Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Chitosan Type and Concentration on Physical Characteristics and Drug Release Behavior of Andrographolide-Chitosan Microparticles Prepared by Ionic Gelation – Freeze Drying

Andrographolide is low in solubility and bioavailability, with short half-life (t1/2 = 1.5-2 hours), with chitosan micro-particles expected to improve its dissolution. Therefore, the objective of this research was to investigate the effect of chitosan concentration and the in vitro release of andrographolide from micro-particles prepared by ionic gelation using sodium tripolyphosphate (TPP) as cross-linker. Furthermore, the micro-particles used were prepared with two different concentration of chitosan namely 19 cPs and 50 cPs. The result showed that the micro-particles obtained were spherical in shape and heterogeneous in size, with Infrared spectra indicating ionic bonding between chitosan and TPP. The DTA thermogram and XRD diffractogram exhibited a change in crystal structure to a more amorphous form. The entrapment efficiency (EE) of andrographolide in micro-particles was about 78%, with no impact on the chitosan concentrations (19 cPs and 50 cPs) affected by EE. The kinetics released was followed by the Higuchi model in phosphate buffer, with pH 7 ± 0,05 higher than the andrographolide substance. It was investigated that andrographolide-chitosan micro-particles had the ability to enhance its dissolution rate.
Anticancer Property of Triacetylphloroglucinol and Its Molecular Interactions With Human Serum Albumin

Phloroglucinols, the phenolic compounds have been gaining importance as pharmacophores owing to the wide array of innate biological potentials such as antimicrobial, antioxidant, anticancer, antifungal, anticoagulant, anti-inflammatory and antimetastatic properties. In the present investigation, for the first time, we report the anticancer potential of 2,4,6-triacetylphloroglucinol (TAPG), a phloroglucinol derivative and its molecular interactions with human serum albumin (HSA). HSA is considered to be a good carrier to deliver anticancer drugs due to its accumulation in tumor cells. The preliminary anticancer potential was assessed through anti-proliferative assay and the inhibitory concentration (IC50) was determined. , Cell cycle analysis, reactive oxygen species (ROS) generation and determination of mitochondrial membrane potential were done using IC50 value. TAPG showed potent cytotoxicity towards lung (A549), breast (MDA MB-231), colon (HCT-15) and cervical (HeLa) cancer cells in dose-dependent manner at 48 h without showing any toxic effect to normal cells such as human peripheral blood mononuclear cells (PBMCs) and human embryonic kidney (HEK-293) cells. Cell cycle analysis revealed G1 phase arrest in cancer cells upon TAPG treatment. Also, loss in mitochondrial membrane potential with increased ROS production was observed in TAPG-treated cancer cells. The drug-protein interactions were explored through steady-state fluorescence, time-resolved fluorescence (TRF), circular dichroism (CD), isothermal titration calorimetry (ITC) and computational studies. Fluorescence spectroscopy of HSA-TAPG interaction depicted strong static quenching of intrinsic fluorescence of HSA. Further, TRF study confirmed the ground-state complex formation. CD spectroscopy analysis depicted the secondary structural changes with decrease in α-helical content in protein upon interaction of TAPG with HSA. Both fluorescence and ITC studies exhibited the reversible and moderate affinity of TAPG towards HSA which favours the easy diffusion of molecule to target tissue. ITC study for TAPG-HSA interaction further demonstrated the spontaneity and exothermic reaction with involvement of electrostatic interactions. It was also confirmed through molecular docking and molecular dynamic simulation studies that TAPG showed stable interactions and strong affinity towards HSA in its site I than site II. Thus, present investigation demonstrates the anticancer potential of TAPG and its favourable binding with HSA. <br><br>
Vietnamese Immigrant Entrepreneurship: A Comparison of Self-Employment in Vietnam and the United States

In this paper, we examine the case of Vietnam as an example of this phenomenon. In Vietnam, the World Bank Indicators show a self-employment rate of 60% among men in 2008, whereas the United States rate is less than 15%. This wide gap between the two countries even greater than that between Mexico and the U.S. (Fairlie and Woodruff, 2007). We use census microdata from Vietnam and the U.S. as well as an understanding of the economic systems in the two countries to understand the differences in the self-employment rate of the Vietnamese in Vietnam versus Vietnamese immigrants in the U.S. and to give some insight into the nature of self-employment in the two countries. We utilize two analysis methods on various variables to investigate the differences in the rates of self- employment. This diversified analysis enables one to understand the differences between the two economies and the differences between Vietnamese workers in Vietnam and Vietnamese immigrants in terms of self-employment. Through these analyses, we can explain some parts of the differences.
2Gram Features Based Prediction of Membrane Protein Types Using Ensemble Classifiers Methods

Membrane proteins are an essential type of protein used in a cell as receptors, channels, and energy transducers. Moreover, they do many of the functions imperative to the survival of the cell. Therefore, it is necessary to build up machine learning methods to correctly identify membrane protein types to recognize protein function, disease occurrence, and drug therapy design. So the function prediction of membrane protein performs a key task. In this paper, membrane protein types are detected by using 2-gram and sequence length feature. The predictor ensemble classifiers used to predict a membrane protein, with sequence length encodes single descriptor value and 2-gram exchange group, encodes multilabel feature types. Were, 2-gram special kinds of feature type focus on its different modes of protein information to emulate the fundamental functions that are enormously unknown in complex protein sequences and find a relation between residues. The proposed method avoids biasing among the differentiation between types of membrane proteins. Ensemble classifiers well handle the imbalanced protein datasets.
IBEECP: Improved Biogeography Optimization Based Energy Efficient Clustering Protocol for Wireless Sensor Networks

Biogeography-based Optimization (BBO) is a new nature inspired computing algorithm for solving optimization problems. Cluster head selection is a well-known optimization problem in wireless sensor networks (WSN). The life of network is depends on selecting the cluster head (CH) for transferring the data to the base station in Wireless Sensor Network. This paper proposes Improved Biogeography Optimization Based Energy Efficient Clustering Protocol (IBEECP) for Wireless sensor networks for cluster head selection. The concept of IBCEEP is by using efficient CH selection inspired by biogeography-based optimization for efficient energy utilization of WSNs. The performance of simulation results through MATLAB shows IBEECP enhance the network lifetime, quality of network, network stability period and throughput compared with heterogeneous protocols in WSN are stable election protocol (SEP), evolutionary routing protocol (ERP), intelligent hierarchical clustering routing protocol (IHCR) and biogeography based energy efficient cluster protocol (BEECP).
Devising UIF Model in Designing Usable User Interface Impacted from Shorter-Time-To-Market Phenomena

The research goal is set in devising a UIT (Usability interface Factors) model in designing user interface when there is a time pressure and investigating the todays software company’s tendency to shift towards shorter-time-to-market phenomena, while still carrying about high quality of User Interface- UI and User Experience-UX. Currently there is only few published research analysing this tendency and habit of today’s companies. Also, there is no published research that addresses efforts in handling this habit by analysing and assessing the impact of IU design systems for increasing the level of usability using UI consistent. However, the complexity of the research landscape and the diverse set of approaches and goals impedes the analysis and advancement of research and the identification of promising research areas, challenges, and research directions. As research, method will use analyses of published research and then surveys and questionnaires to collect information from a representative sample of a broad population using specifically devised questionnaire. Insights and recommendations are provided.
Monoclinic Angle, Shear Response, and Minimum Energy Pathways of NiTiCu Martensite Phases - An Ab Initio Investigation

Ti<sub>50</sub>Ni<sub>50-x</sub>Cu<sub>x</sub> alloys are observed to exhibit multiple martensitic transformations from B2 to an orthorhombic B19 and a monoclinic B19′ phase. In addition, DFT calculations have predicted a B19ʺ phase with a higher monoclinic angle as the thermodynamically stable ground state. This study investigated the effects of Cu content and shear stress on the monoclinic angle, phase stability, the minimum energy pathway, and the relative total energy among the various phases in this pseudo-equiatomic Ti(Ni<sub>50-x</sub>Cu<sub>x</sub>) system. A new monoclinic phase (B19<sub>M</sub>) with a monoclinic angle lower than that of B19′ was found at above a critical Cu content. This confirms the formation of an intermediate phase in the martensitic transformation sequence of the pseudo-equiatomic Ti(Ni<sub>50-x</sub>Cu<sub>x</sub>) system but contradicts the crystal structure of the experimentally observed phase. It was found that the monoclinic angles of both B19<sub>M</sub> and B19ʺ decrease with increasing the magnitude of an opposing shear stress to their monoclinic distortion. At above certain critical values of the opposing shear stress, the B19<sub>M</sub> and B19ʺ phases destabilise and transform to lower monoclinic angle phases. In addition, the evidence suggests that the experimentally observed monoclinic B19′ phase is in fact a distorted B19ʺ with a reduced monoclinic angle under an opposing shear stress. With the same argument, the experimentally reported B19 phase is a metastable phase formed under the effect of an opposing shear stress to the monoclinic distortion of B19<sub>M</sub>.
Identification and Prediction of Factors Responsible for Diabetes by Machine Learning Approaches

This research paper has been made to get a better insight of diabetes and understand the factors responsible for diabetes in people by machine learning approaches. We have used machine learning to predict the accuracy of a person who can have diabetes based on some well understood factors. We have acquired datasets of various patients from the internet and analyzed their reports by structurally organizing the data and then applying various algorithms to predict the accuracy. This paper is aimed to give a better visualization of data and predict the possibility of a person to have diabetes in the future.
Investigating Influence of Palladium Metal Surface as a Component of Fuel Cells

Adsorptive studies focusing on palladium metal can model a situation in which some parts of platinum are replaced with palladium in a fuel cell, or some kind of PdPt alloy is used. Correspondingly thus, adsorptions of small molecules such as carbon monoxide and hydrogen are studied using density functional theory and the neural network method of generating higher dimensional potential energy surfaces. Various lower index surfaces of palladium metal are considered, and the adsorption energies appear to increase according to: Pd(110) Pd(211) &gt; Pd(100) &gt; Pd(111) with the adsorption of both molecules. The adsorption energies are in the range 0.79 eV to 1.86 eV with the adsorption of carbon monoxide while the values are between 0.93 eV to 1.76 eV with the adsorption of hydrogen. The large adsorption energies mean only energetic interactions with the ions from air such as oxygen ion can detach the adsorbates from the surface thereby forming water and carbon monoxide as by-products. Exploring possible adsorption sites gives insight on better ways in which charge exchanges are maximized while at the same time forming of the by-products becomes efficient. Charge transfers give some insight into the study of electrification process in the system. These values are computed to be about 1.06e with the adsorption of hydrogen and upto 0.90e with the adsorption of carbon monoxide. Training of the system potential energies using the neural network method shows promising opportunity to study further complex problems in such systems.
Kannada Speaker Recognition Using Mel Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN)

Speaker Recognition is a most prominent emerging technology in today’s society Speaker recognition is a process of authentication of the claimed of a person from voice characteristics. The main goal of speaker recognition is to extract, classify and identify the information about speaker identity. In this paper shows the usage of Mel Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network for Speaker Recognition System of Kannada Languages. The standard multilingual database is not available experiments are carried out on our own created database of ten speakers. The code developed in MATLAB Environment.
Modified Weighted Similarity in Heterogeneous Graph for Prediction of miRNA Disease Association

In the last decade lot of experimental research has witnessed and verified the important roles of miRNA in the development of complex human diseases. Publicly available MiRNA data and different analysis methodologies have given rise to the development of many computational models to predict miRNA disease association. Predicting accurate miRNA disease association is very essential for the proper diagnosis and treatment of diseases. During past few years lots of computational methods have been developed. However, each method has its own limitation and it has not yet been possible to develop an efficient method that can predict miRNA-disease associations accurately. In this paper, weighted meta-graph based computational approach for predicting the association between diseases and miRNAs is proposed. The proposed algorithm is designed by integrating available miRNA functional similarity, miRNA similarity based on Environmental factors, miRNA similarity based on diseases to get the average miRNA similarity, disease semantic similarity and disease functional similarity are also integrated to get the average disease similarity. AUC of 0.9617833 on global LOOCV has been achieved using the proposed method.
Dislocation Mechanics of Extremely High Rate Deformations

High strain rate simulations were performed using the multiscale dislocation dynamic plasticity (MDDP) method to calculate different rise times and load durations in mimicking high deformation rate shock as compared with isentropic (ramp) testing of α-iron and tantalum materials. Smaller differences than expected were found between the two loading behaviors and attributed to the shock loading being relatively weak. Comparisons were also made with thermal activation strain rate analysis (TASRA), phonon drag and dislocation generation predictions for the behaviors.
Drug-Delivering Nanoyarn Could Promote Effective Reconstruction of Functional Urethra in Dog by Inhibiting Wnt/Β-Catenin Signaling

Hypospadias and urethral stricture are common urological diseases which seriously affect voiding function and life quality of the patients. Current clinical treatments using autologous tissues or tissue engineered grafts often result in unsatisfactory clinical outcome with frequent complications at tissue-harvesting organ(s). In the present study, we performed RNA profiling (mRNA and miRNA sequencing) using urethral stricture animal model. Functional enrichment analysis of differential expressed genes showed that CTNNB1-centered Wnt signaling were significantly regulated during the scar formation and fibrosis, suggesting Wnt pathway as a promising therapeutic target for clinical treatment of urethral defects. <i>In vitro</i> experiments confirmed that ICG-001 (a well-established Wnt signaling inhibitor) could effectively prohibit fibroblast proliferation and the expression of fibrosis associated proteins. To testify whether inhibition of Wnt signaling could benefit tissue reconstruction from urethral defects <i>in vivo</i>, we applied a novel drug-delivering nanoyarn scaffold in urethroplasty in dog model, which could continuously deliver ICG-001 during the tissue reconstruction. The ICG-001 delivering nanoyarn could effectively promote urethral recovery and resume fully functional urethra within 12 weeks. Such attempts could be essential in the Next Generation Sequencing (NGS)-guided regenerative medicine to cure urethral defects and might be helpful for broader clinical applications in precision medicine.
An Approach Towards Linguistic Steganography

Steganography is the study of building systems (stegosystems) that encrypt data in a way, such that the datagram created appears to be an innocuous message sent between two endpoints. Steganography aims to provide security by making it difficult for unprivileged endpoints to ascertain that a given message contains any hidden information. Almost all stegosystems, work by embedding the message to be transmitted onto a cover medium and the resulting datagram, which differs from a given innocuous cover by less than a decided threshold value, is then sent over an insecure channel. Most practical stegosystems have thus far used graphic media as a cover channel, but few have used natural language texts (such as English texts) as a cover medium. This paper represents various stegosystems that use natural language as a cover medium and proposes a new approach towards building such a system.
Fractional-Order Diffusion Model for Social Networks

One of the most common areas of real world fractional-order model applications are diffusion processes. In the paper the attempt to use fractional-order diffusion equation to model information diffusion in a social network is made. The example dataset consist of citations for over 34000 arXiv papers. Basic diffusive fractional-order model is compared with Monte Carlo simulation of the flow of information from a particular node (paper) within the network. Experiments shown that the model of diffusion within network can be accurately fitted to the results obtained with the numerical method. Order of the obtained model (from 0.05 to 0.4 depending on the source node of diffusion) indicates, that the investigated citation network is a highly superdiffusive system. The results can be significant for the future works, including fractional-order generalization of epidemic models or diffusion in variable order networks.
Oxide Ion Conductors Developed from the Layer-Structured Ferroelectric Perovskite Bi 4Ti 3O 12

Layer-structured perovskite Bi<sub>4</sub>Ti<sub>3</sub>O<sub>12</sub> is a well-known lead-free ferroelectric and piezoelectric material. By solving the problems of high leakage current and polarization degradation associated with oxygen vacancies, Bi<sub>4</sub>Ti<sub>3</sub>O<sub>12</sub> shows potential applications in both non-volatile memory devices and high-temperature sensors. In this paper, the high leakage is transformed into high level of ionic conductivity and new oxide ion conductors are developed from Bi<sub>4</sub>Ti<sub>3</sub>O<sub>12</sub>, and the oxygen transport mechanism in the layer-structured perovskite is investigated. Different Bi-stoichiometric compositions are presumably mixed hole and ion conductors with bulk conductivity (σb) of 10-4 and 10-3 S/cm orders of magnitude at 600 °C and 700°C, respectively. It is interesting that a combination of Bi deficiency and Mg doping on the Ti site results in high-performance oxide ion conduction for Bi<sub>4</sub>Ti<sub>3</sub>O<sub>12</sub>. Nominal starting composition with a formula of Bi<sub>3.88</sub>Ti<sub>2.92</sub>Mg<sub>0.08</sub>O<sub>11.74</sub> exhibits oxide ion conductivity with σb of ∼10<sup>-2</sup> S/cm and ionic transport number (tion) of 0.92 at 650 °C, which are well comparable to those of other known solid oxide electrolyte materials. Furthermore, this composition shows low conductivity degradation and good chemical stability in reducing atmosphere (5%H2/95%N2). This study opens up new horizons for Bi<sub>4</sub>Ti<sub>3</sub>O<sub>12</sub> and other A-site Bi-based layer-structured perovskites as oxide ion conductors in addition to ferroelectric- and piezoelectric-based applications.
Biochemical Investigation of Marine Seaweeds Colpomenia Sinuosa and Halymenia Porphyroides Collected Along Southeast Coast of Tamilnadu, India

In the current study the biochemical evaluation of the seaweeds were conducted which were collected along the south east coast of India. The marine brown macro alga Colpomenia sinuosa (Mertens ex Roth) Derbes and Solier as well as the marine red alga Halymenia poryphyroides Boergesen were used in this study. The relationship between the nutritive components and the variation of the biochemical composition like organic and inorganic constituents were mainly analyzed in the current study. The present analysis revealed that both the seaweeds were rich in protein content whereas more carbohydrate content was observed in marine red alga Halymenia porphyroides compared to the marine brown alga Colpomenia sinuosa. Similarly the analysis of inorganic biochemical constituents revealed that calcium, magnesium, iron and phosphorus were present in higher amount in marine brown alga Colpomenia sinuosa whereas sodium and potassium contents were observed in higher amount in marine red alga Halymenia porphyroides. The presence of high organic and inorganic biochemical constituents in these seaweeds may be of economic importance and ass potential health food for human diets.
Structure and redox properties of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) adsorbed on a silica surface. A DFT M05 computational study

Adsorption of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) on (001) surface of α-quartz was studied at the M05/tzvp level using cluster approximation. Hydrogen bonds between nitramines and silica surface were analyzed by atoms in molecules (AIM) method. Electron attachment causes significant change in geometry of adsorbed complexes. Redox properties of adsorbed RDX and HMX were compared with those of gas-phase and hydrated species by calculation of the ionization potential, electron affinity, oxidation and reduction Gibbs free energies, oxidation and reduction potentials. Calculations show that adsorbed RDX and HMX have lower ability to undergo redox transformations than hydrated ones.
The Potential of Moldovan Agri-food Products on EU Markets

Competitiveness has become a key issue on international markets since it can be considered as the major source of export development. A country that utilizes the best its resources within its agricultural sector may benefit from comparative advantage on international agricultural markets. Due to the Deep and Comprehensive Free Trade Agreement signed recently between Moldova and European Union, it is of vital importance for local agri-food products to be competitive on the EU market and to assess the potential gains of the increased demand. Moldova’s share on agri-food exports is still large, but various changes occurred in the total value and the geographical structure of trade flows during the analyzed period. The aim of this research is to analyze the changes that happened in the pattern trade flows and to evaluate the consequences and potential benefits of DCFTA on agri-food exports of Moldova. In the given research data from the National Bureau of Statistics for the period 2001-2012 will be used. The analysis of competitiveness will be carried out by assessing trade indices of intra and inter industrial trade (RTA, GL).