Miracle-dz/UniFAR-FTUs huggingface dataset:
SCREENING AND IDENTIFICATION OF POTENTIAL INHIBITORS AGAINST UDP-N ACETYL GLUCOSAMINE ENOLPYRUVYL TRANSFERASE(MURA) IN STREPTOCOCCUS PNEUMONIA: AN INSILICO APPROACH

Streptococcus pneumoniae are the most common cause of pneumonia and meningitis in infants and adults. Peptidoglycan layer is an essential component of cell wall composed of alternating units of N-acetylglicosamine (GlcNAc) and N-acetylmuramic acid (MurNAc). UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) is a key enzyme involved in peptidoglycan synthesis. The Biosynthesis of peptidoglycan in cytoplasm is catalysed by MurA which transfer enolpyrul group from phosphophenol pyruvate (PEP) to UDP Nacetylglucosamine to form UDP -Nacetylenolpyruvate. Present study was aimed to develop novel antimicrobial agents against MurA enzyme through insilico analysis approach. The three dimensional structure of MurA enzyme was modelled computationally by using Modeller9v10. Later this enzyme model subjected to molecular dynamics simulations using NAMD 2.5 software with CHARMM27 force field tip 3p model of water. Initially energy minimization carried 5000 runs for 10 ps time and subsequent minimized model was simulated with 1,00,000 runs for 2 ns time period. The final resolved model reliability was assessed by prochek using Ramchandran plot calculations, verify 3D and WHATCHECK programs. From Zinc database 5000 similar structure compounds with fosfomycin were virtually screened against MurA by Autodock vina in PyRx virtual screening tool. The docking results reveal that the compounds Zinc50247, Zinc3020559, Zinc1884559, Zinc3154681, Zinc19286884, Zinc58219 and Zinc3978065 have -10.3, -10.1, -9.8, -9.8, 9.2, -9.1 and -9.0 k.cal/mol binding affinity respectively, with MurA enzyme. We found that, the Amino acids Asp306, Tyr329, Gly115, Arg121, Arg322, Ser125, Leu91 and Phe125 present in the enzyme binding pocket are showing molecular interactions with ligands. MurA being a potential drug target in treating Streptococcus pneumonia infections, in this study we found that Zinc50247 effectively inhibits the MurA enzyme and can act as potential therapeutic agent.
An On-Line Early Warning and Preventive Control System for Static Voltage Stability

The functional framework,algorithm characteristics and application effect of an on-line early warning and preventive control system for static voltage stability are presented. By applying minimum singular value and improved continuous power flow algorithm the on-line calculation of stability indices,the real-time monitoring of the load margin of regional power network and whole grid and the power transmission limits of key tie-lines are implemented,as well as prompting of early warning level;a high-performance contingency screening subsystem is realized by distributed calculation architecture,thus a preventive control strategy for severe fault can be offered. The presented system can be integrated with automatic voltage control(AVC) system to ensure the voltage statbility of power grid after the control. The numerical test results of the presented system in dispatching center of North China power grid and Jiangxi provincial power grid show that this on-line early warning and preventive control system is effective.
Research on transient-based faulty phase selection for double-circuit lines on the same tower

This paper proposes a novel approach of transient-based faulty phase selection for double circuit lines on the same tower.This phase-selection algorithm uses the wavelet transform to extract the transient energy of current faulty component in each phase and takes the transient energy as phase selection criterion. First, according to the fault equivalence principle of the single line and double circuit lines on the same tower, this approach identifies the fault phase preliminarily. Then the transient energy magnitudes are compared to select the same-name-phase fault lines and further identify the fault phase precisely. At last, this paper presents a complete faulty phase selection based on the single terminal quantities of double circuit lines. The effectiveness of the approach has been verified by simulating different fault types, fault location, fault time and different transition resistance with PSCAD/EMTDC.
Three axis weak magnetic field detection system based on Lab VIEW

According to requirement of weak magnetic field detection,Lab VIEW virtual instrument technology developed three axis of weak magnetic field detection system,using 3D Mag IC( PNI12927) chip to drive the three axis magnetic sensor,configuration parameters and communication control on sensing device are carried out by STC12LE2052 AD MCU,and multi-function visualization system software is compiled based on Lab VIEW. By calibrating the sensitivity and linearity of the detection system,the systematic error of which is pre-compensated by means of software,real-time measurement of magnetic field at the level of 15 n T is realized. Ceomagnetic field measurement results verify the feasibility of the method.
Designing Internet of Behaviors Systems

The Internet of Behaviors (IoB) puts human behavior at the core of engineering intelligent connected systems. IoB links the digital world to human behavior to integrate human-driven design, development, and adaptation processes. This paper defines the novel IoB concept with a constructed model based on a collective effort interacting with software engineers, human-computer interaction scientists, social scientists, and cognitive science communities. The model for IoB is created based on an exploratory study that synthesizes state-of-the-art analysis and experts interviews. The architecture of a real industry 4.0 manufacturing infrastructure helps to explain the IoB model and its application. The conceptual model was used to successfully implement a socio-technical infrastructure for a crowd monitoring and queue management system for the Uffizi Galleries, Florence, Italy. The experiment, which started in the fall of 2016 and was operational in the fall of 2018, used a data-driven approach to feed the system with real-time sensory data. It also incorporated prediction models on visitors’ mobility behavior. The system’s main objective was to capture human behavior, model it, and build a mechanism that considers changes, adapts in real-time, and continuously learns from repetitive behaviors. In addition to the conceptual model and the real-life evaluation, this paper provides recommendations from experts and gives future directions for IoB to become a significant technological advancement in the coming years.
Investigation of fast-NMPC and deep learning approach in fixed-point-based hierarchical control

This paper explores some variations of a hier-archical control framework that has been recently proposed. This framework is dedicated to the control of a network of interconnected subsystems such as those describing cryogenic processes or power plants. Recent studies have shown that handling constraints and non-linearities could challenge the real-time feasibility of the approach. This paper investigates and combines two successful directions, namely the use of truncated fast gradient and deep-neural-network-based controller modeling, to reduce the computational time of the most critical subsystem. It is also shown that by doing so, the control update period can be significantly reduced and the closed-loop performance is greatly improved. This paper can therefore be seen as a concrete implementation and validation of some key ideas in the design of real-time distributed NMPCs. All concepts are validated using the realistic and challenging example of a real cryogenic refrigerator.
Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting

Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with time-efficient training method is still an open challenge. Thus, this paper introduces a novel univariate time series forecasting technique, which is composed of a group of randomized high order FCM models labeled R-HFCM. The novelty of the proposed R-HFCM model is relevant to merging the concepts of FCM and Echo State Network (ESN) as an efficient and particular family of Reservoir Computing (RC) models, where the least squares algorithm is applied to train the model. From another perspective, the structure of R-HFCM consists of the input layer, reservoir layer, and output layer in which only the output layer is trainable while the weights of each sub-reservoir components are selected randomly and keep constant during the training process. As case studies, this model considers solar energy forecasting with public data for Brazilian solar stations as well as Malaysia dataset, which includes hourly electric load and temperature data of the power supply company of the city of Johor in Malaysia. The experiment also includes the effect of the map size, activation function, the presence of bias and the size of the reservoir on the accuracy of R-HFCM method. The obtained results confirm the outperformance of the proposed R-HFCM model in comparison to the other methods. This study provides evidence that FCM can be a new way to implement a reservoir of dynamics in time series modelling.
Automatic Related Work Generation: A Meta Study

Academic research is an exploration activity to solve problems that have never been resolved before. By this nature, each academic research work is required to perform a literature review to distinguish its novelties that have not been addressed by prior works. In natural language processing, this literature review is usually conducted under the"Related Work"section. The task of automatic related work generation aims to automatically generate the"Related Work"section given the rest of the research paper and a list of cited papers. Although this task was proposed over 10 years ago, it received little attention until very recently, when it was cast as a variant of the scientific multi-document summarization problem. However, even today, the problems of automatic related work and citation text generation are not yet standardized. In this survey, we conduct a meta-study to compare the existing literature on related work generation from the perspectives of problem formulation, dataset collection, methodological approach, performance evaluation, and future prospects to provide the reader insight into the progress of the state-of-the-art studies, as well as and how future studies can be conducted. We also survey relevant fields of study that we suggest future work to consider integrating.
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria

A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate. This is particularly critical in situations when other agents have hidden, possibly misaligned motivations and goals. Social deduction games offer an avenue to study how individuals might learn to synthesize potentially unreliable information about others, and elucidate their true motivations. In this work, we present Hidden Agenda, a two-team social deduction game that provides a 2D environment for studying learning agents in scenarios of unknown team alignment. The environment admits a rich set of strategies for both teams. Reinforcement learning agents trained in Hidden Agenda show that agents can learn a variety of behaviors, including partnering and voting without need for communication in natural language.
Dynamic Control of Soft Manipulators to Perform Real-World Tasks

Dynamic motions are a key feature of robotic arms, enabling them to perform tasks quickly and efﬁciently. Soft continuum manipulators do not currently consider dynamic parameters when operating in task space. This shortcoming makes existing soft robots slow and limits their ability to deal with external forces, especially during object manipulation. We address this issue by using dynamic operational space control. Our control approach takes into account the dynamic parameters of the 3D continuum arm and introduces new models that enable multi-segment soft manipulators to operate smoothly in task space. Advanced control methods, previously afforded only to rigid robots, are now adapted to soft robots; for example, potential ﬁeld avoidance was previously only shown for rigid robots and is now extended to soft robots. Using our approach, a soft manipulator can now achieve a variety of tasks that were previously not possible: we evaluate the manipulator’s performance in closed-loop controlled experiments such as pick-and-place, obstacle avoidance, throwing objects using an attached soft gripper, and deliberately applying forces to a
Cluster assembly and the origin of mass segregation in the STARFORGE simulations

Stars form in dense, clustered environments, where feedback from newly formed stars eventually ejects the gas, terminating star formation and leaving behind one or more star clusters. Using the STARFORGE simulations, it is possible to simulate this process in its entirety within a molecular cloud, while explicitly evolving the gas radiation and magnetic fields and following the formation of individual, low-mass stars. We find that individual star-formation sites merge to form ever larger structures, while still accreting gas. Thus clusters are assembled through a series of mergers. During the cluster assembly process a small fraction of stars are ejected from their clusters; we find no significant difference between the mass distribution of the ejected stellar population and that of stars inside clusters. The star-formation sites that are the building blocks of clusters start out mass segregated with one or a few massive stars at their center. As they merge the newly formed clusters maintain this feature, causing them to have mass-segregated substructures without themselves being centrally condensed. The merged clusters relax to a centrally condensed mass segregated configuration through dynamical interactions between their members, but this process does not finish before feedback expels the remaining gas from the cluster. In the simulated runs the gas-free clusters then become unbound and breakup. We find that turbulent driving and a periodic cloud geometry can significantly reduce clustering and prevent gas expulsion. Meanwhile, the initial surface density and level of turbulence have little qualitative effect on cluster evolution, despite the significantly different star formation histories.
High fill factor confocal compound eyes fabricated by direct laser writing for better imaging quality

We fabricate two kinds of 100% fill factor compound eye structures using direct laser writing, including conventional compound eyes (CVCEs) with the same focal length of each microlens unit, and specially designed confocal compound eyes (CFCEs). For CFCEs, the focal length of each microlens unit is determined by its position and is equal to the distance between the microlens unit and the image sensor. In this letter, the optical properties of CVCEs and CFCEs are tested and compared. It is found that compared with CVCEs, CFCEs can improve the focusing efficiency by about 7%, enlarge the imaging area by about 25%, and have better imaging quality at the edge of the field of view.
Ecce Signum: An R Package for Multivariate Signal Extraction and Time Series Analysis

The package provides multivariate time series models for structural analysis, allowing one to extract latent signals such as trends or seasonality. Models are fitted using maximum likelihood estimation, allowing for non-stationarity, fixed regression effects, and ragged-edge missing values. Simple types of extreme values can be corrected using the device of entropy maximization. Model adequacy is assessed through residual diagnostics, and model-based signal extraction filters can be assessed in time domain and frequency domain. Extracted signals are produced with uncertainty measures that account for sample edge effects and missing values, and the signals (as well as the original time series) can be forecasted.
POCO: Point Convolution for Surface Reconstruction

Implicit neural networks have been successfully used for surface reconstruction from point clouds. However, many of them face scalability issues as they encode the isosurface function of a whole object or scene into a single latent vector. To overcome this limitation, a few approaches infer latent vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to answer occupancy queries. In doing so, they lose the direct connection with the input points sampled on the surface of objects, and they attach information uniformly in space rather than where it matters the most, i.e., near the surface. Besides, relying on fixed patch sizes may require discretization tuning. To address these issues, we propose to use point cloud convolutions and compute latent vectors at each input point. We then perform a learning-based interpolation on nearest neighbors using inferred weights. Experiments on both object and scene datasets show that our approach significantly outperforms other methods on most classical metrics, producing finer details and better reconstructing thinner volumes. The code is available at https://github.com/valeoai/POCO.
Electrical-Readout Microwave-Free Sensing with Diamond

While nitrogen-vacancy (NV-) centers have been extensively investigated in the context of spin-based quantum technologies, the spin-state readout is conventionally performed optically, which may limit miniaturization and scalability. Here, we report photoelectric readout of ground-state cross-relaxation features, which serves as a method for measuring electron spin resonance spectra of nanoscale electronic environments and also for microwave-free sensing. As a proof of concept, by systematically tuning NV centers into resonance with the target electronic system, we extracted the spectra for the P1 electronic spin bath in diamond. Such detection may enable probing optically inactive defects and the dynamics of local spin environment. We also demonstrate a magnetometer based on photoelectric detection of the ground-state level anticrossings (GSLAC), which exhibits a favorable detection efficiency as well as magnetic sensitivity. This approach may offer potential solutions for determining spin densities and characterizing local environment.
Splitting of localized disturbances in viscoelastic channel flow

Abstract We examine the response of an inertialess viscoelastic channel flow to localized perturbations. We thus performed an experiment in which we perturbed the flow using a localized velocity pulse and probed the perturbed fluid packet downstream from the perturbation location. While for low Weissenberg numbers the perturbed fluid reaches the measurement location as a single velocity pulse, for sufficiently high Weissenberg numbers and perturbation strengths, a random number of pulses arrive at the measurement location. The average number of pulses observed increases with the Weissenberg number. This observation constitutes a transition to a novel elastic pulse-splitting regime. Our results suggest a possible new direction for studying the elastic instability of viscoelastic channel flows at high elasticity through the growth of localized perturbations.
A model for an epidemic with contact tracing and cluster isolation, and a detection paradox

Abstract We determine the distributions of some random variables related to a simple model of an epidemic with contact tracing and cluster isolation. This enables us to apply general limit theorems for super-critical Crump–Mode–Jagers branching processes. Notably, we compute explicitly the asymptotic proportion of isolated clusters with a given size amongst all isolated clusters, conditionally on survival of the epidemic. Somewhat surprisingly, the latter differs from the distribution of the size of a typical cluster at the time of its detection, and we explain the reasons behind this seeming paradox.
Blind Source Separation over Space: An Eigenanalysis Approach

We propose a new estimation method for the blind source separation model of Bachoc et al. (2020). The new estimation is based on an eigenanalysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and, therefore, can handle moderately high-dimensional random fields. The consistency of the estimated mixing matrix is established with explicit error rates even when the eigen-gap decays to zero slowly. The proposed method is illustrated via both simulation and a real data example.
Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns

The Resource Description Framework (RDF) is a framework for describing metadata, such as attributes and relationships of resources on the Web. Machine learning tasks for RDF graphs adopt three methods: (i) support vector machines (SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational graph convolutional networks. In this paper, we propose a novel feature vector (called a Skip vector) that represents some features of each resource in an RDF graph by extracting various combinations of neighboring edges and nodes. In order to make the Skip vector low-dimensional, we select important features for classification tasks based on the information gain ratio of each feature. The classification tasks can be performed by applying the low-dimensional Skip vector of each resource to conventional machine learning algorithms, such as SVMs, the k-nearest neighbors method, neural networks, random forests, and AdaBoost. In our evaluation experiments with RDF data, such as Wikidata, DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM. We also compare our method with the two approaches: RDF graph embeddings such as RDF2vec and relational graph convolutional networks on the AIFB, MUTAG, BGS, and AM benchmarks.
DeepMLS: Geometry-Aware Control Point Deformation

We introduce DeepMLS, a space-based deformation technique, guided by a set of displaced control points. We leverage the power of neural networks to inject the underlying shape geometry into the deformation parameters. The goal of our technique is to enable a realistic and intuitive shape deformation. Our method is built upon moving least-squares (MLS), since it minimizes a weighted sum of the given control point displacements. Traditionally, the inﬂuence of each control point on every point in space ( i.e., the weighting function) is deﬁned using inverse distance heuristics. In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks. Our geometry-aware control point deformation is agnostic to the surface representation and quality; it can be applied to point clouds or meshes, including non-manifold and disconnected surface soups. We show that our technique facilitates intuitive piecewise smooth deformations, which are well suited for manufactured objects. We show the advantages of our approach compared to existing surface and space-based deformation techniques, both quantitatively and qualitatively.
How well do we know the gluon polarization in the proton?

We perform the first simultaneous global QCD analysis of spin-averaged and spin-dependent parton distribution functions (PDFs), including single jet production data from unpolarized and polarized hadron collisions. We critically assess the impact of SU(3) flavor symmetry and PDF positivity assumptions on the quark and gluon helicity PDFs, and find strong bias from these, particularly on the gluon polarization. The simultaneous analysis allows for the first time extraction of individual helicity-aligned and antialigned PDFs with a consistent treatment of uncertainties. 1 ar X iv :2 20 1. 02 07 5v 1 [ he pph ] 6 J an 2 02 2