Miracle-dz/UniFAR-FTUs huggingface dataset:
Feshbach resonances and the three-body problem

By applying a magnetic field across a trapped ultracold gas of alkali metal atoms, it is possible to alter the binding energies of near threshold molecular states. The proximity of molecular states to the scattering threshold has a strong effect on the threshold scattering characteristics. Because of this the magnetic field strength can be used to control low energy scattering within the gas. This resonant phenomena is referred to as a magnetically tuneable Feshbach resonance. This thesis looks at these and related phenomena in the context of trapped potassium atoms. First, we perform coupled-channels calculations in order to characterise a group of previously unreported Feshbach resonances, occurring in a host of different collision channels. Next, we compare these characterisations to empirical data, and more simplified models. Second, a detailed investigation into the photodissociation of weakly bound Feshbach molecules is carried out. This allows us to gain new insights into the structure of the exit channel interaction. We make semi-classical arguments which enable experimentalists to directly measure the height of potential barriers. We also perform detailed numerical calculations which, in conjunction with experimental data, allow us to develop a fine-tuned potential model for the exit state. Third, we model weakly bound triatomic states and their impact on near threshold scattering. Then, we discuss the use of three body recombination as a tool for observing Efimov states. Furthermore, we model the three body recombination rate using a simplified two channel model. Finally we suggest a method intended to provide the basis for numerical calculations using realistic Born-Oppenheimer potentials. It could also be used to analytically study near threshold three body physics.
Risk-Return Analysis of Momentum Buying & Selling using EMA of Equity Prices in India

Present study has been conducted to examine the risk and return on equity prices using Exponential Moving Average (EMA) for the period of twelve years starting from January 1, 2006. The study is based on 31 companies out of 50 companies which constitute NSE NIFTY Index whose data was available for the whole period. The returns on buys as well as sell positions were significant in 49.39 percent, 70.79 percent and 51.61 percent cases but the aggregate return of momentum buying and selling were insignificant in most of the cases on the basis of EMA 5-20, EMA 5-50 and EMA 5-200 respectively. The study also observed positive alpha in cases of 18 stocks, 20 stocks and 20 stocks out of the 31 stocks taken in the study on the basis of EMA 5-20, EMA 5-50 and EMA 5-200 respectively but the value were low. The study concluded that it is not possible to beat the market return on the basis of momentum buying and selling indicated by EMA 5-20, EMA 5-50 and EMA 5-200.
Face Recognition System for Complex Surveillance Scenarios

In recent years, with the continuous development of the Internet and artificial intelligence, face recognition technology has also been widely used in many application scenarios. Facing complex surveillance scenarios, face recognition technology still faces great challenges. This paper focuses on implementing real-time and efficient face recognition systems in complex surveillance scenarios, such as insufficient lighting, small faces, dense crowds, and sides at 45 ° environment. The system is mainly based on RetinaFace for face detection and face alignment, and uses lightweight mobilenet (0.25) as the backbone network of RetinaFace. Facial feature extraction is based on deep residual neural network combined with ArcFace loss, and feature matching is performed by Euclidean distance. The experimental results show that the face recognition system has good real-time performance, accuracy and robustness.
Arginine culture turns on the elusive nitrogen starvation signal during robust phototrophic growth in Chlamydomonas

Under nitrogen (N) starvation, photosynthetic organisms search for other N sources while slowing down photosynthesis by downregulating light harvesting and electron transport to balance the carbon/nitrogen ratio and eventually stopping growth due to N limitation. To investigate the elusive N starvation-specific signaling mechanisms, we sought a way to induce N starvation responses without limiting photosynthesis or cell growth. In the chlorophyte Chlamydomonas reinhardtii, gametogenesis is exclusively induced during N starvation except in arginine culture. We showed that the arginine-grown culture turned on N starvation responses including hundreds-fold induction of N starvation-induced genes, reduced chlorophyll content, and increased carbon storage in the form of lipid droplets. Arginine culture supported robust phototrophic growth but not heterotrophic growth, indicating that arginine catabolism contributes CO2 to Rubisco without directly fueling ATP synthesis. Based on in silico analysis, we propose the possible routes of arginine catabolism that may bypass critical steps for monitoring of cellular N status and thereby trigger N starvation responses. Our results describe a study system where the N starvation responses are constantly induced without compromising photosynthesis or growth, paving ways to discover the mechanisms that sense and respond to cellular N status in eukaryotic phototrophs. Highlights Arginine catabolism leads to the activation of nitrogen starvation responses while supporting robust photosynthesis and growth, presenting ways to investigate N starvation signaling mechanisms in photosynthetic organisms.
Looking Beyond Wine Risk-Adjusted Performance

Abstract In this paper, we use copula-GARCH models applied to daily data from March 2010 to March 2018 to test the time-varying dependence of the Liv-ex 50, a secondary market fine wine index comprised of the ten most recent vintages of the five Bordeaux First Growths, with a portfolio composed of the six main stock markets (S&P 500, CAC 40, DAX 30, FTSE 100, and Hang Seng). Our results suggest that the Liv-ex 50 underperforms the six stock indexes, but provides diversification benefits in terms of volatility, asymmetry, and extreme events. (JEL Classifications: G110, G120, Q14)
Microwave Assisted Synthesis of Benzotriazole Derivatives for Anti-Corrosive Study on Mild Steel in Acidic Medium

Four novel benzotriazole derivatives 1-(Chloromethyl)-1H-Benzotriazole; 1, N-(benzo[e] [1, 2, 4]triazin-4(3-H)-ylmethylbenzenamine; 2, 1-((1-H-benzo[d][1,2,3]triazol-1-yl)methyl) hydrazine; 3 and 1-((1-H-benzo[d][ 1,2,3]triazol-1-yl)methyl) phenyl hydrazine; 4 were synthesized through micro wave irradiation (MWI) method. These derivatives were characterized and tested for anticorrosive action on iron coupons in 0.5M acidic medium at different concentration. The anti-corrosive study was done using weight loss method and corrosion rate (CR) analysis. All the synthesized derivatives gave remarkable corrosion protection effect. Major outcome of the study shows corrosion inhibitor efficiency of derivatives follows order 3, 4, 2 and 1 at 100mg/L, 3, 2, 4 and 1 at 200mg/L, 3, 4, 2 and 1 at 300mg/L. Compound 1-((1-H-benzo[d][1,2,3]triazol-1-yl)methyl)hydrazine; 3 emerges as leader in all concentration in this study while 1-(Chloromethyl)-1H-Benzotriazole; 1, shows moderate impact. This study imparts an understanding for the new class of anticorrosive benzotriazole derivatives which can be of industrial use.
Improving Growth and Yield of Wheat under Drought Stress via Application of SiO2 Nanoparticles

Silicon (Si) and its derivatives have beneficial effects on a wide variety of plant species, especially under both biotic and abiotic stresses. Yet, their effects on wheat ( Triticum aestivum L.) plants under drought stress are not well known. Therefore, in order to evaluate the effects of SiO 2 NanoParticles (NPs) under drought stress, wheat seeds were separately sown in pots. Then, the SiO 2 NPs were added to them through soil and foliar application at three stages of plant growth. Results indicated that drought stress significantly decreased majority of the studied traits compared to the normal irrigation. Soil application of NPs, under drought stress, significantly increased leaf greenness (SPAD) and Relative Water Content (RWC) by 12.54 and 84.04%, respectively, compared to the control (NPs= 0 ppm). Moreover, under drought stress, wheat yield also increased by 25.35 and 17.81%, respectively, by foliar and soil application of NPs. Under the same irrigation regimes, soil application of NPs significantly increased plant height and biomass compared to the foliar application of NPs. Finally, our results highlight that usage of the SiO 2 NPs, especially at rates of 30 and 60 ppm, can mitigate adverse effects of drought stress in wheat plants.
Spherical representation of light detection and ranging data for three-dimensional object detection

Autonomous vehicles are one of the most attractive applications for light detection and ranging sensors, where they help with scene understanding. For this understanding, object detection is crucial, and it must be done in a frame by frame basis. This detection on a single frame is a challenging task due to the sparse and disordered nature of the information. This paper presents an alternative spherical representation for this data aiming to improve object detection. This proposal registers the light detection and ranging data in a 2-dimensions angle map using most of the 3-dimensions points in three layers, adding reflectivity information, and a logarithmic representation of distance. For evaluating this representation, we employed an object detector based on the algorithm: you only look once version 3, and we used a public reference dataset of 3-dimensional objects for training. This framework yielded a classification accuracy of 85.9% and 74.5% of intersection over union factor when estimating seven classes simultaneously. This approach presents an alternative for processing this data that helps to benefit the most from the light detection and ranging information with high accuracy, helping in the reduction of the associated risks of autonomous vehicles.
Physicochemical Properties and Sensory Evaluation of Fermented Sausage using Probiotic Bifidobacterium

Probiotic food products are very popular on domestic and international markets. The application of probiotic in meat products is still being explored. Six types of sausages were developed from beef sausage (with and without probiotic), mutton sausages (with and without probiotic) and chicken sausages (with and without probiotic) and stored in the refrigerator at 4°C and sampling were performed at1, 10, 20 and 30 days in order to analyze their physicochemical and sensorial properties. During the storage period, ash, dry matter, pH, and titrable acidity, water holding capacity, fat, and protein (p
Deep Reinforcement Learning for Dynamic Multichannel Access in Multi-Cognitive Radio Networks

We propose a reinforcement learning framework based on deep recurrent learning to solve the dynamic spectrum access problem in the scenario where multiple cognitive networks coexist. In this scenario, the shared spectrum is divided into multiple channels, and the channel occupation by the primary user is modeled as a Markov model. The observation of the channel status by secondary users in this area obeys the partially observed Markov process, that is, each user can only observe the status of one channel in each time slot, and cannot obtain global information. To avoid collision in different cognitive networks, we adopt the distributed multi-agent deep reinforcement learning method. Without the interaction of different cognitive networks, based on the current partial observations, a suitable channel access policy can be obtained by a neural network. Comparing with Slotted-aloha and DQN-DSA Algorithm, the result indicates DRQN-DSA proposed in this paper performs better.
Generation of Human-Induced Pluripotent Stem Cells from Peripheral Blood Mononuclear Cells using Small-Molecule Compound VC6TFZ

BACKGROUND: Induced pluripotent stem cells (iPSCs) were generated from somatic cells through reprogramming process. Peripheral blood mononuclear cells (PBMNCs) were an attractive source cells due to the ease of accessibility, need minimal invasive procedure, and can be stored frozen. Small-molecule compound VC6TFZ has been successfully reprogrammed iPSCs from mouse fibroblast, but it has not been proven in human. AIM: This study aims to determine whether the small-molecule compound VC6TFZ can induce pluripotency of PBMNC to generate iPSCs. METHODS: Mononuclear cells were isolated from peripheral venous blood using centrifugation gradient density method. Mononuclear cells were cultured for 6 days in expansion medium and 48 h using hanging drop method. Pluripotency induction process using small-molecule compound VC6TFZ was done in 14 days then the medium changed to 2i medium for 7 days. Identification of iPSCs based on colony morphology and expression of pluripotency marker OCT4 and SOX2. RESULTS: Colonies appeared on day 9 of reprogramming process. These colonies had round, large, and cobble stone morphology like embryonic stem cell. These colonies had positive expression of pluripotency markers OCT4 and SOX2. All experimental groups had significantly higher expression of OCT4 and SOX2 than control group. CONCLUSION: Small-molecule compound VC6TFZ could induce pluripotency of human PBMNC to generate iPSCs.
The role of the teacher’s emotional intelligence for efficacy and classroom management

Comparte Abstract In recent years, several studies have revealed multiple benefits of teachers’ emotional intelligence (EI) concerning their professional performance, regarding teaching and learning process, students’ school performance, job satisfaction, reduction of stress and burnout, and the importance of interpersonal relationships at school. However, few studies have explored how teachers’ EI capacity affect their effectiveness in teaching and classroom management. In this sense, the aim of this study was twofold: to examine the relationship between teacher’s EI capacities, teacher efficacy, and classroom management effectiveness, and to analyze the relationship between teacher’s characteristics (gender) and professional background (service time and academic formation) and teacher’s EI capacities. 634 Portuguese teachers filled out a form with personal and professional data and answered an adaptation of the Emotional Skills and Competence Questionnaire, and the Teacher Efficacy in Classroom Management and Discipline Scale. Structured Equation Model analyses were computed and showed that teachers who tended to have higher levels of capacity to perceive, express and manage emotions reported higher levels of teaching effectiveness and classroom management effectiveness. Therefore, is important for Portuguese teachers developing their emotional skills during academic formation, to a more effective future professional activity.
An Uncertain Tuple Density Clustering (UTDC) Algorithm for Uncertain Data

The massive uncertain data generated by network applications has potential value, and it is of great significance to carry out clustering analysis of uncertain data. However, the uncertainty of data brings a serious challenge to traditional clustering algorithms. There are some problems in the existing clustering algorithms for uncertain data. (1) Some algorithms have a lot of meaningless distance calculations when calculating the distance of uncertain objects, and then the calculation complexity of algorithms increases. (2) The data model in clustering algorithms results in the loss of data distribution information, and then the accuracy of the algorithms decreases. In this paper, we propose an uncertain tuple density clustering (UTDC) algorithm for uncertain data. Firstly, we extract the data distribution features of uncertain instances and construct uncertain tuples by introducing the cloud model, which realizes the pruning of clustering objects. Secondly, we apply the EW distance to the traditional density clustering algorithm DBSCAN, which completes the density clustering for uncertain data. The experimental results show that comparing with UK-Means and FDBSCAN, UTDC algorithm effectively reduces the computational complexity and improves the accuracy.
Research on Task-oriented Dialogue Based on Modified Transformer

The traditional end-to-end task-oriented dialogue models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. But in the case of large amounts of data, there are many types of questions. It performs poorly when answering multiple types of questions, memory information cannot effectively record all the sentence information of the context. In view of the above this, this article uses a modified transformer model to overcome the problems mentioned in dialogue tasks. Transformer is a model constructed using attention mechanisms, which completely discards the method of RNN (recurrent neural networks), and its structure includes two sub-parts of Encoder and decoder. It uses residual network, batch normalization, and self-attention mechanism to build the model structure, uses Positional Encoding to capture sentence information, which can speed up model training convergence and capture Longer sentence information. In this paper, we modified the activation function in the transformer and use label smoothing to optimize the training to make the model’s expressive ability better than previous.
Mixing induced by ISWs breaking over a sloping boundary: an analytical heuristic model

<p>We propose an analytical approach to estimate mixing efficiency in Internal Solitary Waves (ISWs) breaking processes. We make use of the theoretical framework of Winters et al. [1995] to describe the energetics of a stratified fluid flow, calculating the Available Potential Energy (APE) of an ISW of depression in a two-layer system, assuming that the symmetric density structure on both sides of the feature is exactly the same. Starting from the definition of mixing efficiency given by Michallet and Ivey [1999], through the Ozmidov and Thorpe length-scales we derive an expression for the mixing efficiency avoiding the use of any wave model (as KdV-type models or strongly nonlinear models) to estimate the wave energy. The model is successfully verified through laboratory experiments performed in a wave tank and is meant to be applied by using real field CTD casts. </p><p>&#160;</p><p>References:</p><p>Winters, K. , Lombard, P., Riley, J., and D&#8217;Asaro, E. (1995). <em>Available potential</em></p><p><em>energy and mixing in density-stratified fluids</em>. J. Fluid Mech. , 289, 115-128. </p><p>Michallet, H. and Ivey, G. (1999). <em>Experiments on mixing due to internal solitary</em></p><p><em>waves breaking on uniform slopes</em>. Journal of Geophysical Research: Oceans,</p><p>104(C6), 13467-13477</p>
Version Locking Mechanism in Database

The distributed database provides a resource sharing environment, where multiple transactions at different sites coexist in order to access the resources. In this paper we investigate multi version locking protocol in distributed environment. Multi-version database has the potential to significantly increase the amount of concurrency in transaction processing as they can avoid read-write conflict by completing the read requests with older versions of data while the write operation is in progress. These algorithms are particularly effective for long queries, which otherwise cannot finish due to the high probability of conflict with other transactions. We have given an improvement on the algorithm proposed by Jie shao. We have not imposed any restriction on the involvement of all the existing sites to obtain a global version which thereby reduces the burden on each individual site. Our scheme involves the collection of local transaction identifier only from the participating site whether it can be one or many. We have proposed our new algorithm to obtain a correct version for a particular data item during read and write operation.
Plasma Warfarin Level and International Normalized Ratio do not Correlate with Bleeding Events in Indonesian Patients of Minangkabau Ethnicity with Atrial Fibrillation

BACKGROUND: Warfarin is the mainstay of anticoagulant therapy to prevent thromboembolism in atrial fibrillation (AF). It has a narrow therapeutic window, rendering monitoring prothrombin time necessary using the international normalized ratio (INR). However, INR value is not always correlated with the clinical risk of bleeding. AIM: We aimed to monitor plasma warfarin concentration and to analyze its correlation with bleeding events in Indonesian patients of Minangkabau ethnicity with AF. METHODS: We consecutively recruited outpatients with AF from January to November 2017 at a tertiary hospital in West Sumatera, Indonesia. At the time of the study, patients had received at least 5 weeks of warfarin. Their characteristics were obtained from medical records, and INR data were collected. Warfarin plasma concentration was analyzed using high-performance liquid chromatography. RESULTS: There were a total of 45 patients (25 males and 20 females; mean age 54.6 years). The number of patients with INR value lower than, within, and higher than target value (2.0–3.0) was 25, 12, and 8, respectively. Half of the patients (n = 23; 51.1%) had subtherapeutic plasma warfarin levels and nearly half (n = 20; 44.4%) of the patients had therapeutic plasma warfarin levels. INR value was not significantly correlated with plasma warfarin level (r = 0.273; p = 0.07). Bleeding events occurred in 14 patients. INR value was not significantly different (p = 0.12), while the plasma warfarin level was marginally significantly different (p = 0.05) between those with bleeding and no bleeding events. CONCLUSION: Neither warfarin plasma concentration nor INR was correlated with bleeding events in Indonesian patients of Minangkabau ethnicity with AF.
Does chronic hepatitis B infection have an impact on fasting blood glucose levels and fatty liver development?

Aim: The relationship between hepatitis B virus (HBV) infection and insulin resistance (IR) appears to be confusing. In this study, the goal was to compare fasting blood glucose (FBG) levels and fatty liver (FL) frequency as a reflection of IR in patients with chronic active HBV infection and to determine whether there is a relationship between liver fibrosis, FBG and FL. Methods: In this case-control study, the study group consisted of 116 chronic HBV patients with HBV DNA levels above 2000 IU/ml. The control group included 120 healthy individuals with matching age, gender, and body mass indexes. Results: There was no difference in FBG levels between the groups (P=0.15), but FL rates were significantly higher in patients with HBV (P=0.01; OR: 2.13, 95% CI 1.26-3.61). Although the FBG levels of groups with regards to severity of fibrosis were similar, there was a significant difference in terms of FL (P=0.07 and P<0.001, respectively). A positive correlation was found between the development of FL and the severity of fibrosis (rs=0.216, P=0.01). Conclusion: While FBG level appears to be unaffected by chronic active HBV infection, the frequency of FL is markedly increased in these patients. Viral factors are likely responsible for the development of FL rather than metabolic factors.