Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential Protective effects of Cauliflower on osteoporosis induced prednisolone acetate

Cauliflower (Brassica oleracea L.) was a nutrient – good source of phenolic compounds , minerals, vitamin K and C. The objective of the study was to investigate the effect cauliflower on induce osteoporosis in rats . Thirty female of albino rats (Sprague dawley) weighting 150±5g Rats classified into five equal groups (6 rats each). The first group kept as negative control group which fed on standard diet only ,while the other four animals groups were administered methyl prednisolone acetate 0.2mg/kg/b.w. t. injected subcutaneously injection 3 times/ week to induce osteoporosis, one of them used as a positive control (+ve) and the other groups fed on standard diet containing 5%, 10% & 15% cauliflower, treatment groups". The study was assigned for seven weeks. The results revealed that all of the treated groups showed significant increase in BMD, BMC, estrogen ,alkaline phosphate, GSH Rd ,CAT, and osteocalcin in comparing to positive control group.
Flicker in the Networks with Distributed Generation Plants and Its Removal Using Group Prognostic Controller

The use of distributed generation (DG) plants in electrical energy system (EES) produces unambiguous effect on power quality. The presence of DG plants allows to reduce losses associated with power transmission and maintain the required voltage levels. In this case, the presence of DG can cause voltage fluctuation, leading to the appearance of flicker, which is understood as a feeling of instability of visual perception. Similar processes can occur at sharp disturbances close proximity to the DG. The situation can be aggravated by improperly configured DG generators controllers. Therefore, it is necessary to conduct an accurate assessment of the DG plants impact on the power grid, which is a rather time consuming task. The article presents results of the EES working modes simulation with a DG plant implemented on the basis of synchronous turbine generators. The results obtained indicated that during temporary connection of heavy load in the connection unit of DG plant and the use of non-concordantly tuned controllers, there are fluctuations in rotor speed and voltage of generators, the analysis of which indicates the presence of flicker. The same effect can obtained a sudden change in the forecast time for individual controllers of turbine generators speed. Flicker can be removed by applying group control of generators speed controllers.
The role of engagement among farmers in developing farming knowledge: evidence from northern Thailand

Several studies found science-based knowledge has only able to reach a small fraction of their desired recipient. To compensate for the lack of formal sources, farmers often relied on informal sources of knowledge within their farming community. This study investigates the role of farmer’s social engagement in developing farming knowledge and farmer’s decision-making. A structural equation modelling was used to test the hypothesised moderating role of social engagement among farmers on the effects of service access, training, and knowledge-sharing on farming knowledge. The study used the case of rice farmers in Northern Thailand, wherein a focus group discussion and a series of survey interviews were conducted. Study results found that social interactions among farming communities significantly moderated the effects of training and knowledge-sharing. The findings support the critical role of social engagement among farmers in increasing information flow and experiential knowledge exchange in developing farming knowledge. Furthermore, social interactions promote farming innovation and management practices through advice-seeking with other farmers. Hence, in supporting farming sustainability, extension support should also focus on network building among actors within the farming community and understand how farmers exchange experiential knowledge to compensate for the lack of formal sources of knowledge.
Algorithms for Detecting and Preventing Attacks on Machine Learning Models in Cyber-Security Problems

Machine learning algorithms can be vulnerable to many forms of attacks aimed at leading the machine learning systems to make deliberate errors. The article provides an overview of attack technologies on the models and training datasets for the purpose of destructive (poisoning) effect. Experiments have been carried out to implement the existing attacks on various models. A comparative analysis of cyber-resistance of various models, most frequently used in operating systems, to destructive information actions has been prepared. The stability of various models most often used in applied problems to destructive information influences is investigated. The stability of the models is shown in case of poisoning up to 50% of the training data.
Electric vehicles in Smart Grids: Performance considerations

A Smart Grid offers a two-way communication between the source and the load; integrates renewable sources into the generation system; and provides reliability and sustainability in the entire power system from generation through to ultimate power consumption. Unreliability in continuous production poses challenges for deploying renewable sources in a real-time power delivery system. Different storage options could address this unreliability issue, but they consume electrical energy and create significant costs and carbon emissions. An alternative is using electric vehicles and plug-in electric vehicles, with two-way power transfer capability (Grid-to-Vehicle and Vehicle-to-Grid), as temporary distributed energy storage devices. A perfect fit can be charging the vehicle batteries from the renewable sources and discharging the batteries when the grid needs them the most. This will substantially reduce carbon emissions from both the energy and the transportation sector while enhancing the reliability of using renewables. However, participation of these vehicles into the grid discharge program is understandably limited by the concerns of vehicle owners over the battery lifetime and revenue outcomes. A major challenge is to find ways to make vehicle integration more effective and economic for both the vehicle owners and the utility grid. This research addresses problems such as how to increase the average lifetime of vehicles while discharging to the grid; how to make this two-way power transfer economically viable; how to increase the vehicle participation rate; and how to make the whole system more reliable and sustainable. This research also investigates the economic benefits of using the vehicle batteries in their second life as energy storage units thus reducing storage energy costs for the grid operators, and creating revenue for the vehicle owners.
Introduction of Fog Computations to Measuring System Collection and Accounting of Data of Distributed Internet of Things

The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device’s behaviour at a given time interval.The practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given.
Microwave tomography of lossy objects from monostatic measurements

This paper addresses the problem of microwave tomographic imaging from far-field monostatic measurements. In the first-order Born approximation, a simple Fourier relationship exists between the measured data and dielectric contrast of the object under test. For a lossy object, the contrast function depends on frequency, and this prevents monostatic data from being used directly since multifrequency probing radiation must be used to get sufficient information. To overcome this difficulty, we propose a data preprocessing strategy that eliminates frequency from the function to be reconstructed. The Fourier data obtained through preprocessing could be immediately inverted to give a bandpass estimate of the unknown function. Despite the drawbacks inherent in this approach, in small-scale tomographic applications, the space region occupied by the object under test, as well as the sign of the unknown function, are normally known. This extra information allows us to apply a projected Landweber algorithm to uniquely reconstruct the unknown, thus avoiding the above-mentioned drawbacks. We outline the preprocessing and reconstruction techniques adopted, and show some preliminary results from experimental and simulated data.
Safety related study of Expanding Pin systems application in lifting and drilling equipment within Construction, Offshore, and Marine sectors

A questionnaire-based survey has been performed among original equipment manufacturers (OEMs), sub-suppliers, engineering companies, end-users, service & maintenance, and “others”, as part of an investigation to clarify their relationship to expanding pin system, compared to standard, cylindrical pins. In addition, a short literature study on onshore cranes is conducted. The survey is based on 9 questions about safety for personnel and machine, breakage, and wear of pins and supports, and installation and retrieval easiness of pins. The analysis of the responses indicates that safety for personnel and machines/equipment is regarded mainly as either “Important” or “Crucial and decisive”, and that the expanding pin solution is regarded as “better” or “equal” compared to the standard, cylindrical pin, both for “safety level”, “risk for breakage of pin & support”, “tear & wear on pin & support” and “installation and retrieval time”.
Data-driven prediction of mean wind turbulence from topographic data

This study presents a data-driven model to predict mean turbulence intensities at desired generic locations, for all wind directions. The model, a multilayer perceptron, requires only information about the local topography and a historical dataset of wind measurements and topography at other locations. Five years of data from six different wind measurement mast locations were used. A k-fold cross-validation evaluated the model at each location, where four locations were used for the training data, another location was used for validation, and the remaining one to test the model. The model outperformed the approach given in the European standard, for both performance metrics used. The results of different hyperparameter optimizations are presented, allowing for uncertainty estimates of the model performances.
Modelling trade-offs in design-accompanying life cycle cost calculation

It is becoming increasingly important to integrate into cost calculation processes, all costs throughout a product's development and life span. Cost performance in design, manufacture, operation and disposal is associated with several intertemporal trade-offs – a subject about which there has been little research. This paper (1) highlights the main trade-offs in product design, (2) models unidirectional and bidirectional trade-offs between product design and later life cycle stages and (3) presents a profit-maximising solution for these trade-offs. The model's approach enables the assignment of changing costs in manufacturing, operation and disposal in relation to their dependency on development decisions, ensuring appropriate unidirectional and bidirectional trade-offs. It shows that neglecting these trade-offs may lead to weak design decisions. This new model will enforce the accountants' contribution to successful product design.
Algorithm of Dynamic Forming One Whole Raster Photo Map of the Seabed During its Shooting by an Autonomous Uninhabited Underwater Vehicle

An algorithm has been developed for creating one whole raster photo map of the seabed from images obtained from vertically downward cameras of autonomous underwater vehicles (AUV) using tile graphics. Tile representation of graphical information allows to quickly access the images after lifting the AUV to the surface and reduce the time spent by the operator to analyse the results of the mission. The images were combined using simple geometric transformations based on the data received from the navigation systems of the AUV and the parameters of its camera, so the algorithm can be implemented on the AUV with low-performance onboard computer, as shown in the experiment.
Research of Methods of Optimal Design of Special Electric Drives

Problems of optimization of special electrical and electromechanical systems in modeling, creation and design are solved mainly by methods of mathematical programming. The task of mathematical programming is to find extremes of the function of many variables in the presence of restrictions on variables, which creates fundamental difficulties. To solve such problems, the number of methods for solving the general problem of mathematical programming is currently expanding significantly. In this regard, the trend in the development of mathematical programming is following the path of highlighting and studying various subclasses of problems. The use of certain specific features of the tasks of the selected subclass creates opportunities for their more effective investigation and solution. Examples of such subclasses give convex, quadratic, linear programming problems, transport-type problems, and others. Geometric programming is also a section of mathematical programming that research a certain class of optimization problems. However, when using geometric programming, you have to apply linear, nonlinear programming, the concept of convex programming. Using optimization methods will allow you to correctly investigate, design and create special electric drives.
Microcomputer Based Data Acquisition System for Dynamic Testing

The availability of capable and relatively inexpensive microcomputers and versatile expansion devices has revolutionized education, research, and practice in engineering. A data acquisition and analysis system for use in full scale and large model dynamic testing based on a popular series of microcomputer has been developed. “Quick-look” and preliminary frequency analysis of data are completed in the microcomputer environment, while more detailed frequency domain analysis of single and dual channel data are completed in the mainframe environment. The system has been used for ambient and forced vibration testing of several prototype structures and large models.
ATTRIBUTES TEMPTING STUDENTS’ ONLINE PURCHASE INTENTION: THE MEDIATING ROLE OF PRE-PURCHASE SEARCHING

This study aims to examine the relationship between perceived usefulness (PU), perceived risk (PR) and online purchase intention (OPI). Furthermore, this study has endeavoured to elucidate the mediating role of pre-purchase searching (PS) between perceived risk (PR) and perceived usefulness (PU) and online purchase intention. This research is a correlational study conducted among students of Universiti Sains Malaysia to examine the purchase intention for online shopping. The variables are neither controlled nor manipulated. Data would be collected from the students in Universiti Sains Malaysia who are online purchase consumers. The results have supported the hypothesized direct and mediated relationship. The present study encompasses the body of knowledge through testing the Theory of Perceived Risk (TPR) and postulates empirical evidence on the hypothesized relationship. Moreover, this research study has contributed to the prevailing theory by assessing pre-purchase intention mediating by using partial least square structural modelling (PLS-SEM).
Dielectric Materials and Applications

Theory dielectric measuring techniques dielectric materials and their applications - dielectric materials insulation strengths of high pressure gases and of vacuum liquid dielectrics plastics as dielectrics ceramics dielectrics in equipment dielectrics in power and distribution equipment dielectrics in electronic equipment dielectrics in capacitors rubber and plastics in cables problems of the cable engineer dielectric materials as devices rectifiers piezoelectric transducers and resonators magnetic and dielectric amplifiers memory devices dielectric requirements of the armed forces tables of dielectric materials
Testing a conjecture on the origin of space, gravitation and mass

A Planck-scale model for the microscopic degrees of freedom of space and gravitation is explored in detail. The conjectured model is based on a single fundamental principle that involves fluctuating one-dimensional strands. First, the fundamental principle is used to deduce classical and quantum properties of space and gravitation, from the field equations of general relativity to gravitons. Secondly, the fundamental principle is used to deduce a detailed list of predictions. They include the lack of any change to general relativity at all sub-galactic scales, the validity of black hole thermodynamics, the lack of singularities, the lack of quantum foam, the lack of unknown observable quantum gravity effects, the maximum local momentum flow or force c4/4G, and the maximum local luminosity or power c5/4G. All predictions are tested against experiments and are found to agree with observations so far. Finally, it is shown that the fundamental principle implies a model for elementary particles that allows deducing ab-initio upper and lower limits for their gravitational mass values.
A Deep Learning Model for Extracting Consumer Sentiments using Recurrent Neural Network Techniques

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.
International Scientific Global Journal in Engineering, Science and Applied Research (ISGJESAR), Vol. 1, No. 1, May, 2016 | ISSN: Applied

In this paper, we are presenting hybrid methods for analysis of radiological image, such as CT, MRI, etc., to get the interested area from the medical image. This approach integrates fuzzy segmentation methods (FSM), localizing region-based active contours (LRBAC) and self-organizing map (SOM). Such integration of these methods is useful in focusing region of interest in medical images and optimizing the result. Firstly, we use fuzzy methods to get an initial segmentation result. Then we use LBARC and SOM to get smooth, accurate and optimized result. This hybrid approach is automated, since the whole segmentation procedure doesn’t need much manual intervention, except the initial seed position selection for fuzzy connectedness segmentation.
Weighted-nuclear-norm minimization for low-rank approximation -- application to multi- shot diffusion-weighted MRI reconstruction

Synopsis Low rankness of image/k-space data has been exploited in many different MRI applications. In this work, we introduce weighted-nuclear-norm minimization for MRI low-rank reconstruction, in which smaller thresholds are used for larger eigenvalues to reduce information loss. Our simulation results demonstrate that weighted nuclear norm could serve as a better rank approximation compared to (unweighted) nuclear norm. With this technique, we achieve 10-shot DWI and high-fidelity half-millimetre DWI reconstruction with significantly reduced ghosting artifacts.
Age-related differences in visual P300 ERP during dual-task postural balance

Standing and concurrently performing a cognitive task is a very common situation in everyday life. It is associated with a higher risk of falling in the elderly. Here, we aim at evaluating the differences of the P300 evoked potential elicited by a visual oddball paradigm between healthy younger (< 35 y) and older (> 64 y) adults during a simultaneous postural task. We found that P300 latency increases significantly (p < 0.001) when the elderly are engaged in more challenging postural tasks; younger adults show no effect of balance condition. Our results demonstrate that, even if the elderly have the same accuracy in odd stimuli detection as younger adults do, they require a longer processing time for stimulus discrimination. This finding suggests an increased attentional load which engages additional cerebral reserves.
Parasocial Relationships and the Formation of Hyperreality in the BTS World Game

The rapid development in the gaming business makes games, besides being a means of entertainment, also become the promotional tools for companies. One of them is the BTS World game released by Big Hit Entertainment to promote the K-Pop idol group, BTS. From the fandom perspective as the game users, BTS World is a form of media artifact facilitated by the company. BTS World is a video game as an adaptation of fan fiction, in which most of the story contents are the interaction between users and BTS members as the characters of the game. From the contents, it is possible to create a parasocial relationship and hyperreality felt by its players. The researcher conducted a study using a qualitative approach and phenomenological method to describe the meaning of the BTS World game and the experiences felt by the players both in parasocial relationships and forming hyperreality. Based on the results, the parasocial relationship, the meaning between parasocial friendship and parasocial, and the formation of hyperreality were found, which leads to the meaning of pseudo interaction and control of the characters’ life.