Creation Of Interface Research Articles

INTERACTIVE TRAINING OF PERSONNEL ON ELECTRICAL SAFETY ISSUES FOR AN OIL PUMPING COMPANY

The article is devoted to electrical safety issues in the oil industry, where a significant proportion of injuries are associated with personnel errors in the performance of their work functions. In order to reduce personnel errors and, consequently, reduce the level of injuries and accidents associated with the action of the human factor, a simulator has been developed for training employees of an oil pumping enterprise in the safe handling of electrical equipment. The article analyzes modern approaches to training, including traditional methods and the use of simulators that simulate both current work and emergency situations. Practical application of such solutions will improve the competence of workers in electrical safety issues by practicing the skills of performing work in conditions close to real ones, which emphasizes the relevance of developing simulators for the oil industry. The simulator includes the creation of an intuitive interface and integration with training management systems, consisting of two modules covering safety procedures, working with electrical equipment and filling out work permits. In conclusion, areas for further research are outlined, including the development of new modules to improve work efficiency.

Navigating the Golden Triangle: The Need to Jointly Consider Modularization and Interface Choices When Making Performance, Cost, and Schedule Tradeoffs for Complex System Development

ABSTRACTDecomposition is a critical enabler of complex system development, as it enables both task specialization and efficiency through parallel work. The process of decomposing involves partitioning system parameters into tightly coupled modules and managing any cross‐module coupling by designing passive interfaces or through active coordination. A rich literature has developed algorithms and tools to support this process. However, we contend that this view has placed too much emphasis on module selection, and not enough on the interaction with interface design. This perspective has significant implications for lifecycle costs and development time. To that end, this study explores how earlier consideration of interface design can create more valuable options to better navigate performance, cost, and schedule tradeoffs. Specifically, through an abstract simulation experiment, we demonstrate that (1) a sequential approach that first selects modules and then designs interfaces to support those modules, yields lower performance than an integrated approach that considers modules and supporting interfaces simultaneously; and (2) this result is even stronger when schedule and cost are considered as part of the evaluation. In other words, an integrated approach provides more options for project managers seeking to navigate the performance‐cost‐schedule tradeoff known as the golden triangle. These results emphasize the need for a decomposition aid that adopts a holistic view of the optimization problem, accounting for interface creation, intra‐organization collaboration, and valuing nonperformance measures of effectiveness.

AN APPROACH OF DOM RECORD MANAGEMENT COMPONENT UI CREATION

This paper presents an approach of Document Object Model database record management component user interface (UI) creation. Mainstream of UI development approaches concentrates in few directions, omitting holistic view on the fundamental database operations. The proposed solution uses method lay on of standard clauses adopted by the ECMA Script edition sixth and above. The program logic for the UI component was built with the intention - The existing functionality of such UI component to be extended and complemented.

Predicting metastable oxide-to-hydroxide phase transformations by bulk and interface thermodynamics: Application to the phase stability of aluminium oxides and hydroxides in water

Fundamental understanding of the phase stability of oxides in aqueous environments is of eminent importance for a broad range of disciplines, such as corrosion, tribology, catalysis, medical implants, biosensing and environmental sciences. Most oxide-to-hydroxide phase transformation sequences proceed by consecutive formation of different metastable hydroxide phases towards the most stable bulk hydroxide phase, in accord with Ostwald's Rule of Stages and thus in contradiction with bulk thermodynamics. In this work, a novel unified thermodynamic model is presented for predicting such metastable oxide-to-hydroxide phase transformation sequences by accounting for the energy barrier(s) associated with the creation of new interface(s) between the competing parent and product phase(s). To this end, semi-empirical expressions for the estimation of the interface energies between metals, oxides and/or hydroxides were derived based on the macroscopic atom approach. Application of the model to the phase stability of aluminium in water (at neutral pH) for 298 ≤ T ≤ 500 K predicts a solid-state phase transformation sequence from Al → am-Al2O3 → (pseudo)boehmite, in accord with experimental observations. Subsequent competing formation of the trihydroxide phases bayerite and gibbsite by a dissolution-precipitation on the (pseudo)boehmite surface is equally favoured and critically depends on the hydrolysis of dissolved Al3+ species in solution, as governed by e.g. pH, temperature and the presence of foreign ions in solution.

Assessment of a multiphase formulation of one-dimensional turbulence using direct numerical simulation of a decaying turbulent interfacial flow

The interaction between turbulence and surface tension is studied numerically using the one-dimensional-turbulence (ODT) model. ODT is a stochastic model simulating turbulent flow evolution along a notional one-dimensional line of sight by applying instantaneous maps that represent the effects of individual turbulent eddies on property fields. It provides affordable high resolution of interface creation and property gradients within each phase, which are key for capturing the local behavior as well as overall trends, and has been shown to reproduce the main features of an experimentally determined regime diagram for primary jet breakup. Here ODT is used to investigate the interaction of turbulence with an initially planar interface. The notional flat interface is inserted into a periodic box of decaying homogeneous isotropic turbulence, simulated for a variety of turbulent Reynolds and Weber numbers. Unity density and viscosity ratios serve to focus solely on the interaction between fluid inertia and the surface-tension force. Statistical measures of interface surface density and spatial structure along the direction normal to the initial surface are compared to corresponding direct-numerical-simulation (DNS) data. Allowing the origin of the lateral coordinate system to follow the location of the median interface element improves the agreement between ODT and DNS, reflecting the absence of lateral nonvortical displacements in ODT. Beyond the DNS-accessible regime, ODT is shown to obey the predicted parameter dependencies of the Kolmogorov critical scale in both the inertial and dissipative turbulent-cascade subranges. Notably, the probability density function of local fluctuations of the critical scale is found to collapse to a universal curve across both subranges. Published by the American Physical Society 2024

Predicting mothers' exclusive breastfeeding for the first 6 months: Interface creation study using machine learning technique.

Machine learning techniques (MLT) build models to detect complex patterns and solve new problems using big data. The present study aims to create a prediction interface for mothers breastfeeding exclusively for the first 6 months using MLT. All mothers who had babies aged 6-24 months between 15.09.2021 and 15.12.2021 and to whom the surveys could be delivered were included. 'Personal Information Form' created by the researchers was used as a data collection tool. Data from 514 mothers participating in the study were used for MLT. Data from 70% of mothers were used for educational purposes, and a prediction model was created. The data obtained from the remaining 30% of the mothers were used for testing. The best MLT algorithm for predicting exclusive breastfeeding for the first 6 months was determined to be the Random Forest Classifier. The top five variables affecting the possibility of mothers breastfeeding exclusively for the first 6 months were as follows: "the mother not having any health problems during pregnancy," "there were no people who negatively affected the mother's morale about breastfeeding," "the amount of water the mother drinks in a day," "thinking that her milk supply is insufficient," "having no problems breastfeeding the baby". Using created prediction model may allow early identification of mothers with a risk of not breastfeeding their babies exclusively for the first 6 months. In this way, mothers in the risk group can be closely monitored in the early period.

Development of Smart Hydroponics System using AI-based Sensing

This paper proposes a smart hydroponic system that operates automatically using a fuzzy logic algorithm, integrating IoT functionalities to support smart agriculture. The system allows for remote monitoring and control via the internet, providing real-time data on water levels, pH levels, temperature, and nutrient solution temperature. Precise dosing and temperature control are critical for optimal plant growth, and the system schedules temperature measurements to ensure stability. Unstable temperature can affect pH levels, thereby impacting nutrient absorption. The proposed system employs sensors to continuously monitor the electrical conductivity (EC) and pH levels of the nutrient solution. Fuzzy control is utilized to regulate the nutrient solution pump, automatically adjusting EC and pH levels to promote optimal plant growth. This approach reduces the time burden on producers and provides more precise control over the nutrient solution, resulting in improved growth outcomes. The main contributions of this work are as follows: the development and implementation of an AI-based system integrating a controller, IoT environment, fuzzy logic algorithm, and NFT (nutrient film technology) hydroponics; the creation of a user-friendly interface for farmers through the Smart-Hydroponic application, enabling hybrid monitoring and control of hydroponic farms; the establishment of an IoT-based cloud environment for sensor data monitoring; the implementation of a smart hydroponic system for nutrient sensing, monitoring, and control; and a comparative analysis between smart and conventional hydroponics based on morphological results.

Human-robot interactions in autonomous hospital transports

The integration of robotics in nursing is a significant shift in healthcare, driven by the aging global population and the increasing demand for care. Robots in nursing can handle less technical tasks such as patient transport and rehabilitation activities. This support allows caregivers to focus on less strenuous nursing duties and more direct patient care. Human-Robot Interaction (HRI) plays an important role in this challenging context. In this research, we present an autonomous hospital transport system based on the ROS 2 framework, focusing on enhancing HRI in the healthcare environment. It encompasses the development of a control architecture for autonomous robot behavior, the implementation of machine learning for emergency detection, and the creation of a user-friendly interface for both patients and staff. The proposed concepts were validated in real-world scenarios in three different hospitals in Germany. This not only demonstrates the practical application of this system but also shares insights and methods, encouraging further advancement in the field of healthcare robotics.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2.

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

A bioinspired surface tension-driven route toward programmed cellular ceramics

The intriguing biomineralization process in nature endows the mineralized biological materials with intricate microarchitected structures in a facile and orderly way, which provides an inspiration for processing ceramics. Here, we propose a simple and efficient manufacturing process to fabricate cellular ceramics in programmed cell-based 3D configurations, inspired by the biomineralization process of the diatom frustule. Our approach separates the ingredient synthesis from architecture building, enabling the programmable manufacturing of cellular ceramics with various cell sizes, geometries, densities, metastructures, and constituent elements. Our approach exploits surface tension to capture precursor solutions in the architected cellular lattices, allowing us to control the liquid geometry and manufacture cellular ceramics with high precision. We investigate the geometry parameters for the architected lattices assembled by unit cells and unit columns, both theoretically and experimentally, to guide the 3D fluid interface creation in arranged configurations. We manufacture a series of globally cellular and locally compact piezoceramics, obtaining an enhanced piezoelectric constant and a designed piezoelectric anisotropy. This bioinspired, surface tension-assisted approach has the potential to revolutionize the design and processing of multifarious ceramic materials for structural and functional applications in energy, electronics and biomedicine.

Perancangan Aplikasi Sistem Tata Kelola Rukun Tetangga Menggunakan Pendekatan Design Thinking

The emergence of service system problems that are carried out manually which causes inefficiencies that take a long time. The absence of records and data archive management becomes difficult to track finances, decision-making processes and evaluate the overall performance of RTs making a lack of transparency and accountability. The purpose of this research is to design the user interface of the RT administration system application with the design thinking method so as to produce an easy-to-use application user interface as needed as a solution to existing problems. This research uses the design thinking method. The nature of design thinking is human-centered. Design thinking uses strategic innovation to design for users through an empathetic process by trying to understand user needs and focusing on human behavior and emotions to produce optimal innovation. As a result, the creation of the RT administration application user interface utilizes the design thinking method that produces prototypes according to user needs. This prototype became the basis for designing an RT service application with an easy-to-use and intuitive UI. The application of the design thinking method has been proven to help produce an easy-to-use application, which is expected to improve RT services and create a more engaged and informed community of citizens.

Rancang Bangun, Penyerahan, dan Sosialisasi Sistem Informasi Jasa Katering Berbasis Web

Catering services are integral to the food and beverage industry, particularly in providing meals for various events such as weddings and corporate functions. However, many catering businesses still rely on outdated, conventional systems that result in inefficiencies and difficulties in reaching potential customers. To address these challenges, this research focuses on developing a modern information system tailored to the needs of catering enterprises. The development process began with the design of a Use Case Diagram, followed by the creation of a user-friendly interface and a robust database structure. The final product is a fully functional website that streamlines the ordering process, enhances customer interaction, and provides a platform for suppliers to effectively promote their products. The system was rigorously tested, and user feedback was gathered through questionnaires. The results indicated a satisfaction rate of 80%, demonstrating that the website meets the users' needs and successfully resolves issues related to service accessibility and promotional outreach. This project highlights the importance of integrating modern technology into traditional business models to improve efficiency and customer satisfaction.

MnO2 anchored N–Ti3C2 MXene as a bifunctional electrode for enhanced water splitting

The domain of energy research is vigorously exploring a wide array of materials, from advanced carbon-based substances like graphene and carbon nanotubes to emerging contenders like MXenes. Ti3C2 MXene offers exceptional performance in electrochemistry, benefiting from its remarkable electronic conductivity, considerable surface area, chemical stability, cost-effectiveness, hydrophilicity, and eco-friendliness. However, it undergoes self-accumulation, which diminishes the number of electrochemically active sites, resulting in decreased performance. In this study, MnO2 particles are intricately anchored onto the surfaces and within the layers of nitrogen-doped Ti3C2 (N–Ti3C2), resulting in the creation of innovative interface engineered N–Ti3C2/MnO2 nanosheets. Due to its distinctive heterostructure and favourable interfacial interaction, the N–Ti3C2/MnO2 electrode shows better performance in both the hydrogen and oxygen evolution reactions, exhibiting low overpotentials of 130 mV and 289 mV, respectively, at a current density of 10 mA cm−2. Furthermore, it requires a cell voltage of 1.7 V to achieve a current density of 10 mA cm−2 during the overall water splitting process. The N–Ti3C2/MnO2 composite also maintains sustained durability for a period of 4 h. This enhanced electrochemical activity of N–Ti3C2/MnO2 can be due to the synergistic effects resulting from the intricate contact between N–Ti3C2 and MnO2. This research presents a simple methodology for designing MXenes-based multicomponent electrodes for electrochemical water splitting reactions and its potential application for electrochemical water splitting.

Enhancing Friction Stir Welding: Quality Machine Learning Based Friction Stir Welding Tool Condition Monitoring

Ensuring the quality and optimizing the tool in Friction Stir Welding (FSW) process is quite complex and the solution relies on implementing Condition Monitoring. The major impact of this process yields good quality welds and cuts down the non-operational timing and cost. Condition Monitoring is the key to find a solution to the challenging problem of ensuring quality and optimizing the tool in the FSW process. The creation of a graphical user interface (GUI) and the development and comparison of several models, including Decision Tree (DT), Random Forest (RF), Light Gradient Boosted Machine (LGBM), and Extreme Gradient Boosting (XGBoost), are the main objectives of this study. By offering an uniform interface for tracking and evaluating tool condition data, GUI can make it easier for operators and the maintenance crew to collaborate. Vibration analysis is the first step in tool condition monitoring. Al5083 and AZ31B are used as the workpiece and H13 as the tool in this investigation. The signals are obtained from the experimental setup via DAQ, and LabView processes them. A Python script converts the raw signals into statistical data. Following that, the data was loaded into ML models and optimized using Optuna. TKinter has been used to create the GUI. For prediction, the best models were included in the GUI. By the deployed models, LGBM generates 96% for 1000 rpm, 96.55% for 1200 rpm, and 95.90% for 1400 rpm for Al5083 93.22% for 1000 rpm, 99.29% for 1200 rpm, and 91.50% for 1000 rpm for AZ31B. For real-time prediction, these models are thus connected to a graphical user interface. In each case, the LGBM classifier topped the others. This work served as an initial basis for the creation of a semi-onboard diagnostic approach that requires minimal human input.

Medical Report Management and Distribution System on Blockchain

The security and effective management of health records is a key issue for today's digitized healthcare environment. In terms of data security, integrity and accessibility, traditional centralized systems face challenges. We propose a novel system of medical report management and dissemination using the Block-chain technology, as well as SHA1 encryption for addressing these issues. In order to store hashed medical reports, our system uses a decentralized block-chain network to ensure tamper-proof and immutable records. SHA1 is used to hash every medical report, generating a unique identifier that's stored on the Block-chain. The integration of Flask, Python's lightweight web framework, facilitates the creation of an easy user interface to interact with a system easily. Secure storage, effective distribution and granular control of medical reports are among the key features of our system. In order to maintain the integrity and confidentiality of data, patients, physicians or authorized persons should be able to use secure means for accessing and updating their healthcare records. The use of SHA1 increases system security by providing a reliable cryptographic hash. Our system is aimed at simplifying the management and distribution of medical reports, thereby improving efficiency and patient outcomes in addition to enhanced data protection and integrity. By ensuring security, transparency and decentralized solutions, we are confident that the proposal for a medical records management system has the potential to revolutionize this area.

π-π Stacking at the Perovskite/C60 Interface Enables High-Efficiency Wide-Bandgap Perovskite Solar Cells.

Interface passivation is a key method for improving the efficiency of perovskite solar cells, and 2D/3D perovskite heterojunction is the mainstream passivation strategy. However, the passivation layer also produces a new interface between 2D perovskite and fullerene (C60), and the properties of this interface have received little attention before. Here, the underlying properties of the 2D perovskite/C60 interface by taking the 2D TEA2PbX4 (TEA=C6H10NS; X=I, Br, Cl) passivator as an example are systematically expounded. It is found that the 2D perovskite preferentially exhibits (002) orientation with the outermost surface featuring an oriented arrangement of TEACl, where the thiophene groups face outward. The outward thiophene groups further form a strong π-π stacking system with C60 molecule, strengthening the interaction force with C60 and facilitating the creation of a superior interface. Based on the vacuum-assisted blade coating, wide-bandgap (WBG, 1.77eV) perovskite solar cells achieved impressive records of 19.28% (0.09cm2) and 18.08% (1.0cm2) inefficiency, respectively. This research not only provides a new understanding of interface processing for future perovskite solar cells but also lays a solid foundation for realizing efficient large-area devices.

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool.

We report here the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package that processes data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data visualization stack, including DataShader and Param, to create an interactive visualization of the mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS and that the visualization methods could be useful for other open-source mass spectrometry tools.

Fasta2Structure: a user-friendly tool for converting multiple aligned FASTA files to STRUCTURE format

BackgroundThe STRUCTURE software has gained popularity as a tool for population structure and genetic analysis. Nevertheless, formatting data to meet STRUCTURE's specific requirements can be daunting and susceptible to errors, especially when handling multilocus data. This article highlights the creation of a graphical user interface (GUI) application tailored to streamline the process of converting multiple sequence alignments into a single, cohesive file that is compatible with the STRUCTURE software.ResultsThe application has been developed utilizing Tkinter for the GUI and Biopython for handling FASTA files. This program processes the files, pinpoints variable sites, and converts the sequences into a binary format. Subsequently, the sequences are concatenated and presented within the graphical interface's text area, enabling users to review and confirm the results. Furthermore, the program stores the concatenated results in a file, delivering a ready-to-use input for the STRUCTURE software.ConclusionThis application offers an efficient and dependable solution for transforming multiple aligned FASTA files into a concatenated binary format file, which is compatible with the STRUCTURE software. With its user-friendly graphical interface and error-reduction approach, this tool proves invaluable for researchers engaged in population structure and genetic analysis.

Leveraging Django and Machine Learning to Craft a Health Forecasting System, Tailoring Personalized Wellness Strategies

Abstract: The goal of this thesis is to develop a health forecasting system that combines Django with cutting-edge machine learning in order to customize wellness plans according to personal health information. The study addresses model selection, data preprocessing, and user interface creation as it investigates the potential synergies between machine learning and web development. Predictive health analytics potential and gaps are identified through a review of the literature. The goal of the project is to create a real-time, scalable health forecasting system while protecting user privacy. Neural networks and decision trees are two examples of machine learning methods that are utilized. The efficacy of the system in delivering precise health projections and augmenting user involvement in proactive wellness management is evaluated through empirical assessment and case studies. In order to further personalized healthcare solutions, practitioners, researchers, and developers can benefit greatly from the research's insightful contributions.