Convenient Interface Research Articles

RANCANG BANGUN MESIN PEMURNIAN VIRGIN COCONUT OIL (VCO) DENGAN METODE SENTRIFUGAL BERBASIS INTERNET OF THGINGS (IoT)

Virgin Coconut Oil (VCO) is a virgin coconut oil produced from the extraction process of fresh coconut meat without the use of chemicals or high temperatures. VCO has high economic value with various health benefits. The process of making VCO involves a centrifugal machine to separate the oil from coconut milk. However, the control of the VCO Purification machine still relies on manual intervention by the operator, which can lead to human error and affect production efficiency. In order to improve efficiency and reduce the risk of human error, Internet of Things (IoT) and Mobile Application technologies can be applied in the control of VCO Purification machines. The programming language used is PHP because it can be accessed on various platforms, especially Mobile. The sensor used is an IR (Infrared) speed sensor for measuring rotational speed. The Mobile Application can be a convenient interface for operators to control and record the operation history of the VCO purification machine. This research has succeeded in developing an IoT and Mobile Application-based control system that is effective in controlling VCO purification machines, increasing production efficiency, and reducing the risk of human error.

РАЗРАБОТКА ПРОГРАММЫ РАСЧЕТА ПАРАМЕТРОВ СХЕМЫ ЗАМЕЩЕНИЯ ТРЕХФАЗНОГО АСИНХРОННОГО ЭЛЕКТРОДВИГАТЕЛЯ

The article is devoted to developing a program for calculating the parameters of a T-shaped equivalent circuit of three-phase asynchronous electric motors. The relevance of this work is due to the need to obtain reliable results of computer simulation modelling of electric drive systems in software packages. The aim of the study is to implement in a convenient software interface an analytical technique for calculating the parameters of an asynchronous motor equivalent circuit with minimal deviations from the catalogue data. For the software calculation of the equivalent circuit parameters the paper considers analytical approximation and iterative counting techniques, since they do not require experiments on industrial electrical equipment. A comparative analysis of such techniques is performed using the example of a three-phase asynchronous motor brand BA280S4 with a power of 110 kW. The results of the compar-ative analysis show that the highest reliability of the mechanical and electromechanical characteristics is achieved in the case of using the iterative counting technique with minimizing the objective function of the sum of the weighted squares of the deviations of the control point values from the catalogue data. The authors implement the program for calculating the equivalent circuit parameters according to the selected calculation method in the Embarcadero Delphi environment. The reliable values of the electric motor parameters calculated by the program will allow for computer to model electric drive systems that best matches real objects.

DiscovEpi: automated whole proteome MHC-I-epitope prediction and visualization

BackgroundAntigen presentation is a central step in initiating and shaping the adaptive immune response. To activate CD8+ T cells, pathogen-derived peptides are presented on the cell surface of antigen-presenting cells bound to major histocompatibility complex (MHC) class I molecules. CD8+ T cells that recognize these complexes with their T cell receptor are activated and ideally eliminate infected cells. Prediction of putative peptides binding to MHC class I (MHC-I) is crucial for understanding pathogen recognition in specific immune responses and for supporting drug and vaccine design. There are reliable databases for epitope prediction algorithms available however they primarily focus on the prediction of epitopes in single immunogenic proteins.ResultsWe have developed the tool DiscovEpi to establish an interface between whole proteomes and epitope prediction. The tool allows the automated identification of all potential MHC-I-binding peptides within a proteome and calculates the epitope density and average binding score for every protein, a protein-centric approach. DiscovEpi provides a convenient interface between automated multiple sequence extraction by organism and cell compartment from the database UniProt for subsequent epitope prediction via NetMHCpan. Furthermore, it allows ranking of proteins by their predicted immunogenicity on the one hand and comparison of different proteomes on the other. By applying the tool, we predict a higher immunogenic potential of membrane-associated proteins of SARS-CoV-2 compared to those of influenza A based on the presented metrics epitope density and binding score. This could be confirmed visually by comparing the epitope maps of the influenza A strain and SARS-CoV-2.ConclusionAutomated prediction of whole proteomes and the subsequent visualization of the location of putative epitopes on sequence-level facilitate the search for putative immunogenic proteins or protein regions and support the study of adaptive immune responses and vaccine design.

ThunderBoltz: an open-source direct simulation Monte Carlo Boltzmann solver for plasma transport, chemical kinetics, and 0D modeling

Abstract Plasma-neutral interactions, including reactive kinetics, are often either studied in 0D using ODE-based descriptions, or in multi-dimensional fluid or particle-based plasma codes. The latter case involves a complex assembly of procedures that are not always necessary to test effects of underlying physical models and mechanisms for particle-based descriptions. Here we present ThunderBoltz, a lightweight, publicly available 0D direct simulation Monte Carlo code designed to accommodate a generalized combination of species and arbitrary cross sections without the overhead of expensive field solves. It can produce electron, ion, and neutral velocity distributions in applied AC/DC E-field and/or static B-field scenarios. The code is built in the C++ standard library and includes a convenient Python interface that allows for input file generation (including compatibility with cross section data from the LXCat database), electron transport and reaction rate constant post-processing, input parameter constraint satisfaction, calculation scheduling, and diagnostic plotting. These codes can be accessed at the repository: https://github.com/lanl/ThunderBoltz. In this work we compare ThunderBoltz transport calculations against Bolsig+ calculations, benchmark test problems, and swarm experiment data, finding good agreement with all three in the appropriate field regimes. In addition, we present example use cases where the electron, ion, and background neutral particle species are self-consistently evolved to model the background kinetics, a feature that is absent in fixed-background Monte Carlo and n-term Boltzmann solvers. The latter functionality allows for the possibility of particle-based chemical kinetics simulations of the plasma and neutral species as a new alternative to ODE-based approaches.

Real-time object detection, tracking, and monitoring framework for security surveillance systems

The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging.

TRIQS/Nevanlinna: Implementation of the Nevanlinna Analytic Continuation method for noise-free data

We present the TRIQS/Nevanlinna analytic continuation package, an efficient implementation of the methods proposed by J. Fei et al. (2021) [53] and (2021) [55]. TRIQS/Nevanlinna strives to provide a high quality open source (distributed under the GNU General Public License version 3) alternative to the more widely adopted Maximum Entropy based analytic continuation programs. With the additional Hardy functions optimization procedure, it allows for an accurate resolution of wide band and sharp features in the spectral function. Those problems can be formulated in terms of imaginary time or Matsubara frequency response functions. The application is based on the TRIQS C++/Python framework, which allows for easy interoperability with other TRIQS-based applications, electronic band structure codes and visualization tools. Similar to other TRIQS packages, it comes with a convenient Python interface. Program summaryProgram Title:TRIQS/NevanlinnaCPC Library link to program files:https://doi.org/10.17632/4cbzfy5rds.1Developer's repository link:https://github.com/TRIQS/NevanlinnaLicensing provisions: GPLv3Programming language:C++/PythonExternal routines/libraries:TRIQS 3.2[1], Boost >= 1.76.0, Eigen >= 3.4.0, cmake >= 3.20.Nature of problem: Finite-temperature field theories are widely used to study quantum many-body effects and electronic structure of correlated materials. Obtaining physically relevant spectral functions from results in the imaginary time/Matsubara frequency domains requires solution of an ill-posed analytic continuation problem as a post-processing step.Solution method: We present an efficient C++/Python open-source implementation of the Nevanlinna/Caratheodory analytic continuation.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

Utilizing Real – Time Face Recognition Based Bio-Metric System for Online Transaction

A crucial component of contemporary banking is now online banking. Due to the present password- based authentication paradigm’s inadequacies in terms of efficiency and robust, as well as their suspectibility to automated attacks, several attempts are successful in gaining access to social network accounts. The easiest solution is to add more identifying features, like one-time PIN numbers that are created by the user’s own device(like a smart phone) or sent to them via SMS to the single factor(Password-based) authentication procedure. With the help of this technology, client’s identities may be instantly and conveniently verified. The goal of this project is to create an online banking system that authenticates customer’s using real-time facial recognition technology. The system will be made to offer a safe and convenient user interface that enables users to perform financial operation like bill payment, money transfers, and balance queries. A facial recognition algorithm, such Grassmann learning, which can record and evaluate customer’s facial traits in real time, will be included into the system. To confirm customer’s identification, the algorithm will match the customer’s facial traits with those in the bank’s database. The technology would give users a safe and convenient interface to conduct real-time banking transactions. Notifications about banking amount transactions are sent to the user in this suggested netbanking application.

Designing Cost-Effective SMS based Irrigation System using GSM Module

This paper describes the development and application of an SMS-based, affordable, and easily understandable irrigation control system for agricultural purposes. The system leverages the processing power of a Raspberry Pi Pico microcontroller and the remote communication capabilities of a GSM module to create a robust and versatile solution. By incorporating a suite of sensors for soil moisture, temperature, and humidity, the system automates irrigation cycles based on user-defined thresholds and real-time sensor data. This data-driven approach ensures optimal water usage, minimizing labor requirements and promoting sustainable farming practices by reducing water waste. Additionally, the system transmits SMS alerts for critical situations such as high temperature, low moisture, or pump malfunctions, enabling timely intervention to prevent crop stress or equipment failure. Users can remotely control the pump and receive status updates through SMS commands, providing a convenient interface for managing irrigation tasks from anywhere with cellular reception. This innovative system contributes to the field of smart agriculture by demonstrating how readily available technology can be effectively used to improve water management efficiency, minimize labor requirements, and ultimately increase agricultural productivity.

A language‐directed virtual human motion generation approach based on musculoskeletal models

AbstractThe development of the systems capable of synthesizing natural and life‐like motions for virtual characters has long been a central focus in computer animation. It needs to generate high‐quality motions for characters and provide users with a convenient and flexible interface for guiding character motions. In this work, we propose a language‐directed virtual human motion generation approach based on musculoskeletal models to achieve interactive and higher‐fidelity virtual human motion, which lays the foundation for the development of language‐directed controllers in physics‐based character animation. First, we construct a simplified model of musculoskeletal dynamics for the virtual character. Subsequently, we propose a hierarchical control framework consisting of a trajectory tracking layer and a muscle control layer, obtaining the optimal control policy for imitating the reference motions through the training. We design a multi‐policy aggregation controller based on large language models, which selects the motion policy with the highest similarity to user text commands from the action‐caption data pool, facilitating natural language‐based control of virtual character motions. Experimental results demonstrate that the proposed approach not only generates high‐quality motions highly resembling reference motions but also enables users to effectively guide virtual characters to perform various motions via natural language instructions.

Гибридная интеллектуальная система машинного обучения для моделирования процессов обработки фосфатного рудного сырья

The results of a study are presented, the purpose of which was to create an intelligent machine learning system for modeling the processes of charge agglomeration during processing of phosphate ore raw materials. The relevance of the study is justified by the need to improve the information support of technological systems management processes in the context of the digital transformation of the production environment, carried out within the framework of the Fourth Industrial Revolution and characterized by the massive introduction of the industrial Internet of things, which leads to an avalanche-like increase in the volume of technological data. Their processing using modern analysis methods, including artificial intelligence methods, can improve the quality of decisions made and provide competitive advantages. The scientific novelty of the research results is the structure of the proposed hybrid intelligent machine learning system for modeling phosphate ore processing processes, which is based on the joint use of a dynamic model of the sintering process in the Simulink environment and a deep neural network. The architecture of the neural network was developed taking into account the specifics of the mathematical description of the agglomeration process and includes input fully connected layers that receive measurement results of process variables, as well as a recurrent layer that processes the combined sequence from the outputs of fully connected layers. The integration of a Simulink model and a deep neural network makes it possible to quickly adapt an intelligent system to a specific sintering machine through the use of a two-stage machine learning procedure – first on a Simulink simulation model, and then on a real object. Taking into account the significant inertia of the processes accompanying agglomeration, this approach ensures prompt changes in the settings of the hybrid intelligent machine learning system for the new composition of raw materials and technological parameters. A program has been developed that provides a convenient graphical interface for preparing and using an intelligent system, and simulation experiments have shown that the process of additional training for new technological parameters is much faster than initial training while maintaining high accuracy of the obtained modeling results.

Novel Approach for Network Data Exchange Among Safety-Related Control Systems of Nuclear Reactors

Industrially mature digital networking technologies form the backbone of the distributed control architectures of nuclear reactors. Typically, hundreds of control systems responsible for assuring safer and secure plant operations are located in the field and interconnected via wired isolated local area networks, which are also connected to centralized servers and operating consoles located in the main control room. Many of such control systems require data processed by other systems situated far away in the plant, which mandates multiple concurrent reliable TCP sessions to be active or live for such field-level control systems. Multi-socket communication beyond 10 is practically difficult for embedded systems with limited memory and processing capabilities; hence, data accumulation, extraction, and routing are typically done with the help of commercially available high-performance servers and switches. The servers and operating consoles of a plant are generally Linux-based commercial-off-the-shelf (COTS) industrial computers, whose development lifecycle is not accessible for review and in accordance with safety guides; hence, qualifying such components for safety operations is extremely difficult. This paper presents a novel way of independently offloading safety-related data transactions from such COTS components (reducing dependency) with the help of very simple, reliable, and secure hardware, which is referred to as a data exchange and processing unit (DEPU). An approach is proposed to realize distributed control architecture with this proposed hardware (to simplify qualification tasks) along with existing servers and operating consoles without compromising powerful and convenient human-machine interface as well as the data logging capabilities of such components. Hardware and software design perspectives, performance characterization, and qualification testing of the DEPU in accordance with nuclear power plant instrumentation and control system design guidelines are also briefed in this paper.

ЗНАХОДЖЕННЯ КОМПОНЕНТІВ КОРИСТУВАЦЬКОГО ІНТЕРФЕЙСУ ЗА ДОПОМОГОЮ МАШИННОГО НАВЧАННЯ

Modern technologies and products are becoming more complex and multi-component, which requires the creation of a convenient and intuitive user interface. One way to ease the interface development process is to use machine learning techniques to find user interface components and generate ready-made templates for subsequent software development. There are several approaches and methods that can be used to detect UI components on a finished design using machine learning: - Image segmentation; - Layout Analysis; - Component Recognition Libraries; - Neural networks; This mechanism will allow in the future to be used in various areas related to the development and design of the user interface.

User Experience Evaluation of a Spinal Surgery Robot: Workload, Usability, and Satisfaction Study.

Robotic spine surgery has continued to evolve since its US Food and Drug Administration approval in 2004, with products now including real-time video guidance and navigation during surgery. As the market for robotic surgical devices evolves, it is important to consider usability factors. The primary objective of this study was to determine the user experience of a surgical-assistive robotic device. The secondary objective was to evaluate workload, usability, the After-Scenario Questionnaire (ASQ), and the System Usability Scale (SUS). In addition, this study compares the workload, usability, and satisfaction survey of the device among different occupational groups using the device. Doctors (n=15) and nurses (n=15), the intended users of the surgical assistant robot, participated in the usability evaluation. Participants performed essential scenarios for the surgical assistant robot and provided scenario-specific satisfaction (ASQ), workload (NASA Task Load Index), and usability (SUS) scores. Both doctors and nurses had task success rates of 85% or higher for each scenario. ASQ results showed that both doctors and nurses were least satisfied with ease of completing the task of registration (group 1: mean 4.73, SD 1.57 and group 2: mean 4.47, SD 1.8), amount of time it took (group 1: mean 4.47, SD 1.63 and group 2: mean 4.40, SD 2.09), and support information satisfaction (group 1: mean 5.13, SD 1.50 and group 2: mean 5.13, SD 1.89). All participants had low workloads, and the overall Task Load Index score had a P value of .77, which is greater than .05. The SUS results showed that the overall usability mean for doctors was 64.17 (SD 16.52) and the mean for nurses was 61.67 (SD 19.18), with a P value of .84, which is greater than .05, indicating no difference between the 2 groups. In this study, doctors and nurses evaluated the interaction of the device in a simulated environment, the operating room. By evaluating the use experience and usability of the device with real intended users, we can develop a more effective and convenient user interface.

Reduced-order equivalent source modeling of aeroacoustic sources

The acoustic far-field prediction of high-fidelity aerodynamic simulation data is still not accurately possible when accounting for the surface reflections from an aircraft’s fuselage or wings. By presenting a general method for order-reduced equivalent source modeling of aeroacoustic sound sources, this work is bridging the gap between high-fidelity aerodynamic simulations and its integration to auralization frameworks. At the example of a numerically simulated jet, this study shows the computation of an equivalent source model with minimal complexity by means of spherical harmonic (SH) coefficients. The minimal source extension, in which all acoustic sources of a flow field are confined, is computed by using spherical Hankel extrapolation of sound pressure data from virtual concentric microphone arrays. The result of the SH transform shows that the dominant energy contribution can be associated to nine elementary sound sources. The resulting equivalent source model of jet noise provides a convenient date interface between large-scale computational fluid dynamics and acoustics simulations.

Convenient interface engineering of hierarchical flower spherical Bi-MOF/Bi2WO6 heterostructures for high-performance visible light photocatalytic degradation of tetracycline hydrochloride

Convenient interface engineering of hierarchical flower spherical Bi-MOF/Bi2WO6 heterostructures for high-performance visible light photocatalytic degradation of tetracycline hydrochloride

Software Implementation of Methods for Modeling the Interaction of Electromagnetic Radiation with Carbon Nanocomposites

Methods for modeling the interaction of electromagnetic radiation with a carbon nanostructured composite containing various structural elements, such as carbon fibers, nanoparticles, air gaps, and transition shells are considered. It is shown that for effective and adequate modeling of the interaction of electromagnetic radiation with a carbon nanostructured composite, it is necessary to create software due to the presence of a large set of input parameters, as well as the complexity of calculations. Functional requirements for the software are formulated, reflecting the choice of models and the presence of a convenient interactive interface. The architecture of the software that implements models of interaction of electromagnetic radiation with a carbon nanostructured composite is described, and the software implementation (class structure, methods, user interface) is considered. The developed code is based on proven physical and mathematical models, which make it possible to obtain results close to experimental data. The software architecture allows to effectively manage the modeling process, and also includes the ability to modify models and take into account new structural elements. An interactive interface for users has been developed that allows to easily manage the model parameters and analyze the results of numerical experiments.

CMD + V for chemistry: Image to chemical structure conversion directly done in the clipboard

AbstractWe present Clipboard‐to‐SMILES Converter (C2SC), a macOS application that directly converts molecular structures from the clipboard. The app focuses on seamlessly converting screenshots of molecules into a desired molecular representation. It supports a wide range of molecular representations, such as SMILES, SELFIES, InChI's, IUPAC names, RDKit Mol's, and CAS numbers, allowing effortless conversion between these formats within the clipboard. C2SC automatically saves converted molecules to a local history file and displays the last 10 entries for quick access. Additionally, it incorporates several SMILES operations, including canonicalization, augmentation, as well price‐searching molecules on chemical vendors for the cost‐effective purchasing option. Beyond the one‐click conversion from clipboard to molecular structures, the app offers continuous monitoring of the clipboard which automatically converts any supported representations or images detected into SMILES. The convenient interface, directly in the status bar, as well as availability as macOS application, makes C2SC useful for a broad community not requiring any programming expertise. Most conversions are performed locally, notably the image‐to‐SMILES conversion, with internet access only necessary for specific tasks like price lookups. In summary, C2SC provides a user‐friendly and efficient solution for converting molecular structures directly from the clipboard, offering seamless conversions between comprehensive chemical representations and can be directly downloaded from https://github.com/O-Schilter/Clipboard-to-SMILES-Converter.

IMPLEMENTATION OF ELECTRONIC DOCUMENT MANAGEMENT IN THE ENTERPRISE MANAGEMENT SYSTEM

The management of information support of the enterprise is closely linked to both the availability of information resources and with the possibility of introducing information innovations in ordering the documentation and information flows of the enterprise. Effective processing of continuously growing amounts of information, which is based on documents and arrays of data, is possible only if they are automated. The introduction of electronic document management technology at enterprises is an effective tool for managing information support of enterprises, which is a key factor in successful and successful business and provides an opportunity to increase the competitive advantages of enterprises in strict market conditions. Concerning the issues of the theory and practice of using information systems and technologies in the management system of enterprises in this article, the implementation of a certain range of tasks in the design of EDMS, which consists in creating an intuitive, convenient, convenient interface that requires minimal learning. The article analyzes the peculiarities of the introduction of electronic document circulation into the enterprise management system. Electronic document management systems, requirements for them, security and accessibility problems are paid to the enterprise. The implementation of a certain range of tasks in the design of EDMS is analyzed in creating an intuitive, convenient, convenient interface that requires minimal learning. EDMS efficiency is investigated, which integrates with existing systems in the organization, as well as integration with ERP systems, which allows you to effectively synchronize the processes of document management with wider business operations. The article deals with the introduction of encryption protocols, which is one of the fundamental aspects of EDMS security. Accordingly, this allows a wide range of EDMS users to increase the efficiency of both professional activity and to significantly improve the degree and extent of the specialists who have chosen the specified field of professional activity for their life path. The use of modern electronic document management systems and the search for an effective mechanism for their further development make it possible to reduce the cost of labor resources of workers and increase productivity.

Design and development of human-computer interactive multimedia application software based on Python language

Abstract Interactive multimedia systems have been widely used because of their good interactivity and flexible operation. In this paper, a support platform for human-computer interactive media applications is designed based on the three-layer model, which provides an efficient multimedia-oriented communication mechanism and a convenient development interface for application developers. The M/M/1 queue model is established by considering the load of nodes and links, and the average delay for error repair of the SRM and TORM protocols is calculated. Finally, the interactive performance of multimedia applications is examined through the construction of an experimental platform. The experimental results show that the interaction probability of selecting the distance range of 800~1200 mm is greater. The selection probability is more than 12.45%, and the interaction scenario built by Python language can effectively improve the interaction performance of multimedia applications.