Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Microphysical Properties of Ice Clouds as Inferred from the Polarization of Electromagnetic Waves

On the Microphysical Properties of Ice Clouds as Inferred from the Polarization of Electromagnetic Waves. (August 2011) Benjamin H. Cole, B.S., University of Dallas Co-Chairs of Advisory Committee: Dr. Ping Yang Dr. Sarah Brooks Uncertainties associated with the microphysical and radiative properties of ice clouds remain an active research area because of the importance these clouds have in atmospheric radiative transfer problems and the energy balance of the Earth. In this study, an adding/doubling model is used to simulate the top of atmosphere (TOA) radiance and full Stokes vector from an ice cloud at the wavelength λ = 865 nm with many different combinations of assumed ice habits (shapes) and different degrees of ice surface roughness, and the polarized radiance at a wide range of scattering angles is derived. Simulated results are compared with polarized radiance data from the POLDER (POLarization and Directionality of the Earth’s Reflectances) instrument on board the PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) satellite. Bulk ice scattering properties are obtained by using five different size distributions collected during field campaigns ranging in effective diameter from 10 μm
Ideal hypothesis testing and algorithmic information transfer

In this talk we study influence testing for two discrete time series of equal length, by defining two types of universal influence-free enumerable semimeasures. We prove a coding result that characterizes the semimeasure approximately by a variant of conditional prefix-free Kolmogorov complexity, and describe how influence tests in engineering literature can be considered as approximations of these ideal definitions. We show that these have some nice additive properties. The results shed light on the much more involved case of general ideal hypothesis testing, where many questions are left open. In statistics a simple hypothesis is defined as a set of logical statements that allow the inference of a unique semimeasure over the set of all a priori possible outcomes of an experiment. Assume that two simple hypothesis have semimeasures P0 and P1. The statistical test d(x) = P1(x)/P0(x) has an optimal power for any significance level [5]. Composite tests are tests that specify a set S of semimeasures. Multiplicative dominance defines a partial order on the semimeasures, and in some cases the enumerable semimeasures in S have a maximal element, which is called the the universal element of the hypothesis. The likelihood-ratio test can now define in the same way notions of significance and power, which under some assumptions, can have the same meaning as traditional significances and powers. The hypothesis of a time series x being influence-free from another time series y with equal length l(x) = l(y) = n, is a composite hypothesis. We show that with this hypothesis there corresponds a universal enumerable length conditional semimeasure, whose logarithm is approximately
Technologies for a Diagnostic Technique for HVAC Systems Using IoT and Cloud-Based Architecture

Optimizing the energy efficiency in Heating, Ventilation, and Air Conditioning (HVAC) systems is becoming increasingly important as costs rise and the environment needs to be protected. Existing plants are often not adequately digitized to identify unnecessary excess consumption. The overall goal of the project described in this work is the development of an Internet of Things (IoT)-based diagnostic technique for those systems. Our intended system will allow the evaluation of the performance of HVAC systems with as few additional measurement points as possible. The main contributions of this paper are (a) a comparison of different cloud providers, (b) a description of the architecture of the system, and (c) an initial evaluation of the value added to the case study enterprise by the system in use.
IAPC2: Improved and Automatic Classification of Polyp for Colorectal Cancer

The CRC (Colorectal Cancer) is the lethal disease now a day in all over the world. Detect the polyp and predict about colorectal cancer increased the rate of patient survival. An improved and automated detection system is developed to detect and classify polyp. The images-based dataset plays a crucial role in the field of medical by applying Deep learning with TL approaches the proposed research implements two models: Shuffled and ResNet-50. These models enhanced the accuracy and sort outset the gaps of formal work. The prior work were expensive, time taken, use less size of data and no of classes was also low. There a there are different parameter applied to achieve the results. Before training models dataset was processed and changed the dimension of all classes in to 224 x 224.Both models were altered according to the problem like the last 3 layers of the models. The overall accuracy achieved was 97.0 by using above mentioned models respectively. This method indicates the high accuracy which will helpful in the reduction of mortality rate of colorectal cancer.
Laser Doppler Anemometer Measurements of Particle Velocity in a Laboratory Precipitator

A laboratory precipitator was constructed to permit direct measurement of particle velocities by means of laser Doppler anemometry. Dimensions of the ESP were chosen to reflect realistic precipitator designs, and the flow conditions were carefully established. Measurements of particle velocities were carried out for a variety of particles sizes and electrical conditions. These velocities were interpreted in light of current theories for electric field configuration and particle charging. Generally, the measured velocities exceeded those predicted. On the assumption that corona wind was responsible for the excess velocities, several corona wind hypotheses were tested. The results are in fair agreement with previous work, but the phenomena are too complex to describe adequately with simple theories.
Moment maps and diffeomorphisms

Atiyah and Bott pointed out, in [1], that the curvature of a connection on a bundle over a surface can be viewed as the “momentum” corresponding to the action of the gauge group. This observation, together with various extensions, has stimulated a great deal of work and provides a conceptual framework to understand many phenomena in Yang-Mills theory. Our purpose in this paper is to explore some similar ideas in the framework of diffeomorphism groups. We begin by identifying a moment map in a rather general setting, and then see how the ideas work in some more specific situations. We hope to show that the moment map point of view is useful, both in understanding certain established results and also in suggesting new problems in geometry and analysis. While these analytical questions are the main motivation for the work, we will concentrate here on the formal aspects and will not make any serious inroads on the analysis.
H I survey of face-on galaxies - the frequency of distortions in H I disks

The full results of an H I survey of face-on galaxies are presented and it is shown that narrow H I profiles are rare in normal spiral galaxies. This is due in part to the wider-than-expected range of the integral dispersion and in part to the frequent occurrence of large-scale distortions in the H I disk. These factors reduce the number of galaxies with half-power widths less than 30 km/s to about 24 percent of those that would occur if galaxies generally had quiescent, coplanar H I disks. Two useful subsets may be drawn from this study of 212 face-on galaxies with axial ratios greater than 0.87. Fifty-two spirals of all morphological types have half-power widths smaller than 100 km/s and may be used for studies that benefit from a small velocity spread and an enhanced beam-filling factor. About 40 galaxies have velocity widths much larger than expected and are of interest in studies of dynamically peculiar systems. 50 references.
Time Series Recognition with Convolutional and Recursive Neural Networks in BSPM

This paper presents biomedical time series from Body Surface Potential Mapping (BSPM) recognition using various convolutional and recurrent neural network structures. The BSPM signal is in a form of time series form 102 channels located around the chest. The time series are then transformed to time windows to allow recognition of heart diseases. The several options of neural network structures were compared: one-dimensional convolutional neural network, Long-Short-Term Memory neural network, and Gated Recurrent Unit neural network. The article showcases different convolutional and recurrent neural network architectures for recognizing patterns in biomedical time series measured with Body Surface Potential Mapping. The study compared three types of neural network structures: Long-Short-Term Memory neural network, Gated Recurrent Unit neural network, and one-dimensional convolutional neural network. The main goal of paper is to find optimal machine learning solution for heart disease recognition on the basis of BSPM signal. The best results are obtained using model with GRU layer.
IoT-based Spatial Monitoring and Environment Prediction System for Smart Greenhouses

Agriculture has seen several technological transformations in recent years, allowing accelerated growth in production to meet the greater consumer demand for food. In particular, the adoption of the internet of things (IoT) is rapidly transforming the future of agriculture. In this paper, we present a data-driven climate prediction model for greenhouses using an effective and low-cost IoT-based spatial monitoring system. The IoT infrastructure comprises four stages: data gathering, transmission, analysis and processing of information, and visualization. This system was deployed and tested in a real greenhouse for three months, monitoring the temperature, relative humidity, and CO2 levels. The behavior of these environmental variables were predicted 24h in advance, obtaining an advantage in prediction accuracy. Additionally, we developed a spatial monitoring strategy based on packing density theory as a solution to the climate variability within greenhouses, offering a compromise between effectiveness and cost.
A Three-Phase Multistepped-Voltage Inverter Combined with a Single-Phase Inverter through Three Switching Devices

A new multistepped voltage waveform inverter with a total harmonic distortion (THD) of 6.25% is described. The proposed system consists of a three -phase inverter whose conduction interval is smaller than l80°, a triple-frequency single-phase inverter, and three power semiconductor switches that link the two inverters. The advantages of the proposed system over conventional stepped-voltage inverters are that it requires a only small number of switching devices, no three-phase output transformers, and an output filter of reduced size. The relation between THD and the number of steps of an output voltage waveform is discussed. Comparison with other stepped voltage inverters, and experimental results using power transistors are also presented.
Improved picture total scrambling algorithm based on sort transforma1tion

An improved image total scrambling algorithm based on chaotic system,which utilizes the random-alike,erogic and extreme sensitivity to parameters and initial values of chaotic system is proposed in this paper,and detailed procedure is also given.The algorithm generates a image total scrambling matrix through a chaotic system and then perform scrambling on the total image,it has better scrambling performance compared with sort transformation based on chaotic system in literature.In the meantime,as the algorithm have lower time complexity,and also the total scrambling matrix has the strong irregularity,so the proposed new image total scrambling algorithm based on chaotic system possesses high-level security.At last,some simulations and experiments verified the effectiveness of the algorithm.
Stem Cell Therapy in Inflammatory Bowel Disease: A Review of Achievements and Challenges

Abstract Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a group of chronic inflammatory diseases of the gastrointestinal tract. Repeated inflammation can lead to complications, such as intestinal fistula, obstruction, perforation, and bleeding. Unfortunately, achieving durable remission and mucosal healing (MH) with current treatments is difficult. Stem cells (SCs) have the potential to modulate immunity, suppress inflammation, and have anti-apoptotic and pro-angiogenic effects, making them an ideal therapeutic strategy to target chronic inflammation and intestinal damage in IBD. In recent years, hematopoietic stem cells (HSCs) and adult mesenchymal stem cells (MSCs) have shown efficacy in treating IBD. In addition, numerous clinical trials have evaluated the efficiency of MSCs in treating the disease. This review summarizes the current research progress on the safety and efficacy of SC-based therapy for IBD in both preclinical models and clinical trials. We discuss potential mechanisms of SC therapy, including tissue repair, paracrine effects, and the promotion of angiogenesis, immune regulation, and anti-inflammatory effects. We also summarize current SC engineering strategies aimed at enhancing the immunosuppressive and regenerative capabilities of SCs for treating intestinal diseases. Additionally, we highlight current limitations and future perspectives of SC-related therapy for IBD.
CLIQUE: A Web-Based Queue Management System with Real-Time Queue Tracking and Notification of Units for Angeles University Foundation Office of the University Registrar

The CLIQUE is a web-based queue management system with real-time monitoring of queues and notification of units, which was developed to improve the queue system of the University Registrar at Angeles University Foundation. The system aims to deliver an enhanced web-based queue management system organized for maximum productivity and excellent service by reforming the traditional process in a way that utilizes technology. The method used in developing the system is Design Thinking, a framework designed to help designers solve various complicated problems and figure out solutions for clients. The development tools of the system are; HTML, CSS, Bootstrap, JavaScript, Model-View-Controller, ASP.NET, C#, and MySQL. As a result, the developed queue management system is more suitable for clients than the traditional queuing process due to the fact that the clients and staff can track and monitor queues. Also, the developed system has lessened clients’ confusion upon entering the Office of the University Registrar, and the staff would work efficiently in assessing clients. Henceforth, the clients, staffs, and admins were able to manage the queuing process because of the developed queue management system.
SAR ATR method based on sparse representation

In order to recognize SAR target accurately, an identification method based on sparse representation is proposed.The training samples after dimensionality reduction using principal component analysis are used to build a sparse linear model. The sparse coefficient solution x of the test sample is solved by ? 1-minimization. The identification task is solved by utilizing the sparse distribution of the sparse coefficient. Experimental results with MSTAR dataset verify that the identification method based on sparse representation in a certain characteristic dimension can obtain good recognition performance, and the recognition rate can reach more than 98% without knowing the target azimuth.
REVIEW ON BIO-DETOXIFICATION OF AFLATOXINS BASED ON LACTIC ACID BACTERIA: MECHANISM AND APPLICATIONS

Access to healthy food, without any undesirable contamination, is one of fundamental human right. Some of mycotoxins, especially aflatoxins (AFs), contamination feed and food that cause problems such as acute damage liver, irritation, and cancer of the liver and teratogenic complications. Among the physical, chemical and biological methods used to prevent the production, reduction, elimination and deactivation of AF in contaminated food, biological methods have been considered, due to maximum efficiency, low cost, eco-friendly and non-degradation of nutritional quality. The protective effect of lactic acid bacteria as probiotic microorganisms against mutagenic factors, such as polycyclic amines, N-nitrosamine compounds, and mycotoxins has been proven. Therefore, in addition to the beneficial properties using these probiotic bacteria with the ability to remove AF can help to enhance food safety. Although some naturally-occurring bacteria in the intestine can be attached to harmful components such as toxins and prevent them from binding to the intestinal layer, but probiotics have the potential to inactivate toxins through surface binding, due to high adhesion properties in their cell wall proteins. Polysaccharides and peptidoglycans in the cell wall are two of the main ingredients for decomposition, bonding and binding of AFs to lactic acid bacteria. Therefore, this review examines the economic and health impacts of AF contamination in foods. Further, this review discusses how lactic acid bacteria are able to detoxify common food AFs.
Soft Information-Based Localization for 5G Networks and Beyond

Accurate location information is crucial for a variety of new verticals and use cases enabled by 5th generation (5G) wireless networks. While existing localization techniques for cellular networks are continuously evolving in the standardization process, the stringent key performance indicator requirements defined by the 3rd Generation Partnership Project (3GPP) have not been met yet. This paper first provides an in-depth review of the standardized reference signals and time/ angle-based measurements that can be used for localization of user equipments in current 5G networks. Then, the paper details the development of a soft information (SI)-based approach that significantly improves localization accuracy for 5G and beyond 5G networks. Results are obtained in full conformity with 3GPP standards in two standardized scenarios, namely urban microcell and indoor open office, using time/angle-based measurements. Results show that the proposed SI-based localization methods significantly outperform existing techniques, especially when harsh propagation conditions and higher (millimeter Waves) frequencies are considered, paving the way to new services and performance enhancements in 5G and beyond wireless networks.
Indonesian Generation Z’s Awareness of Data Privacy in the Use of Social Media

Sharing personal interest and activity in social media has become a trend nowadays. While passwords are the main access gate in social media accounts, many people still use their personal information as passwords, which inadvertently makes password vulnerable and increasing the chance for unauthorized parties to access or even control user’s account. This research examines Generation Z awareness toward data privacy in multiple social media platforms. By using an online questionnaire to gather data in quantitative approach the data is summarized using descriptive statistics to gain deeper insight. The result shows most of Generation Z are quite aware of data privacy, although their behavior does not indicate any preventive action to protect their data.
Parameter estimation and outliers detection based on Bayesian method

Outliers detection is an important research field in the current data analysis. Outliers in the data will affect the modeling,estimating parameters,forecasting and other issues directly. The conventional methods of outliers detection based on the model are to estimate the model parameters firstly,and then detect the abnormal value. The presence of outliers affects the parameter estimation,which results the in unreliability of outlier detection consequently; On the contrary,the presence of outliers will affect the parameter estimation. In this paper,we propose a new outliers detecting method based on Bayesian method,which can estimate parameters and detect outliers simultaneously. This method is introducing classification variables into linear regression model. Using Gibbs sampling a procedure for computing the posterior probabilities of classification variables and obtaining the estimation of parameters is designed. The outliers can be detected by comparing the posterior probabilities of these classification variables. A large number of simulation experiments illustrate that the proposed method is more sensitive to outliers compared with traditional methods.
Earthquake Magnitude With DAS: A Transferable Data‐Based Scaling Relation

Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning.
The regional forecasting systems of the Italian seas

ABSTRACT Over the last 15 years, five operational systems for the forecasting of the circulation of the Italian seas have been developed, which provide products of use for the management and safety of marine activities and for the protection of the marine environment. The first systems which focused on the Adriatic Sea and the Sicily Strait, have been added to over the years, to cover the Tyrrhenian Sea, the seas around Sardinia, and, finally, the region around the Bonifacio Strait, a small area interested by an intense marine traffic. In this work, the main features of the five regional systems are described and examples are given of their capabilities and their use for revealing new features of marine circulation.
Effect of Mesotrione 40% SC on Weed Growth, Yield and Economics of Maize (Zea mays L.)

Newer herbicides are required to reduce the losses cause by the weeds in maize crop. Therefore, a field experiment was conducted during kharif 2019 at Research Farm, AICRP on Forage Crops, Department of Agronomy, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur (Madhya Pradesh) to study the effect of different weed management practices on productivity of Maize (Zea mays L.). The experiment was conducted in randomized block design with eight treatments (six herbicidal treatments with hand weeding twice at 20 and 40 DAS and weedy check) replicated thrice. Observations were recorded for various weed parameters and crop growth parameters. All the weed management treatments significantly reduced the total weed density and total dry weight of weeds as compared to weedy check. Among herbicidal treatments, mesotrione 350 g ha-1 recorded the lowest total weed density and total dry weight of weeds as compared to other herbicides. While highest total weed density and total dry weight were recorded under weedy check treatment. Significantly higher growth parameters and yield attributing traits were recorded with the application of mesotrione 350 g ha-1 which resulted in higher grain and stover yields (2447.22 and 21804.72 kg ha-1) followed by mesotrione 300 g ha-1. Maximum net returns and Benefit: Cost ratio was also obtained with the application of mesotrione 350 g ha-1.