Minimum Computation Time Research Articles

An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling

Heterogeneity among factories in distributed manufacturing significantly expands the solution space, complicating optimisation. Traditional centralised scheduling methods lack the scalability to adapt to varying factory scales. This paper proposes an end-to-end decentralised scheduling framework based on deep reinforcement learning (DRL) for dynamic distributed heterogeneous permutation flowshop scheduling problem (DDHPFSP) with random job arrivals. The framework utilises a multi-agent architecture, where each factory operates as an independent agent, enabling efficient, robust, and scalable scheduling. Specifically, the DDHPFSP is formulated as a partially observable Markov decision process (POMDP), with a state space reflecting heterogeneity and permutation characteristics and a new tailored reward function addressing sparse rewards and high reward variance. An end-to-end policy network with dual-layer architecture is developed, incorporating a feature extraction network to capture intrinsic relationships between jobs and heterogeneous factories, enhancing the agent's self-learning and policy evolution. Moreover, a backward swap search (BSS) method based on greedy heuristics optimises the pre-scheduling plan during the online phase with minimal computation time. Experimental results demonstrate the framework outperforms the best comparison methods by 39.76% on 540 baseline instances and 59.95% on 2430 generalisation instances. Furthermore, the framework's effectiveness improves by 68.9% with the introduction of the BSS method.

Optimizing cryptographic protocols against side channel attacks using WGAN-GP and genetic algorithms

This research introduces a novel hybrid cryptographic framework that combines traditional cryptographic protocols with advanced methodologies, specifically Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) and Genetic Algorithms (GA). We evaluated several cryptographic protocols, including AES-ECB, AES-GCM, ChaCha20, RSA, and ECC, against critical metrics such as security level, efficiency, side-channel resistance, and cryptanalysis resistance. Our findings demonstrate that this integrated approach significantly enhances both security and efficiency across all evaluated protocols. Notably, the AES-GCM algorithm exhibited superior performance, achieving minimal computation time and robust side-channel resistance. This study underscores the potential of leveraging machine learning and evolutionary algorithms to advance cryptographic protocol security and efficiency, laying a robust foundation for future advancements in cybersecurity.

Significance of variable electrical conductivity on unsteady EMHD flow of nanofluid over a moving wedge with time-dependent chemical reaction

A model based on physical concepts studied the unsteady flow of viscous nanofluid over a wedge with variable electrical conductivity. Electric and magnetic fields are applied at right angles to each other and in the direction of the flow. The electrical conductivity of the medium is considered a power law function. The flow incorporates Brownian diffusion and thermophoresis. The model partial differential equations are transformed into associated nonlinear ordinary differential equations, and these equations are then numerically solved using a boundary value solver. The results of physical interest, like combined electric field and magnetic field, power law index of electrical conductivity, and Brownian motion parameter, are depicted graphically. The results obtained are validated with previous publications, and it was found an excellent agreement between these results. A grid convergence test is performed to find the optimum grid size, which helps to achieve accurate results with minimum computational time. It is observed from the simulation result that the maximum drop in surface friction under the action of Lorentz force occurs when the power law index is zero. Further nanoparticle concentration layer thickness builds up during a generative chemical reaction in the presence of a time-dependent chemical reaction with thermophoresis particle deposition.

Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers

Nervous system cancers exhibit diverse transcriptional cell states influenced by normal development, injury response, and growth. However, the understanding of these states’ regulation and pharmacological relevance remains limited. Here we present “single-cell regulatory-driven clustering” (scregclust), a method that reconstructs cellular regulatory programs from extensive collections of single-cell RNA sequencing (scRNA-seq) data from both tumors and developing tissues. The algorithm efficiently divides target genes into modules, predicting key transcription factors and kinases with minimal computational time. Applying this method to adult and childhood brain cancers, we identify critical regulators and suggest interventions that could improve temozolomide treatment in glioblastoma. Additionally, our integrative analysis reveals a meta-module regulated by SPI1 and IRF8 linked to an immune-mediated mesenchymal-like state. Finally, scregclust’s flexibility is demonstrated across 15 tumor types, uncovering both pan-cancer and specific regulators. The algorithm is provided as an easy-to-use R package that facilitates the exploration of regulatory programs underlying cell plasticity.

Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg-Based Implants.

Computational models of electrochemical biodegradation of magnesium (Mg)-based implants are uncertain. To quantify the model uncertainty, iterative evaluations are needed. This presents a challenge, especially for complex, multiscale models as is the case here. Approximating high-cost and complex models with easy-to-evaluate surrogate models can reduce the computational burden. However, the application of this approach to complex degradation models remains limited and understudied. This work provides a workflow to quantify different types of uncertainty within biodegradation models. Three surrogate models-Kriging, polynomial chaos expansion, and polynomial chaos Kriging-are compared based on the minimum number of samples required for surrogate model construction, surrogate model accuracy, and computational time. The surrogate models are tested for three computational models representing Mg-based implant biodegradation. Global sensitivity analysis and uncertainty propagation are used to analyze the uncertainties associated with the different models. The findings indicate that Kriging proves effective for calibrating diverse computational models with minimal computational time and cost, while polynomial chaos expansion and polynomial chaos Kriging exhibit greater capability in predicting propagated uncertainties within the computationalmodels.

EEGER – A Model for Recognition of Human Emotion Using Brain Signal

Emotions significantly impact human thinking, judgment, health, and communication. EEG-based emotion detection has advanced with the use of Brain-Computer Interface (BCI) technology, proving more effective than other physiological data. Despite progress in affective computing, emotion recognition remains a challenge. However, it's increasingly common in brain–machine interfaces, and research shows EEG brain waves are valuable in identifying emotional states. This research introduces a novel automated system for emotion recognition using deep learning techniques on EEG data collected from the GAMEEMO dataset, where participants played emotional assessment games. The system is designed to identify four emotions experienced during gameplay. The proposed model, called EEGER, was trained exclusively on EEG signals and demonstrated a 99.99% accuracy with minimal computational time. Key to its efficiency is the use of LSTM (Long Short-Term Memory) classifiers, which simplify the process by automatically extracting relevant features. The system was tested across different learning rates and epoch values, showing that 10 epochs with a learning rate of 0.0001 were sufficient to achieve the best accuracy. EEGER was also compared with other methods like K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Adaptive Boosting (AdaBoost), outperforming them in both accuracy and efficiency. These findings suggest that EEGER offers a promising new approach to EEG-based emotion recognition, optimizing performance with lower complexity and computation time.

Leveraging neural networks to correct FoldX free energy estimates.

Proteins play a pivotal role in many biological processes, and changes in their amino acid sequences can lead to dysfunction and disease. These changes can affect protein folding or interaction with other biomolecules, such as preventing antibodies from inhibiting a viral infection or causing proteins to misfold. The ability to predict the effects of mutations in proteins is crucial. Although experimental techniques can accurately quantify the effect of mutations on protein folding free energies and protein-protein binding free energies, they are often time-consuming and costly. By contrast, computational techniques offer fast and cost-effective alternatives for estimating free energies, but they typically suffer from lower accuracy. Enhancing the accuracy of computational predictions is therefore of high importance, with the potential to greatly impact fields ranging from drug design to understanding disease mechanisms. One such widely used computational method, FoldX, is capable of rapidly predicting the relative folding stability ( ) for a protein as well as the relative binding affinity ( ) between proteins using a single protein structure as input. However, it can suffer from low accuracy, especially for antibody-antigen systems. In this work, we trained a neural network on FoldX output to enhance its prediction accuracy. We first performed FoldX calculations on the largest datasets available for mutations that affect binding (SKEMPIv2) and folding (ProTherm4) with experimentally measured . Features were then extracted from the FoldX output files including its prediction for . We then developed and optimized a neural network framework to predict the difference between FoldX's estimated and the experimental data, creating a model capable of producing a correction factor. Our approach showed significant improvements in Pearson correlation performance. For single mutations affecting folding, the correlation improved from a baseline of 0.3 to 0.66. In terms of binding, performance increased from 0.37 to 0.61 for single mutations and from 0.52 to 0.81 for double mutations. For epistasis, the correlation for binding affinity (both singles and doubles) improved from 0.19 to 0.59. Our results also indicated that models trained on double mutations enhanced accuracy when predicting higher-order mutations (such as triple or quadruple mutations), whereas models trained on singles did not. This suggests that interaction energy and epistasis effects present in the FoldX output are not fully utilized by FoldX itself. Once trained, these models add minimal computational time but provide a substantial increase in performance, especially for higher-order mutations and epistasis. This makes them a valuable addition to any free energy prediction pipeline using FoldX. Furthermore, we believe this technique can be further optimized and tested for predicting antibody escape, aiding in the efficient development of watch lists.

Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters

Diabetes mellitus poses a global health concern, prompting the development of machine learning algorithms designed to construct a model for the accurate classification of patients, enabling precise diagnoses and early-stage treatment. However, the efficacy of classifying diabetes patients through machine learning relies on datasets, often plagued by imbalance, leading to biased classification and inaccurate diagnoses. Previous research attempts, employing techniques like random sampling (under-sampling and oversampling) and the Synthetic Minority Oversampling Technique (SMOTE), have struggled to achieve optimally balanced datasets. Additionally, setting the best parameters for machine learning classifiers remains a challenging task. To address these issues, this research focuses on devising a methodological metaheuristic optimization, a machine learning algorithm tailored for diabetes data balancing, and classifier hyperparameter tuning. Leveraging Particle Swarm Optimization (PSO) algorithm for diabetes data balancing and a genetic algorithm to select the optimal architecture for various machine learning classifiers. The study compares the performance of the proposed metaheuristic data balancer and classifier architecture parameter tuner using classification metrics (F1 score, Average Precision–Recall (APR), AUC, and accuracy). The PSO balanced dataset emerges as the most effective in classifying diabetes, with an Average Percentage Improvement (API) in classification performance metrics: 20.78% accuracy, 16.79% area under the curve for receiver operating characteristics, and a significant 32.78% enhancement in APR. Moreover, the XGBOOST classifier trained with a genetic algorithm demonstrates minimal computational training time for the Centre for Disease Control and Prevention (CDC) diabetes dataset compared to the artificial neural network and random forest classifier. Notably, the imbalanced CDC diabetes dataset exhibits the least APR compared to random under-sampling and the PSO data balancing technique.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

AI-assisted optimal energy conversion for cost-effective and sustainable power production from biomass-fueled SOFC equipped with hydrogen production/injection

This study introduces a novel energy conversion and management framework to reduce carbon emissions in the energy sector and expedite the global shift towards sustainable practices. The system is driven by biomass-based solid oxide fuel cells for efficient power generation. Central to this approach lies the integration of additional hydrogen injection provided by a thermally-driven vanadium chloride cycle, aiming to enhance the quality of the syngas entering the fuel cells. The system is also combined with a super-critical CO2 cycle that generates power by passively enhancing performance through flue gas condensation. The proposed model's feasibility is evaluated in depth, techno-economically, considering thermodynamics and specific cost theories. As part of artificial intelligence, a neural network model is coupled with the genetic algorithm to determine the best operating status while minimizing computation time. According to the results, the suggested new integration results in higher efficiency and lower cost than a similar system without hydrogen injection. The results further show that the triple-objective optimization achieves output power, second-law efficiency, and overall system cost of 3425 kW, 48.5 %, and 2.3 M$/year, respectively. Eventually, the gasifier is the main contributor to the highest level of exergy destruction, and fuel utilization and current density are the most important parameters in modeling.

Dempster Combination Rule-Aided Multi-Sensor Decision-Level-Based Data Fusion in Industrial Internet of Things (IIoT) over Zero-Trust Security

Data fusion is a process of accommodating data from discrete sources to generate more relevant information with enhanced accuracy and compatibility rather than the data gathered and information produced by the identical or particular source. To achieve better accuracy in the information inferred by the Industrial Internet of Things (IIoT) system, data fusion is an emerging technology that takes data from different involved devices as input and generates more consistent and accurate information. To safeguard the IIoT data involved in communication foremost is to ensure the authenticity of the devices to block unauthorized access and thus enhance the data confidentiality and data integrity as well. Zero-trust security is thus employed in the IIoT infrastructure to meet the security requirements in question. The proposed approach, Dempster combination rule-aided multi-sensor decision-level-based data fusion (MS-DLDF) in Industrial Internet of Things (IIoT) over zero-trust security, breaks down the whole concept into phases – initially, the device authenticity is ensured before entering and accessing the network, next, the device data is then encoded to avoid data theft or breach from any unauthorized device, and lastly, the data fusion algorithm is executed to gather data from multiple sources as input and reach to more appropriate and relevant information as compared to the information inferred from a single source. The proposed MS-DLDF concludes with the outstanding results of the Dempster combination rule with the increased number of sensors in complex networks as compared to the single-source data fusion and Bayesian estimation data fusion with enhanced accuracy by 6%, minimal computation time by 15%, increased precision rate 6%, reduced false positive rate by 31%, and the amplified data confidentiality rate by 2%, respectively.

Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT

The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder’s activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method’s consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%.

Design of Adaptive Target Tracking Algorithm for Robots Based on Visual Attention Mechanism

Adaptive target tracking with visual attention represents a sophisticated approach to object detection and localization in dynamic environments. With principles inspired by human visual perception, this methodology employs mechanisms of selective attention to prioritize relevant visual information for tracking moving targets. By dynamically adjusting attentional focus based on salient visual cues and target motion characteristics, adaptive target tracking enhances the efficiency and accuracy of object localization in cluttered scenes. This research presents a novel adaptive target tracking algorithm designed for robotic systems, integrating a visual attention mechanism with the Fuzzy Clustering Multi-Point Tracking utilizing the Green Channel (FC-MPT-GC) approach. The proposed FC-MPT-GC model comprises of Fuzzy Clustering for the extraction of features in the robots-based environment. The FC-MPT-GC model uses the estimation of green channels in the classification environment. With the estimation of features in the environment with Fuzzy C-means clustering green channels are deployed in the deep learning, The proposed algorithm aims to enhance the adaptability and precision of target tracking in dynamic environments. By incorporating a visual attention mechanism, the algorithm dynamically allocates attentional focus to salient regions of the visual input, optimizing the tracking process for moving targets. The FC-MPT-GC methodology further refines target localization by utilizing fuzzy clustering and multi-point tracking strategies, particularly leveraging information from the Green Channel to improve robustness in various lighting conditions. Simulation analysis demonstrated that the proposed FC-MPT-GC model tracking accuracy is achieved at 95.1% with the minimal computation time of 15.2 ms.

Predicting viscosity for steelmaking slag: A stacking regression approach

The viscosity of steel slag plays a pivotal role in steelmaking, impacting various aspects of the steelmaking operation and product quality. This paper introduces a novel approach for predicting the viscosity of turndown slag from a basic oxygen furnace using a stacking regression model. The stacking model in this approach incorporates four base models: multiple linear regression (LR), random forest (RF), k-nearest neighbour (KNN) and AdaBoost (ADB), while random forest is selected as a meta-model. The selection of meta-models is done through a systematic framework provided for identifying suitable meta-models in a stacking model. Similarly, the framework also demonstrated the workflow for constructing and evaluating a series of stacking models with different configurations. On the evaluation of 20 stacking models (including two and three-layers), the optimal configuration is found to be LR + KNN with R2 value of 0.993 and the shortest training time. Additionally, a correlation analysis of slag viscosity with composition and temperature is done, which will help the operators of steel melting shops in optimize the steelmaking process based on the required slag viscosity. Ultimately, this study emphasizes the efficacy of optimal-configured stacking regression models in industrial applications, achieving both maximum accuracy and minimal computational time.

A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach.

Duration and resource constraint prediction models for construction projects using regression machine learning method

PurposeThe construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.Design/methodology/approachThe dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.FindingsThe prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.Research limitations/implicationsThe researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.Practical implicationsThe research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.Social implicationsDelays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.Originality/valueThis is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.

Advanced Prognostic Models for Bearing Health: A Comparative Analysis of BiLSTM and ANFIS

Bearings play a critical role in the operation of rotary machines, serving as essential components. Their failure often leads to unexpected shutdowns, posing a significant risk to the entire system. To mitigate these risks, it is imperative to implement proactive maintenance measures and strategic planning to prevent system breakdowns. This article introduces a comparative analysis between two predictive modelling approaches: Bidirectional Long Short-Term Memory (Bi-LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) networks, aiming to enhance bearing prognostics. The proposed methodology involves a two-step process. Firstly, data undergoes pre-processing through wavelet packet decomposition (WPD). Subsequently, a degradation model is employed for predicting the remaining useful life (RUL). To validate the accuracy of the proposed approach, extensive testing is conducted using a bearing's life dataset obtained from a run-to-failure experiment. The results demonstrate that the ANFIS model exhibits remarkable capabilities in learning and accurately estimating the system's RUL, achieving this with minimal computation time compared to alternative methods, thus presenting a more efficient and precise solution.

An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

An Efficient Algorithm for Basic Elementary Matrix Functions with Specified Accuracy and Application

If the matrix function f(At) posses the properties of f(At)=gf(tkA, then the recurrence formula fi−1=gfi,i=N,N−1,⋯,1,f(tA)=f0, can be established. Here, fN=f(AN)=∑j=0majANj,AN=tkNA. This provides an algorithm for computing the matrix function f(At). By specifying the calculation accuracy p, a method is presented to determine m and N in a way that minimizes the time of the above algorithm, thus providing a fast algorithm for f(At). It is important to note that m only depends on the calculation accuracy p and is independent of the matrix A and t. Therefore, f(AN) has a fixed calculation format that is easily computed. On the other hand, N depends not only on A, but also on t. This provides a means to select t such that N is equal to 0, a property of significance. In summary, the algorithm proposed in this article enables users to establish a desired level of accuracy and then utilize it to select the appropriate values for m and N to minimize computation time. This approach ensures that both accuracy and efficiency are addressed concurrently. We develop a general algorithm, then apply it to the exponential, trigonometric, and logarithmic matrix functions, and compare the performance with that of the internal system functions of Mathematica and Pade approximation. In the last section, an example is provided to illustrate the rapid computation of numerical solutions for linear differential equations.

An Interactive Pedagogical Tool for Simulation of Controlled Rectifiers

Active learning approaches, incorporating student engagement through experimentation and problem solving, effectively foster higher-level thinking abilities and enhance academic performance. Interactive tools like simulators align with these methodologies, but commercially available simulators have limitations; particularly, their high cost and lack of customization features pose significant challenges for many educational institutions. This article presents CORES, a web-based educational application designed to simulate controlled rectifier circuits. CORES eliminates the need for intricate circuit assembly and software installation by providing pre-built circuits so that users can concentrate on analyzing circuit behavior by manipulating the thyristor firing angle and load characteristics, while the application generates output voltage and current waveforms under steady-state conditions, minimizing computation time. CORES has proven to be a valuable pedagogical tool, surpassing commercial simulators in terms of accessibility, ease of use, and enriched learning experiences for power electronics students and educators.