Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug Potency Prediction of SARS-CoV-2 Main Protease Inhibitors Based on a Graph Generative Model

The prediction of a ligand potency to inhibit SARS-CoV-2 main protease (M-pro) would be a highly helpful addition to a virtual screening process. The most potent compounds might then be the focus of further efforts to experimentally validate their potency and improve them. A computational method to predict drug potency, which is based on three main steps, is defined: (1) defining the drug and protein in only one 3D structure; (2) applying graph autoencoder techniques with the aim of generating a latent vector; and (3) using a classical fitting model to the latent vector to predict the potency of the drug. Experiments in a database of 160 drug-M-pro pairs, from which the pIC50 is known, show the ability of our method to predict their drug potency with high accuracy. Moreover, the time spent to compute the pIC50 of the whole database is only some seconds, using a current personal computer. Thus, it can be concluded that a computational tool that predicts, with high reliability, the pIC50 in a cheap and fast way is achieved. This tool, which can be used to prioritize which virtual screening hits, will be further examined in vitro.
A Survey of Path Loss Prediction and Channel Models for Unmanned Aerial Systems for System-Level Simulations

Unmanned aerial systems (UAS) have recently gained popularity, and they are envisioned as an integral parts of the current and future wireless and mobile-radio networks. Despite the exhaustive research on air-to-ground channels, there are insufficient studies, experimental campaigns and general channel models related to air-to-space (A2S) and air-to-air (A2A) wireless links. This paper presents a comprehensive review of the available channel models and path-loss prediction for A2S and A2A communications. Specific case studies attempting to extend current models’ parameters and provide important knowledge of the channel behavior in combination with UAV flight characteristics are also provided. A time-series rain-attenuation synthesizer is also presented that describes quite accurately the impact of the troposphere at frequencies above 10 GHz. This specific model can be also applied to both A2S and A2A wireless links. Finally, scientific challenges and gaps that can be used for future research on the upcoming 6G networks are highlighted.
Direct Titration Method for Lime Requirement Estimates of Ultisols in Southeastern Vietnam

ABSTRACT Determination of lime requirement (LR) is a routine test due to the wide use of liming materials to remediate acid soils. The titrations using several base additions reportedly work well on many soils but have not yet been tested on Ultisols (0–30 cm depth) in Southeastern Vietnam. Results showed that a titration using two base [saturated Ca(OH)2 solution] additions to 1:1 suspensions of soil in 0.01 M CaCl2 (0.10 g CaCO3 kg−1 soil per addition), a 90-min equilibrium, and a continuous shaking mode yielded reliable LR values on these soils. It estimated on average 86% of LR by the reference method and was of highest accuracy in soils with LR of around 1.58 t CaCO3 ha−1. To improve the prediction, two distinct equations were recommended for soils with LR, respectively, below and above this value. The titration under the defined conditions was likely to be suited for routine use on Ultisols in Southeastern Vietnam, with further testing, adjustment, and validation being needed.
Revision programming: a knowledge representation formalism

OF DISSERTATION Inna Valentinovna Pivkina The Graduate School University of Kentucky 2001 REVISION PROGRAMMING: A KNOWLEDGE REPRESENTATION FORMALISM ABSTRACT OF DISSERTATIONOF DISSERTATION A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the University of Kentucky By Inna Valentinovna Pivkina Lexington, Kentucky Co-Directors: Dr. Wiktor Marek, Professor of Computer Science and Dr. Miroslaw Truszczynski, Professor of Computer Science Lexington, Kentucky 2001 ABSTRACT OF DISSERTATIONOF DISSERTATION REVISION PROGRAMMING: A KNOWLEDGE REPRESENTATION FORMALISM The topic of the dissertation is revision programming. It is a knowledge representation formalism for describing constraints on databases, knowledge bases, and belief sets, and providing a computational mechanism to enforce them. Constraints are represented by sets of revision rules. Revision rules could be quite complex and are usually in a form of conditions (for instance, if these elements are present and those elements are absent, then this element must be absent). In addition to being a logical constraint, a revision rule specify a preferred way to satisfy the constraint. Justified revisions semantics assigns to any database a set (possibly empty) of revisions. Each revision satisfies the constraints, and all deletions and additions of elements in a transition from initial database to the revision are derived from revision rules. Revision programming and logic programming are closely related. We established an elegant embedding of revision programs into logic programs, which does not increase the size of a program. Initial database is used in transformation of a revision program into the corresponding logic program, but it is not represented in the logic program. The connection naturally led to extensions of revision programming formalism which correspond to existing extensions of logic programming. More specific, a disjunctive and a nested versions of revision programming were introduced. Also, we studied annotated revision programs, which allow annotations like confidence factors, multiple experts, etc. Annotations were assumed to be elements of a complete infinitely distributive lattice. We proposed a justified revisions semantics for annotated revision programs which agreed with intuitions. Next, we introduced definitions of well-founded semantics for revision programming. It assigns to a revision problem a single “intended” model which is computable in polynomial time. Finally, we extended syntax of revision problems by allowing variables and implemented translators of revision programs into logic programs and a grounder for revision programs. The implementation allows us to compute justified revisions using existing implementations of the stable model semantics for logic programs.
Serum Interleukin-36 α as a Candidate Biomarker to Distinguish Behçet’s Syndrome and Psoriatic Arthritis

Behçet’s syndrome (BS) is a rare systemic vasculitis characterized by different clinical manifestations. As no specific laboratory tests exist, the diagnosis relies on clinical criteria, and the differential diagnosis with other inflammatory diseases can be challenging. Indeed, in a relatively small proportion of patients, BS symptoms include only mucocutaneous, articular, gastrointestinal, and non-typical ocular manifestations, which are frequently found also in psoriatic arthritis (PsA). We investigate the ability of serum interleukin (IL)-36α—a pro-inflammatory cytokine involved in cutaneous and articular inflammatory diseases—to differentiate BS from PsA. A cross-sectional study was performed on 90 patients with BS, 80 with PsA and 80 healthy controls. Significantly lower IL-36α concentrations were found in patients with BS as compared to PsA, although in both groups IL-36α was significantly increased compared to healthy controls. An empirical cut-off of 420.6 pg/mL displayed a specificity of 0.93, with a sensitivity of 0.70 (AUC 0.82) in discriminating PsA from BS. This cut-off displayed a good diagnostic performance also in BS patients lacking highly specific BS manifestations. Our results indicate that IL-36α might be involved in the pathogenesis of both BS and PsA, and might be a candidate biomarker to support the differential diagnosis of BS.
Self-Assured Deep Learning With Minimum Pre-Labeled Data for Wafer Pattern Classification

Data quality plays an important role during the training stage of machine/deep learning models. The annotation hinges on the experiences of domain experts. To acquire the expert’s knowledge in the context of machine learning, manual data labeling, a tedious and time-consuming task in supervised learning, should be given a top priority. However, the domain experts in the line of plentiful manual annotation may easily get distracted or fatigued after long-time work, causing judgment errors, mislabeling, etc. The pattern recognition of wafer defect map is investigated in this paper, the primary goal of which is to train the convolutional neural network (CNN) model through a very limited number of manually labeled data so that the trained model is capable of performing pseudo labeling. Subsequently, a self-assured adaptive ensemble learner in terms of a series of shallow neural networks is proposed to filter wafer map samples with untrusted pseudo-labels. In the result, the amount of human annotations is significantly reduced by 61% for training a highly accurate classifier. A minimum number of manually labeled data is suggested while the equally high classification performance of wafer defect pattern is maintained. For the evaluation purpose, the proposed self-assured learning is compared with the confidence learning.
THE ROLE OF NON-COGNITIVE FACTORS IN THE INTRODUCTORY STATISTICS CLASSROOM

The study reported in this paper aimed to not only understand student attitudes towards statistics, but also their beliefs about their ability to perform statistical tasks (self-efficacy), the value they place on carrying out course and statistical tasks (task value), and level of effort they perceive when engaging in these tasks (cost) across a semester in an undergraduate introductory statistics course. Findings indicated that students viewed statistics as more difficult, less valuable, and more costly to engage in as the semester progressed. Further, students also reported an increase in avoidant help-seeking behaviors (e.g., not seeking help when needed). Non-cognitive factors were not found to predict course retention. Variables of affect and self-efficacy, however, were shown to predict overall course grade.
Automatic Gripper-Finger Design, Production, and Application: Toward Fast and Cost-Effective Small-Batch Production

Tactile robot-based assembly line adaptation of new products is still limited by the manual redesign, manufacturing, and exchange of the end-effector setup since the gripper fingers must often be adapted to the geometry of the product components to ensure a successful assembly process. In this article we present an automatic finger design, production, and evaluation pipeline, developed to improve this adaptation process. Two different form-closure–based design principles have been implemented to automatically generate the fingertip geometry: a projected surface representation-based approach as well as a Bézier surface fitting strategy. The resulting fingertips are printed via an automatic production unit and experimentally evaluated based on pick and insertion tasks for three different manipulation objects. To illustrate the potential usage of the introduced design methods for machine learning-based fingertip design approaches, we set up the training and testing process for a neural network-based design method. The proposed automatic fingertip design, production, and application framework represent a step further toward small-batch–size production, since the assembly adaptation effort, flexibility, and scalability are significantly improved.
The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations

AbstractA linear wave theory for the Madden–Julian oscillation (MJO), previously developed by Sobel and Maloney, is extended upon in this study. In this treatment, column moisture is the only prognostic variable and the horizontal wind is diagnosed as the forced Kelvin and Rossby wave responses to an equatorial heat source/sink. Unlike the original framework, the meridional and vertical structure of the basic equations is treated explicitly, and values of several key model parameters are adjusted, based on observations. A dispersion relation is derived that adequately describes the MJO’s signal in the wavenumber–frequency spectrum and defines the MJO as a dispersive equatorial moist wave with a westward group velocity. On the basis of linear regression analysis of satellite and reanalysis data, it is estimated that the MJO’s group velocity is ~40% as large as its phase speed. This dispersion is the result of the anomalous winds in the wave modulating the mean distribution of moisture such that the moistur.
Social Networking: Strategies for Exploring Innovation in the Indonesian Manufacturing Industry

In Indonesia, SMEs in the manufacturing sector are around 16.65 percent of the existing SMEs. The processing industry is a variety of production activities that change the form of raw materials into finished or semifinished goods. At this time, the Covid-19 pandemic has resulted in SMEs in the processing industry being unable to survive only by exploiting existing businesses. Changes in demand and supply chains, require the industry to explore new things while continuing to develop the business that is currently owned. This will be difficult for SMEs in Indonesia, because in general, SMEs are family companies, with limited resources. To overcome the shortage of resources, SMEs need to collaborate with external partners and form social networks. This study uses a survey method to analyze the influence of networks on the competence of exploration in SMEs in the processing industry. From 100 respondents, it was found that social networking has a positive effect on the competence of exploring innovation. This happens because from social networks, SMEs can obtain additional resources, information, and new knowledge so that they can strengthen exploration competencies.
Co)algebraic Foundations for Effectful Recursive Definitions

A pervasive challenge in programming theory and practice are feature combinations. Here, we propose a semantic framework that combines monad-based computational effects (e.g. store, nondeterminism, random), underdefined or free operations (e.g. actions in process algebra and automata, exceptions), and recursive definitions (e.g. loops, systems of process equations). The joint treatment of these phenomena has previously led to models tending to one of two opposite extremes: extensional as, e.g., in domain theory, and intensional as in classical process algebra and more generally in universal coalgebra. Our metalanguage for effectful recursive definitions, designed in the spirit of Moggi's computational metalanguage, flexibly combines these intensional and extensional aspects of computations in a single framework. We base our development on a notion of complete Elgot monad, whose defining feature is a parametrized uniform iteration operator satisfying natural axioms in the style of Simpson and Plotkin. We provide a mechanism of adjoining free operations to such monads by means of cofree extensions, thus in particular allowing for a non-trivial semantics of non-terminating computations with free effects. Our main result states that the class of complete Elgot monads is closed under such cofree extensions, which thus serve as domains for effectful recursive definitions with free operations. Elgot monads do not require the iterated computation to be guarded, and hence iteration operators are not uniquely determined by just their defining fixpoint equation. Our results however imply that they are uniquely determined as extending the given iteration in the base effect and satisfying the axioms. We discuss a number of examples formalized in our metalanguage, including (co)recursive definitions of process-algebraic operations on side- effecting processes.
Geometric and topological methods in protein structure analysis

With the recent success of the Human Genome Project, one of the main challenges in molecular biology in this post-genomic era is the determination and exploitation of the three-dimensional structure of proteins and their function. The ability for proteins to perform their numerous functions is made possible by the diversity of their three-dimensional structures. Hence, to attack the key problems involved, such as protein folding and docking, geometry and topology become important tools. Despite their essential roles, geometric and topological methods are relatively uncommon in computational biology, partly due to a number of modeling and algorithmic challenges. This thesis describes efficient computational methods for describing and comparing molecular structures by combining both geometric and topological approaches. In particular, in the first part of the thesis, we study three geometric descriptions: (i) the writhing number of protein backbones, which measures how many times a backbone coils around itself; (ii) the level-of-details representation of protein backbones via simplification, which helps to extract main features of backbones; and (iii) the elevation of molecular surfaces, which we propose to identify geometric features such as protrusions and cavities from protein surfaces. We develop efficient algorithms for computing these descriptions. The second part of the thesis focuses on molecular shape matching algorithms. By modeling a molecule as the union of balls, we propose algorithms to compute the similarity between two such unions by (variants of) the widely used Hausdorff distance. We also study the protein docking problem, which, from a geometric perspective, can be considered as the problem of searching for configurations with maximum complementarity between two molecular surfaces. Using the feature information computed from the elevation function, we describe an efficient algorithm to find promising initial relative placements of the proteins.
NTNU-TRH system at the MultiGED-2023 Shared on Multilingual Grammatical Error Detection

The paper presents a monolithic approach to grammatical error detection, which uses one model for all languages, in contrast to the individual approach, which creates separate models for each language. For both approaches, pre-trained embeddings are the only exter-nal knowledge sources. Two sets of embed-dings (Flair and BERT) are compared as well as two approaches to the problem of multi-lingual rammar detection, building individual and monolithic systems for multilingual grammar error detection. The system submitted to the test phase of the MultiGED-2023 shared task ranked 5th of 6 systems. In the subsequent open phase, more experiments were conducted, improving results. These results show the individual models to perform better than the monolithic ones and BERT embeddings working better than Flair embeddings for the individual models, while the picture is more mixed for the monolithic models.
Is a compact organization important for defensive success in elite soccer? – Analysis based on player tracking data

The interest in tactical analysis in soccer has increased in the latest years, especially with the growing availability of player tracking data. With it, the defending team's compact organization, which is considered by practitioners to be an important factor in defense, was repeatedly examined. However, the connection between this defensive principle of play and the defending success remains unclear. Therefore, this study aims to investigate the relation of the principle of playing defensive compact organization to the success of the defense. Based on tracking and event data of 153 games of the German Bundesliga (season 2020/21), the compactness (surface area, spread of the team, and of defending subgroups) and the organization (distances between formation lines) of the defending team was compared between successful and unsuccessful defensive plays. There were almost no differences in the compactness of the whole team, and the organizational measures between successful and unsuccessful defensive plays. The defending subgroup of five defenders closest to the ball showed a higher compactness (smaller surface area and smaller spread) in successful defensive plays compared to unsuccessful ones (−0.08 ≤ d ≤ −0.16). Our results indicate that the compactness of players in areas close to the ball seems crucial for defensive success. However, the compact organization of the entire team does not seem important to regain the ball in defense.
Cultural consumption and social identities (a study of the museum’s image through the eyes of visitors)

This article describes the study of the relationship between the practices of cultural consumption and the processes of constructing territorial, professional and other social identities. The studyexplores materials of an empirical sociological study conducted in the summer and autumn of 2022 "The image of the historical and architectural museum" Stroganov's Chambers"in Usolye through the eyes of visitors. " The study found that pensioners are more likely to demonstrate a behavior model that fits into the formulas of “status high consumption” and “heritage tourism”, while young people are mainly driven by the values of the “culture of curiosity”, and middle-aged persons feature both motives. The article interprets the results obtained through the theory of social identities and conclude that the motivation for visiting museums and the choice of cultural consumption practices are directly related to the professional, territorial and generational identities of the informants. This conclusion allows us to see new promising formats for working with museum visitors.
Dirac–Fock calculations of X-ray scattering factors and contributions to the mean inner potential for electron scattering

Tables of X-ray scattering factors for naturally occurring elements and ions have been calculated using a multiconfiguration Dirac–Fock code. The complete data set takes up about 250 kbytes of space. For accurate values, it is preferable to interpolate directly. For consistency with previous work, parametric fits are also presented with error estimates and a range of validity. The limiting values of the electron scattering factor as sinθ/λ tends to zero, which are needed to calculate the mean inner potential for electron scattering, are also tabulated. The results show that the mean inner potential can be very sensitive to ionization state.
Cardiac Tamponade Risk Associated With Anticoagulation for Atrial Fibrillation in Dialysis-Associated Pericarditis: A Case Report

Heparin is a preferred initial anticoagulant in patients with new-onset atrial fibrillation (AF). Despite continuous debate about the risk, there has been a concern about heparin-induced hemorrhagic pericarditis and cardiac tamponade. We present a case of a new onset atrial fibrillation (AF) in a patient with renal impairment and evidence of pericardial effusion complicated by hemopericardium development after starting anticoagulation. Although the risk of hemorrhagic conversion of uremic pericarditis induced by heparin in ESRD patients with new onset AF was suggested in the literature, this case raises the possibility of a similar complication in dialysis-associated pericarditis. Therefore, we aim to heighten alertness regarding this potential complication of a commonly used medication in clinical practice. We also aim to review the current anticoagulation recommendations in this setting.
Efficient Grid Treatment of the Time Dependent Schrödinger Equation for Laser-Driven Molecular Dynamics

We present an efficient method to solve the time dependent Schrodinger equation for modeling the dynamics of diatomic molecules irradiated by intense ultra- short laser pulse without Born-Oppenheimer approximation. By introducing a vari- able prolate spheroidal coordinates and discrete variable representations of the Hamil- tonian, we can accurately and efficiently simulate the motion of both electronic and molecular dynamics. The accuracy and convergence of this method are tested by sim- ulating the molecular structure, photon ionization and high harmonic generation of H + .