Minimum Processing Time Research Articles

Design and Implementation Plagiarism Checker Application with DetectGPT using Scheduler Algorithm

The increasing use of Generative AI in the field of writing in the academic world creates problems in the field of plagiarism. this problem has prompted an urgent need for plagiarism detection tools. However, not all universities are able to implement a system that is able to detect sentences or paragraphs resulting from generative AI. Recent research seeks to overcome this obstacle using AI itself, specifically through the DetectGPT model. However, the implementation of this technology has limitations, such as requiring large computing resources and quite a long examination time. Using a GPU can speed up the process, but not all users can implement it. Solutions to minimize processing time include using more than one worker, however, scheduling is essential to maximize plagiarism detection efficiency. This research proposes the implementation of the above system with the help of the scheduler. The results obtained in this system prototype are an average waiting time for checking of 457,778 seconds to complete 10 tasks with the help of 3 workers running at the same time.

Application of AC superimposed DC waveforms to bismuth electrorefining

Electrorefining in molten salts is used for purifying actinides. Optimizing electrorefining is key to minimizing processing time and radiological waste. One possible way of improving electrorefining efficiency is using an AC superimposed DC waveform. This waveform has demonstrated potential benefits in aqueous solutions but has never been utilized in a molten metal, molten salt application. This work investigates the effects of using an AC superimposed DC waveform on molten bismuth electrorefining in a molten LiCl–KCl–CaCl2 eutectic. Bismuth has been identified as a potential surrogate for plutonium electrorefining and a potential cathode in electrorefining used nuclear fuel (UNF). All electrorefining runs resulted in a high purity cathode ring and high yield with exception of the run using a low-frequency, high-amplitude superimposed AC waveform, which experienced some contamination and a lower yield. The other three AC superimposed DC runs experienced an average yield 6.7 % higher than the average yield of the DC runs. The electrorefining run using the high-frequency, high-amplitude superimposed AC signal had the highest yield. It is recommended in future studies to investigate the statistical variability of electrorefining yield and current efficiency and the impact of AC superimposed DC waveforms on solidified bismuth anodes.

Reorganization of the Warehouse Activities Processes with the Framing of their Problems and Solutions

Purpose: The aim of this study is to examine and identify goods processing operations in a warehouse and propose a reorganization of tasks to minimize processing time. Theoretical framework: Current literature highlights the need to reorganize and adapt the operations chain (LOMBA Cédric, 2018). Some areas still need to be studied in depth to improve the supply chain. The literature on warehouse management has not sufficiently studied partial time management or closed-loop time management in the supply chain (CLSC) from goods-in to goods-out. The potential links between the risks of lost time and the management of the quality of the goods handling service have not been studied in depth. Design/Methodology/Approach: The in-depth analysis of recent international studies published on warehouse management and the supply chain enables us to identify the studies that have the greatest impact on research. Findings: The result of this work is the development of a double typology, one linked to the chain of operations and the other to the constraints linked to the CLSC in a warehouse. Tools will be applied to diagnose the constraints and factors adversely goods handling operations. Research, Practical & Social implications: To help logisticians and researchers, we have proposed an operational reformulation of a warehouse, enriched with a logical framework linking the problems and their potential solutions. Originality/Value: The article proposes an original vision to improve the spatio-temporal organization of warehouses, by synthesizing its different entities, operations, and processes while presenting all the existing problems and identifying their possible solutions.

An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm.

Autonomous mobile robot encompasses modules such as perception, path planning, decision-making, and control. Among these modules, path planning serves as a prerequisite for mobile robots to accomplish tasks. Enhancing path planning capability of mobile robots can effectively save costs, reduce energy consumption, and improve work efficiency. The primary slime mold algorithm (SMA) exhibits characteristics such as a reduced number of parameters, strong robustness, and a relatively high level of exploratory ability. SMA performs well in path planning for mobile robots. However, it is prone to local optimization and lacks dynamic obstacle avoidance, making it less effective in real-world settings. This paper presents an enhanced SMA (ESMA) path-planning algorithm for mobile robots. The ESMA algorithm incorporates adaptive techniques to enhance global search capabilities and integrates an artificial potential field to improve dynamic obstacle avoidance. Compared to the SMA algorithm, the SMA-AGDE algorithm, which combines the Adaptive Guided Differential Evolution algorithm, and the Lévy Flight-Rotation SMA (LRSMA) algorithm, resulted in an average reduction in the minimum path length of (3.92%, 8.93%, 2.73%), along with corresponding reductions in path minimum values and processing times. Experiments show ESMA can find shortest collision-free paths for mobile robots in both static and dynamic environments.

GUI development for SSDL to calibrate photon measuring equipment

Current legislation mandates the inspection and calibration of operational survey radiation monitoring instruments used in nuclear medicine, radiotherapy departments, and other fields utilizing ionizing radiation sources. To comply with national and international radiation protection standards, Morocco's National Secondary Standard Dosimetry Laboratory provides reliable calibration results with high accuracy and covers various measurement ranges using attenuators provided by the automated Gamma G10 irradiator or validated beam qualities produced by the X-ray irradiator type X80–320 kV.This study aims to develop a digital graphical user interface using Python programming language, designed for calibrating radiation protection measuring instruments . The interface is intended to facilitate all operations and calculations related to determining calibration factors and measurement uncertainties in accordance with the ISO 4037 standard, ensuring minimal processing time and minimizing potential error sources . The interface enables calculations to be recorded, as well as the establishment and electronic archiving of the calibration certificate and the report in PDF format using the Hypertext Preprocessor FPDF library (PHP FPDF). With the development of this interface, multiple instruments can be processed per day with high accuracy, streamlining the calibration process and improving efficiency.•The importance of compliance with international standards to ensure the quality and reliability of measurements in radiation protection was examined.•Description of X-ray and Gamma-ray irradiators designed for the calibration of radiation protection measuring instruments within the Secondary Dosimetry Calibration Laboratory (SSDL) which is a member of the WHO/IAEA network within the National Center for Radiation Protection of Morocco•Graphical User Interface using python for the calibration of photon measurement instruments for radiation protection purposes was developped.

Data-based modelling of proton exchange membrane fuel cell performance and degradation dynamics

Proton exchange membrane fuel cell (PEMFC) is in the commercial adoption process for hard-to-decarbonize applications such as transport. However, its long-term durability, reliability, diagnostics, and performance remain a critical challenge. In this study, the data-based modelling technique, artificial neural network (ANN), is adopted to comprehend performance degradation and thermal and pressure dynamics. Experimental data consisting of polarization and cyclic voltammetry data are acquired after every 6,000 accelerated stress test (AST) degradation cycles as stipulated by the US Department of Energy guidelines. Over 40,000 test cases were considered for training and evaluating the neural network comprising data from 30,000 full-cell AST cycles. Fuel cell operating temperatures, pressures, flow rates, and relative humidities are varied to capture the entire spectrum of PEMFC performance characteristics. Parameters such as the number of AST cycles endured or instantaneous catalyst electrode surface area, operating current density, reactant flow rate, relative humidity, system temperature and pressure are considered as feature vectors (inputs) to predict cell output voltage, reactant outlet pressure and temperature for both anode and cathode streams. Data preprocessing and batch learning are implemented in Python to improve prediction accuracy and computational time; considering the substantial nonlinear data sets, various Keras library optimizers and corresponding hyperparameters are investigated for better convergence. The trained neural network model is evaluated on 15,000 test cases resulting in an R2 ≥ 0.995 for all the predicted variables, demonstrating the capability of data-based models to accurately predict the nonlinear behavior of such electrochemical systems with minimal processing time, advocating for their application in real-time system monitoring, controls and diagnostics.

Genomic Signal Processing Methods in DNA Mapping Schemes for Prediction of Exon in a Gene Using Digital Filters

Genomic signal processing (GSP) is an engineering domain involved with the analysis of genomic data using digital signal processing (DSP) approaches after transformation of the sequence of genome to numerical sequence. One challenge of GSP is how to minimize the error of detection of the protein coding region in a specified deoxyribonucleic acid (DNA) sequence with a minimum processing time. Since the type of numerical representation of a DNA sequence extremely affects the prediction accuracy and precision. The impact of different DNA statistical representations on the identification of coding sequences (exons) was researched. In this study using the IIR inverse Chepyshev filter for twenty benchmark human genes. In order to accomplish this, the sensitivity, specificity, and correlation coefficient of the four most modern DNA numerical representation schemes GCC, FNO, atomic number, and 2-bit binary were measured and contrasted with those of EIIP, the most used technique for locating protein-coding regions.

Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.

The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.

An integrated approach ofIoTandWSNusing wavelet transform and machine learning for the solid waste image classification in smart cities

AbstractSolid waste management is one of the utmost vital issues allied with the smart city, which harms our society's health and the environment. Solid waste management has been a significant hurdle in developing smart cities, impacting the community's living standards. Sustainability is the circumstances that help to achieve natural and social development simultaneously in a prolific accord. We can allege that sustainable growth brings out stability in the necessities of the environment. It helps to preserve resources available for the coming generations. Waste management is also a concern for many authorities; researchers have started focusing on this area. This research article presents a unique method for waste management by employing internet of things for waste monitoring, a wireless sensor network for data communication and discrete wavelet transforms, image processing, and machine learning for image categorization. Implementing machine learning for image categorization has gained attention in the research area of artificial intelligence. The paper presents a way to classify waste images into different Classes such as domestic waste, e‐waste, and medical waste. The input image is down‐sampled, and channels are split to extract the color moments for minimal processing time. We have created a convolution neural network (CNN) trained using pre‐processed data for feature extraction and classification. We have used K‐nearest neighbor classifier (KNN) as another classifier to compare the results obtained by the proposed methodology. The findings demonstrate that, compared to the KNN, the suggested model shortens the processing time and achieves a high accuracy rate. The paper presents a novel approach for Solid waste classification by using pre‐processed image data for image processing and CNN for waste image classification, for the sake of proving the efficiency of the proposed method we have compared the results with the KNN classifier and found that the accuracy of classification is better in proposed method than the KNN classifier.

An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.

A security model for smart grid SCADA systems using stochastic neural network

AbstractDetection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.

Smart GreenGrocer: Automatic Vegetable Type Classification Using the CNN Algorithm

In the food industry, separating vegetables is done by visually trained professionals. However, because it takes plenty of time to sort a large number of different types of vegetables, human errors might arise at any time, and using human resources is not always effective. Thus, automation is needed to minimize process time and errors. Computer vision helps reduce the need for human resources by automatizing the classification. Vegetables come in various colors and shapes; thus, vegetable classification becomes a challenging multiclass classification due to intraspecies variety and interspecies similarity of these main distinguishing characteristics. Consequently, much research is made to automatically discover effective methods to group each type of vegetable using computers. To answer this challenge, we proposed a solution utilizing deep learning with a Convolutional Neural Network (CNN) to perform multi-label classification on some types of vegetables. We experimented with the modification of batch size and optimizer type. In the training process, the learning rate is 0.01, and it adapts on arrival in the local minimum for result optimization. This classification is performed on 15 types of vegetables and produces 98.1% accuracy on testing data with 25 minutes and 45 seconds of training time.

Solución práctica para el problema de reconfiguración en redes eléctricas de distribución: un enfoque heurístico constructivo

The problem regarding the reconfiguration of electrical distribution grids is addressed in this research through the implementation of a practical solution using a constructive heuristic algorithm. The most important characteristic of the proposed heuristic approach is its low-computation effort, given that few power flow solutions are required in order to solve the reconfiguration problem. The constructive algorithm starts its exploration of the solution space by closing all the tie lines form a fully meshed distribution network. The distribution line with the minimum current is permanently opened. A new power flow evaluation is made for the new distribution system, and the distribution line with the minimum current is opened if and only if this action does not generate isolated nodes. This procedure is repeated until the number of closed lines is equal to the number of nodes minus one, which is a condition required to maintain a radial configuration. Numerical validations in test feeders composed of 16, 33, 69, 84, 136, and 415 nodes demonstrate that the proposed constructive algorithm finds adequate solutions with minimum processing times. The proposed approach is practical for distribution companies since its implementation only requires a power flow tool for distribution networks that can deal with radial and meshed configurations.

Internet of energy (IoE) adoption for a secure semi-decentralized renewable energy distribution

Peer-to-peer power systems are becoming more common due to the increasing popularity of renewable energy and the need for clean energy security in electrical grids. The Internet of Energy proposes energy routers to connect devices, optimize energy dispatch, and manage energy flow. Reliable transfer needs an effective and secure energy routing algorithm. This research outlines an innovative algorithm to manage routing communication securely and minimize processing time and energy loss. It uses a semi-decentralized method to offload computing tasks onto energy routers and a central database for routing information.The proposed energy routing algorithm is tested in MATLAB to assess its handling rate, security, malware filtering, and performance. The results showed a handling rate of over 99.7%, an infection probability of less than 1%, a packet delivery rate of over 99%, and no data loss. Finally, given minimum power loss, the proposed energy routing algorithm requires an energy inquiry of 3.78 seconds per energy router inquired and 2.11 seconds in energy routing per distribution line and energy router combined.

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Facial recognition technology has been used in many fields such as security, biometric identification, robotics, video surveillance, health, and commerce due to its ease of implementation and minimal data processing time. However, this technology is influenced by the presence of variations such as pose, lighting, or occlusion. In this paper, we propose a new approach to improve the accuracy rate of face recognition in the presence of variation or occlusion, by combining feature extraction with a histogram of oriented gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the Canny contour detector techniques, as well as a convolutional neural network (CNN) architecture, tested with several combinations of the activation function used (Softmax and Segmoïd) and the optimization algorithm used during training (adam, Adamax, RMSprop, and stochastic gradient descent (SGD)). For this, a preprocessing was performed on two databases of our database of faces (ORL) and Sheffield faces used, then we perform a feature extraction operation with the mentioned techniques and then pass them to our used CNN architecture. The results of our simulations show a high performance of the SIFT+CNN combination, in the case of the presence of variations with an accuracy rate up to 100%.

Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

Production scheduling in a knitting workshop is an important method to improve production efficiency, reduce costs and improve service. In order to achieve a reasonable allocation of parallel machines as well as cooperation between different machines within the workshop, thereby ensuring the optimal scheduling of production plans, this paper proposes a scheduling method using an improved genetic algorithm (IGA) based on tabu search. Firstly, the production scheduling model of a knitting workshop is established. Secondly, an IGA based on the minimum processing time rule, the priority idle machine rule and the production order ranking code is used to optimize the solution. Finally, an experiment platform for knitting workshop production is built to verify the proposed scheduling method. The experimental results show that the proposed IGA based on tabu search performs well in terms of preconvergence speed, global search capability and local search capability. The IGA converges faster than the traditional genetic algorithm by about 25%, reduces the redundancy time of scheduling, meets the production requirements of the knitting intelligent workshop and has a good reference value for promoting the intelligent development of knitting production.

Investigation of mechanical properties and microstructure characteristics on ultrasonic metal welding of aluminum alloy 8011 sheets

The non-traditional machining processes are widely used to manufacture geometrically complex and precision parts for the aerospace, electronics, and automotive industries. In which, the ultrasonic welding technique can be used for welding various materials ranging from metals to non-metals and composites with minimum processing time and cost. The present work is to understand the workings of the ultrasonic metal welding process as well as to determine the best welding parameters, such as weld pressure and time for three different thicknesses aluminum sheets using the design of experiment approach. Based on the experimental design, a series of experiments are conducted using L27 orthogonal array and a data collection system tensile testing equipment was used to compute the weld strength for each specimen. The highest weld strength of thickness 0.1, 0.2, and 0.3 mm are 98.2 MPa, 98.38 MPa, and 90.23 MPa respectively. Also, the relation between weld strength and grain area is observed using microstructural analysis. Grain area often decreases as weld strength increases. The coefficient of multiple determination is used to validate the findings of experimental work, and it shows that the results are good.

Green solvent exfoliation of few layers 2D-MoS2 nanosheets for efficient energy harvesting and storage application

In this study, a facile, cost-effective, and green solvent exfoliation route with bio extract was derived from Cissus quadrangularis (MP) and it was used to exfoliate to get the few layers of MoS2. Compared to conventional chemical exfoliation, the green solvent exfoliation method showed quick delamination from the bulk MoS2. To compare the effectiveness of the exfoliation in green solvents, the exfoliation analysis of the bulk MoS2 was conducted in N-Methyl-2-pyrrolidone and another bio-extract with low Hansen properties was derived from Mussa acuminate. The MP-derived green extract showed excellent delamination of the few layers of MoS2 nanosheets with a minimal processing time of 2 h. It was confirmed that the layered structure with a lateral size of 150 nm and a d-spacing was 0.63 nm by HRTEM, and also Raman spectrum revealed the minimum frequency difference (24.6 cm−1) for MP, which confirmed the presence of the few-layer MoS2. The obtained few-layered MoS2 nanosheets were further examined as an electrode material for dye-sensitized solar cells (DSSC) and supercapacitor applications. The fabricated DSSC with MP-electrode showed increased Voc with a photo conversion efficiency (PCE) of 5.14 %. As a supercapacitor electrode, it exhibited a significant potential window expansion of 0.1 V with a specific capacitance of 183 F/g at a scan rate of 5 mVs−1. This work opens up a simple, inexpensive, and eco-friendly green solvent exfoliation methodology to obtain the layers of 2D materials.

Dyeing of Recycled Electrospun Polyamide 6 Nanofibers: Implications of Dye Particle Size

The dyeing of recycled polyamide nonwoven fabrics based on nanofibers (PA-NWNF), which were fabricated from polyamide wastes, was conducted in this study. Since PA-NWNF exhibited a high surface area to volume ratio, it was dyed with different particle sizes of Disperse Red 167 dye (DR 167, DR 167-B, and DR 167-C) without auxiliary agents to prevent further environmental pollution. The undyed and dyed PA-NWNF, as well as the applied dyes, were characterized by SEM, BET, XRD, and FT-IR techniques. Both color yield (K/S) and fastness of dyed PA-NWNF were also evaluated. The morphology of dyed DR 167-C owned homogeneity and smooth nanofibers. In addition, DR 167-C dye (the smallest particle size) provided numerous advantages, including high particle dispersion, low dyeing temperature, minimum processing time, and greater color yield. At a concentration of 15%, DR 167-C produced 55.1 and 33.18% color yields which were higher than DR 167 and DR 167-B, respectively. Also, DR 167-C achieved a better colorfastness to washing (very good, 4) compared to other studied dyes.

Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy

Providing real-time information on the chemical properties of hydrocracking bottom oil (HBO) as the feedstock for ethylene cracker while minimizing processing time, is important to improve the real-time optimization of ethylene production. In this study, a novel approach for estimating the properties of HBO samples was developed on the basis of near-infrared (NIR) spectra. The main noise and extreme samples in the spectral data were removed by combining discrete wavelet transform with principal component analysis and Hotelling’s T2 test. Kernel partial least squares (KPLS) regression was utilized to account for the nonlinearities between NIR data and the chemical properties of HBO. Compared with the principal component regression, partial least squares regression, and artificial neural network, the KPLS model had a better performance of obtaining acceptable values of root mean square error of prediction (RMSEP) and mean absolute relative error (MARE). All RMSEP and MARE values of density, Bureau of Mines correlation index, paraffins, isoparaffins, and naphthenes were less than 1.0 and 3.0, respectively. The accuracy of the industrial NIR online measurement system during consecutive running periods in predicting the chemical properties of HBO was satisfactory. The yield of high value-added products increased by 0.26 percentage points and coil outlet temperature decreased by 0.25 °C, which promoted economic benefits of the ethylene cracking process and boosted industrial reform from automation to digitization and intelligence.