Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Using Coolant on the Formation of Microcracks, Burr and Delamination in Bone Drilling Process

Direct approach for bone fracture treatment usually involves restoring the fractured parts to their initial position and immobilizing them with plates, screws and wires. This approach needs a bone surgery drilling to produce hole for screw insertion. But this drilling process causes mechanical damages, i.e microcracks, burr formation and delamination, that can reduce the stability of the fixation. One of the ways to minimize it is by using coolant. Moreover, it is noted that bone has anisotropic microstucture. The object of this study is to understand the effect of coolant on mechanical damages that occur in bone drilling and to understand the effect of microstructure difference on microcracks that occur in the drilled walls holes. Adult bovine bones and adult goat bones were used in this study as the specimens to represent differences in cortical bone microstructure. Five consecutive holes from the distal to the proximal in each specimen were generated using manual hand-drill (spindle speed (n) = 1000 rpm; drill bit (d) = 4 mm diameter) with the use of coolant as variation. The drilling holes then stained and observed using a microscope. As the result, it was found that the use of coolant can significantly reduce the drilling temperature. Microcracks, burr formation and delamination were found to be quite large in the drilling holes without coolant. However, there is no microcrack found in the drilling holes with coolant, there is only a small number of burr formation was found. In addition, it was found that the differences in bone microstructure affect the number and length of microcracks that occur in the wall of the hole. It can be concluded from this study that the application of coolant is very effective to reduce the drilling temperature and enhancing the quality of the hole generated by bone drilling and the higher the density of osteon in cortical bone, the easier the microcrack to initiate and propagate.
LINK PREDICTION IN SOCIAL MINING

: Online social networking sites have become very popular over the last few years. So, new researchers are interested to work in social network. Different methods are applied to network containing millions of user. Unfortunately, links between individuals may be missing either due to online network. The primary goal of link prediction technique is extracting structural features required for classify links. In this paper, we focus on link prediction in social networks. We provide an efficient and effective link prediction method, which consists of three steps as follows: (1) we locate the similar nodes of a target node; (2) we identify candidates that the similar nodes link to; and (3) we rank candidates using weighing schemes. One of the methods used to uncover missing information in social networks is referred to as link prediction.
Between solidarity and competitive threat? The ambivalence of anti-immigrant attitudes among ethnic minorities

Attitudes toward immigrants are typically investigated from the perspective of the dominant native majority group versus the subordinate ethnic minority group, but there are not apparent reasons why established minority groups would be exempt of negative attitudes towards new immigrant groups. This article investigates the roots of anti-immigrant attitudes among Belgians of Turkish and Moroccan descent. For that purpose, we analyze survey data from the Belgian Ethnic Minorities Elections Study 2014. Our results confirm that negative predispositions toward the arrival of new immigrants in general and Eastern Europeans in particular are also present among Turkish and Moroccan Belgians. Furthermore, we find that feelings of unfair treatment shape anti-immigrant attitudes in important ways. However, the direction of the relationship (positive vs. negative) depends crucially on the specific setting of unfair treatment. While perceived unfair treatment in the labor market arouses interminority hostility, experiences of unfair treatment by the government or in daily life lead to positive attitudes towards newcomers. Identification with the nation rather than with the ethnic group is significantly linked to more negative attitudes towards new immigrants in general (but not towards Eastern European immigrants). We furthermore find that the strength of the relationship between unfair treatment/identity and interminority attitudes depends on the particular immigrant group being evaluated.
OSPF IMPLEMENTATION & APPLCATIONS IN MATLAB

: Due to the shortage of RIP protocol, OSPF protocol is used in large network. OSPF is shortened form of Open Shortest Path First. It is a dynamic routing protocol used in Internet Protocol networks. Specifically, it is a link-state routing protocol and falls into the group of interior gateway protocols, operating within a single Autonomous system. It gathers link state information from available routers and constructs a topology map of the network. The topology determines the routing table presented to the Internet Layer which makes routing decisions based solely on the destination IP address found in IP datagram. OSPF was designed to support Variable-length subnet masking (VLSM) or Classless Inter-Domain Routing (CIDR) addressing models. OSPF detects changes in the topology, such as link failures, very quickly and converges on a new loop-free routing structure within seconds. It computes the shortest path tree for each route using a method based on Dijkstra's Algorithm, a shortest path first algorithm. Dijkstra algorithm is a solution to the single-source shortest path problem in graph theory. Works on both directed and undirected graphs. However, all edges must have nonnegative weights. It follows Greedy Approach. There are two types of routing-Link State routing and Distance Vector routing. Dijkstra is based on Link State routing. In Link State routing each router keeps track of its incident links and cost on the link, whether the link is up or down. Each router broadcasts the link state to give every router a complete view of the graph. Each router runs Dijkstra’s algorithm to compute the shortest paths and construct the forwarding table. Thus, it chooses the path with min hops and topology changes can be detected with the help the help of beacons this algorithm is based on iterations. The topology of the network can be generated by collecting the OSPF message.
MONITORING OF OVERHEAD LINES WITH AUTONOMOUS FLYING PLATFORMS

The paper presents a method for the monitoring of MV and HV overhead lines with an autonomous flying video platform, equipped with multi spectral cameras that perform life line inspection recordings. For this research project a multicopter with six rotors was chosen. The platform is equipped with communication and video transmission links to obtain data and life-images during the flight. The navigation is controlled by GPS and different sensors to maintain a stable and defined position in the air, predefined flight tracks can be flown without human action. The Electromagnetic Interference (EMI) caused by corona, magnetic and electric fields can affect the radio control communication and influence the operation of sensors and thus the flight behaviour. Therefore extensive laboratory test have been performed to assure safe and stable operation. The paper shows first results from the on-going project with regard to image recordings, different spectral resolutions, tests of the prototype platform in a high voltage lab, and the first experiences with In-Field-Operations.
NOVAL TECHNIQUE TO FIND SHORTEST PATH PROBLEM A* ALGORITHM FOR ENHANCING THE DURING REAL TIME IMFORMATION

: To facilitate intelligent traffic communication, a reliable, realistic, and network is required for Adhoc communication between vehicles. Many requirements compete in the different existing vehicular networking systems. However, one solution to suit all purposes has not been determined. While the vehicle-to infrastructure, such as standardized GPRS, HSPAM, and LTE, is currently used, vehicle-to-vehicle and vehicle to-roadside units are more or less under construction. To find the shortest distance always suffers struggle and more complexity rather than simple and easy path. Here we are finding the position & shortest path to an object from image taken from camera or any visual sensing device by making use of fuzzy logic & Artificial Intelligence. Here we are making use of AI to track the position to the object & then finding the shortest path from source to destination. Shortest path is the best path to reach from source to destination & it will make use of AI that helps in decision making power. Position Tracking System to find shortest path to object aimed to track the position to object and then find the shortest path to objects from source to destination where, the shortest path problem is the problem of finding the best path between two objects. This inclusion gives us more dynamic model and this real-time situation provides it a best model for future. This enhancement brings us more closely to real situations. So real life implication of this model give us more real parameter to the world and real time scenario for path travelling. This saves time for travelers. Further we implement the results of tests using wireless sensor network (WSN) radios of different kinds to communicate with moving vehicles. Our aim is to provide measured background information about different kinds of vehicular networking methodologies in order to find an optimal solution for different kinds of usage scenarios. The proposed technique use of real time information gives us more dynamic model to find the shortest path. It provide real information makes it a best model for future and enhancement brings us more closely to real time situations & save time for travelers.
Amortized Variational Inference for Type Ia Supernova Light Curves

Markov Chain Monte Carlo (MCMC) methods are widely used for Bayesian inference in astronomy. However, when applied to datasets coming from next-generation telescopes, inference becomes computationally expensive. We propose using amortized variational inference to estimate the posterior of a supernova light curve parametric model. We show that amortization with a recurrent neural network is signiﬁcantly faster than MCMC while providing competitive estimates of the predictive distribution. To the best of our knowledge, this is the ﬁrst time this fast amortized framework is applied to supernova light curves. This approach will be essential when estimating the posterior of astrophysical parameters for terabytes of data per night that next-generation telescopes will produce.
A NOVEL HYBRID CONTINUOUS DENSITY HIDDEN MARKOV MODEL-PROBABILISTIC NEURAL NETWORK FOR MULTIPLE SOURCE PARTIAL DISCHARGE PATTERN RECOGNITION

: Partial Discharge (PD) is inherently a non-Markovian process exhibiting significant statistical variability in the correlated patterns that describe the nature of discharges. Hence, the utility of Hidden Markov Model (HMM) as a tool to recognize spatio-temporal sequences of dynamically varying patterns becomes perceptible. Though a few researchers have attempted PD pattern recognition utilizing the standard version of HMM, such studies are confined to stationary Discrete Density HMM (DDHMM) versions which reported a relatively lower success rate. Since PD patterns exhibit dynamic behaviour, a non-stationary Continuous Density HMM (CDHMM) which best describes the hidden state transition probabilities as time dependant estimates coupled with multivariate Gaussian observation densities is taken up for study. This research focuses on implementing a novel hybrid HMM-Probabilistic Neural Network (HMMPNN) PD pattern recognition system which utilizes the complementary advantages of both the models i.e., ability of HMM in recognizing spatio-temporal sequences and capability of NN in classifying patterns. Exhaustive studies and analysis is carried out on laboratory models and industrial objects to determine the effectiveness of classification of the proposed hybrid system to cater to large training dataset. Comparison of performance of the algorithms with various pre-processing schemes that utilize statistical operators has also been carried out.
JAMBURA JOURNAL OF MATHEMATICS

Paying taxes is an example of public service. In the process of serving, the service is often synonymous with the queuing process. Queuing is a condition in which several people or objects from a waiting line to be served are generally caused by the need for services to exceed the service capacity or service facilities so that users of arriving facilities cannot immediately receive service. Therefore, overcoming many complaints due to queues can be done by improving services and maximizing time efficiency using the Petri net model. In this study, a Petri net model of the 5-year tax payment service system for a motor vehicle at SAMSAT Oku Timur 1 was made as many as 17 places, 15 transitions, two operators, and 30 arcs using WOPED 3.2. 0 software.
Asres SYNTHESIS , CHARACTERIZATION AND APPLICATION OF WS 2 NANOWIRE-NANOFLAKE HYBRID NANOSTRUCTURES

Transition metal dichalcogenide (TMD) materials crystalize in a layered structure with a stoichiometry MX2 where M is a transition metal (Mo, W, Tc, Re, V, Nb, Ta, Ti, Zr, Hf) and X is a chalcogen (S, Se, Te). While there is a strong covalent bond between the chalcogen and the metal atoms in each 2-dimensional (2D) sheet, the bulk 3-dimensional crystals are held together by weak van der Waals forces acting on the adjacent 2D sheets allowing for micromechanical and liquid phase exfoliation into nanostructures composed of either a single layer or a few layers. Since the electronic band structure depends not only on the chemistry but also on the number of layers, a whole new range of metal, semimetal and semiconductor materials may be achieved. These properties, among many other advantages (e.g. tunable band structure, high mobility of carriers, easy intercalation with ions), make TMDs appealing and timely for applications in solar cells and photodetectors, heterogeneous catalysis, electrocatalytic electrodes, energy storage and in (electro) chemical sensing. Motivated by the anticipated fascinating properties of TMDs, this research work focuses on the synthesis, characterization and application of a novel hybrid WS2 nanomaterial. While the original goal of the research work was to develop a simple method to synthesize WS2 nanowires, it became clear that instead of nanowires a hybrid nanowire-nanoflake (NW-NF) structure could be synthesized by a simple thermal sulfurization of hydrothermally grown WO3 nanowires. The structure, morphology and composition of the new materials were analyzed by X-ray diffraction, Raman spectroscopy, electron microscopy and X-ray photoelectron spectroscopy. Temperature dependent electrical measurements carried out on random networks of the nanostructures showed nonlinear characteristics and a negative temperature coefficient of resistance indicating that the hybrids were semiconducting. Resistive gas sensors were prepared and exposed to H2S, CO, NH3, H2 and NO and to which the devices displayed ultra-high sensitivity (0.043 ppm-1) towards H2S with a detection limit of 20 ppb. The results suggest further exploration of gas sensing with TMDs as potential competitive alternatives to the classical metal oxide based devices. Moreover, photodetector devices with excellent visible light response were also demonstrated using an individual WS2 NW-NF hybrid as well as its random networks having photoresponsivity of up to 400 mAW-1. This was two orders of magnitude higher than that measured for other 2D materials based devices. Overall, the WS2 nanowire-nanoflake hybrid is a truly multipurpose and multifunctional semiconductor making it a promising material for advanced micro, nano and optoelectronics devices.
Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.
Numerical prediction of the drag coefficient of bluff bodies in three- dimensional pipe flow

Article info: Article history Received: 13/07/2020 Accepted: 00/00/0000 Revised : 30/10/2021 Online : 00/00/0000 Key wards : Drag coefficient, Pressure coefficient, Turbulence model, Computational Fluid Dynamics, bluff body, Friction coefficient * Corresponding author : tamilchandran@fcriindia.com Abstract Numerical analysis of Drag coefficient of three-dimensional bluff bodies such as flat plates, cylinder, triangular prism, semicircular profiles located in the flow path of the pipe was performed. Bluff bodies of various lengths are analysed using a turbulence model. The effect of bluff body thickness on drag coefficient was analysed. A significant observation of the study is, reduction in drag coefficient with increase in thickness. Effect of pressure coefficient, on drag coefficient was evaluated. The study confirms that frictional coefficient has negligible effect on drag coefficient in the studied Reynolds number range. Change in drag coefficient over a wide range of Reynolds number was studied and is reported. Irrespective of geometry and length, the study indicates that there is a significant difference in drag coefficient between two dimensional and three dimensional simulation studies. It is also brought out that for a bluff body, in a confined domain; its length has a significant effect on its drag coefficient.
A REVIEW ON STATUS AND FEASIBILITY OF USING PERVIOUS CONCRETE PAVEMENT FOR CONSTRUCTION OF NEW ROADS IN INDIA

Flooding and water logging problems are the Major environmental issues now a days Particularly in a country like India for this sustainable development has become a necessity. Various sustainable and Eco-friendly methods are being implemented to fight with these problems. The concrete technology has made remarkable progress in past decades. Pervious concrete pavement is one of them. Pervious Concrete is a light-weight concrete produced by omitting the fines from conventional concrete. Also known to as “No-fine Concrete” or “Porous Concrete” is material comprised of narrowly graded coarse aggregates, cement materials, water and admixture. The purpose of this review paper is to consolidate the wide range and spread the relevant works on permeable pavement systems, and also to recommend the future areas of research work. Pervious concrete pavement is an environmentally friendly paving material that allows water to drain directly through the pavement structure and infiltrate into the sub grade. By reducing runoff, pervious concrete pavement decreases the demand on the storm water management system. The pervious pavement have wider application for construction of parking pavements, residential streets, sidewalks, and walkways, apartmentways where there is light traffic and the loading intensity is low. The void content in Pervious concrete ranges from 1530% with compressive strengths ranging from 3.5 MPa to 28 MPa . This paper makes an attempt to put forth the facts of the pervious concrete and the suitability of its use in India. This review paper includes literature reviews related to pervious concrete and its applicability for Indian roads.
Inferring dark matter substructure with global astrometry beyond the power spectrum

Astrometric lensing has recently emerged as a promising avenue for characterizing the population of dark matter clumps—subhalos—in our Galaxy. By leveraging recent advances in simulation-based inference and neural network architectures, we introduce a novel method to look for global dark matter-induced lensing signatures in astrometric datasets. Our method shows signiﬁcantly greater sensitivity to a cold dark matter population compared to existing approaches, establishing machine learning as a powerful tool for characterizing dark matter using astrometric data
COMPUTATIONAL FLUID-STRUCTURE INTERACTIONS OF A 3D FLEXIBLE FLAPPING WING

In this paper, we focus on the dynamic mechanical properties of flapping wings with highly flexible structures, which has a structure of anisotropic and batten-enforced, allowing a better understanding of the resulting fluidstructure interactions (FSI). Based on the ANSYS Workbench platform, we developed a three-dimensional (3D) aeroelastic framework for the flapping wing, which has a flexible multibody system subjected to an external incompressible turbulent flow. The Transient Structural module is used to calculate the structural deformation of the flexible wing. The flow field around the flexible wing is calculated in the Fluid Flow (Fluent) module. And the dynamic mesh and overset mesh methods are combined to update the mesh of the fluid domain. The displacement data of the flexible wing is exchanged with the force data in the flow field around it in the System Coupling module. In order to verify the effectiveness of the method, we selected the experimental model in a reference, and compared the calculated results with the experimental results.
Loan Evergreening Through Banks’ Lenses: Evidence from Credit Product-Level Data

Banks’ incentives to implement a policy of forbearance in order to avoid increasing their loan loss reserves leads to loan “evergreening”, through which they grants additional credit to firm when it faces repayment difficulties. Exploiting granular data of different types of corporate loans, we identify banks’ loan evergreening strategies as instances when firms receive bullet loans of a similar amount to their contemporary amortizing loan repayment. We study the determinants of these strategies and their consequences. The main bank-level determinant is solvency: lower solvency leads to more loan evergreening. When a bank provides loan evergreening to a firm, it tends to also increase amortizing credit to it in the following months. Firms receiving loan evergreening are more likely to end up delinquent, especially when provided by banks with low solvency. On the other hand, when firms borrow from two or more banks, they are more likely to become delinquent with the bank that did not provide loan evergreening. Finally, we find some evidence of crowding-out credit to other firms. JEL: G21, G28, E44.