Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of the Possibility of Achieving the Same Per-Port Outflow in a Dividing Manifold

There have been several attempts to optimise fluid flow manifolds; these, however, have shown are limited and further investigation into the efficiency of these systems is needed. This work focuses on improving the distribution manifolds efficacy in outflow division, i.e. attaining the same flow rate per each exit port of the manifold. Water has been selected to be the working fluid. A numerical investigation utilising CFD (by ANSYS Fluent R16.2) analysis into two-dimensional, incompressible, and turbulent flow has been carried out to resolve the flow manifold problem using two turbulence modelling, Standard k-ε and RNG k-ε, approaches. Four values of flow rate have been considered, which are specified by the Reynolds numbers 101×10, 202×10, 303×10, and 404×10. These values correspond to the fluid inlet velocities 0.5, 1.0, 1.5, and 2.0 m/s, respectively. The manifold configuration is defined by the given area ratio (total cross-sectional area for laterals /header cross-sectional area). Three values of area ratio are considered; these are 0.703125, 0.84375, and 0.984375. The results indicate that the flow uniformity has a reverse proportional relationship with the fluid flow rate and area ratio for all manifold arrangements. However, there is no significant effect of the flow rate increase on flow mal-distribution. Also, the use of RNG k-ε model has shown higher values of the non-uniformity coefficient than those obtained by the Standard k-ε model. The outcomes of this analysis have been compared with experimental data and a good agreement among them has been found.
AN IMPROVISED APPROACH TO GENERATE SIGNIFICANT ASSOCIATION RULES FROM CUSTOMER TRANSACTION DATABASE-EMPIRICAL ANALYSIS

There are techniques available in order to remove redundant and irrelevant association rules from transactional database. This paper emphasizes on identifying the significant rules from pool of rules generated using an improvised approach of the algorithm. A score model is proposed to find essential rules out of many rules. An attempt is made to find significant rules by using a proposed algorithm RuleGenerator which is based on an approach of adjacency lattice and depth first search to generate association rules. The proposed algorithm is less complex as compared to the existing one and generates rules in less number of edges. The empirical research analysis is done on the customer transaction database using Maruti Suzuki cars, it results in the vital customer choices and rules. The application in automobile sector, cars results in rules which help the Organization in identifying the segment of cars preferred by the certain category of customers. The study also focuses on the implication in detail, the usage of significant rules in various decision making activities.
EXPLORING THE ROLE OF SOCIAL CAPITAL IN IMPLEMENTING A COLLABORATIVE SCM SYSTEM

In recently years, since uncertainty increases in business environment and market competition intensifies, the importance of collaborative supply chain management (SCM) is increasing. Companies need to collaborate with partner Firms within the supply chain from the perspective of strategy and system to respond quickly to dynamic market changes. Therefore, this study was conducted with the aim to seek ways to implement the collaborative supply management system of manufacturing Firms from the perspective of social capital. To achieve the purpose of this study, the author examined the concept and dimensions of social capital and supply chain collaboration through literature review and empirically verified the influencing relationship between the social capital, collaborative supply chain management system, and supply chain performance of manufacturing Firms. This study is meaningful in that the author confirmed the importance of social capital formation and collaborative SCM system establishment for enhancing the competitiveness of manufacturing Firms, and suggested the strategic directions of the supply chain management that can contribute to improving supply chain performance.
Potential benefit of clove oil sedation on animal welfare during salmon smolt, Salmo salar L. transport and transfer to sea

The purpose of this study was to determine the eiect of clove oil (4.0 mg L � 1 ) sedation, compared with nonsedation, on the primary (plasma cortisol), secondary (osmoregulation) and tertiary (mortality) stress responses in Atlantic salmon smolts during transport and transfer to sea. Clove oil sedation during on- and oi-loading su⁄ciently reduced the primary stress response to lower mortality (2.1%) during transfer to sea compared with unsedated ¢sh, which experienced a mortality rate above 12.2%. The unsedated ¢sh experienced an acute mortality that stabilized only 6 days after the transport. None of the secondary stress responses measured in this experiment could contribute towards explaining this phenomenon, with the possible exception of plasma magnesium (Mg 21 ). Plasma Mg 21 diiered between the groups; while plasma Mg 21 in the clove oil sedated group returned to pre-stress levels 72 h after transport, the unsedated group showed no such recovery even 1 week after transport, which may indicate a disturbance in the hydromineral balance, and provides a plausible explanation for the delayed mortality in this group. Eugenol-based anaesthetics appear to be promising as a stress-reducing sedative for Atlantic salmon smolts, and, if used properly, this chemical could improve animal welfare and survivability during and after common aquaculture-related incidents.
Chamfer Matching을 이용한 실시간 템플릿 기반

In this paper we describe an approach for template based detection and tracking of objects by chamfer matching in real time video. Detecting and tracking of any objects is the key problem in computer vision. In our case we try for hand and head of human for detection and tracking by chamfer matching technique. Matching involves correlating the templates with the distance transformed scene and determining the locations where the mismatch is below a certain user defined threshold
1-NRTSVM via ADMM for Automatical Feature Selection and Classiﬁcation Simultaneously

: In this paper, a novel classiﬁer with linear and nonlinear versions for data classiﬁcation and automati-cal feature selection simultaneously is proposed and named as 1-norm regularized twin support vector machine (1-NRTSVM). By means of the alternating direction method of multipliers (ADMM), two implementation algorithms for 1-NRTSVM are presented. A major feature of the proposed method is directly solving primal problems not dual problems. Experiment results show that the proposed 1-NRTSVM is an effective and competitive classiﬁer for data classiﬁcation and automatical feature selection.
Steganography with Public-Key Cryptography for Videoconference

In this work, we show a model for a case where extreme security is needed. In such case steganocryptography (steganography and cryptography) is used. In this model we use Diﬃe-Hellman, RSA and cryptography with ir-rational numbers. We use also steganography in DCT coeﬃcients to send a message through the frames.
Phylogenetic relationships of Hamadryas (Nymphalidae: Biblidinae) based on the combined analysis of morphological and molecular data

A new phylogenetic hypothesis for the Neotropical butterfly genus Hamadryas, based on the combination of a morphological matrix, one mitochondrial (COI) and four nuclear markers (CAD, RpS5, EF1a, and Wingless), is presented. Results from analyses of the molecular evidence are compared with a previously published morphological phylogeny. Molecular data and the analysis of the complete dataset support the monophyly of Hamadryas and most sister groups suggested by morphological data alone. The addition of DNA sequences to the morphological matrix helped define species groups for which no morphological synapomorphies were found. Partitioned Bremer support indicates that COI, CAD, and morphology were consistently in agreement with the combined evidence tree. In contrast, signal from the nuclear markers Rps5, EF1a, and Wingless showed indifference at most levels of the tree, and minor conflict at nodes solving the relationships between species groups. Though resolved, the combined evidence tree shows low resample values, particularly among species groups whose relationships were characterized by short internodes. A reassessment about the pattern of character change for sound production is presented and discussed.
A Programming Framework for OpenDP∗†

In this working paper, we propose a programming framework for the library of differentially private algorithms that will be at the core of the OpenDP open-source software project, and recommend programming languages in which to implement the framework. Note: The current version of this paper is intended as a proposal for discussion at the May 2020 OpenDP Community Meeting, rather than as a scholarly paper that identifies novel contributions. In particular, proper credits to and comparisons with prior work are still missing (but will be added in a future version). ∗CC BY 4.0: https://creativecommons.org/licenses/by/4.0/ †We thank the rest of the OpenDP Design Committee (Mercè Crosas, James Honaker, Gary King, Aleksandra Korolova, and Ilya Mironov) for numerous discussions and feedback that contributed to this paper. ‡Department of Computer Sciences, Boston University. Supported by NSF through grants 1565365 and 1845803. §Department of Computer Sciences, Colgate University. Supported by NSF through grant 1409125 and by DARPA and SPAWAR under contract N66001-15-C-4067. ¶School of Engineering and Applied Sciences, Harvard University. Supported by a grant from the Sloan Foundation and a Simons Investigator Award.
Butterfly (Lepidoptera: Rhopalocera) Fauna of East Calcutta Wetlands, West Bengal, India

East Calcutta Wetlands (ECW), lying east of the city of Kolkata (formerly Calcutta), West Bengal in India, demonstrates the usage of city sewage for traditional practices of fisheries and agriculture. As a Ramsar Site, the wetland demands exploration of its bioresources for better understanding and management of the ecosystem operating therein. Butterflies (Lepidoptera: Rhopalocera) being potent pollinators and ecological indicators, are examined in the present study. The diversity study, conducted for two consecutive years (Jan. 2007-Nov. 2009) in all the three seasons (pre-monsoon, monsoon and post-monsoon), revealed seventy-four species. As butterflies depend on preferred host and nectar plants during their larval and adult stages respectively, the lack of these sources in some parts of ECW indicate degraded habitats with low species richness. Ongoing unplanned anthropogenic activities like habitat modifications (conversion of wetlands to agricultural lands) are resulting in the loss of wetland biodiversity and hence ecosystem integrity in ECW.
USING FUZZY SOFT SET ASSOCIATION RULE MINING APPROACH TO IDENTIFY THE STUDENT SKILL DATA ASSOCIATION

Graduates shall see the importance of improving their skills to prepare themselves to get the right jobs. Considering such important requirements, therefore many universities have designed various skill development programs in their curriculum. The students enrol in these programs and eventually, their skills performance will be evaluated using certain scores. This research does not aim to calculate the scores. Instead, we focus on how to find the relationship between parameters presented for the evaluation. In this study, we use the fuzzy-soft-association-rule mining (FSAR) approach and proposed the fast algorithm for finding association rule on fuzzy soft set. FSAR is a tool that combines Fuzzy Soft Set concepts and Association Rule Mining. We found that FSAR is an effective method to describe the relationship between parameters in large size data. Using FSAR, we will find a significant parameter or uncorrelated parameters for further analysis. This study recommends the selected parameters for determining the type of training for students. By selecting the right training, the University can reduce training cost significantly.
Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods

This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics of STL provide a robustness metric, that is, how much a signal satisfies or violates an STL specification. In this work, we devise a systematic methodology for translating STL robustness formulas into computation graphs. With this representation, and by leveraging off-the-shelf automatic differentiation tools, we are able to efficiently backpropagate through STL robustness formulas and hence enable a natural and easy-to-use integration of STL specifications with many gradient-based approaches used in robotics. Through a number of examples stemming from various robotics applications, we demonstrate that STLCG is versatile, computationally efficient, and capable of incorporating human-domain knowledge into the problem formulation.
Assessing the Impact of Head-Related Transfer Function Individualization on Task Performance: Case of a Virtual Reality Shooter Game

This paper presents the results of an extended experiment to assess the impact of individualized binaural rendering on player performance in an ecologically valid use context, specifically that of a VR “shooter game,” as part of a larger project to characterize the impact of binaural rendering quality in various VR type applications. Participants played a simple game in which they were faced with successive targets approaching from random directions on a sphere. While audio-visual cues allowed for general target localization, only sections of the game that relied on audio cues were used for analysis. Two HRTF exposure protocols were used, comprising best and worst-match HRTFs from a “perceptually orthogonal” optimized set of HRTFs, during the course of six game sessions. Two groups performed the game sessions exclusively using either their best or worst-match HRTF. Two additional groups performed the game sessions alternating between best and worst-match HRTFs. Results suggest that HRTF quality had minimal general impact on in-game participant performance and improvement rate. However, performance for extreme elevation target positions was affected by the quality of HRTF matching. In addition, a subgroup of participants showed higher sensitivity to HRTF choice than others.
Decreased Neuronal Excitability in Medial Prefrontal Cortex during Morphine Withdrawal is associated with enhanced SK channel activity and upregulation of small GTPase Rac1

Rationale: Neuroadaptations in the medial prefrontal cortex (mPFC) and Nucleus Accumbens (NAc) play a role in the disruption of control-reward circuits in opioid addiction. Small Conductance Calcium-Activated Potassium (SK) channels in the mPFC have been implicated in neuronal excitability changes during morphine withdrawal. However, the mechanism that modulates SK channels during withdrawal is still unknown. Methods: Rats were exposed for one week to daily morphine injections (10 mg·kg-1 s.c.) followed by conditional place preference (CPP) assessment. One week after withdrawal, electrophysiological, morphological and molecular biological methods were applied to investigate the effects of morphine on SK channels in mPFC, including infralimbic (IL), prelimbic (PrL) cortices and NAc (core and shell). We verified the hypothesis that Rac1, a member of Rho family of small GTPases, implicated in SK channel regulation, modulate SK channel neuroadaptations during opiate withdrawal. Results: One week after morphine withdrawal, the neuronal excitability of layer 5 pyramidal neurons in IL was decreased, but not in PrL. Whereas, the excitability was increased in NAc-shell, but not in NAc-core. In mPFC, the expression of the SK3 subunit was enhanced after one-week of withdrawal compared to controls. In the IL, Rac1 signaling was increased during withdrawal, and the Rac1 inhibitor NSC23766 disrupted SK current, which increased neuronal firing. Suppression of Rac1 inhibited morphine-induced CPP and expression of SK channels in IL. Conclusions: These findings highlight the potential value of SK channels and the upstream molecule Rac1, which may throw light on the therapeutic mechanism of neuromodulation treatment for opioid dependence.
Assimilating satellite soil moisture retrievals to improve operational water balance modelling

A simple and robust method for assimilating satellite soil moisture (SM) products into the Australian Water Resources Assessment (AWRA) model was developed and tested via the community modelling system, AWRA-CMS. The method requires time series of two satellite soil moisture products, along with AWRA simulations of upper-layer soil water storage for an offline determination of weights for use in the optimal merging of models and observations via the triple collocation (TC) technique. The candidate data sources were near real-time products from the Soil Moisture Active/Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer on MetOp satellite (ASCAT) production systems. Evaluation of AWRA model performance with and without data assimilation (DA) was conducted for key variables including upper-layer soil water storage, root-zone soil water storage, evapotranspiration and streamflow against in-situ networks. The comparisons demonstrated conclusively that the assimilation of satellite SM considerably improved the accuracy and representation of AWRA model surface soil moisture across Australia. The temporal correlation was increased by 0.2 correlation units on average after the assimilation compared to open-loop across in-situ SM monitoring sites. Positive impacts were found on the simulation of streamflow over majority of catchments with an increase in correlation by up to 0.4. The impact of SM assimilation on the other variables was not as significant, largely as a result of the indirect way SM assimilation imparts constraint on those variables. Finally, an investigation into the impact of SM data assimilation on forecast accuracy was conducted through driving AWRA model with forecast meteorological forcing 9 days into the future. Improved skill in estimating surface soil moisture of AWRA were found to persist up to 4 days, and likely longer. Results of this study demonstrated the benefit of constraining model outputs with satellite soil moisture observation on improving model simulation, as well as the importance of accurate initial hydrological states on improving forecast skill. Improved SM is vital for assessing and predicting water availability and assisting policy making.
SME Credit Guarantee in China and Risk Evaluation

As a product of the development of the market economy, credit guarantee is relatively new to China, and there are some defects in the system. It is of significance to construct and improve the current credit guarantee system, which helps to improve the credit rationing of banks and to solve the financing problem of SME (small and medium-sized enterprise) effectively. This paper provides a comprehensive analysis of the current situation of domestic and international credit guarantee market, with a focus on its development in China. I discuss current credit evaluation models, and extends the pricing method into multi-stage based on the current two-stage VaR (Valuation at Risk) model. Overall, this paper provides suggestions conducive to SME credit financing system design.
Effect of Sever Plastic Deformation on The-Microstructure of Al-Si/Mg2Si in Situ Composites

In this work, the Al-Si/Mg 2 Si in-situ composites were fabricated using casting technique, followed by repetitive equal-channel angular pressing (ECAP) to refine the microstructure. (ECAP) as sever deformation was carried out through a die made of high chromium carbon steel at250ºC for four passes of deformation. Microstructures investigation was conducted using an optical microscope, electron back scattering, EDS, elemental mapping and electron backscattered diffraction (EBSD). After sever deformation , the results showed fragmentation of eutectic Mg 2 Si and eutectic Si , reduction of the primary Mg 2 Si particle size , and fragmentation of the dendrite of Mg 2 Si particles to smaller ones. On other hand, the columnar α-Al phase changed to nearly equiaxed grains after severe deformation. The results of (EBSD)revealed that the grain size of the in site composite matrix showed decrease to less than5µm after four passes ,and the grains after deformation have more orientation of homogenization, and there are fine grains less than1µmin size surrounded by high angled boundaries
Factors Influencing Behavioural Intention to Use Mobile Banking in Champawat District of Uttrakhand

India has more mobile connections compared to banking accounts, therefore GoI in Economic Survey 2014-15, proposed JAM (Jan-Dhan Yojana, Aadhar Number and Mobile Number) trinity to use ICT for more efficient and effective spread of formal banking even to the hilly areas where brick and mortar banks are challenging to build and sustain. Also, financial inclusion cannot happen without economic activity, and Mahatma Gandhi National Rural Employment Act (MGNREGA) is a policy which significantly helped to enhance the economic activity of rural India. Because of penetration of mobile technology and involvement of the same in financial inclusion, this research will contribute to understanding the constructs of mobile banking adoption in hilly rural area of Champawat District, Uttrakhand w.r. t population registered with MGNREGA. The authors found research is scarce for mobile banking adoption in hilly rural areas of India. (Mehta et, al 2019) may be the only study for Champawat district of Uttrakhand, using technology acceptance model (TAM) and total interpretive structural modelling (TISM) to develop a model. This paper takes model from Mehta et al. 2019 and examines the relationship between the constructs using structured equation modelling (SEM).
NAIVE BAYES CLASSIFIER FOR PREDICTING THE FACTORS THAT INFLUENCE DEATH DUE TO COVID-19 in CHINA

SARS CoV 2 or Coronavirus has spread rapidly throughout the world. Much remains unknown about this new virus. The rapid spread makes many countries must be vigilant in facing this new virus. This study uses the patient dataset in the early stage of the spread of SARS CoV 2 in China. Data is processed using data mining techniques with the Naive Bayes method. In addition the simulation process is carried out to find the optimal value of the findings.
An Exercise Assistant for Practical Networking Courses

Supporting students with feedback and guidance while they work on networking exercises can be provided in on-campus universities by human course advisors. A shortcoming however is that these advisors are not continuously available for the students, especially when students are working on exercises independently from the university, e.g. at home using a virtual environment. In order to improve this learning situation we present our concept of an exercise assistant, which is able to provide feedback and guidance to the student while they are working on exercises. This exercise assistant is also able to verify solutions based on expert knowledge modelled using description logic.