Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of stabilizers and cryoprotectants on the characteristics of freeze-dried PLGA nanoparticles containing Morus Alba stem extract

This study aimed to investigate the effects of stabilizers and cryoprotectants on the characteristics of poly (lactic-coglycolic) acid (PLGA) nanoparticles containing Morus alba stem extract (MSE). The stabilizers used in the preparation of the nanoparticles were polyvinyl alcohol (PVA) and poloxamer 188 at a concentration of 2% w/v. The effects on freeze-drying of cryoprotectants, including glucose, sucrose, maltose and mannitol at concentrations of 5% and 10% w/v, were investigated. The results showed that the presence or the absence as well as the type of stabilizer affected size and charge of the nanoparticles. They also affected the loading capacity and the release of MSE. In freeze-drying, the type of cryoprotectant greatly influenced the physical stability of the nanoparticles and the chemical stability of encapsulated MSE. The nanoparticles prepared using 2% w/v PVA as the stabilizer and freeze-dried in the presence of 5% sucrose were the optimal carriers of MSE.
Virtual Assistant for the Visually Impaired using Deep Learning

: - Vision impairment is one of the top disabilities among mankind. Indians with this disability roughly account to one-third of the world’s blind population. Hence accessible visual information improves independence and safety of visually impaired people. With the explosion of data and multimedia, researchers are exploring new avenues to train devices that detect and classify multiple objects within an image. At the outset, the advancements in the field of deep learning can be extended to enhance the life of the visually challenged through smart devices also. Inspired by these findings in literature, a virtual assistant for the visually challenged has been proposed. The assistant detects and classifies objects with an image to provide a voice output for the detected objects. This system has been designed using Mobilenet and Single Shot Detector (SSD) algorithm pertaining to Deep Learning, to incorporate deep learning network for PC and mobile devices and also finds its application for illiterates.
From model to radar variables: a new forward polarimetric radar operator for COSMO

Abstract. In this work, a new forward polarimetric radar operator for the COSMO numerical weather prediction (NWP) model is proposed. This operator is able to simulate measurements of radar reflectivity at horizontal polarization, differential reflectivity as well as specific differential phase shift and Doppler variables for ground based or spaceborne radar scans from atmospheric conditions simulated by COSMO. The operator includes a new Doppler scheme, which allows estimation of the full Doppler spectrum, as well a melting scheme which allows representing the very specific polarimetric signature of melting hydrometeors. In addition, the operator is adapted to both the operational one-moment microphysical scheme of COSMO and its more advanced two-moment scheme. The parameters of the relationships between the microphysical and scattering properties of the various hydrometeors are derived either from the literature or, in the case of graupel and aggregates, from observations collected in Switzerland. The operator is evaluated by comparing the simulated fields of radar observables with observations from the Swiss operational radar network, from a high resolution X-band research radar and from the dual-frequency precipitation radar of the Global Precipitation Measurement satellite (GPM-DPR). This evaluation shows that the operator is able to simulate an accurate Doppler spectrum and accurate radial velocities as well as realistic distributions of polarimetric variables in the liquid phase. In the solid phase, the simulated reflectivities agree relatively well with radar observations, but the simulated differential reflectivity and specific differential phase shift upon propagation tend to be underestimated. This radar operator makes it possible to compare directly radar observations from various sources with COSMO simulations and as such is a valuable tool to evaluate and test the microphysical parameterizations of the model.
Incomplete explanations in physics teaching: discussing the rainbow with student teachers

Inspired by Shulman’s question ‘where do teachers’ explanations come from? ’, this study explores two issues: the extent to which student teachers (STs) of physics can detect consequential gaps in a given explanation (RQ1), and the extent to which they will sacrifice simplicity for completeness when designing an explanation for a given audience (RQ2). The chosen topic was the rainbow; focussing on the case of the primary rainbow, a very limited explanation based on the interaction of a raindrop with a ray of sunlight was discussed with 33 STs, using two successive questionnaires. The results suggest that the STs were often largely unaware that the explanation was deeply incomplete (RQ1). This was accompanied by the lack of a ‘functional approach’ questioning how a changed variable might affect the phenomenon in question, even where this involves accepting a patently incoherent explanation. In relation to RQ2, the results suggest that the simplicity-coherence issue often remained unresolved for STs even after a critique of the explanation’s incompleteness. The final part of the paper centres on teacher preparation and the need to more explicitly address the simplicity-coherence dilemma in order to help STs to develop their critical awareness and responsible choice of explanations.
Classification of Admission Data Using Classification Learner Toolbox

The analysis of data useful for further process and find the usefulness for the futher. In education sector, Admission is hottest topic. For the counselling of the admission in reputed institution or foreign university, the past data of the person are taken into the consideration. In this Paper, Admission data taken from the reputed Website (Dataset Weblink) and conduct the study of classification methods like Naïve bayes and its accuracy using Matlab. The performance of different classification methods from MATLAB classifier learner toolbox for admission data. Finally, the paper choosen the suitable classification method for the admission data set.
k-Split andk-Non Split Domination of some Zero-Divisor Graphs and its ϑ-Obrazom

The concept of split domination number was introduced by Kulli and Janakiram. Fink and Jacobson introduced the notion of k-domination in graphs. In this paper, we acquaint with the concept of k-split and k-non split domination for some zero-divisor graphs and ϑ-Obrazom for zero-divisor graphs.
Big Data Technology for Developing Learning Resources

Endeavours for adopting network data greatly from social media allude to give the specific methods in extracting value from data space, for example, conversion, transaction, message and others, where structured information sources originate from big business assets information and unstructured information sources originate from video and audio. It very well may be accomplished to extend the way toward extracting value from social network for designing the information sources to satisfy the association objective. This paper means to uncover the method for approach of big data in extracting information esteem from information complexity including velocity and variety into volume. This investigation was led employing contents analysis by looking into certain literary works in chapters, books and peer-audited journals and procedures in creating prototype employing data analytics related from users, time analytics and topic. The discoveries uncover that big data rising technology with analytics procedure gives specific favourable circumstances to change the trend of data fitted into innovative atmosphere of OLR i.e. online learning resources to upgrade in building up learning resources. Both model and prototype information extraction value can be upgraded to encourage the learning atmosphere in supporting the implementations with convenience and ease. This investigation is relied upon to add to improve the outcomes and learning atmosphere with achievement and performance by upgrading the learning process development of students to give online resources in the higher education setting.
Neighborhood Prime Labeling in Some Product And Power Digraphs

Let D(p, q) be a digraph. A function f: V → {1, 2, …, n} is said to a neighborhood prime labeling of D if it is both in and out degree neighborhood prime labeling. In this paper, we introduce the concept of neighborhood prime labeling in digraphs and also investigate the existence of neighborhood prime labeling in some digraphs.
Maximization of Cr Removal in Continuous Counter-current Liquid-Solid Fluidized Bed: A Machine Learning Approach

Continuous counter-current liquid solid (CCLS) fluidized bed is used as an adsorption column. Due to the counter current contacting pattern and easiness in handling the fresh as well as used adsorbent continuously, CCLS adsorption column can be preferred over packed adsorption column. In the present work experimental data for chromium removal from the wastewater using CCLS adsorption has been used for developing models using artificial neural network (ANN), a well-known machine learning technique. The percentage removal of Cr is mapped as function of liquid velocity, solid velocity, particle diameter, initial concentration, and height of the column through ANN. The developed ANN model is used as objective function for the design of the process using genetic algorithm (GA), a metaheuristic tool for optimization. The results provide specific guidelines for achieving optimum Cr removal.
An Enhanced Fuel Consumption Machine Learning Model Used in Vehicles

In the present world, some of the people are not able to pay expenses for petrol/diesel. The model which we are generating will be useful for many people. The system which we are generating is a data summary approach will be based on distance rather than traditional conventional time period when developing personalized machine learning model for fuel consumption. This system is utilized within conjunction with vehicle pace Also seven predictors inferred starting with way review to prepare a neural system model utilizing machine Taking in that predicts Normal fuel utilization done vehicles. The proposed model can be easily developed for each individual vehicle and fitted into one fleet to optimize fuel consumption over entire fleet. The model’s predictors are comprehensive on fixed window sizes and on the distance travelled. Different window sizes are evaluated and the results mean that the 1km window can estimate the fuel consumption with a coefficient of 0.91 and it also means less than 4% peak to peak percentage error for routes that include both city and highway duty cycling sections.
Logarthmic Corrections to the entropy of Hairy black hole in (2 + 1) dimension gravity Journal of Physics: Conference Series

The entropy recives the logarthanmic corrections in the black hole due to the quantum fluctuation close to the horizon. We study these logarithmic corrections of entropy for hairy BTZ black hole. By applying the WKB approximation calculation of the states is being done which is used to determine free energy of the scalar field. It is seen that the entropy and the free energy are diverging logarithmically in both cut off parameters and these divergences can be engrossed in the renormalized equation of the gravitational constant.
NUMERICAL INVESTIGATION ON EFFECT OF EXPANSION RAMP ON STAGED TRANSVERSE INJECTION OF FUEL IN A SUPERSONIC COMBUSTOR

This work involves an application of the computatio nal fluid dynamics to a problem associated with the flow inside the supersonic combustor of a scram jet engine. A method for augmenting the mixing of a ir and the fuel in the existing canonical model by int roducing the expansion ramps on both the sides of t he injectors suggested and the effectiveness of expans ion ramps was numerically analysed. The combustor models were simulated using the finite volume based co e FLUENT and the governing equations were solved by means of a coupled explicit method using hear-stress transport (SST) Kω turbulence model. The numerical simulations of the flow through the c ombustor model with the expansion ramp are reported in comparison with the canonical model. The investigat ion shows that the use of the expansion ramp contri butes to the formation of the streamwise vortices and pro motes mixing between air and fuel.
An End-to-End Spiking Neural Network Platform for Edge Robotics: From Event-Cameras to Central Pattern Generation

Learning to adapt one’s gait with environmental changes plays an essential role in the locomotion of legged robots which remains challenging for constrained computing resources and energy budget, as in the case of edge-robots. Recent advances in bio-inspired vision with dynamic vision sensors (DVSs) and associated neuromorphic processing can provide promising solutions for end-to-end sensing, cognition, and control tasks. However, such bio-mimetic closed-loop robotic systems based on event-based visual sensing and actuation in the form of spiking neural networks (SNNs) have not been well explored. In this work, we program the weights of a bio-mimetic multigait central pattern generator (CPG) and couple it with DVS-based visual data processing to show a spike-only closed-loop robotic system for a prey-tracking scenario. We first propose a supervised learning rule based on stochastic weight updates to produce a multigait producing spiking-CPG (SCPG) for hexapod robot locomotion. We then actuate the SCPG to seamlessly transition between the gaits for a nearest prey tracking task by incorporating SNN-based visual processing for input event-data generated by the DVS. This for the first time, demonstrates the natural coupling of event data flow from event-camera through SNN and neuromorphic locomotion. Thus, we exploit bio-mimetic dynamics and energy advantages of spike-based processing for autonomous edge-robotics.
WORKER BEES (APIS MELLIFERA L.) ARE ABLE TO UTILIZE STARCH AS FUEL FOR FLIGHT WHILE DRONES ARE NOT

When old worker bees, sampled from a feeding station, were fed a 2 molar glucose solution which additionally contained an equivalent of soluble starch of 1 molar glucose, they increased the period of flight in a roundabout by 24 percent. Flight-drones which were fed with the same amounts of sugar and starch did not increase, but instead reduced flight time significantly by 23.6 percent, compared to when they were fed a pure 2 molar glucose solution. The two feeding regimes did not alter the mean velocity in workers and drones as well. Our result indicates that workers can utilise starch during flight while male honey bees cannot. This provides evidence that the enzyme amylase found in the hypopharyngeal glands of older workers increases their efficiency of utilising complex carbohydrates and underlines their important role as food processors in the highly evolved social system of the honeybee society.
Understanding Migration from Monolithic to Microservice Architecture and its Challenges

: Cloud computing has already become a core sector for the enterprises in the current digital transformation. It’s of prime importance for companies to choose suitable architectures and migration techniques to gain the benefits of the cloud. Microservice architecture blends well with cloud computing and turns out to be a de facto standard for all enterprise level applications. Most of the applications in the early stages start as a monolithic application. So, companies should migrate monoliths into microservices as the complexity increases. This is a difficult task. This paper discusses the challenges of migration, pre-assessment needed for migration and the various methods for migration.
Human activity recognition in RoboCup@home: Inspiration from online benchmarks

—Human activity recognition is an important aspect of many robotics applications. In this paper, we discuss how well the RoboCup@home competition accounts for the importance of such recognition algorithms. Using public benchmarks as an inspiration, we propose to add a new task that speciﬁcally tests the performance of human activity recognition in this league. We suggest that human-robot interaction research in general can beneﬁt from the addition of such a task as RoboCup@home is considered to accelerate, regulate, and consolidate the ﬁeld.
Synthesis of 4 to 16 Reversible Decoder

– Reversible logic has become an emerging field for research. The concept of reversible logic is being applied both in sequential as well as combinational circuits. This paper describes a 4 to 16 decoder using reversible logic. The decoder involves the use of Fredkin gate which is basically a reversible gate. The circuit has been implemented in Xilinx 8.2 . The simulator used is Xilinx Simulator. The results have been shown and verified with the irreversible 4 to 16 decoder.
Language independent null subject prediction for statistical machine translation

Null subject prediction in statistical machine translation (SMT) is challenging for the following two reasons. First, SMT is not well designed to generate target phrases that are not aligned to source sentences. A special mechanism is required to predict the missing subjects accurately. Second, the null subject is not a rare phenomenon that happens only in a limited number of languages. In fact, a considerable number of languages exhibit null subjects. They include most Romance languages (Italian, Spanish, Portuguese, Romanian), Balto-Slavic languages (Czech, Polish, Russian, Croatian), Arabic, Greek, Hungarian, Turkish, Korean, Chinese, Thai and Japanese. The method for null subject predictions should ideally be language independent to handle these many NSLs with minimum effort.
Filipino Engineering Students’ Perceptions about Teaching and Motivations to Pursue Teaching Career

Quality education depends primarily on the quality of teachers. This is particularly true in the case of Science, Technology, Engineering, and Mathematics (STEM) education. Understanding the factors that motivate students and professionals to choose a teaching career in STEM is crucial in teacher recruitment, retention, and performance. It is for this reason that this study sought to determine the motivation to teach among Filipino engineering students as well as their perceptions about this profession. Fifty-seven engineering students answered a questionnaire adopted from Watt and Richardson’s Factors Influencing Teaching Choice (FIT-Choice) Scale. Results suggest that top motivators include altruistic-intrinsic reasons such as the desire to make social contributions, help shape the future of children/adolescents, and promote social equity. Prior experiences in learning and teaching were also determined to be influential among the respondents. At the same time, the respondents also perceived teaching as a demanding task that requires high level of expertise. Low salaries and experiences of dissuasion from others were also noted as potential barriers to choosing teaching as a career. It is recommended that more comprehensive studies be done not only with engineering students but also with students of other STEM fields. It is hoped that more support and encouragement will be accorded to these prospective STEM educators by understanding their motivations in choosing and beliefs about teaching.
Perspective of Tourist in Bagan Relating to Heritage Site in Myanmar

The aim of this study was to understand the tourists’ perspective status at the Bagan related to heritage Site such as temples, pagodas and naturel beauty based on satisfaction, experience and memory ensure visitors as positive perspective. Bagan, representing one of the most popular destinations in Myanmar, is in fact facing a sharp increase in tourist arrivals since the country opened up to the outside world in 2011. Due to the fast changing environment and the recent political events, such figures are expected to rise even more enormously. This research project examined tourists’ perspective of Bagan region as a tourist destination. By questioning 270 domestic and international tourists about their travel habits, this project constituted the first research of its kind in the region. The objectives of the project were to provide a profile of international and domestic tourists, to study their travel satisfaction, experience and memory destinations visited preferred accommodation and mode of transportation, information sources used average length of stay, and satisfaction levels. Future sustainable tourism management will require catering for both domestic and international needs. The sample showed an almost equal distribution of gender, with one quarter of participants being relatively young 25-35 years. The largest group of foreigners was retired, followed by Photographer, Journalist, Office worker, Teacher and Government officer. Europeans made up more than 60% of all international respondents. Most domestic and international tourists were highly educated. In terms of travel parties, more than half of Myanmar people visited the region as part of a group tour, while only one third of foreigners traveled in a group tour. Nearly one quarter of all internationals visited the region as a couple, while only 1% of Myanmar nationals visited the area with their partners. Almost all international visitors visited in Bagan heritage site. Myanmar nationals, in contrast, spread themselves much more widely across the region than their foreign counterparts. The main motivation for visiting Bagan for foreigners was cultural experience. Most tourists get satisfaction about sightseeing to temples and pagodas. The most tourists get the best experience are hot air balloon over the Bagan. The most tourists of the best memory are taking photography about Bagan heritage site such as temples and pagodas. Most of international tourists visited the temples, stupas and pagodas in the region, followed by a guided book tour of Bagan and visiting the markets. Nearly all participants, whether they were Myanmar nationals or foreigners, were generally quite happy with what was on offer in the region. Almost all respondents indicated they would recommend the destination to their peers. When asked what they enjoyed most about the Bagan area. Keyword: Tourists’ satisfaction, experience, memory, and motivation related to heritage site in Bagan.
Evaluation of antiproliferative properties of Lavatera cachemeriana roots

Brest cancer is the leading cause of deaths across the world and plant derived bioactive molecules are considered as potent and safer agents to tackle and prevent therapeutically challenged breast cancer incidences. The current study investigated In vitro anti-proliferative activity of Lavatera cachemiriana methanol root extracts against breast cancer cell line (MCF-7) through MTT assay, L-6 cell line acted as a negative control. Results have demonstrated that extracts showed insignificant levels of antiproliferative activities against tested cell lines (IC50> 1000 μg/ml). The morphology of both treated and untreated cells was same with no changes reported. In conclusion, the methanol root extract possesses no marked anti-proliferative activities against breast cancer cell line at the tested extract concentrations.