Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar Silicon Array Performance in Generating a Ratio of Electrical Energy for Hercules Aircraft

This paper presents existent model for solar silicon cell and introduces different parts of Hercules electrical system which proposed cell is used for generating a ratio of electrical energy for Hercules. Then, novel electrical energy distribution system based on solar cell will be presented. This model will contributed in generating aircraft start-up power and act as backup electrical system boosting flight reliability. Moreover, considering different values for cell parameters, effect of selecting appropriate model and parameter on solar array performance will be evaluated. Next, the result of temperature on proposed model will be assessed considering maximum output power and total generated power by solar arrays.
Network Traffic Classification Model Based on Multi-Task Learning

A multi-task learning network traffic classification model (MTL-NTC) is experimentally studied. The identification task of the MTL-NTC model contains three tasks: identifying the type of traffic, predicting the bandwidth, and the duration of the flow. Identifying the type of traffic is the main task. It includes the classification of five types of traffic. The bandwidth prediction and the duration of the flow are auxiliary tasks, which are used to improve the performance of the main task. In the public network traffic data set QUIC, the MTL-NTC model and the common model are used for comparative experiments. The experimental results show that the recognition accuracy of this model is 97.33%, which is better than the common models.
Vehicle wheel detection using micro doppler effect

With the advent of autonomous driving, safety have become the top priority. To adhere vehicle safety standard for autonomous vehicle, the sensors play the primary role in sensing the environment. With high frequency resolution radar, micro motions of the vehicle wheel can be detected. Time-Frequency distribution is used to detect the frequency changes with respect to time. MUSIC is a high resolution direction finding algorithm, which can estimate the direction of arrival of echo signals. Micro Doppler effect provides the vehicle wheel information related to the number of spokes and its spoke size. With this information, it is possible to detect the exact position of the wheels in the vehicle.
Do Moody's and S&P firm's ratings differ?

Credit rating agencies produce public statements about the financial health of companies, institutions, geographical entities and financial assets. The main available information about firms, useful for analyzing their long-term creditworthiness is their public accounts about the activities and results, besides the audit reports and their credit ratings. The agencies’ results are mainly based on these data, but they claim to use additional qualitative information, with a methodology only partially disclosed. When different agencies produce long-term ratings about a particular firm, it should be expected that they were coincident, or at least similar, so that investors could use any of them to assess the potential financial risk. This is not the case, as the same companies can be rated differently by different agencies. This is the case with Standard and Poor's and Moody's: although their rating methods are not coincident, but their aim is to measure a similar latent variable – the firm's credit risk. These divergences could be caused, at least in part, by possible conflicts of interest or by a phenomenon called 'rating inflation'. A difference index is proposed to measure the differences in ratings when comparing several agencies' evaluations. The situation with the two main agencies is examined, using two large samples in a five-year period: clear discrepancies are observed, in some economic sectors, and similarities in others, with some evidence about getting higher ratings depending on a chosen agency. Also, a convergence of ratings during the period of 2014-2018 is observed, more prominent in some sectors, suggesting that additional regulation is needed to increase the market transparency.
Analysis, fabrication and testing of a sandwich composite for an UAV wing

This paper includes the study on analysis, fabrication and testing of a lightweight sandwich composite made by balsa wood core and fiber-glass/polyester resin for an UAV wing. The analysis for von mises stress distribution is done using COMSOL software. The materials play an important role in the design of any engineering process and therefore must have the ideal properties required for a particular function. It is more important for the airframe designers as weight is a major criterion in aircraft design. The sandwich structure exhibits high flexural strength which is the primary necessity of a wing to counter bending loads. The balsa wood core structure exhibits better structural strength properties than that of polyurethane foam-based cores. The sandwich composite developed exhibits satisfactory mechanical properties for the required application.
Input voltage controlled full‐bridge series resonant converter for LED driver application

: In this study, a phase shift controlled full-bridge series resonant converter (SRC) integrated with buck converters for light-emitting diode (LED) driver application is proposed. This LED driver supplies four LED loads simultaneously. The current through LED loads is controlled by phase shift control of the SRC, and its output is added to the input voltage. Zero-voltage switching (ZVS) switching of all the switches is achieved by operating the SRC at lagging power factor for all input voltage variations. As the only part of the output power is processed through the SRC and all the switches operating with ZVS, the efficiency of the converter is 95.7%, which is high. A 142 W LED driver is designed, and its performance is tested with PSPICE simulations and experimental prototype.
SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20.
Cascade single stage Detector using full convolution network

For object detection, the two-stage approach always achieves better performance than one-stage. But one-stage is more effective. The main shortcomings of one-stage approach are: class imbalance problem during the training phase and sometimes the correct object score is lower. To address those problems, we propose a novel single-shot detector, called Cascade detection. It is composed of several stages, and each stage is composed of several detection layers. The main point of our method is: we design an anchors to anchors module (A2AM), which gives one-stage approach the ability of training stage by stage. It can not only address the class imbalance problem in the training phase, but also increase the object scores and the Iou between prediction and ground truth in the inference phase. The multitask loss function enables us to train the whole network in an end-to-end way. Experiments on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO prove that our method achieves state-of-art performance detection accuracy and computation efficiency.
Modeling Level 2 Passive Microwave Precipitation Retrieval Error Over Complex Terrain Using a Nonparametric Statistical Technique

The representation of precipitation variability over mountainous regions by ground-based sensors is an open problem in hydrometeorological applications that necessitates the use of satellite-based precipitation products (SPPs). An extended network of ground-based X-band radar (GR) deployments over complex terrain areas, including the northeastern Italian Alps, North Carolina, Olympic Mountain, and the southern tip of Vancouver Island, is used in this study as a benchmark rainfall data set for error characterization and modeling of Level 2 PMW retrievals (Goddard profiling (GPROF) V05 algorithm) for the different sensors: the Microwave Humidity Sounder (MHS), the Special Sensor Microwave Imager/Sounder (SSMIS), the Global Precipitation Measurement Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Matchups of Level 2 PMW/GR rainfall are extracted based on a matching methodology that identifies GR volume scans with PMW overpasses, and scales GR parameters to the satellite products’ nominal spatial resolution. The error model is the nonparametric machine learning tree-based quantile regression forest (QRF), which we developed using matchups of PMW/GR rainfall data from the different study areas. Validation of the error model is conducted using three cross-validation techniques: the k-fold, leave-one region out, and enforced. All validations showed that the error model-based corrections can significantly reduce both the mean relative error and the random component of PMW products. Moreover, the error reduction demonstrated with the leave-one region out cross-validation technique indicated that the error model is transferable among complex terrain regions. Algorithm developers may find this error model useful to integrate in the Level 3 products.
Different Trends in Antarctic Temperature and Atmospheric CO2 During the Last Glacial

We analyze the past 67,000 years of climate using Antarctic ice‐core records to constrain the mechanisms involved in (a) the “bipolar seesaw” relationship between Greenland and Antarctic surface temperature variations, and (b) mechanisms of millennial‐scale atmospheric CO2 concentration variations. Specifically, we determine for each Greenland Stadial the rate of Antarctic temperature and atmospheric CO2 rise. We find that Antarctic warming rates significantly decrease as the climate cools during the glacial period, whereas the rate of atmospheric CO2 rise does not significantly change. Also, we find that the rates of Antarctic warming and atmospheric CO2 rise are both insensitive to whether a given stadial contains a Heinrich event. These results challenge the view that a single Southern‐Ocean‐based mechanism dominates the observed glacial variability in Antarctic temperature and atmospheric CO2. Instead, our results are consistent with an important contribution of low‐ and mid‐latitude processes to millennial‐scale atmospheric CO2 changes.
Iatrogenic cyanosis in children, a pitfall of atrial septal defect repair

AbstractToday surgical repair of the atrial septal defect (ASD) is considered as relatively a simple and safe procedure with minor to no morbidity and mortality. When a complication occurs after ASD repair, is associated with considerable morbidity and mortality, which is much higher than primary surgical procedure. We report two patients that underwent ASD repair with caval inflow occlusion without CPB with late presentations of iatrogenic diversion of IVC to LA and subsequent complications of right to left shunting.
Synthesis, Characterization and in vitro Antioxidant Activity of New Chiral N-boc Organotellurium Compounds, (CH3)3OC(O)NHCH(R)C(O)NHCH2-CH2Te-C6H4-4-OCH3, Containing Carbamate and Peptide Groups

Synthesis, characterization and antioxidant activity of a new series of chiral N-boc oraganotellurium compounds, (CH3)3OC(O)NHCH(R)C(O)NHCH2CH2Te-C6H4-4-OCH3, containing carbamate and peptide groups have been reported in this paper. These chiral peptides were synthesized in good to excellent yields, via acid-amine coupling reaction of N-boc L-amino acids with 2-(4-methoxyphenyltelluro) ethylamine in presence of dicyclohexyl carbodimide (DCC) at room temperature. The elemental analyses, Fourier transform infrared (FTIR), 1H and 13C {1H} nuclear magnetic resonance (NMR) spectra and mass spectra were characteristic. Specific optical rotation (SOR) was also determined. In vitro antioxidant activity of these multi-functional compounds in methanol has been evaluated against 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals with 2,6-di-tert-butyl-4-methylphenol (BHT) as a standard reference compound. The IC50 (inhibitory concentration 50) values of these chiral peptides revealed significant inhibition against DPPH radicals and found to be effective antioxidants.
Positive Correlation Between the Hip Adduction Moment Impulse During the Stance Phase and the Hip Joint Contact Force Impulse

Background: Excessive mechanical loading, in the form of the joint contact force, has been reported to promote osteoarthritis in vitro and vivo in mice. However, it has also been reported that an excessive hip adduction moment impulse during the stance phase likely contributes to the progression of hip osteoarthritis. The relationship between the hip adduction moment impulse and hip joint contact force (impulse, and first and second peaks) during the stance phase is unclear. The objective of the present study was to clarify this relationship. Methods: A public dataset pertaining to the overground walking of 84 healthy adults, in which the participants walked at a self-selected speed, was considered. The data of three trials for each participant were analyzed. The relationship between the hip adduction moment and hip joint contact force, in terms of the impulse and first and second peaks, during the stance phase was evaluated using correlation coefficients.Results: The hip adduction moment impulse during the stance phase was positively correlated with the hip joint contact force impulse and not correlated with the first and second peak hip joint contact forces. Furthermore, the first and second peak hip adduction moments during the stance phase were positively correlated with the first and second peak hip joint contact forces, respectively. Conclusions: These findings indicate that the hip joint contact force impulse during the stance phase can be used as an index to determine the risk factors for the progression of hip osteoarthritis.
Rapid and sensitive detection of neotame in instant grain beverages by paper‐based silver nanoparticles substrates

Neotame is an arti ﬁ cial sweetener with increasing consumption in recent years, excessive intake of it may bring potential health risk. In this work, a rapid method was developed to detect neotame in instant grain beverages, which was based on surface-enhanced Raman scattering (SERS) technique and ﬁ lter paper-based silver nanoparticles (AgNPs@FP) substrates. The designed substrate exhibits good SERS activity with an enhancement factor of 10 5 because of the synergistic effect of the concentrated silver nanoparticles and ﬁ lter paper. Meanwhile, the stability and repeatability of this fabricated substrate have been investigated with the relative standard deviation of 6.1%. In addition, it shows an excellent linear relationship ( R 2 = 0.997) between the SERS signal and the logarithm concentration of neotame with a wide concentration range (0.05 – 5 g/kg) in the quantitative analysis. The limit of detection of neotame can be as low as 0.01 g/kg in instant grain beverages, which is below the maximum allowable addition level for neotame in instant grain beverages set in China (GB2760-2014, 0.16 g/kg). The proposed method has great potential for the identi ﬁ cation and quanti ﬁ cation of neotame in food safety applications with high sensitivity.
Multi-level hybrid recommender decision support system with verbal output

The article describes decision-making methods based on intelligent learning algorithms, for the construction of which verbal elements are used. Such algorithms and methods operate in calculations with strictly quantitative data, however, taking into account the human way of perceiving information in verbal form. A person does not directly participate in the process of constructing a model, that is, its structure does not depend on expert or other human opinions, however, qualitative verbal information (for example, elements of regulatory acts, documents, orders, etc.) is embedded in the algorithm in encoded form. Unlike the classical methods of fuzzy logic and fuzzy inference, the proposed models consist of several interconnected classical and author’s models, using author’s numerical methods. This increases their accuracy and adequacy. The great advantage is the automatic way to build these models, only on the basis of sets of initial quantitative and qualitative data, which will allow you to quickly and efficiently create recommender systems for solving a wide range of decision-making problems. The system is built in three stages: First, the fuzzy logical inference Takagi – Sugeno – Kang system (TSK) is automatically built on the sounding of the available quantitative and qualitative data. The advantage of such a system is that it can be retrained (adjust its parameters). Then, after the system is built according to the available data, at the second stage its accuracy is increased using a fuzzy neural network. The drawback of the constructed system is that it does not give out a verbal answer. At the third stage, based on the adjusted parameters of the TSK system, a Mamdani type system is constructed using a special algorithm. As a result, it can produce a verbal answer from a recommender system, and at the same time gets all the advantages of a neural network refinement. Computational experiments are presented.
Directing moral inquiry: A rejoinder to Cam, Sowey, Lockrobin, Splitter, Sprod and Knight

In this rejoinder to the foregoing responses to my article ‘Moral education in the community of inquiry’, I address what I take to be the four most fundamental objections to my proposed expansion of the community of inquiry (CoI) method. My proposal is that we make room in the CoI for directive teaching of moral standards we know to be justified or unjustified, in addition to nondirective teaching of moral standards whose justificatory status is unknown. The four objections I consider are: (i) that the dominant conception of the CoI method already permits directive moral teaching; (ii) that permitting directive moral teaching in the CoI would edge out other valuable kinds of inquiry; (iii) that all moral standards are controversial; and (iv) that the task of distinguishing justified and unjustified moral standards from controversial ones is unreasonably demanding. I argue that none of these objections is successful.
Application of multi-agent approach in the electric power control systems within the active energy complex

This paper provides an analytical overview of artificial intelligence technologies used in the tasks of technological management of the electric power industry. The relevance of using multi-agent systems is proved for solving energy problems. The concept of an intelligent energy system is considered with an active adaptive network (IES AAN). A block diagram of the electric power system management system is given within the IES AAN. It is shown that the developed centralized management principles are not fully applicable for small distribution networks – microgrid, with the use of storage devices and renewable sources. The article describes the active energy complex in Russia in the context of an analog of the Microgrid concept. The required functionality of a intelligent controllable connection (ICC) is studied, the main algorithm of the ICC operation is given, and the proposed structure is described for building a distributed system of intelligent multi-agent control of the AEC that implements the required functions.
Exploring the Effect of a Scaffolding Design on Students’ Argument Critique Skills

We designed scaffolded tasks that targeted the skill of identifying reasoning errors and conducted a study with 472 middle school students. The study results showed a small positive impact of the scaffolding on student performance on one topic, but not the other, indicating that student skills of writing critiques could be affected by the topic and argument content. Additionally, students from low-SES families did not perform as well as their peers. Student performance on the critique tasks had moderate or strong correlations with students’ state reading and writing test scores. Implications of the scaffolding and critique task design are discussed.
Systematic literature review of photovoltaic output power forecasting

: Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.
The role of self-construal in EFL vocabulary learning

Abstract Vocabulary learning is often assigned as out-of-class learning, which learners need to autonomously initiate and be motivated to sustain. Under such learning modes, though independent learners may need less motivational scaffolding, learners who prefer a more interactive study environment may need to be provided with assistance to boost their motivation. Focusing on such personal determinants, this study examines the role of self-construal in vocabulary learning by employing self-determination theory. The participants were 155 engineering students from a Japanese technical college. Path and mediation analyses were performed based on vocabulary test scores and questionnaire responses. Results revealed that independent self-construal had a significant impact on more self-determined types of both motivation (i.e., intrinsic motivation and identified regulation) and amotivation, but interdependent self-construal was statistically irrelevant to them, in the context of vocabulary learning. Furthermore, perceived autonomy and competence mediated the relationship between independent self-construal and motivation. These findings indicate that vocabulary learning motivation is shaped and regulated by self-construal and may be enhanced through support of the mediators.