Miracle-dz/UniFAR-FTUs huggingface dataset:
ACMAC’s PrePrint Repository Signal to noise ratio analysis in virtual source array imaging Garnier Josselin and Papanicolaou George and Semin Adrien and Tsogka Chrysoula

We consider correlation-based imaging of a reflector located on one side of a passive array where the medium is homogeneous. On the other side of the array the illumination by remote impulsive sources goes through a strongly scattering medium. It has been shown in [J. Garnier and G. Papanicolaou, Inverse Problems 28 (2012), 075002] that migrating the cross correlations of the passive array gives an image whose resolution is as good as if the array was active and the array response matrix was that of a homogeneous medium. In this paper we study the signal to noise ratio of the image as a function of statistical properties of the strongly scattering medium, the signal bandwidth and the source and passive receiver array characteristics. Using a Kronecker model for the strongly scattering medium we show that image resolution is as expected and that the signal to noise ratio can be computed in an essentially explicit way. We show with direct numerical simulations using full wave propagation solvers in random media that the theoretical predictions based on the Kronecker model are accurate.
Causal Web Determination from Texts

The research aim is to determine a causal web from downloaded guru web-board documents. The causal web which benefits a diagnosis service assistant of a problem-solving system consists of several cause-effect pair sequences where each cause-effect pair has a cause-effect relation and the last cause-effect pair of each cause-effect pair sequence has the same effect concept. Each causative/effect concept is expressed by an elementary discourse unit or a simple sentence. The research has three problems; how to determine the cause-effect pair with an overlap problem between a causative-verb concept set and an effect-verb concept set, how to determine cause-effect pair sequences including causative/effect boundary determination, and how to determine the causal web on the extracted cause-effect pair sequences without redundant sequences. We use a word co-occurrence to represent a sentence’s event/state with a causative/effect concept. We then propose using a self-Cartesian product on a collected word co-occurrence set and Naïve Bayes including categorized verb groups to extract each cause-effect pair sequence including the boundary determination without the verb-concept-overlap influence. And we use a dynamic template matching technique to determine the causal web without the redundancy. The research result has a high percentage correctness of the causal web determination.
Voxel octree intersection based 3 D scanning

Recent developments in the field of three dimensional (3D) printing have resulted in widely available low-cost 3D printers. These printers require 3D models, which are traditionally created in 3D modeling software or are created from 3D scans of existing objects. To be printable, these models must exhibit the property of being watertight. In this thesis, a technique is developed which, in combination with a custom built low-cost 3D scanner, produces watertight 3D models. Models produced by this technique – the voxel octree intersection technique – do not require any additional processing prior to 3D printing. Results from using this technique with the custom built scanner are examined, along with the effects of changing various parameters to the technique.
X-(2) Modulator With 40-GHz Modulation Utilizing BaTiO3 Photonic Crystal Waveguides

— Future telecommunication and data center networks as well as quantum optical communication systems will require optical modulators with wide bandwidths, large extinction, low operating voltage, and small size. We report the first quantitative demonstration of slow light enhancement of the electro-optic (EO) coefficient in a χ (2) ferroelectric waveguide at microwave modulation frequencies. This is demonstrated in a compact (1 mm) photonic crystal device with a voltage-length product ( 𝑽 𝝅 ⋅ 𝑳 ) of 0.66 V-cm at 10 GHz and measured electro-optic modulation out to 40 GHz. A local enhancement factor of 12 and effective electro-optic coefficient of 900 pm/V is measured in the PC region at a wavelength of 1530 nm. By further optimizing the PC structure, devices with EO 3 dB bandwidths greater than 40 GHz and voltage-length product of 0.16 V-cm are predicted with 100 µm interaction length.
A Deep Learning Approach to Video Processing for Scene Recognition in Smart Office Environments

The field of computer vision, where the goal is to allow computer systems to interpret and understand image data, has in recent years seen great advances with the emergence of deep learning. Deep learning, a technique that emulates the information processing of the human brain, has been shown to almost solve the problem of object recognition in image data. One of the next big challenges in computer vision is to allow computers to not only recognize objects, but also activities. This study is an exploration of the capabilities of deep learning for the specific problem area of activity recognition in office environments. The study used a re-labeled subset of the AMI Meeting Corpus video data set to comparatively evaluate different neural network models performance in the given problem area, and then evaluated the best performing model on a new novel data set of office activities captured in a research lab in Malmö University. The results showed that the best performing model was a 3D convolutional neural network (3DCNN) with temporal information in the third dimension, however a recurrent convolutional network (RCNN) using a pre-trained VGG16 model to extract features and put into a recurrent neural network with a unidirectional Long-Short-Term-Memory (LSTM) layer performed almost as well with the right configuration. An analysis of the results suggests that a 3DCNN’s performance is dependent on the camera angle, specifically how well movement is spatially distributed between people in frame.
Bayesian Estimation of High-dimensional and Time-varying Parameters in Longitudinal Data Analysis

In social science, regularized regression methods have been successfully applied to analyze the data with many covariates and interaction terms, possibly larger than the sample size. Yet, structural changes in longitudinal data pose a serious challenge to the application of existing regularization methods because time-varying effects can be over-regularized. Motivated by two existing studies from social science where the exact timing and scope of structural changes is unknown, we develop a hidden Markov Bayesian bridge model that investigates parametric change-points in high-dimensional longitudinal data. The proposed method successfully uncovers change-points in the relationship between government partisanship and economic growth and the relationship between food aid and the civil war on-set.
Genetic Mapping of QTLs for Physiological Traits in Rice (Oryza sativa L.) by using Danteswari/Daggad Deshi Ril Population

Rice is the predominant food crop for more than three billion of the world’s population and contributes up to 80% of the daily calories’ intake, specifically in Asia. Because of its semi-aquatic nature, smaller root system rice is severely affected by drought (Sahebi et al., 2018). Millions of lowland rainfed areas in Asia are adversely affected by drought stress, which results in a drastic reduction in crop productivity by 13-35%. Water stress can arise in early growth stages of the crop; from flowering to grain filling, depending on the duration and intensity of stress (Wade et al., 1999). Development of rice cultivars with augmented drought tolerance is thus pivotal in boosting production, strengthening yield stability, and allaying poverty in communities contingent on rainfed production.
Evaluation of CH4MODwetland and Terrestrial Ecosystem Model (TEM) used to estimate global CH4 emissions from natural wetlands

Abstract. Wetlands are the largest and most uncertain natural sources of atmospheric methane ( CH4 ). Several process-based models have been developed to quantify the magnitude and estimate spatial and temporal variations in CH4 emissions from global wetlands. Reliable models are required to estimate global wetland CH4 emissions. This study aimed to test two process-based models, CH4MOD wetland and Terrestrial Ecosystem Model (TEM), against the CH4 flux measurements of marsh, swamp, peatland and coastal wetland sites across the world; specifically, model accuracy and generality were evaluated for different wetland types and in different continents, and then the global CH4 emissions from 2000 to 2010 were estimated. Both models showed similar high correlations with the observed seasonal/annual total CH4 emissions, and the regression of the observed versus computed total seasonal/annual CH4 emissions resulted in R2 values of 0.81 and 0.68 for CH4MOD wetland and TEM, respectively. The CH4MOD wetland produced accurate predictions for marshes, peatlands, swamps and coastal wetlands, with model efficiency (EF) values of 0.22, 0.52, 0.13 and 0.72, respectively. TEM produced good predictions for peatlands and swamps, with EF values of 0.69 and 0.74, respectively, but it could not accurately simulate marshes and coastal wetlands (EF ). There was a good correlation between the simulated CH4 fluxes and the observed values on most continents. However, CH4MOD wetland showed no correlation with the observed values in South America and Africa. TEM showed no correlation with the observations in Europe. The global CH4 emissions for the period 2000–2010 were estimated to be 105.31 ± 2.72 Tg yr −1 by CH4MOD wetland and 134.31 ± 0.84 Tg yr −1 by TEM. Both models simulated a similar spatial distribution of CH4 emissions globally and on different continents. Marshes contribute 36 %–39 % of global CH4 emissions. Lakes/rivers and swamps are the second and third greatest contributors, respectively. Other wetland types account for only approximately 20 % of global emissions. Based on the model applicability, if we use the more accurate model, i.e., the one that performs best as evidenced by a higher model efficiency and a lower model bias, to estimate each continent and wetland type, we obtain a new assessment of 116.99–124.74 Tg yr −1 for the global CH4 emissions for the period 2000–2010. Our results imply that performance at a global scale may conceal model uncertainty. Efforts should be made to improve model accuracy for different wetland types and regions, particularly hotspot regions, to reduce the uncertainty in global assessments.
Cytokinins in Plant Pathogenic Bacteria and Developing Cereal Grains

Cytokinin analysis by immunoaffinity chromatography (IAC), high-performance liquid chromatography (HPLC) and radioimmunoassay (RIA) or enzyme-linked immunosorption assay (ELISA) has been used to study two separate topics: the role of tRNA in bacterial cytokinin biosynthesis and the changes in cytokinin concentration which occur during cereal grain development. Transfer RNA isopentenylation in the gall-forming plant pathogen Agrobacterium tumefaciens is encoded by the chromosomal miaA locus. Mutation of miaA reduces tRNA isopentenylation significantly and preliminary data suggest that turnover of isopentenylated tRNA is responsible for low level secretion of free N6-isopentenyladenine (iP) by the bacteria. However, the major route of cytokinin biosynthesis by gall-forming plant patho- genic bacteria is not via tRNA turnover but by direct biosynthesis mediated by dimethylallylpyro- phosphate: 5'-AMP transferase (DMAPP :AMP transferase) encoded by such genes as ipt, tzs (from A, tumefaciens) or ptz (from Pseudomonas savastanoi). Analysis of cytokinin levels in developing wheat and rice grains in the period immediately following pollination showed large transient increases in zeatin (Z) and zeatin riboside (ZR) which coincided with the period of maximum endosperm cell division reported by others. Detailed analyses of maize kernels, where development can be staged readily, showed that Z and ZR concentrations peaked 9 days after pollination (DAP). During the period 8-10 DAP, cytokinin oxidase underwent a significant increase in specific activity, indicating that cytokinin catabolism was enhanced as endosperm cell division ended.
Characterization Methods for the Effect of Microbial Mineralization on the Microstructure of Hardened C3S Paste

Microbial mineralization has a significant effect on the hydration process of cement-based materials. This paper mainly studied the characterization methods for hydration degree and hydration product of C3S in hardened paste under microbial mineralization. Quantitative X-ray diffraction (QXRD), thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FT-IR), and electron backscatter diffraction (EBSD) were used and compared. The results showed that microbial mineralization increased the hydration degree of T-C3S. QXRD and EBSD could be used to characterize the content of C3S, and there were few differences between the two methods. TG could accurately characterize the content of Ca(OH)2 and CaCO3 at different depths of the sample, and FT-IR could qualitatively characterize the presence of Ca(OH)2 and CaCO3.
A Novel approach based on Curvelet Transform for Detecting Climate Signal using Time Series

Climate Signal identifying and attribution of observed record plays vital role in incorporate information of the climate system. Traditional techniques for detecting and attributing changes due to statistical forcing require for large number of general circulation model statistically results of various primary conditions and forcing scenarios, and these have been completed with a average number of GCMs. In this paper, we proposed a novel approach based on Curvelet Transform to identify the climate change in time series statistics. The parameters like auto-correlation and maximum likelihood ratio through global mean temperature are estimated for statistical climate detection of climate change. The object of this paper is discussed about natural climate variability; It is various claims of anthropogenic signal detection. Numerical results illustrate that our proposed method is self-consistent, accurate and quantitative results than established methods. The detection and attribution analysis can be further extended to recognize the effects of non climatic localized effects like land use and land cover changes, urbanization.
An overview of percutaneous endoscopic gastrostomy tube placement in the intensive care unit

Critically ill patients are at increased risk for malnutrition as they often have underlying acute and chronic illness, stress related catabolism, decreased appetite, trauma and ongoing inflammation. Malnutrition is recognized as a leading cause of adverse outcomes, higher mortality, and increased hospital costs. Percutaneous endoscopic gastrostomy (PEG) tubes provide a safe and effective route to provide supplemental enteral nutrition to these patients. PEG placement has essentially replaced surgical gastrostomy as the modality of choice for longer term feeding in patients. This is a highly prevalent procedure with 160,000 to 200,000 PEG procedures performed each year in the United States. The purpose of this review is to provide an overview of current knowledge and practice standards with regards to placement of PEG tube in the Intensive Care Unit (ICU). When a patient is considered for a PEG tube, it is important to evaluate the treatment alternatives and identify the best option for each patient. In this review, we provide the advantages and disadvantages of various feeding modalities and devices. We review the indications and contraindications for PEG tube placement as well as the risks of this procedure. We then describe in detail the per-oral pull, per-oral push, and direct percutaneous techniques for PEG tube placement. Additionally, we review the feasibility of having interventional pulmonologists place PEG tubes in the ICU.
Design of Application To Tracking System and Temperature Monitoring During Delivery Based on IoT Device

In the industry 4.0 era – a variety of new technologies to added value. Industry 4.0 is the end – to – end digitalization of all physical assets and integration into the digital supply chain with value chain technologies. Digital supply chain brings down those walls and the chain becomes a completely connected that is transparent to all the actors involved from the shippers of goods, delivery service companies, and finally to the customers. Logistics as part of the supply chain has an important role in Indonesia. Specifically, in delivery services. Currently, the tracking of the delivery service company can only be executed from one drop point to another drop point. A trip between drop points cannot be traced so that the tracking becomes not transparent. Logistics roles in delivery service company should utilize the Internet of Things (IoT) in handling customer who needs real-time transparency routes and monitoring of temperature-sensitive pharmaceutical cargo and perishable cargo. If this is implemented, delivery service companies in Indonesia can be crowned as the most advanced delivery service company in the world. The research information system uses a quantitative method. The data collection technique used in this research was collected by filling out a questionnaire. The result shown indicates that delivery service companies in Indonesia may use Traceable e-Seal that are connected to the MyTraceable application as a solution to transparency route, real-time monitoring of temperature-sensitive goods. We prompt to use MyTraceable application and smart lock because it can increase customer satisfaction and delivery service company performance.
COMPUTATIONALLY EFFICIENT PRIVATE INFORMATION RETRIEVAL PROTOCOL

This paper describes a new computationally efficient private information retrieval protocol for one q-ary symbol retrieving. The main advantage of the proposed solution lies in a low computational complexity of information extraction procedure, as well as the constructive simplicity and flexibility in choosing the system parameters. Such results are based on cosets properties. The proposed protocol has communication complexity slightly worse than the best schemes at the moment, which is based on locally decodable codes, but it can be easily built for any parameters of the system, as opposed to codes. In comparison with similar solutions based on polynomials, the proposed method gains in computational complexity, which is important especially for servers which must service multiple requests from multiple users.
Analysis The Sentiments Of Amazon Reviews Dataset By Using Linear SVC And Voting Classifier

: Any opinion of a person that can convey emotions, attitudes, or opinions is known as a sentiment. The data analyzes that are collected from media reports, consumer ratings, social network posts, or microblogging sites are classified as opinion mining research. Analysis of sentiment should be viewed as a way of evaluating people for particular incidents, labels, goods, or businesses. The amount of views exchanged by people in micro-logging sites often increases, which makes nostalgic interpretations more and more common today. All sentiments may be categorized as optimistic, negative, or neutral under three groups. The characteristics are derived from the document term matrix using a bi-gram modeling technique. The sentiments are categorized among positive and negative sentiments. In this analysis, the Python language is used to apply the classification algo for the data obtained. The detailed accomplishment of LinSVC demonstrates greater precision than other algos.
A Refined Non-Driving Activity Classification Using a Two-Stream Convolutional Neural Network

It is of great importance to monitor the driver’s status to achieve an intelligent and safe take-over transition in the level 3 automated driving vehicle. We present a camera-based system to recognise the non-driving activities (NDAs) which may lead to different cognitive capabilities for take-over based on a fusion of spatial and temporal information. The region of interest (ROI) is automatically selected based on the extracted masks of the driver and the object/device interacting with. Then, the RGB image of the ROI (the spatial stream) and its associated current and historical optical flow frames (the temporal stream) are fed into a two-stream convolutional neural network (CNN) for the classification of NDAs. Such an approach is able to identify not only the object/device but also the interaction mode between the object and the driver, which enables a refined NDA classification. In this paper, we evaluated the performance of classifying 10 NDAs with two types of devices (tablet and phone) and 5 types of tasks (emailing, reading, watching videos, web-browsing and gaming) for 10 participants. Results show that the proposed system improves the averaged classification accuracy from 61.0% when using a single spatial stream to 90.5%.
INFLUÊNCIA DA TEMPERATURA NA BIOLOGIA DE TRIATOMÍNEOS.XX. Triatoma maculata (ERICHSON, 1848) (HEMIPTERA, REDUVIIDAE)

Estudou-se o efeito da temperatura no ciclo evolutivo de Triatoma maculata {Erichson, 1848) paraestabelecer a sua influencia no desenvolvimento do triatomineo, conhecer alguns aspectos de sua biologia cdominar tecnicas de criacao para viabilizar o uso dessa especie nos testes experimentais em doenca deChagas. O periodo medio de incubacao dos ovos de T.maculata, para machos e femeas, foi de 29 e de 16dias, a temperatura de 25° e 30°C, respectivamente.A temperatura de 25°C, a duracao media do cicloevolutivo, para machos e femeas, foi, respectivamente, de 223,5 e 222,3 dias e, de 168,7 e 167,2 dias, a30"C. Os experimentos foram realizados a 25 ± 0,5°C e 30 ± 1,0°C, com umidade de 70 ± 5% e fotoperiodode 12 horas.
Energy saving smart waste segregation and notification system

The increase in the generation of waste due to the continuous growth of urbanization and industrialization has become a severe problem for the government. Population density, lifestyles, economic conditions have a great impact on alarming waste composition. Hence, waste segregation is necessary to save our environment. The traditional manual method used for the segregation of waste in Bangladesh is time-consuming and can have adverse effects on the health of the involved people. Proper segregation can bring the economic values of the wastes as well. The proposed smart segregation system will notify the garbage boy after reaching the amount of waste at a certain level. If waste is not collected from that smart bin in time, the system will send a notification to the City Corporation. The proposed system will help to reduce the intensity of greenhouse gases as well. The system will consume electricity when an absence of sunlight, otherwise it consumes solar energy which saves energy consumption on sunny days.
Mobile Marketing Strategies and Customer Patronage of Mobile Telecommunication Services

This study empirically examines the relationship between mobile marketing strategies and customer patronage of mobile telecommunication services. The study applied a cross-sectional survey design in a structured questionnaire to collect data from 400 accessible telecom subscribers across the 4 major telecom operators which include; MTN, Glo, Aitel, and 9 Mobile. A total of 4 hypotheses were proposed and Spearman’s Rank Correlation Coefficient tool was employed with the help of SPSS version 21.0, in statistically testing them. Findings revealed a positive and significant relationship between the dimensions of mobile marketing strategies and measures of customer patronage. The study, therefore, concludes that mobile marketing is an effective tool in improving purchase intention and referral. Therefore, the research recommends that telecom operators who want to improve patronage of subscribers should adopt mobile websites and SMS marketing; as these tools have been further verified to have a huge capacity to enhance purchase intention and customer referral.