Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of substitution of two hydrophobic moieties on the properties of guar gum based hydrogels

Purpose – The purpose of this study is to modify guar gum (GG) into guar gum acetate (GGA) and phthaloyl guar gum (PHGG) by transesterification and phthaloylation, respectively. Design/methodology/approach – GG has been modified into GGA through transesterification reaction between GG and vinyl acetate and PHGG through esterification reaction with phthalic anhydride. The modified GG was characterized by solubility test, Fourier-transform infra-red (FTIR) spectroscopy, proton nuclear magnetic resonance (H NMR) spectroscopy, X-ray diffractometry (XRD) and thermogravimetric analysis. Swelling properties of GGA and PHGG hydrogels in water were evaluated. Findings – These two types of modified GG have better solubility such as in dimethyl sulfoxide and N,N-dimethylformamide but no true organosolubility was achieved. The modifications were confirmed through FTIR with new absorption peaks at 1,733 cm 1 for GGA and 1,709 cm 1 for PHGG coupled with observed substitution peaks at 1.80 to 2.20 ppm and 7.40 to 7.90 ppm, respectively, from H NMR spectroscopies. XRD revealed both GGA and PHGG are less crystalline than native GG. GGA was found to be more thermally stable than native GG, whereas PHGG was slightly less thermally stable than native GG. The swelling property in distilled water for native GG, PHGG and GGA was 918.43 6 46.62%, 537.046 2.87% and 393.046 13.42%, respectively. Research limitations/implications – The GGA and PHGG hydrogels are expected to be useful for biomedical fields such as tissue engineering and drug-delivery. Originality/value –Modifications of native GG into GGA using vinyl acetate and PHGG using phthalic anhydride are novel.
Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust

In this study, we experimentally evaluated the application of multiple scattering theory for measuring ultrasonic attenuation. Based on the independent approximation theory, the method adopted for calculating the attenuation of coherent waves through air with fine dust is discussed. To obtain a scattering wavefield, a unique ultrasonic scattering hardware was developed, and signal processing schemes were suggested. Four cases of standard particle doses (0, 0.004, 0.008, and 0.012 g) were investigated inside a chamber. The results obtained from the experiments demonstrate that the proposed signal processing approach successfully calculates the scattering attenuation, which correlates well with the applied doses of fine dust. In addition, we discuss the irregular shape and composition of fine dust relative to the scattering cross-section.
Critical Manufacturing Prerequisites for Successful Reshoring

Reshoring is an emergent theme in research and practice. It is important for researchers to understand the motivating force and prerequisites for these reshoring activities, and to develop a method that supports sustainable reshoring decisions. The purpose of this study is to shed light on the prerequisites for successful reshoring. The preparation for this study consists of a systematic literature review. The findings presented here, however, are revelatory and emerged during a workshop. The findings were further researched at a total of four companies, which were part of data collection in a larger project. This research identified three scenarios, in which preceding manufacturing step, creation of parallel steps, or exploration of overcapacity are central. The paper also presents a stage-gateprocess for reshoring decisions as well as a typology of reshoring alternatives. This research sheds light on prerequisites and contextual factors that enable companies to reshore. The importance of manufacturing capabilities can be seen in literature, but has not yet, to our knowledge, been given sufficient attention. The main practical implication is related to offshoring. Overly-aggressive offshoring can complicate future reshoring. As such, reshoring needs to be considered during offshoring, specifically what manufacturing capabilities that are kept at the domestic location.
Correlation of PAPP-A values with maternal characteristics, biochemical and ultrasonographic markers of pregrancy

Objective: Our aim is to investigate whether there is a correlation of pregnancy-associated plasma protein A (PAPP-A) values with other variables in pregnancy and maternal characteristics. Materials and Methods: We retrospectively analyzed the relation between the PAPP-A levels, demographics, biochemical and ultrasonographic markers of the first trimester screening of 11,842 pregnant women seen at a tertiary hospital between November 2002 and November 2008. Results: A significant difference between PAPP-A values of the diabetic and non-diabetic pregnant women were observed (p=0.0005, Mann-Whitney U test). In terms of weight, crown-rump length, Beta-hCG values, significant differences were observed between low and medium level PAPP-A subgroups and between low and high level PAPP-A subgroups. PAPP-A levels were found to differ significantly between the pregnant women of Caucasian origin and other racial origins. Conclusions: Pregnant women with different ethnic and medical backgrounds have different PAPP-A values and other markers of the aneuploidy screening. To make patient specific risk predictions, understanding these interactions and differences is important. Future studies are needed to understand the pathopyhsiology behind these differences.
Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved.
FACE MASK DETECTION COVID-19 USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Coronavirus Disease 2019 (COVID-19) mendapat perhatian khusus di dunia. Penularan virus COVID-19 menyebar hampir keseluruh dunia, termasuk Indonesia. Indonesia mengalami krisis terutama pada sektor kesehatan dan ekonomi akibat pandemi COVID-19, sehingga pemerintah melakukan Pembatasan Sosial Berskala Besar (PSBB) di setiap daerah karena penyeberan virus yang dinilai sangat cepat. Sehingga pelayanan publik atau tempat umum mengharuskan masyarkat untuk menggunakan masker. Selama ini pendeteksian masker dilakukan manual dengan pengamatan dari petugas keamanan. Dimana sering terjadi human error pada saat pendeteksian masker. Dalam penelitian ini akan menerapkan sistem deteksi masker (Face mask detection) dengan menggunakan pengolahan citra. Face mask detection adalah pendeteksian apakah seseorang menggunakan masker atau tidak menggunakan masker. Pada penelitian ini menggunakan metode Convolutional Neural Networks (CNN). Sistem ini menggunakan kombinasi klasifikasi deteksi objek, gambar, dan pelacakan objek sehingga dapat mengembangkan sistem yang mendeteksi wajah bermasker atau tidak bermasker dalam gambar atau video. Dataset yang diambil bervariasi dengan gambar wajah menggunakan hijab, topi dan tidak menggunakan atribut. Selain itu, gambar yang diambil dari berbagai negara seperti asia, eropa dan amerika. Pada pengujian sistem face mask detection mendapatkan nilai akurasi sebesar 0.9933% dan training loss 0,0213%.
What Can Electric Noise Spectroscopy Tell Us on the Physics of Perovskites?

Electric noise spectroscopy is a non-destructive and a very sensitive method for studying the dynamic behaviors of the charge carriers and the kinetic processes in several condensed matter systems, with no limitation on operating temperatures. This technique has been extensively used to investigate several perovskite compounds, manganese oxides (La1−xSrxMnO3, La0.7Ba0.3MnO3, and Pr0.7Ca0.3MnO3), and a double perovskite (Sr2FeMoO6), whose properties have recently attracted great attention. In this work are reported the results from a detailed electrical transport and noise characterizations for each of the above cited materials, and they are interpreted in terms of specific physical models, evidencing peculiar properties, such as quantum interference effects and charge density waves.
Parenting of schoolchildren’s television viewing in Addis Ababa, Ethiopia

ABSTRACT Although children are increasingly being exposed to television in Ethiopia, much is unknown about related parental responses. This study investigated parenting practices associated with schoolchildren’s television viewing, with a focus on parental concern and mediation strategies. Three hundred and eighty-seven parents of primary schoolchildren (grades 1 through 4; Mage = 9.6 years, SD = 1.81; age: 6 to 15 years) attending six schools in Addis Ababa, completed a validated measure of concern and mediation. Results indicated that children viewed on average 2.32 h (SD ± 1.70) of television per day, and parents reported a moderate level of concern about the negative effects of television on children. The study further showed that parents mostly used restrictive mediation over instructive and co-viewing practices. Regression analysis showed that while demographic variables, together with child and parent viewing hours, significantly predicted all three mediation practices, parental concern significantly predicted restrictive and instructive mediations but not co-viewing. As children’s television exposure has become an emerging societal concern in Ethiopia, future research is recommended for contextualized understanding and intervention.
Exploring data driven initiatives for smart city development: empirical evidence from techno-stakeholders’ perspective

ABSTRACT The aim of this article is to provide a critical understanding of techno-stakeholders’ perspective by considering data driven initiatives for smart city development. Particularly, a model is proposed based on a systematic literature review to explore techno-stakeholders’ perspective in smart cities. Findings from this study identifies that data driven initiatives that influence techno-stakeholder’s perspectives for smart city development comprises of stakeholder involvement, data ownership, data access, policies, regulations, trust, and data privacy. Evidence from this study offer insight into techno-stakeholder’s perspectives by proposing a model that could be utilized as a governance tool to benchmark and evaluate urban data transformation.
Lower Income Levels Correlate With Increased Prevalence of Gestational Diabetes Mellitus: A Systematic Review and Network Meta-Analysis

BackgroundIncome level is an important factor that influences the occurrence of gestational diabetes. Thus, this systematic review and network meta-analysis aimed at evaluating the correlation between income levels and the prevalence of gestational diabetes. MethodsRelevant published studies were searched in Pubmed, Web of Science, Cochrane library, and Ovid from the establishment time of database to January 3, 2020. Finally, 13 eligible clinical studies involving 1,817,801 women were selected from a total of 3776 studies and included in this study. The statistical softwares Revman5.3 and Stata14.0 were used to compare the prevalence of gestational diabetes in five different income levels; low, lower middle, medium, upper middle, and high. ResultsThe incidences of gestational diabetes in people with different economic income levels were: high income <middle income <upper middle income <lower middle income <low income. ConclusionThere is no linear correlation between income levels and incidences of gestational diabetes. However, the overall prevalence of gestational diabetes is inversely proportional to income level, that is, the higher the income level, the lower the prevalence of gestational diabetes.
Open-Pit Mine Road Extraction From High-Resolution Remote Sensing Images Using RATT-UNet

With the further development of the construction of “smart mine,” the technology of mine unmanned truck has developed rapidly. Road data are an important prerequisite for the mature application of mining and transportation dispatching system and unmanned driving, so it is particularly important to extract the road in open-pit mines timely and accurately. Different from urban roads, mine roads have no clear road edge and more background interference. In view of the above problems, a convolutional neural network called RATT-UNet (R: residual connection; ATT: attention), which combines residual connection, attention mechanism, and U-Net, is proposed to extract mine road from high-resolution remote sensing images. The advantages of the proposed method are listed as follows: first, the well-designed RATT unit combining residual connection and attention mechanism is used to construct the proposed neural network, which improves the network’s perception ability of detailed features. Second, the abundant skip connections and residual connections can improve the information flow, allowing a better network to be designed with fewer parameters. Third, a composite loss function based on structural similarity is proposed, which effectively alleviates the noise and edge blurring phenomenon and improves segmentation quality. Finally, we perform postprocessing optimization operations on the road extraction results. Experimental results demonstrate that the proposed RATT-UNet outperforms all the comparing network models in terms of the quality of extraction results and evaluation indicators in the mining road extraction task.
Modal Analysis of the Lysozyme Protein Considering All-Atom and Coarse-Grained Finite Element Models

Proteins are the fundamental entities of several organic activities. They are essential for a broad range of tasks in a way that their shapes and folding processes are crucial to achieving proper biological functions. Low-frequency modes, generally associated with collective movements at terahertz (THz) and sub-terahertz frequencies, have been appointed as critical for the conformational processes of many proteins. Dynamic simulations, such as molecular dynamics, are vastly applied by biochemical researchers in this field. However, in the last years, proposals that define the protein as a simplified elastic macrostructure have shown appealing results when dealing with this type of problem. In this context, modal analysis based on different modelization techniques, i.e., considering both an all-atom (AA) and coarse-grained (CG) representation, is proposed to analyze the hen egg-white lysozyme. This work presents new considerations and conclusions compared to previous analyses. Experimental values for the B-factor, considering all the heavy atoms or only one representative point per amino acid, are used to evaluate the validity of the numerical solutions. In general terms, this comparison allows the assessment of the regional flexibility of the protein. Besides, the low computational requirements make this approach a quick method to extract the protein’s dynamic properties under scrutiny.
Gráficas de trayectorias escolares de estudiantes de bachillerato

Para las autoridades escolares es importante conocer cómo los alumnos de su institución avanzan en sus estudios, porque de esta forma pueden detectar cualquier tipo de dificultades que se presenten; sin embargo, esa información no siempre se la conoce a tiempo. El objetivo de esta investigación es proponer una metodología que permita visualizar las trayectorias escolares de los estudiantes y de esta forma se identifiquen las dificultades que tienen en la asignatura de matemáticas. Para mostrar el potencial de la metodología, se investigó a los alumnos de bachillerato de una institución educativa ecuatoriana entre 2015 y 2019, se procesaron sus calificaciones y se generaron gráficas de trayectorias escolares que presentan la evolución de sus calificaciones durante el período escolar. Mediante esta metodología se facilita la identificación de trayectorias individuales de bajo rendimiento, además, se encontró que las actividades colaborativas tienen calificaciones más altas que las individuales, también se evidenciaron las diferencias de rendimiento entre cursos. La presentación de las calificaciones mediante gráficas es una herramienta poderosa para la gestión académica, ya que facilita la detección de dificultades y la toma de decisiones oportunas.
Survey and Analysis on Machine Learning Approaches for Image Annotation

In current years, a large amount of image data is being collected worldwide, which is majorly generated by corporate organizations, health industry and social networking sites. With the strength of substantial level depiction of images, Annotating image has numerous applications not only in image understanding and analysis but also in some of the concern domain like medical research, rural and urban management. Automatic Image Annotation (AIA) has been raised since the late 1990s due to inherent weaknesses of manual image annotation. In this paper, a deep review of the most recent stage in the development of AIA methods is presented by synthesizing 32 literatures published during the past decades. We classify AIA methods into five categories: 1) Kernel Logistic Regression (KLR), 2) Tri-relational Graph (TG), 3) Semantically Regularised CNNRNN (S-CNN-RNN), 4) Label Correlation guided Deep Multi-view (LCDM), and 5) Multi-Modal Semantic Hash Learning (MMSHL). Considering inspiration on the basis of main idea, framework of model, complexity of computation, time complexity and accuracy in annotation Comparative analysis for various AIA methods are done.
Aplicaciones y potencial de las técnicas de diagnóstico por imagen en la investigación biomédica de la enfermedad de Chagas

Las tecnicas de diagnostico por imagen son metodos no invasivos o minimamente invasivos que permiten la visualizacion de organos internos y el estudio de su funcion. Incluyen la ecografia, radiologia, tomografia computarizada, resonancia magnetica y medicina nuclear, entre otras. El diagnostico por imagen se ha convertido en un componente esencial en las investigaciones cientificas que requieren el seguimiento in vivo de cambios patologicos. En esta revision, presentamos estas tecnicas y sus aplicaciones para el estudio de la infeccion por Trypanosoma cruzi , parasito causante de la enfermedad de Chagas; con enfasis en la ecografia, la tecnica mas ampliamente empleada por su bajo costo y la unica que proporciona imagenes en tiempo real. La ecografia se utiliza actualmente en el Centro de Investigaciones Regionales “Dr. Hideyo Noguchi” de la Universidad Autonoma de Yucatan, para el estudio de la infeccion por T. cruzi en el modelo murino. Ha demostrado ser util para la evaluacion in vivo decambios morfologicos y funcionales del corazon, el estudio de la progresion de la infeccion y la efectividad de nuevos farmacos. Adicionalmente, las imagenes obtenidas nos han permitido el desarrollo de sistemas automatizados auxiliares en el diagnostico de la infeccion, los cuales se encuentran en fase experimental. La ecografia es util para responder preguntas que aun permanecen sin explorar sobre la enfermedad de Chagas como, el estudio de la progresion del dano organos durante la infeccion.
‘It was not okay because you leave your friends behind’: A prospective analysis of transition to adult care for adolescents living with perinatally-acquired HIV in South Africa

ABSTRACT Our objective was to determine facilitators and barriers to successful transition to adult care for adolescents living with perinatally-acquired HIV in South Africa. We prospectively enrolled 30 adolescents living with perinatally-acquired HIV after their pediatrician deemed them ready for transition to adult care but prior to their transition. Eighteen months after enrollment, we measured transition status, engagement in care (i.e., viral load within 12 months of transition), and viral suppression (<200 copies/ml). Additionally, we conducted in-depth interviews with adolescents before and after transition to explore facilitators and barriers to successful transition. A total of 19/30 (63%) adolescents transitioned to adult care. Of those who transitioned, 11 (58%) were retained in care and 7 (37%) were virally suppressed one year after transition to adult care. Insufficient staff training, lack of availability of pediatric ART formulations in adult clinics, and insufficient clinical monitoring contributed to delayed transition. Rigid clinical scheduling that interfered with school and loss of clinic relationships with peers and clinical staff were major factors in contributing to poor engagement in care after transition. Maturity of the adolescent, reduced distance to clinic, and reduced length of time in the clinic were seen as facilitators to transition to adult care. In conclusion, improved preparation for transition by pediatric and adult clinical staff, including restructuring of care delivery, may improve successful transition of adolescents living with perinatally acquired HIV to adult care. Transition readiness assessments are needed to determine optimal timing of transition and which adolescents are ready to transition to adult care.
Assessment of sugar cane bagasse bio-oil as an environmental friendly alternative for pavement engineering applications

ABSTRACT Due to the depletion of conventional asphalt binder resources and price hiking, researchers are focused on introducing environmental friendly and sustainable materials as a replacement to the conventional binder. On the other hand, the production of a large quantity of sugarcane bagasse (SCB) leads to severe environmental problems. Therefore, effective utilisation of SCB bio-oil as a replacement to conventional binder can help in reducing the aforementioned problems. In this study, the conventional binder and mixture were modified at 5% and 10% SCB bio-oil. The rheological behaviour of the modified binder was analysed using conventional tests and dynamic shear rheometer. The performance of asphalt mixtures was then evaluated using dynamic modulus, cooper wheel tracker and four point beam fatigue test. The ability of modified mixtures against moisture damage was also assessed. The results indicate that the 10% SCB bio-oil sample possessed better fatigue resistance among all samples with insignificant effect on rutting resistance. Further, the SCB bio-oil negatively influenced moisture resistance of modified mixtures. So it can be concluded that SCB bio-oil can potentially be used to modify conventional binder and mixture. However, there is a need to improve the moisture resistance of SCB bio-oil modified binder.