Dynamics of dark energy models and centre manifolds

We analyse dark energy models where self-interacting three-forms or phantom fields drive the accelerated expansion of the Universe. The dynamics of such models is often studied by rewriting the cosmological field equations in the form of a system of autonomous differential equations, or simply a dynamical system. Properties of these systems are usually studied via linear stability theory. In situations where this method fails, for instance due to the presence of zero eigenvalues in the Jacobian, centre manifold theory can be applied. We present a concise introduction and show explicitly how to use this theory in two concrete examples.Comment: 11 pages, 1 figur

Adsorption Properties of Malaysian Activated Carbon for Use in Solar Refrigerator

Detail experiments and analyses have been made on the solar refrigerator using activated charcoal and methanol adsorption cycle. A test rig was designed to study adsorption and desorption capability of activated carbon. A comparison has been made between adsorbability of six Malaysian commercial activated carbons. To improve the adsorbability of the carbon a simple method was tested. Base on Dubinin equation, some them10dynamic properties of adsorbent adsorbate necessary for refrigeration calculation had been obtained. In this experiment methanol is used as the refrigerant medium. The amollnt of methanol adsorbed by the activated carbon was measured as a function of temperature of the activated carbon. During the experiment, the temperature of the unadsorbed methanol was kept constant. From Dubinin-Astakhov equation, parameters of adsorption data were determined using graphical analysis. A Claperyon P-T-X (pressure, temperature and concentration) diagram was then constructed

Quintessence with quadratic coupling to dark matter

We introduce a new form of coupling between dark energy and dark matter that is quadratic in their energy densities. Then we investigate the background dynamics when dark energy is in the form of exponential quintessence. The three types of quadratic coupling all admit late-time accelerating critical points, but these are not scaling solutions. We also show that two types of coupling allow for a suitable matter era at early times and acceleration at late times, while the third type of coupling does not admit a suitable matter era.Comment: 11 pages, 8 figures, revte

コレステロールは口腔扁平上皮癌におけるCAV1の局在と細胞遊走能を制御する

新潟大学Niigata University博士（歯学）Cholesterol plays an important role in cancer progression, as it is utilized in membrane biogenesis and cell signaling. Cholesterol-lowering drugs have exhibited tumor-suppressive effects in oral squamous cell carcinoma (OSCC), suggesting that cholesterol is also essential in OSCC pathogenesis. However, the direct effects of cholesterol on OSCC cells remain unclear. Here, we investigated the role of cholesterol in OSCC with respect to caveolin-1 (CAV1), a cholesterol-binding protein involved in intracellular cholesterol transport. Cholesterol levels in OSCC cell lines were depleted using methyl-β-cyclodextrin and increased using the methyl-β-cyclodextrin-cholesterol complex. Functional analysis was performed using timelapse imaging, and CAV1 expression in cholesterol-manipulated cells was investigated using immunofluorescence and immunoblotting assays. CAV1 immunohistochemistry was performed on surgical OSCC samples. We observed that cholesterol addition induced polarized cell morphology, along with CAV1 localization at the trailing edge, and promoted cell migration. Moreover, CAV1 was upregulated in the lipid rafts and formed aggregates in the plasma membrane in cholesterol-added cells. High membranous CAV1 expression in tissue specimens was associated with OSCC recurrence. Therefore, cholesterol promotes the migration of OSCC cells by regulating cell polarity and CAV1 localization to the lipid raft. Furthermore, membranous CAV1 expression is a potential prognostic marker for OSCC patients.International Journal of Molecular Sciences. 2023, 24(7), 6035.新大院博(歯)第550号doctoral thesi

Mechanisms of Fractal Formation in Colloidal Carbon-Bearing Natural System

By using the advanced nano-approach processes and phenomena in self-organizing colloidal systems are studied. The conditions of appearance of self-organized phenomena are determined and also ranges of operation of diffusion, capillary, and fractalization mechanisms are found. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3363

3D-Self-Assemblage and Self-Organization on Natural Colloidal Microinclusions in Mineral Sediments

The results of micro- and nanoscale research of fractal structure sediments from mineral water re-ceived by the drop method are given. Qualitative analysis of the underlying physical phenomena, allowed us to establish the conditions of their 3D-fractalization that consider the size of colloidal nanoparticles, its location and height from the drop center : rmin Rmax hmax and rmax Rmin hmin. It is shown that the main contribution to 3D fractalization is due to surface tension forces and the Coulomb force interaction. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3625

A Study of Resident’s Awareness on Pro Environmental Behaviours (Case Study In Pyay Township, Bago Region) ( Nyein Chan, 2024)

Pro environmental behaviours are important in solving environmental pollution that is facing today. People need to believe that individual’s actions can change the environment. A clean and good environment will be achieved by each individual’s pro environmental behaviours. The objective of this study is to investigate resident’s awareness on pro-environmental behavior in Pyay Township. In this study, descriptive method is used by using primary data from surveying target population . It is found that The residents have good behaviours that is no leaving carbon dioxide that can cause air pollution for their house yard by burning leaves and garbage. They have good behaviours and knowledge about air pollution. The respondents have knowledge about the use of fossil fuel in their cars and bike that can cause carbon dioxide emission and it is the cause of global warming and climate change. When buying a car/bike, they have good behaviour to choose environmental friendly product. They have good behaviours to reuse water and to keep track of daily water usage. They save energy due to the costs of usage and they practise the energy saving methods. The respondents have poor behaviour in buying used items. They have good behaviour to buy long term used material that are repeately damage and throw away. The respondents are active to know and participate in environmental conservation. They have good attitude to practise pro environmental behaviours and to make less impact on environmental to reduce pollutio

Development of Adaptable Human-Machine Interface for Teleoperation Over Time Delay

The Adaptable Human-Machine Interface (AHMI) was developed for the Orbital Robotic Interaction, On-orbit servicing, and Navigation (ORION) Laboratory of the Florida Institute of Technology. The primary project objective was to develop and test a predictive display for mitigating the effects of time delay in teleoperation of space robots and Unmanned Aerial Vehicles (UAV) using quadcopters as a test case. Regardless of the increasing popularity of various autonomous systems, research and development of teleoperating system should not be neglected since it is often utilized as a back-up in most autonomous systems especially in systems for human spaceflight and UAV operations in unpredicted conditions. This project serves as a pilot research project for developing a Human-Machine-Interface (HMI) for teleoperating over time delay, which can be adapted for different flight mechanics and/or systems. The interface has been developed in the Unity3D game engine and implemented for a Parrot A.R. Drone 2.0. Test results suggest that various elements of the head-up display will require to be customized along with the system dynamics model to achieve an effective predictive display. However, the interface software framework in Unity3D can be utilized, adapted, or expanded for different flight mechanics

Sentiment Analysis System in Big Data Environment

Nowadays, Big Data, a large volume of both structured and unstructured data,is generated from Social Media. Social Media are powerful marketing tools andSocial Big Data can offer the business insights. The major challenge facing Social BigData is attaining efficient techniques to collect a large volume of social data andextract insights from the huge amount of collected data. Sentiment Analysis of SocialBig Data can provide business insights by extracting the public opinions. Thetraditional analytic platforms need to be scaled up for analyzing a large volume ofSocial Big Data. Social data are by nature shorter and generally not constructed withproper grammatical rules and hence difficult to achieve high reliable result inSentiment Analysis. Acquiring effective training data is a challenge, although learningbased approaches are good for sentiment classification. Manual Labeling for trainingdata is time and labor consuming. Sentiment analysis based on multiclassclassification scheme is oriented towards classification of text into more detailedsentiment labels. However, multiclass classification with Single-tier architecturewhere single model is developed and entire labeled data is trained may decrease theclassification accuracy. The presence of sarcasm, an interfering factor that can flip thesentiment of the given text, is one of the challenges of Sentiment Analysis. Real-timetracking and analytics is important for Social Big Data because the speed may indeedbe the most important competitive business profits. Compared to batch processing ofSentiment Analysis on Big Data Analytics platform, Real-time analytic is dataintensive in nature and require to efficiently collect and process large volume andhigh velocity of data.In this research, proposed Sentiment Analysis system is implemented withdifferent architectures on different platforms to provide valuable information byanalyzing large scale social data in an efficient and timely manner. Firstly, SentimentAnalysis is implemented on traditional analytics platform by performing modelselection which is evaluated by comparing the performance of three different machinelearning algorithm (Naïve Bayes, Random Forest and Linear Regression). Fordeveloping scalable and high performance Sentiment Analysis system, SentimentAnalysis is implemented on Big Data Analytics Platform (Hadoop MapReduce). Thesystem enables high-level performance of sentiment classification while takingadvantage of combining lexicon-based classifier’s effortless setup process andiiilearning based classifier. Multi-tier Sentiment Analysis system on Big DataAnalytics Platform (MSABDP) is developed for achieving high level performance ofmulticlass classification. This system is implemented by combining lexicon andlearning based classification scheme with Multi-tier architecture. Multi-tier SentimentAnalysis system with sarcasm detection on Hadoop (MSASDH) is proposed toachieve high-level performance of sentiment classification. MSASDH identifiessarcasm and sentiment-emotion by conducting rule based sarcasm-sentiment detectionscheme and sentiment classification with Multi-tier architecture. Real-time Multi-tierSentiment Analysis system (RMSA) is implemented to achieve high levelperformance of multi-class classification in Real-time manner. To improve theclassification accuracy, the suitable classifier is selected by comparing the accuracy ofthree different learning based multiclass classification techniques: Naïve Bayes,Linear SVC and Logistic Regression.On the traditional analytics platform, Naïve Bayes classifier is better and theproposed system can achieved the promising accuracy. The evaluation result showsthat the proposed system on Big Data Analytics Platform has enabled to achieve thepromising accuracy by 84.2% and is able to scale up to analyze the large scale data bydecreasing the running time when adding more nodes in the cluster. The evaluationresults show that the proposed MSABDP is able to significantly improve theclassification accuracy over multi-class classification based on Single-tier architectureby 7%. The evaluation results show that detecting sarcasm can enhance the accuracyof Sentiment Analysis. The evaluation results show that Real-time Multi-tierSentiment Analysis achieves the promising accuracy and Linear SVC is better thanother techniques for Real-time Multi-tier Sentiment Analysis