Minimum Error Research Articles

Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model’s efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model’s robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model’s exceptional generalizability and potential for real-world applications in electric vehicle battery management.

QUALITATIVE ANALYSIS OF TEMPERATURE ERROR OF NEGATIVE PRESSURE WAVE METHOD FOR DETERMINING THE LOCATION OF A PIPELINE LEAK

The article is devoted to the qualitative analysis of the temperature error of the negative pressure wave method for determining the location of a pipeline leak. Recording the arrival time of the negative wave at both ends of the pipeline allows you to determine the location of a pipeline leak. In contrast to known works on this topic, this article proposes a method for qualitative analysis of the error in determining a leak location. Based on the variational optimization method, an optimal ratio of the mode indicators at which the minimum error in determining the location of a leak can be achieved is defined. A method for qualitative analysis of the error in determining the leak location using the negative pressure difference wave method is proposed. The optimal correlation of the mode functions is determined at which the minimum error in determining the leak location is achieved. An expression for estimating the relative error in determining the leak location using the negative pressure difference wave method is obtained.

Reliability and Validity of the L Test in Persons With Multiple Sclerosis.

The L Test has been developed to assess balance and gait disorders. Our aim in this study was to estimate the test-retest reliability and validity of the L Test when used with 82 persons with multiple sclerosis (PwMS). For these participants, we examined the degree of agreement between the results of a first and second administration of the L Test (separated by one day), using Bland-Altman analysis and intra-class correlation coefficients (ICCs). We computed minimal detectable change (MDC) and standard error of measurement (SEM) values for the L Test and evaluated concurrent validity by correlating L Test results with the Timed Up and Go test (TUG) and the 10-minute Walk Test (10MWT). Prior to administering these measurements, we randomized the sequence of the test administrations to our participants. The Bland-Altman analysis showed that L Test was reproducible, with upper and lower limits of agreement of 0.99 and -1.45 seconds, respectively. The L Test demonstrated excellent test-retest reliability, with an ICC value of 0.996 (95% CI: 0.994-0.998). Cronbach's alpha coefficient was 0.996. The performance of the L Test is measured by seconds required to complete the task, and we found the L Test SEM value to be 0.35 seconds, and its MDC value to be 0.97 seconds. The L Test showed a strong correlation with both the TUG test (rho = 0.936; p < .001) and the 10MWT (rho = 0.925; p < .001). We concluded that the L Test is a reliable and valid \ tool for assessing functional mobility and balance in PwMS.

Prediction of the Mean Time to Failures of a Complex System Using the Monte Carlo Simulation Method

This paper presents a Monte Carlo-based algorithm for predicting the Mean Time to Failure (MTTF) of complex structures, specifically a “Bridge-type network” with five elements exhibiting various failure distributions. The proposed algorithm involves generating element lifetimes through the inverse of their failure distribution functions, providing a robust approach to evaluating MTTF for systems beyond traditional series or parallel configurations. The approach was implemented in MATLAB, and the software underwent extensive testing across different scenarios, including both exponential and Weibull distributions. The results demonstrated the method’s accuracy and its capability to handle diverse failure distributions with minimal error. This tool offers reliability engineers a versatile solution for predicting and improving the reliability of complex systems. In summary, the proposed method and software significantly advance the reliability assessment of intricate structures and offer a solid foundation for further research and practical applications in the field of reliability engineering.

Mathematical Modeling of Biogas Production in Sanitary Landfills

Objective: The objective of this study is to evaluate the biogas production potential in sanitary landfills using the Verhulst mathematical model, which is employed to represent natural phenomena constrained by a saturation point. Theoretical Framework: It was based on references on energy use from waste, possible conversion routes, in addition to mathematical models that estimate the energy potential for biogas generation. Method: The methodology adopted for this research involves the use of data collected in an experimental cell, subsequently applying the De Verhulst model through a regression with minimization of errors using the Least Squares Method (LSM). Results and Discussion: The results indicated that the developed model correlates well with the observed data, demonstrating its suitability as a predictive model for biogas production. Research Implications: The research contributes to the construction of a theoretical framework for the adaptation, elaboration and use of mathematical models to predict the energy potential in the generation of biogas in landfills. Originality/Value: This study contributes to the literature by using a population growth model (Verhulst) to predict landfill biogas production.

Truncation Error of the Network Simulation Method: Chaotic Dynamical Systems in Mechanical Engineering

This article focuses on the study of local truncation errors (LTEs) in the Network Simulation Method (NSM), specifically when using the trapezoidal method and Gear’s methods. The NSM, which represents differential equations through electrical circuit elements, offers advantages in solving nonlinear dynamic systems such as the van der Pol equation. The analysis compares the performance of these numerical methods in terms of their stability and error minimization, with particular emphasis on LTE. By leveraging circuit-based techniques prior to numerical application, the NSM improves convergence. This study evaluates the impact of step size on LTE and highlights the trade-offs between accuracy and computational cost when using the trapezoidal and Gear methods.

Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of proteins and their interactions. To gain insights into the spatial effects shaping intracellular regulation, we apply machine learning (ML) to explore a previously developed spatial model of the epidermal growth factor receptor (EGFR) signaling. The model describes the reactions between molecular species inside of cells following the transient activation of EGF receptors. To train our ML models, we conduct 10,000 numerical simulations in parallel where we calculate the cumulative activation of molecules and transcription factors under various conditions such as different diffusion speeds, inactivation rates, and cell structures. We take advantage of the low computational cost of ML algorithms to investigate the effects of cell and nucleus sizes, the diffusion speed of proteins, and the inactivation rate of the Ras molecules on the activation strength of transcription factors. Our results suggest that the predictions by both neural networks and random forests yielded minimal mean square error (MSEs), while linear generalized models displayed a significant larger MSE. The exploration of the surrogate models has shown that smaller cell and nucleus radii as well, larger diffusion coefficients, and reduced inactivation rates increase the activation of transcription factors. These results are confirmed by numerical simulations. Our ML algorithms can be readily incorporated within multiscale models of tumor growth to embed the spatial effects regulating intracellular pathways, enabling the use of complex cell models within multiscale models while reducing the computational cost.

Assessment of a Low-cost Canine Uterine Simulator as a Tool for Teaching the Ovariohysterectomy Technique to Veterinary Students.

The acquisition of skills to perform an ovariohysterectomy (OVH) is crucial for veterinary medicine students. It has been demonstrated that the most effective way to develop these skills is through repetitive training on simulators. Unfortunately, commercial simulators are expensive, limiting their use and highlighting the need for the development of more cost-effective alternatives. This study aimed to assess the effectiveness of a low-cost, easily constructed textile-based simulator for the canine ovaries and uterus in training veterinary students on the OVH technique. The impact of tutor guidance on student learning was also assessed. Participants were divided into two groups: simulator and tutor guidance (SG) and simulator only (SO). Each student performed 20 repetitions of the three-clamp OVH technique, and the number of errors and execution time were quantified. The SG group reached the learning curve plateau in terms of minimum errors on the 7th repetition and attained the fastest time on the 6th repetition. The SO group reached the learning curve plateau in terms of minimum errors and attained the fastest time both on the 15th repetition. From individuals in the SG group, there were no requests for tutor guidance from the 11th attempt. This low-cost simulator is ideal for training veterinary students in the early stages of surgical learning, as it effectively facilitates learning the OVH procedure without the use of live animals.

Enhancing Thermal–Hydraulic Performance in Nuclear Reactor Subchannels with Al2O3 Nanofluids: A CFD Analysis

In this paper, the performance of aluminum-based nanofluids with a possible application in pressurized water reactors is numerically investigated. A 605 mm long 4-rod array square (2 × 2) subchannel geometry with a uniform heat flux of 50 kW/m2 has been used in CFD simulation. This analysis has been carried out using the RNG k-epsilon turbulence model with standard wall function in ANSYS FLUENT 2022R1. The impact of various flow conditions and nanofluid concentrations has been examined. The effects of variable velocities on nanofluid performance have been studied using different Reynolds numbers of 20,000, 40,000, 60,000, and 80,000. The analysis was conducted with Al2O3/water nanofluid concentrations of 1%, 2%, 3%, and 4%. A comparison of the Nusselt number based on five different correlations was conducted, and deviations from each correlation were then presented. The homogeneous single-phase mixer approach has been adopted to model nanofluid characteristics. The result shows a gradual enhancement in the heat transfer coefficient with increasing volume concentrations and Reynolds numbers. A maximum heat transfer coefficient has been calculated for nanofluid at maximum volume concentrations (ϕ = 4%) and highest velocities (Re = 80,000). Compared to the base fluid, heat transfer was enhanced by a factor of 1.09 using 4% Al2O3. The Nusselt number was calculated with a minimal error of 3.62% when compared to the Presser correlation and the maximum deviation has been found from the Dittus–Boelter correlation (13.77%). Overall, the findings suggest that aluminum-based nanofluids could offer enhanced heat transfer capabilities in pressurized water reactors.

Design and Development of An Integrated Monitoring System for Nebulizer and Infusion for Lung Disease Patients Based on IoT

This research develops an Internet of Things (IoT)-based monitoring system for nebulizers and infusion devices for patients with lung diseases, specifically Acute Respiratory Infections (ARI). The aim is to improve nursing efficiency and minimize the risk of complications due to delayed patient care by enabling remote monitoring and providing real-time information on the status of nebulizers and infusion devices via a website. The system is designed to stop the infusion fLow when the required amount is reached and to automatically turn off the nebulizer when there is no liquid. It uses an ESP32 microcontroller to control IR obstacle sensors, relays, and a servo motor for the infusion clamp, as well as an LCD display for digital information and feature displays. The system is also equipped with an alarm to indicate when the infusion liquid or nebulizer is depleted and a website to provide real-time status information to medical personnel. Testing shows that the system has a high accuracy of 98.7% and minimal error of approximately 1.82% in monitoring and controlling nebulizer and infusion fluids. In conclusion, the system successfully improves nursing efficiency and patient safety while reducing the workload of nurses through the automation of medical devices that were previously managed manually.

Machine learning-aided enhancement of white tea extraction efficiency using hybridized GMDH models in microwave-assisted extraction

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes.

Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

The Photometric Testing of High-Resolution Digital Cameras from Smartphones—A Pilot Study

Luminance is the fundamental photometric quantity representing the technical meaning of brightness. It is usually measured from a distance using a matrix sensor, which is the basis of the professional instrument. However, specific technical requirements must be fulfilled to achieve accurate results. This paper considers whether modern high-resolution smartphone cameras are suitable for luminance measurements. Three cameras from high-end smartphones were evaluated on a dedicated laboratory stand. The sensors’ output characteristics showed relatively good linearity of the individual R, G, and B channels. Unfortunately, the spectral sensitivities were unfavorable, as the minimum error achieved was about 17%. This device is classified outside the generally accepted quality scale for photometric instruments. The presented investigation confirmed that none of the high-resolution smartphone cameras tested was suitable for use as a universal luminance camera. However, one of the test devices can be developmental if restrictively calibrated and used only in a specialistic laboratory stand. Using a smartphone (or only its camera) for luminance measurements requires proper advanced calibration. It is possible, but it limits us to only dedicated applications. The pilot study presented in this paper will help create a suitable test stand for spectacle vision systems, e.g., virtual reality equipment.

An innovative multilayered material fabricated through additive manufacturing for structural applications: method and mechanical properties

Abstract Additive manufacturing (AM) is a cost-effective method for fabricating structurally sound components. Fused filament fabrication (FFF) is a popular AM technique known for its design flexibility, minimal material wastage, and recyclability. Poly lactic acid (PLA) is a thermoplastic widely used in aerospace, biomedical, and automobile industries. Wood-PLA, incorporating wood fillers into PLA, finds applications in several industries. This research explores multilayered materials (MLM) for enhanced performance in various sectors such as aircraft, energy, and biomedical. Mechanical properties of MLM were investigated under different load conditions (tensile, bend, compressive). Properties simulated through Finite Element Method (FEM) showed minimal error (less than 1 %). Microscopic analysis, aided by scanning electron microscope (SEM) fractography, reveals a brittle mode of failure in the specimens. This study provides valuable insights into the mechanical behaviour of MLM, offering potential applications in diverse industries.

Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.

Multi-label Random Subspace Ensemble Classification1

In this work, we develop a new ensemble learning framework, multi-label Random Subspace Ensemble (mRaSE), for multi-label classification. Given a base classifier (e.g., multinomial logistic regression, classification tree, K-nearest neighbors), mRaSE works by first randomly sampling a collection of subspaces, then choosing the best ones that achieve the minimum cross-validation errors and, finally, aggregating the chosen weak learners. In addition to its superior prediction performance, mRaSE also provides a model-free feature ranking depending on the given base classifier. An iterative version of mRaSE is also developed to further improve the performance. A model-free extension is pursued on the iterative version, leading to the so-called Super mRaSE, which accepts a collection of base classifiers as input to the algorithm. We show the proposed algorithms compared favorably with the state-of-the-art classification algorithm including random forest and deep neural network, via extensive simulation studies and two real data applications. The new algorithms are implemented in an updated version of the R package RaSEn.

Applications of AI in public health: Improving access to high-quality evidence syntheses

Abstract Issue Using the best available evidence is crucial for effective public health practice. Health Evidence™ provides access to high-quality synthesis evidence relevant to public health. As the volume of peer-reviewed published literature grows, maintaining a database of this magnitude is increasingly resource-intensive. Artificial intelligence (AI) can be used to reduce the maintenance burden thereby ensuring decision makers can easily access high-quality synthesized public health evidence. These innovative strategies may be transferable to enhance efficient maintenance of large curated databases of published literature. Description of the problem In 2020, The Health Evidence™ team conducted extensive training and testing of a supervised machine learning application to explore the accuracy of AI-assisted reference de-duplication and relevance screening. Finding promising results, the team implemented these AI-assisted strategies in August 2020. To assess the impact on the overall screening burden and time saved, implementation data was analyzed between November 2020 to 2023. Results For these 3+ years, AI assisted de-duplication and relevance screening was applied to 394,903 search results. The AI assisted de-duplication application removed 31% (n = 123,903) of references as duplicates. From the remaining reference sets (n = 272,253), the AI assisted screening application removed 70% (n = 190,966) of references as not relevant. Quality assurance spot testing found minimal classification errors (n = 1). In total, AI assisted approaches reduced the need for manual screening by 80%, saving approximately 626 hours of manual screening time over three years (or approximately 17 hours/month). Lessons With the reality of limited public health resources, continued access to high-quality synthesis evidence is critical. Innovative strategies using AI-assisted applications improves the feasibility of maintaining a large database of quality-appraised public health synthesis evidence. Key messages • Access to high quality synthesis evidence is critical for evidence-informed decision making. • Artificial Intelligence can be used to efficiently identify evidence syntheses relevant for public health.

Air Pollutant Concentration Forecasting with WTMP: Wavelet Transform-Based Multilayer Perceptron

Atmospheric pollutants’ real-time changes and the internal interactions among various data make it challenging to efficiently predict concentration variations. In order to extract more information from the time series of pollutants and improve the accuracy of prediction models, we propose a type of Multilayer Perceptron model based on wavelet decomposition, named Wavelet Transform-based Multilayer Perceptron (WTMP) model. This model decomposes pollutant data through overlapping discrete wavelet transforms to extract non-stationarity and nonlinear dependencies in the time series. It combines the decomposed data with static covariate information such as data collection time and inputs them into an improved Multilayer Perceptron (MLP) model, reconstructing and outputting the prediction results. Finally, the model is validated using atmospheric pollutant data collected at a specific location in Ruian City, Zhejiang Province, China. The results indicate that the model performs well with minimal prediction errors.

Weighted Centroid Amorphous Algorithm for Position Error Minimization in WSN

ABSTRACTWireless Sensor Networks (WSNs) have numerous applications, one of which is localization. Localization is crucial for determining the position of unknown sensor nodes in the deployment environment. Localization techniques are broadly classified into two categories: range based and range‐free. Range‐free localization techniques are gaining popularity as they are easy to implement and do not require external hardware. In this proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. Ten nearest beacon nodes are considered to determine the coordinates of a single unknown node. The hop value and hop size of that unknown node are calculated from the 10 nearest beacon nodes, and by using the hop value and hop size, the weight is calculated. After the weight estimation, the weighted sum is calculated, and using the weighted sum, the coordinates of an unknown node are determined. Simulation results indicate that the proposed Weighted Centroid Amorphous method achieves superior accuracy, with rates of 90.41%, 89.57%, and 86.10% compared to the traditional Amorphous, improved Amorphous, and Ensemble approach, respectively.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.