Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Fast Simulation Technique using Deep Autoregressive Model

A probabilistic safety assessment (PSA) has been used to estimate the risk of nuclear power plants (NPPs). To traditional approach to analyze the PSA is Event Tree and Fault Tree (ET/FT). Though there are many advantages of ET/FT methodology, it is natural to anticipate that the traditional PSA techniques based on ETs and FTs intrinsically has high uncertainties sources, such as parameter uncertainty and model uncertainty [1]. To reduce uncertainty of PSA, the ETs has more diversity to branch points considering time, operator action, and multiple branch. However, the problem is that the number of scenarios to be analyzed by precise thermal-hydraulic (TH) codes will drastically increase for a complicated system containing many systems or components. Since each run of the precise TH code generally takes several hours to a day, it is evident that a technique for determining the consequence of each plausible scenario within a short time period is the key to reduce PSA uncertainties. In order to resolve this problem, the deep learning techniques is introduced in previous study. One of the key benefits expecting from these deep learning techniques is that it is possible to create an emulation system that can synchronize the repose (or behavior) of a target system. The suggested techniques are also called a surrogate model or a fast running. In the previous study, feasibility study that the deep learning technic is able to emulate TH code was carried out.
Management of tomato wilt complex caused by Meloidogyne incognita and Fusarium oxysporum f. sp. lycopersici through organic amendments

Tomato (Solanum lycopersicon Mill.) is the second most important vegetable crop. It suffers from various phytopathogenic diseases. Among the fungal diseases, wilt disease caused by Fusarium oxysporum f. sp. lycopersici is most important disease and is limiting factor in its production. The fungus also develops synergistic relationship with Meloidogyne spp. leading to root-knot wilt disease complex. This complex is highly destructive to tomato plants and is characterized by enhanced wilt symptoms. Efficacy of different organic amendments (@5g / kg soil) were evaluated against root-knot wilt disease complex developed by Meloidogyne incognita and Fusarium oxysporum f. sp. lycopersici. Observations were made on plant growth parameters, nematode reproduction and wilt disease incidence in tomato. The experimental results showed that among organic amendments, neem cake was found significantly superior to increase plant growth parameter, reduce nematode population and wilt disease incidence followed by castor cake.
Load-balancing in Large Machines Applied to Weather Forecast: a Preliminary Study

The increase in performance have become synonym of increase in the number of processors. Computers with thousands of processors are becoming common in many computing environments. This fact imposes plenty of challenges for the development, debugging and management of the programs executed on these computers. One of these challenges is load balancing. Several works have been done on load-balancing, but few of them deal with large machines and highly dynamic applications like weather forecasts. This article presents a preliminary study of load imbalances in weather forecast models, that can prevent this kind of application from taking full advantage of large machines. The model chosen for the tests was the BRAMS model, an open source mesoscale model commonly used in Brazil. This work focuses on the source of load imbalance related to the nesting grid technique.
Distilling Arbitration Logic from Traces using Machine Learning: A Case Study on NoC

Arbitration logic is extensively used in modern computer architectures to dynamically determine how shared hardware resources are allocated or accessed. Recent work has shown that machine learning techniques can learn non-obvious yet effective arbitration policies, which in simulation demonstrate superior performance over human-designed heuristics. However, existing methods based on deep learning are too expensive to be directly implemented as an arbitration unit in hardware. While some prior efforts managed to manually analyze and reduce a deep learning model into relatively small circuits in certain cases, such ad hoc and labor-intensive approaches cannot easily generalize. In this work, we propose a new methodology to automatically “distill” the arbitration logic from simulation traces. Starting by training a deep learning model, we leverage tree-based models as a bridge to convert the more complex model to a compact logic implementation. This paper presents a case study of the proposed methodology on a network-on-chip port arbitration task. Compared with an array of combinational multipliers that exactly computes the neural network output, our arbitration logic achieves up to 282x area reduction without significant performance degradation. Under the training traffic, our arbitration logic achieves up to 64x reduction in average packet latency and up to 5% increase in network throughput over the FIFO arbitration policy. The distilled arbitration policy is also able to generalize to different injection rates and traffic patterns.
SPATIO-TEMPORAL VARIATIONS OF YELLOWFIN TUNA CAUGHT BY HANDLINE IN THE INDIAN OCEAN, SOUTH OF NUSA TENGGARA

One of the main targets from small-scale handline tuna fishery is yellowfin tuna (Thunnusalbacares), where most, if not all of its operation depended heavily on the installment of the fish aggregating device (FAD). The objective of the study was to investigate the spatial and temporal distribution of catch and size of yellowfin tuna from FAD-associated handline fishery. The study was conducted from January to December 2018 with main observation location was in Labuhan Lombok fishing port, West Nusa Tenggara. The results showed that the highest catch rate was obtained during April-June, followed by the frequent emergence of large yellowfin tuna (>100 cm FL). On the other hand, January-March was considered as a low season, where most of the catch was dominated by juvenile yellowfin tuna (<50 cmFL). The spatial distribution of yellowfin tuna abundance is closely related to the presence of FADs, where all fishing operations are carried out around FADs. Also, aggregation of fish was more likely influence by temporal variation rather than the quantity of FAD. More abundant yellowfin tuna were found during the east monsoon (April-June) compared to another season where less fish occurred.
Awareness of Green washing amongst College Students

The 21st century witnessed a tremendous increase in the environmental related awareness and also on green washing. This research investigates the Awareness of green washing amongst college students and mainly discusses its impact on the environment and public health. The objective of the study focused on students' awareness, usage of green products and impact of green washing. The study used primary data as the main source and collected the data through a questionnaire. The responses were received from both online and offline mode. It was found out from the study that the term green washing is still unknown to many and there is a need to create awareness of it among the general public.
Nonlinearity Engineering with the Quarton

In this thesis, we show the principles and applications of a new technique we call “nonlinearity engineering” using a recent superconducting qubit, the Quarton. In traditional nonlinear optics, nonlinear effects are usually weak perturbations to linear interactions. Similarly, microwave quantum optics with superconducting circuits relies on the Josephson junction for a negative Kerr nonlinearity that is much weaker than its associated linear energy. Recently, a new superconducting qubit known as the “Quarton” can offer non-perturbatively strong nonlinearity. Here, we demonstrate the general principle of using the Quarton’s positive Kerr and zero linear energy to perform nonlinearity engineering, i.e. the selective design of the nonlinear properties of microwave artifical atoms, metamaterials, and photons in a manner that (to the best of our knowledge) has no optical analog. We show that for Quarton mediated light-matter coupling, the Quarton can erase or amplify the nonlinearity of artificial atoms and metamaterials. Without nonlinearity, matter behaves light-like and we find (to our best knowledge) the first theoretical demonstration of cross-Kerr between linear microwave photon modes. We extend these fundamental results and provide a practical application by designing a Josephson traveling wave photon detector. Thesis Supervisor: Kevin P. O’Brien Title: Assistant Professor of Electrical Engineering and Computer Science
Climate Change, Cryosphere and Impacts in the Indian Himalayan Region

This study has benefitted from collaborations promoted by the Indian Himalayas Climate Adaptation Programme (www.ihcap.in), a project under the Global Programme Climate Change and Environment of the Swiss Agency for Development and Cooperation in cooperation with the Department of Science and Technology, Government of India (GoI), and with support from the Government of Himachal Pradesh and National Mission of Himalayan Studies, Ministry of Environment, Forest and Climate Change, GoI.
Regulatory focus and junk food avoidance: The influence of health consciousness, perceived risk and message framing

The purpose of this research is to examine: (1) the health-related factors namely perceived health consciousness and perceived risk that motivate a person's intention to avoid junk food consumption, and (2) the influence of regulatory focus (promotion vs prevention foci) on a person's intention to avoid junk food consumption under the conditions of gain-framing vs loss-framing health communication and messages. A set of hypotheses are tested across three studies. Study 1 (n = 148) shows that regulatory focus influences the consumers' intention to avoid junk food. Also, health consciousness mediates the relationship between regulatory focus and junk food avoidance intention. Study 2 (n = 132) finds that perceived risk amplifies the relationship between regulatory focus and health consciousness. Study 3 (n = 168) demonstrates that message framing combined with the appropriate regulatory focus positively influences the consumers' intention to avoid junk food. The findings validate and extend the current theoretical framework in relation to unhealthy eating behaviours (e.g., junk food consumption). Based on the findings of this research, the practitioners can utilise the correct type of health information or claims for the appropriate consumer segment (whether prevention or promotion foci) to effectively implement campaigns and programs.
AUC Knowledge Fountain AUC Knowledge Fountain

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the con(cid:28)dence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market e(cid:30)ciency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it’s applications in this domain. The bene(cid:28)t of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price manipulation. This research implements machine learning algorithms in detecting trade manipulations where trade behaviors arti(cid:28)cially impact the National Best Bid and O(cid:27)er (NBBO) of traded stocks. Research methodology implemented is based on feature extraction using signal analysis, taking advantage of the similarity between physical signals measured by machines and raw (cid:28)nancial data. Accordingly, Continuous Wavelet Trans-form (CWT) is applied on actual manipulation data for feature extraction, Principal Component Analysis (PCA) and factor analysis are used for dimensionality reduction and then Machine Learning Classi(cid:28)ers are trained and tested. Tick Bid/Ask Price and volume data of actual 15 manipulation cases published by the Security Exchange Center (SEC) was extracted from an online interface and labeled accordingly. This data was then used to train, and test 3 di(cid:27)erent classi(cid:28)cation models (XGBoost, KNN & SVM) and the outcome was compared accordingly. Results showed that introducing continuous wavelet transform enhances model accuracy, it increased precision results tremendously, while reducing recall values slightly. Adding PCA, reduced run time greatly, yet reduced the quality of some models prediction. Out of the three classi(cid:28)ers XGboost & KNN are showing the highest performance.
Maxillary Canine with Two Roots and Two Root Canals: A Rare Case Report

Detailed examination of the root morphology and anatomy is critical for complete cleaning and shaping of the root canal system for successful outcome. The current case presentation describes a rare case of maxillary canine with two roots. While clinical examination it was reported that the offending tooth had severe pain to cold test. Detailed radiographic examination by cone-beam computed tomography (CBCT) revealed additional root in the maxillary canine. Complete root canal treatment was done and the tooth remained clinically and radiographically asymptomatic at 6 months follow-up.
Development and Control of Modular 5 DoF Robotic ARM using ROS

—This paper presents the design and development of a robust 5-DoF robotic arm. Articulation of the robotic arm is achieved by linear actuators and DC brushed motors. The onboard processing is achieved by Nvidia Jetson Nano along with off-the-shelf motor drivers and microcontrollers for feedback and control. The safety of electronic components and battery management is also kept in mind while designing the sub-system. The software stack is based on Robot Operating System (ROS) to facilitate distributed computing.
Effectiveness of Emotion Efficacy Therapy on Internet Dependency and Negative Cognitive Emotion Regulation Strategies among Students Addicted to Internet: A Quasi-Experimental Design

Background & Aims: Internet addiction is one of the most important problems of the human societies. The aim of this study was to investigate the effectiveness of emotion efficacy therapy on reducing internet dependency and negative cognitive emotion regulation strategies (self-blame, other-blame, rumination, and catastrophizing) among students addicted to internet. Methods & Materials: The research was quasi-experimental design with preand post-test and control group. The study population included all male students of state technical schools addicted to internet in year 2020 in Tabriz, Iran. In order to select the sample, one district from five educational districts was chosen and then a technical school was selected randomly. After that, the students possessing the inclusion criteria to the research were screened by Young’s Internet Addiction Test (YIAT20) and then 40 students were randomly assigned to either experimental or control group, each group consisting of 20 students. The experimental group received eight session emotion efficacy therapy whereas the control group received no intervention. For the collection of data, Young’s Internet Addiction Test (YIAT20) and Cognitive Emotion Regulation Questionnaire (CERQ) were used. The collected data were analyzed using multivariate analysis of covariance. Results: The results from the comparison of the post-tests of the groups indicated that scores of internet dependency (p<0.01), and negative cognitive emotion regulation strategies (self-blame (p<0.01), other-blame (p<0.01), rumination (p<0.01) and catastrophizing (p<0.01) ) of experimental and control groups differed significantly. The analysis of data showed that emotion efficacy therapy reduced internet dependency and negative cognitive emotion regulation strategies (self-blame, other-blame, rumination and catastrophizing) among students addicted to internet. Conclusion: Emotion efficacy therapy is one of the most recently developed approaches used to treat different psychological disorders. Taking into account the results of the present study, emotion efficacy therapy can be used to treat internet dependency and negative and maladaptive emotion regulation strategies such as self-blame, other-blame, rumination, and catastrophizing.
Transactions of the Korean Nuclear Society Virtual Autumn Meeting December 17-18 Monte Carlo Simulation Study for Verification of Target and Beamline on μSR Facility in RAON

Muon Spin Rotation, Relaxation, and Resonance (μSR) facility has been designed as the material science research facilities in Rare isotope Accelerator complex for ON-line experiments (RAON) Heavy Ion Accelerator, which is under construction at the Rare Isotope Science Project (RISP). The facility uses surface muons which is 100% polarized [1]. Through nuclear reaction between 600 MeV proton beam and low-Z target (graphite or Be), pion and other particles are produced, and the pion decays into muon and neutrino, where the muon become 100% polarized. Momentum and spin direction of the muon are in opposite to each other. Muon originating from pion stopped on the target surface is called surface muon. In μSR facility, generated surface muons, which have momentum bite of 28.5 MeV/c ±3%, are collected and transmitted to a sample via muon beamline. In order to achieve enough production of surface muon from target and high transmission rate of surface muon via beamline were designed. In addition, contamination of other particles is removed during transmission. Once the components have been installed, verification experiments on these components will be perform. In this study, proper measurement location and particle identification (PID) method for the verification were determined using Monte Carlo simulations, and the results were discussed.
An Infinite 2D Supramolecular Cobalt(II) Complex Based on an Asymmetric Salamo-Type Ligand: Synthesis, Crystal Structure, and Spectral Properties

A new Co(ΙΙ) complex, [Co2(L)2]·CH3COCH3 based on an asymmetric Salamo-type ligand (H2L, 5-methoxy-4’,6’-dibromo-2,2’-[ethylenedioxybis(nitrilomethylidyne)]diphenol) has been synthesized and characterized structurally by elemental analyses, IR, UV-Vis, and fluorescence spectra. The X-ray crystal structure shows the Co(II) ion is pentacoordinated with geometry of a distorted square-pyramidal symmetry. The Co(ΙΙ) complex consists of two Co(ΙΙ) ions, two deprotonated L2– units, and one noncoordinated acetone molecule. Each Co(ΙΙ) complex molecule links four other molecules into an infinite 2D supramolecular structure. Meanwhile, the Co(II) complex exhibits emission with the maximum emission wavelength λmax = 432 nm when excited at λex = 349 nm.
Raytracing Through Portals

In this paper, we describe a method for rendering a raytraced image that contains portal planes. Every portal planes is connected to another portal plane in such a way that the two portal planes may be said to occupy the same area, despite being in two different locations. In other words, any ray that strikes a portal plane will be instantly transformed so that it is exiting the related portal plane at the same relative position and relative angle. This allows viewing the area behind one portal plane through a different portal plane, as well as other related effects.
Connective ambiguity and compensation by discourse signals in speech and writing

Background. Discourse, either written or spoken, is built upon coherence relations, which specify the nature of the link that connects clauses and other discourse segments. These coherence relations can be additive, contrastive, causal, conditional, etc. While their number and labels vary across frameworks, they have repeatedly been studied in connection with the category of discourse connectives (e.g. Knott & Dale, 1994). Connectives are the prototypical signals for coherence relations. Despite their famous ambiguity, no one would question that whereas in (1) expresses a contrastive relation.
DynaTail: A Method for Hybrid Software Process Tailoring

It is widely accepted that a good software process is essential for developing good software products. International studies have seen that most software companies apply hybrid processes for developing software, i.e., an organized combination of traditional and agile practices. Software process improvement and tailoring have been researched for several years, but there is less experience when dealing with hybrid software processes. Therefore it is not always clear to determine the combination of practices that best ﬁts a certain context, either for the company or a certain project. Moreover, the most appropriate process also depends on a particular goal. In this paper we propose the Dynamic Tailoring for Hybrid Software Processes (DynaTail), a method that aims to achieve three purposes: (1) allow for the tailoring of the organization process in order to adapt to the project context when pursuing certain goal; (2) provide a framework for trying diﬀerent process and context modiﬁcations in order to obtain better results for a particular project. We describe the method in full detail illustrating it with a running example taken from industry. We have found that the proposed method is a promising path for the quantitative evaluation of hybrid software processes.
Transactions of the Korean Nuclear Society Virtual Autumn Meeting December 17-18 Defect Structure of Irradiated Graphite Depending on The Incident Angle of Neutrons: A Molecular Dynamics Study

Graphite is broadly used in nuclear reactor as moderator and reflector. For the case of AGN-201K, which is a nuclear reactor for educational purpose, graphite is used as reflector and it is exposed to the fast neutrons. Therefore, predicting the irradiation damage of graphite by high-speed neutrons is very important in evaluating the aging deterioration of AGN-201K. In this study, a defect structure due to the neutron irradiation damage of graphite was predicted using a simulation technique. We simulated atomistic behavior of graphite after during the neutron irradiation by using a Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), an open-source program to perform a Molecular Dynamic (MD) simulation, to simulate the evolution of the defect structure of irradiated graphite.
Contextual concurrency control

Kernel synchronization primitives are of paramount importance to achieving good performance and scalability for applications. However, they are usually invisible and out of the reach of application developers. Instead, kernel developers and synchronization experts make all the decisions regarding kernel lock design. In this paper, we propose contextual concurrency control (C3), a new paradigm that enables userspace applications to tune concurrency control in the kernel. C3 allows developers to change the behavior and parameters of kernel locks, to switch between different lock implementations and to dynamically profile one or multiple locks for a specific scenario of interest. To showcase this idea, we designed and implemented Concord, a framework that allows a privileged userspace process to modify kernel locks on the fly without re-compiling the existing code base. We performed a preliminary evaluation on two locks showing that Concord allows userspace tuning of kernel locks without incurring significant overhead.