In these experiments, the speed-accuracy tradeoff function was used as the dependent variable to separate the speed of perceptual processing from the subject's criteria for responding fast or accurately. These psychophysical experiments confirmed most predictions of the new algorithm.
Motor Imagery-Based Brain-Computer Interface Using Fusion of Deep Convolutional Neural Network with Wavelet Scattering Network

Brain-Computer Interface (BCI) is a system which is used to interact with the computer system and can be used to control different assistive devices by utilizing the brain signals such as Electroencephalography (EEG). Motor Imagery (MI) is considered one of the prominent fields of BCI systems which are based on EEG signals. These systems have the capability to restore motor ability in humans. Though a good deal of Machine Learning (ML) approaches were investigated in recent years, studies that explore BCI with Deep Learning methods or Wavelet Scattering Transforms have not been extensively used. Also, conventional classification methods show longer computational time and they are incapable of processing non-linear and non-stationary EEG signals. The proposed system aims to explore the area of a calibration-free or a subject-independent model by integrating Deep Convolutional Neural Network (CNN) with Wavelet Scattering Network by utilizing feature maps learned from both CNN and Scattering networks to tackle the non-linearity and non-stationarity of the EEG signals and thereby to improve the classification accuracy of the model to build a more robust and generalized MI-based BCI system. The proposed model outperforms the state-of-the-art techniques, achieving an accuracy of 87% and 93% on BCIC IV 2a and SMR-BCI datasets respectively.
Membrane Vesicles as Drug Delivery Systems: Source, Preparation, Modification, Drug Loading, In Vivo Administration and Biodistribution, and Application in Various Diseases

Bioinspired (or biologically inspired) drug delivery systems (DDSs) have been intensively studied in the last decades. As bioinspired DDSs, membrane vesicles, including extracellular vesicles (EVs) released from eukaryotic cells, outer membrane vesicles (OMVs) from bacteria, cell-bound membrane vesicles (CBMVs) isolated in situ from cell surfaces, membrane vesicles reorganized after the isolation of the plasma membrane of cells, and others have been rapidly developed and are attracting more and more attention. Most recently, a collection of 25 papers on the advances in membrane vesicle-based drug delivery systems was published in a Special Issue of Pharmaceutics entitled “Advances of membrane vesicles in drug delivery systems”. These papers cover many related topics including the source, preparation, modification, drug loading, and in vivo administration and biodistribution of membrane vesicles (mainly extracellular vesicles or exosomes and bacterial outer membrane vesicles), as well as application of membrane vesicles as DDSs in the treatment of various diseases.
Quantum-Dot-Induced Modification of Surface Functionalization for Active Applications of Whispering Gallery Mode Resonators

Quantum dots can modify the properties of the whispering gallery mode resonators (WGMRs) used in various potential applications. A deposition of a suitable nanomaterial for the surface functionalization of WGMRs allows for the achievement of high quality (Q) factors. Here, we show that the WGMR surface can be functionalized using quantum dots. We demonstrate that WGMRs covered with thin layers of HgS and PbS quantum dots are suitable for third-harmonic generation due to the high Q factor of the developed microresonators, thus significantly lowering the pumping power required for nonlinear optical interactions.