Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the position of noise sources in a turbulent subsonic jet using the beamforming technique

The position of the noise sources is the most important information in aeroacoustic research. This is especially true for the noise of a turbulent jet, in which noise is generated by many vortices of various scales interacting with each other. This leads to the fact that noise sources at different frequencies are distributed inside the jet. This information is especially important, for example, for the development of computational shielding models. There are a series of methods to identify the position of noise sources of different frequencies in the noiseproducing region of the flow. The most universal among them today are methods based on conducting acoustic measurements using microphone arrays. Acoustic measurements of the noise of the expiration of an air subsonic turbulent jet were carried out in a anechoic chamber at the PNRPU. Two planar beam microphone arrays with the same number of microphones and their different locations in the plane of the array were used. Postprocessing was carried out using the beamforming technique. The results obtained in this study were compared with the expression for the Strouhal number. It was found that the position of the noise sources in the jet is quite close to the calculated one.
Electric Vehicle Driver Response Evaluation in Multiaggregator Charging Management With EV Routing

Electric vehicle (EV) charging scheduling is a necessary aspect for encouraging the everyday use of EVs. EV charging may be accommodated while benefiting one or more of the stakeholders, i.e., the grid, the aggregator (service provider), and/or the driver. This article presents a profit maximization multiaggregator scheduling scheme, which encapsulates the benefits of the consumer while maximizing the profits of the aggregator in noncollaborative and collaborative scheduling. The manuscript proposes novel fuzzy inference based EV driver response indicator (EVDRI) for gauging the response of the EV driver to a particular scheduling. The EVDRI is dependent on parameters of charging time and cost. Penalties for low satisfaction, along with penalties for any EVs that remain unscheduled are also included in the profit calculation. Furthermore, the potential impact of EV routing on aggregator profits and driver's satisfaction is also explored. Results indicate that the user response is positive (greater satisfaction) in case of collaborative scheduling and with optimum routing. The developed fuzzy EVDRI (F-EVDRI) provides good indication of the satisfaction level of the drivers. Impact of different parameters is also explored on the F-EVDRI wherein charging duration was found to be the parameter that impacts the EVDRI.
REAL WASTE TESTING OF SPHERICAL RESORCINOL-FORMALDEHYDE ION EXCHANGE RESIN

This report presents data on batch contact and column testing tasks for spherical resorcinol-formaldehyde (sRF) resin. The testing used a non-radioactive simulant of SRS Tank 2F dissolved salt, as well as an actual radioactive waste sample of similar composition, which are both notably high in sodium (6 M). The resin was Microbeads batch 5E-370/641 which had been made on the hundred gallon scale. Equilibrium batch contact work focused on cesium at a temperature of 25 C due to the lack of such data to better benchmark existing isotherm models. Two campaigns were performed with small-scale ion exchange columns, first with Tank 2F simulant, then with actual dissolved salt in the Shielded Cells. An extrapolation of the batch contact results with radioactive waste over-predicted the cesium loaded onto the IX sRF resin bed by approximately 11%. This difference is not unexpected considering uncertainties from measurement and extrapolation and because the ion exchange that occurs when waste flows through a resin bed probably cannot reach the same level of equilibrium as when waste and resin are joined in a long term batch contact. Resin was also characterized to better understand basic chemistry issues such as holdup of trace transition metals present inmore » the waste feed streams. The column tests involved using two beds of sRF resin in series, with the first bed referred to as the Lead column and the second bed as the Lag column. The test matrix included two complete IX cycles for both the simulant and actual waste phases. A cycle involves cesium adsorption, until the resin in the Lead column reaches saturation, and then regenerating the sRF resin, which includes eluting the cesium. Both the simulated and the actual wastes were treated with two cycles of operation, and the resin beds that were used in the Lead and Lag columns of simulant test phase were regenerated and reused in the actual waste test phase. This task is the first to demonstrate the treatment of SRS waste with sRF resin and the tests clearly demonstrated cesium decontamination for actual waste. The results of the column tests were similar for both the simulated and the actual waste and demonstrated Cs removal with sRF from both wastes. For a flowrate of 1.4 bed volumes (BV)/hour at 25 C those results with sRF resin were: (1) Simulant and actual waste results are equivalent; (2) Cs breakthrough began between 200 and 250 BV; (3) Cs breakthrough reached 100% at around 400 BV; (4) Cs breakthrough curve from 5% to 100% is approximately linear; (5) Cs elution with 0.5 M HNO3 starts at 2 BV and ends at 6BV; (6) Most, if not all, of Cs adsorbed during treatment is released during elution; (7) At 100% breakthrough of Cs the resin bed adsorbs approximately 85% of full capacity before detection in the effluent; the remaining 15% is adsorbed at saturation; (8) Approximately 90% of resin bed changes (color and volume) are complete by 6 BV; and (9) During elution the resin shrinks to about 80% of its fully working (sodium form) BV. « less
Developing Entrepreneurship Self-Efficacy: A Diagnostic Study Of The Influential Factors In The Confidence To Start A Business

The study objective was to identify the influential factors in the development of the entrepreneurship self-efficacy in students of international business administration of a private university of Lima (Peru). The sample belonged to 110 students, 50 women (45.5 %) and 60 males (55.5 %) between 17 and 32 years of age (Mage = 20.06, SD = 2.86). The results indicated the students possess an average level in the dimensions of the enterprising self-efficacy and there are not significant differences for genre. Also, it was observed the high educational levels of the father predict the efficacy to define the essential objectives of a business. Then, the being of the capital city predicts the promotion of an innovative labor environment. And finally, the self-efficacy to define the central objectives of a business predicts positively the suitable human resource management.
From 6.2 to 0.15 seconds – an Industrial Case Study on Mobile Web Performance

Background. A fast loading web app can be a key success factor in terms of user experience. However, improving the performance of a web app is not trivial and requires a deep understanding of both the browser engine and the specific usage scenarios of the web app under consideration.Aims. This paper presents an industrial case study targeting a large web-based dashboard, where its performance was improved via 13 distinct interventions over a four-month period. Method. Firstly, we design a replicable performance engineering plan, where the technical realization of each intervention is reported in details together with its development effort. Secondly, we develop a benchmarking tool which supports 11 widely-used web performance metrics. Finally, we use the benchmarking tool to quantitatively evaluate the performance of the target web app. Results. We observe a considerable performance improvement over the course of the 13 interventions. Among others, we achieve a 97.56% reduction of the time for the First Contentful Paint (i.e., from 6.29 to 0.15 seconds) and a 19.85% improvement of the Speed Index metric (i.e., from 15.31 to 12.27 seconds). Conclusions. This case study shows the value of a continuous focus on performance engineering in the context of large-scale web apps. Moreover, we recommend developers to carefully plan their performance engineering activities since different interventions require different efforts and can have very different effects on the overall performance of the system.
On the Estimation of Power System Inertia accounting for Renewable Generation Penetration

Large-scale penetration of renewable energy sources in power systems is essentially related to the need of reducing the environmental impact caused by the fossil-fuel. As well known, the interface between the power grid and this kind of energy resource is achieved by power converters, with a consequent dynamic behavior quite different from the synchronous generators. This matter involves negative impacts on the operating conditions of power systems. In this context, it is crucial to individuate estimation techniques able to predict promptly critical conditions which could, in extreme cases, compromise the stability of whole system. In this paper the authors employ an auto-regressive model which can describe the dynamic evolution of the power system inertia. The core of the procedure relies on an inertia model conceived as the sum of a periodic component and a noise stochastic process distributed according a Logistic model. The robustness of a novel estimation procedure, able to capture the dynamic evolution of the inertia, is investigated by testing two scenarios of Italian Transmission Network. The assumptions in terms of the obtained numerical results show the validity of the estimation technique and of the probabilistic characterization of the noise.
Solar Water Pumping System Modelling and Analysis using MATLAB/Simulink

Solar photovoltaic (PV) based water pumping systems provide an appropriate substitute to the traditional water pumps based on electricity and diesel because of low cost and eco-friendly nature. These systems are very suitable for remote locations with no access to electricity grid. The output of solar PV systems varies with varying external conditions like solar irradiance and temperature. In this work, a MATLAB/ Simulink based study of solar powered water pumping system with permanent magnet DC motor driving a centrifugal pump has been done. Perturb and observe (P&O) maximum power point tracking algorithm has been implemented to regulate duty cycle of a DC-DC boost converter. The Simulink model has been tested for three condition (i.e. under standard test condition (STC), varying solar irradiance and partial shading condition).
Improving Bug Localization by Mining Crash Reports: An Industrial Study

The information available in crash reports has been used to understand the root cause of bugs and improve the overall quality of systems. Nonetheless, crash reports often lead to a huge amount of information, being necessary to consolidate the crash report data into groups, according to a set of well-defined criteria. Recent research work have proposed different criteria and techniques to group crash report data, making more effective the process of finding the root causes of a bug and showing the performance of the approaches in the context of open source applications (such as IDEs and web browsers). In spite of that, it is still not clear how these approaches perform in other application domains, such as enterprise systems. In this paper, we present an industrial study in this field. We tailor existing approaches to find and group correlated crash reports, and identify buggy files in the domain of web-based systems. We then evaluate the performance of the resulting criteria and technique in industrial settings – identifying and ranking the classes that are more likely to contribute to a crash and thus might need a fix. We also check if the methods changed by the developers to fix a bug are present in the stack traces of the crash report groups used to identify the buggy classes. Our study provides new pieces of evidence of the potential use of crash report groups to indicate buggy classes and methods using stack traces information. For instance, we successfully identify buggy classes with recall varying from 61.4% to 77.3%, considering the top 1, top 3, top 5, and top 10 suspicious buggy files identified and ranked by our approach. We also found that 80% of changed methods from the closed bug fix issues appeared in related stack traces of the crash report groups. Finally, the approach also received positive response from the project leaders of the evaluated projects to help their bug resolution processes.
An Empirical Demonstration of Unsupervised Machine Learning in Species Delimitation

One major challenge to delimiting species with genetic data is successfully differentiating species divergences from population structure, with some current methods biased towards overestimating species numbers. Many fields of science are now utilizing machine learning (ML) approaches, and in systematics and evolutionary biology, supervised ML algorithms have recently been incorporated to infer species boundaries. However, these methods require the creation of training data with associated labels. Unsupervised ML, on the other hand, uses the inherent structure in data and hence does not require any user-specified training labels, thus providing a more objective approach to species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus). First, we combine mitochondrial data with examination of male genitalic morphology to identify a priori species hypotheses. Then we use single nucleotide polymorphism data derived from sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML algorithms in successfully identifying a priori species, comparing results to commonly used genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses using UCE data. We find that unsupervised ML approaches successfully cluster samples according to species level divergences and not to high levels of population structure, while standard model-based validation methods over-split species, in some instances suggesting that all sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the benefits of better data visualization in two-dimensional space and the ability to accommodate various data types. We argue that ML methods may be better suited for species delimitation relative to currently used model-based validation methods, and that species delimitation in a truly integrative framework provides more robust final species hypotheses relative to separating delimitation into distinct “discovery” and “validation” phases. Unsupervised ML is a powerful analytical approach that can be incorporated into many aspects of systematic biology, including species delimitation. Based on results of our empirical dataset, we make several taxonomic changes including description of a new species.
A Study of Wiggling AA modeling and Its Impact on the Device Performance in Advanced DRAM

In this paper, a wiggling active area (fin) in an advanced 1x DRAM process was analyzed and modeled using the pattern-dependent etch simulation capabilities of the SEMulator3D® semiconductor modeling software. Nonuniformity in sidewall passivation caused by hard mask pattern density loading was identified as the root cause of the wiggling profile. The calibrated model mimicked these phenomena, giving nearly the same output AA shape as the real fabrication process. The wiggling profile’s impact on device performance was assessed using the built-in drift-diffusion solver of SEMulator3D. Our analysis confirmed that the wiggling profile, induced by micro-loading during a pattern-dependent etch, has a large impact on overall electrical performance in the device. This was especially apparent with the off-state leakage, primarily due to a worse drain-induced barrier lowering effect in a fatter fin.
Classification Of Power Relations Based On Email Exchange

In this paper, we try to find out how interactions between two people show power relations between them by studying various research papers and comparing techniques used in them. Several features such as non-lexical, lexical, no. of recipient, were used in power level analysis. Deep learning techniques such as CNN, LSTM have been observed to give better results than traditional NLP methods. We have implemented our own power relation classifier by using the optimum technologies and improving on some of the drawbacks faced by the previous researchers. We have also implemented a four-way classification for the power hierarchy problem.
Seascape genetics at its finest: dispersal patchiness within a well-connected population

Dispersal is a main determining factor of population structure and variation. In the marine habitat, well-connected populations with large numbers of reproducing individuals are common but even so population structure can exist on a small-scale. Variation in dispersal between populations or over time is often associated to both environmental and genetic variation. Nonetheless, detecting structure and dispersal variation on a fine-scale within marine populations still remains a challenge. Here we propose and use a novel approach of combining a clustering model, early-life history trait information from fish otoliths, spatial coordinates and genetic markers to detect very fine-scale dispersal patterns. We collected 1573 individuals (946 adults and 627 juveniles) of the black-faced blenny across a small-scale (2km) coastline as well as at a larger-scale area (<50kms). A total of 178 single nucleotide polymorphism markers were used to evaluate relatedness patterns within this well-connected population. Local retention and/or dispersal varied across the 2km coastline with higher frequency of SHORT-range disperser adults; representing local recruitment; towards the southwest of the area. An inverse pattern was found for juveniles, showing an increase of SHORT-range dispersers towards the northeast. This reveals a complex but not full genetic mixing and suggests oceanic/coastal circulation as the main driver of this fine-scale chaotic genetic patchiness within this otherwise homogeneous population. When focusing on the patterns within one recruitment season, we found large differences in temperatures (from approx. 17oC to 25oC) as well as pelagic larval duration (PLD) for juveniles from the beginning of the season and the end of the season. We were able to detect fine-scale differences in HIGH-range juvenile dispersers, representing distant migrants, depending on whether they were born at the beginning of the season, hence, with a longer PLD, or at the end of the reproductive season. The ability to detect such fine-scale dispersal patchiness will aid in our understanding of the underlying mechanisms of population structuring and chaotic patchiness in a wide range of species even with high potential dispersal abilities.
Behavior-Based Machine Learning Approaches to Identify State-Sponsored Trolls on Twitter

In recent years, there has been an increased prevalence of adopting state-sponsored trolls by governments and political organizations to influence public opinion through disinformation campaigns on social media platforms. This phenomenon negatively affects the political process, causes distrust in the political systems, sows discord within societies, and hastens political polarization. Thus, there is a need to develop automated approaches to identify sponsored-troll accounts on social media in order to mitigate their impacts on the political process and to protect people against opinion manipulation. In this paper, we argue that behaviors of sponsored-troll accounts on social media are different from ordinary users’ because of their extrinsic motivation, and they cannot completely hide their suspicious behaviors, therefore these accounts can be identified using machine learning approaches based solely on their behaviors on the social media platforms. We have proposed a set of behavioral features of users’ activities on Twitter. Based on these features, we developed four classification models to identify political troll accounts, these models are based on decision tree, random forest, Adaboost, and gradient boost algorithms. The models were trained and evaluated on a set of Saudi trolls disclosed by Twitter in 2019, the overall classification accuracy reaches up to 94.4%. The models also are capable to identify the Russian trolls with accuracy up to 72.6% without training on this set of trolls. This indicates that although the strategies of coordinated trolls might vary from an organization to another, they are all just employees and have common behaviors that can be identified.
Comparison of Shallow Neural Network with Random Forest Algorithm in Estimating Lawn Grass Lengths for Robotic Lawn Mowers

This paper states the accuracy comparison of shallow neural network (SNN) with random forest algorithm in the estimation of lawn grass lengths for robotic lawn mowers. This relates to Digital Twin and Virtual Twin of Hybrid Twin approach when controlling the autonomous driving of robotic lawn mower. In its autonomous operating, the length estimation of lawn grasses or of such ground conditions as dirt, gravel, or concrete, etc., are important for precisely controlling the rotation speed of motor and moreover, the estimation must be fast. Therefore, an SNN is constructed, and its performances are compared with those of random forest algorithm through some experiments. The accuracy of SNN is 0.7 point better than that of random forest algorithm and the training time is very fast, that is, less than 0.4 seconds. The SNN is being further improved from the view point of application range and performances.
Association of Cord Blood Telomere Biology with Mother’s Education

Background: Telomere, the biological chronometer, has been found to be influenced by different parameters which are reprogrammed during fetal life. This study was designed to find out influence of education on relative telomere length (RTL) of the maternal and new born and to detect improvement in the genetic remodeling during the fetal life between low and high educational levels of mother. In this cross-sectional study RTL (maternal and cord) was calculated by Quantitative real-time PCR. Statistical analysis used were chi square tests and Mann Whitney U test for variables with two categories and then Kruskal Wallis for variables with more than two categories to examine mean difference between relative telomere length(RTL) and maternal education. The relationship between predictor variable (education) and RTL was done by linear regression. Results: A significant association of education and RTL revealed shorter mean maternal RTL (base pair(bp)) 6380±1128 among with low education and longer 6553±945 in high p (B= 0.009 p = >0.05). RTL low and longer in high education group ( p =0.007) (B= 0.184 p = <0.05) at 95% confidence level. It was also found that newborn among high education with upper middle Socioeconomic status (SES) have significantly longest RTL 7262±1804 ( p =<0.05). Conclusion: Overall longer newborn RTL than mothers were observed among different levels of education in targeted population of Karachi where higher education have a shielding effect on telomere remodeling during the fetal development.