Error Values Research Articles

Using new control strategies to improve the effectiveness and efficiency of the hybrid power system based on the battery storage system

Hybrid energy systems (HESs) are integrated systems that have successfully addressed the problems of meeting the increasing demand for electrical power. Like all known power systems, the energy and stream quality are among the most important issues in addition to the durability of the HES. In this study, the battery-powered HES is presented, where this designed system consists of a wind system and a photovoltaic (PV) system. The strategy of maximum power point (MPP) tracking (MPPT) based on the adaptive neuro-fuzzy inference system (ANFIS) method is used to command the PV system and the wind system, and the MPPT based on the neural method is used. These proposed strategies do not need the mathematical model of the studied system and augment the robustness and stability, where the system performance is great. Also, the fractional-order proportional-integral regulator and the integral sliding mode control approach are combined to control the battery-based storage system, and the particle swarm optimization approach was used to estimate the gain values of the resulting controller. The HES was realized using MATLAB, where the competence is tested under different work scenarios. The results showed excellent efficacy of the designed control and were compared with conventional control. The simulation results showed that using the neural MPPT strategy in the case of the wind speed being 12 m/s, the values of rise time, response time, MPP, and steady-state error (SSE) are improved by rates estimated at 99.32%, 60%, 1.5%, and 60%, respectively compared to the perturbations and observations-based MPPT approach. Compared to the traditional strategy, the ANFIS-MPPT strategy improves the values of MPP, response time, SSE, and rise time in the case of irradiation, which takes the value of 1000 W/m2, by percentages estimated at 18%, 60%, 94.70%, and 69.23%, respectively. Also, the PSO-FOPI-ISMC strategy improves the harmonic distortion of the current value in the second test by 55.20% and 72.90% for mode 1 and mode 2, respectively, compared to the traditional approach. These results make the designed approach of great importance in the future in other industrial applications.

Early stage cost prediction model for Indian building construction projects using artificial neural networks

Purpose This paper aims to enhance early-stage cost estimation in construction projects, a critical factor in project feasibility, funding, resource allocation and scheduling. Traditional cost estimation approaches suffer from limitations such as the absence of structured methodologies, assumptions of linear cost relationships, prolonged processes and expert judgment variations. To address these challenges, this study proposes a reliable cost prediction model based on artificial neural networks (ANNs) for building construction projects in India. Design/methodology/approach To develop cost prediction model, this study collected data from 377 building construction projects in India, encompassing 17 essential cost parameters. The methodology involves data preprocessing, constructing features and fine-tuning ANN hyperparameters meticulously to achieve optimal performance. Findings The research showcases effectiveness of cost prediction model, evident in significantly reduced mean square error values. ANN-based prediction model excels in handling nonlinear cost dependencies and diverse project complexities, making it a valuable tool for early-stage cost estimation. Research limitations/implications ANN-based cost prediction model is primarily designed for predicting costs associated with structural works of building projects. Practical implications The proposed solution offers stakeholders a robust data-driven decision-making tool during initial phases of construction projects. This can lead to more successful and economically viable outcomes. Originality/value This research examines the drawbacks of traditional cost estimation methods by presenting a data-driven approach leveraging machine learning. It significantly improves precision of early cost forecasts in construction projects while offering practical value to industry.

Analysis of differences between keratometric astigmatism and total corneal astigmatism measured by IOLMaster 700

PurposeTo compare keratometric astigmatism (ΔK) and total corneal astigmatism (ΔTK) calculated by the IOLMaster 700 in cataract cases with regular corneal astigmatism ≥ 0.75 diopters (D).MethodsThis study was a secondary analysis of a database of cataract patients who had undergone examination with IOLMaster 700 between May 2021 and March 2022 at Shanghai Eye Disease Prevention and Treatment Center. The Surgically Induced Astigmatism Calculator v2.1 was used to examine vector errors between ΔK and ΔTK. Subgroup analyses were performed based on ΔK axis, ΔK magnitude, age, gender, mean simulated keratometry (Km), and white-to-white corneal diameter (WTW).ResultsIn total, 580 eyes in 580 cases were included. The centroid values of ΔK and ΔTK were 0.28 D at 4° ± 1.55 D and 0.51 D at 4° ± 1.60 D, respectively. ΔTK (1.38 [1.02–1.85] D) was significantly larger than ΔK (1.25 [0.98–1.69] D) (P < 0.0001). The absolute value of the vector error between ΔK and ΔTK (|ΔTK − ΔK|) was 0.24 (0.18–0.31) D. Among groups categorized based on ΔK axis, the |ΔTK − ΔK| was markedly reduced in the with-the-rule (WTR) astigmatism group compared with other groups; ΔTK was starkly lower than ΔK in the WTR astigmatism group (P = 0.0006), while ΔTK was larger than ΔK in the against-the-rule (ATR) astigmatism (P < 0.0001). The |ΔTK − ΔK| increased with the magnitude of ΔK and with age. The difference between ΔK and ΔTK showed no correlations with gender, Km, and WTW (all P > 0.05).ConclusionsA significant difference between ΔK and ΔTK obtained by IOLMaster 700 was found in cataract cases with regular corneal astigmatism ≥ 0.75 D. Such a difference was markedly reduced in the WTR astigmatism group. In contrast, this difference increased with the magnitude of ΔK and age. Moreover, this difference was not associated with gender, Km, and WTW.

Comparison of ERA5-Land and CMPAS reanalysis data for the regional assessment of precipitation in Chongqing, China

Precipitation data play a crucial role in meteorological research, and there are many different sources of precipitation data available. A comprehensive understanding of dataset reliability is essential for conducting research on weather and climate patterns studies. This study utilizes the multi-source hourly precipitation product from the China Meteorological Administration (CMA), derived from CMPAS, and the atmospheric reanalysis fifth-generation product (ERA5-Land) from the European Centre for Medium-Range Weather Forecasts (ECMWF).Through comparative analysis and evaluation of these two reanalysis precipitation products at different resolutions, the following conclusions are drawn: (1) The variation trends of ERA5-Land and CMPAS data are essentially consistent with the observed data. However, CMPAS exhibits smaller deviations, lower root mean square errors, and higher correlation coefficients. (2) In terms of accuracy at various precipitation levels, CMPAS outperforms ERA5-Land, which tends to overestimate low-level precipitation. (3) Across different terrain types, the quality of CMPAS is generally superior to ERA5-Land, particularly in the high-altitude regions of northeastern and southeastern Chongqing where ERA5-Land shows the greatest precipitation deviation and higher error values in mountainous areas compared to hills and plains. (4) Both CMPAS and GPM can effectively reproduce the spatial characteristics of precipitation. However, ERA5-Land and GPM tend to overestimate precipitation to some extent, while GPCP underestimates it, CMPAS is the closest to the observed values.

Integrated Navigation Algorithm for Autonomous Underwater Vehicle Based on Linear Kalman Filter, Thrust Model, and Propeller Tachometer

For the purpose of reducing the cost, size, and weight of the integrated navigation system of an AUV (autonomous underwater vehicle), and improving the stealth of this system, an integrated navigation algorithm based on a propeller tachometer is proposed. The algorithm consists of five steps: ① establishing the resistance model of AUV, ② establishing the thrust model, ③ utilizing the measured speeds obtained from the AUV’s voyage trials for calibration, ④ discrimination and replacement of outliers from the tachometer measurements, and ⑤ establishing a linear Kalman filter (LKF) with water currents as state variables. This paper provides the modeling procedure, formula derivations, model parameters, and algorithm process, etc. Through research and analysis, the proposed algorithm’s accuracy has been improved. The specific values of the localization error are detailed in the main text. Therefore, the proposed algorithm has high accuracy, a strong anti-interference capability, and good robustness. Moreover, it exhibits certain adaptability to complex environments and value for practical engineering.

The effect of wrist position sense and tactile recognition on manual skills in patients with upper extremity neuropathy.

Peripheral neuropathy affects fine motor skills in daily life. However, reports on the effects of position sense and tactile recognition on manual dexterity are quite scarce in the literature. The increasing focus on hand rehabilitation has created a need to examine the effects of sensation on manual dexterity. The purpose of this study is to compare the effect of tactile recognition and wrist position sense on manual skills in healthy and neuropathy individuals and to investigate the relationship between them. Cross-sectional, non-randomized comparative clinical study. Thirty-seven (50 hands) with median and ulnar nerve neuropathy between the ages of 18 and 65years and 32 (64 hands) healthy individuals of similar age and gender were included in the study. Wrist position sense was assessed using the K-FORCE Sens electrogoniometer as target angle, 30° wrist flexion and extension, and 10° radial and ulnar deviation. Shape-Texture Identification Test (STI), Purdue Pegboard test (PPT), and Michigan Hand Outcome Questionnaire were applied for tactile recognition, manual dexterity, and hand functions, respectively. The independent-sample t test and Mann-Whitney U test were used for K-FORCE Sens, STI, and PPT to compare groups. Correlation coefficient was used to determine the relationship between variables. The mean age of individuals with neuropathy and healthy individuals was 45.7±10.3 and 44.5±9.2years, respectively (p>0.05). Seventy-three percent of individuals had median neuropathy and 27% had ulnar neuropathy. Totally, 33 dominant and 17 non-dominant hands of patients with neuropathy were affected. When healthy and neuropathic hands were compared, the mean error values in the dominant hand, flexion (4.4±1.4; 6.5±2.9), and radial deviation (2.4±1.0; 3.3±1.7) degrees were higher in the neuropathic hands (p<0.05). Similar results were also found in the non-dominant hands. However, there was no difference between the mean error values in the extension and ulnar deviation degrees (p>0.05). STI and PPT subtest results were also lower in the neuropathic hands (p<0.05). There was a relationship between the mean error values of position sense in the flexion and radial deviation directions in the neuropathic dominant hands and all subtests of the PPT (p<0.05), while there was a relation in the flexion direction in the non-dominant hand (p<0.05). No relation was found in the Michigan Hand Outcome Questionnaire test (p>0.05). This study has shown that in neuropathy rehabilitation, the assessment of position and tactile sensations should not be ignored in determining participation in manual skills. This article can be used as a starting point for further studies and can be considered as one of the sensory focal points in rehabilitation in the development of manual skills.

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.

Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. Our study aims to leverage the Baidu search index, representing search volumes from China's most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3% in 2015 to 45.7% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 µm or less, and particulate matter with a diameter of 10 µm or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9%, and 0.43, respectively, in the test set. Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks.

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes is one of the key factors that is interlinked with a plethora of diseases. Health management can be achieved through balanced diet, physical exercise, and periodic examination of blood glucose level and hematocrit volume. Our study developed a model to estimate the hematocrit volume (red blood cells) from the correlation of the glucose concentration obtained from a glucometer by employing machine learning techniques. This Article explores the prediction of hematocrit volume in whole blood by applying various machine learning (ML) models such as linear regression (LR), support vector regressor (SVR), decision tree (DT), random forest regressor (RFR), artificial neural network (ANN), and extreme gradient boosting regressor model (XGBoost). We used amperometric signals generated from an electrochemical glucose sensor or glucose strip, which produces current values on glucose concentration. We estimated the hematocrit volume via processing of the amperometric signals to enhance diagnostic capabilities with the least error in the field of biomedical signal processing. The ML models were trained on the data set comprising 80% training set and 20% testing set in the Python programming language. The models were evaluated based on the metrics such as R-squared (R2), mean squared error (MSE), and root mean squared error (RMSE) values, and their reliability was assessed through the three validation mechanisms, namely, the relative error, K-fold cross-validation, and analysis of confidence interval. We observed that the XGBoost regression results were comparatively better than the LR and ANN results as corroborated through reliability analysis. It was concluded that XGBoost demonstrated 15% relative error between actual and predicted data and 68% accuracy with 6% standard deviation in the prediction obtained via a 5-fold cross-validation technique. The XGBoost model demonstrates comparatively better performance in terms of flexibility in tuning and interpretability options, which make it suitable for the regression task in the predictive biomedical analytics.

Modeling of Pedestrian Crash Frequency at Unsignalized Intersections Using Traffic Conflict Indicators

This study develops a predictive model to estimate pedestrian crash frequency at unsignalized intersections by analyzing pedestrian-vehicle conflicts using video data from Cheema Chowk and Duke Chowk in Ludhiana, Punjab. Utilizing AI techniques like YOLO and Deep SORT, key conflict indicators such as Post-Encroachment Time (PET) and Time to Collision (TTC) were extracted. These indicators informed the construction of Extreme Value Theory (EVT) models, with bivariate Copula models outperforming univariate ones in predictive accuracy, evidenced by low Mean Absolute Percentage Error (MAPE) values of 2.164% and 0.60%, respectively. The study demonstrates the efficacy of bivariate Copula models in forecasting pedestrian crashes, providing a proactive tool for enhancing road safety at unsignalized intersections by identifying high-risk areas and facilitating targeted safety interventions. This approach highlights the importance of incorporating multiple conflict indicators to improve predictive accuracy and reduce pedestrian casualties.

Establishment of predictive models for head loss and filtration efficiency of pontoon mesh rotary filter based on dimension analysis

Filters serve as core equipment to ensure the normal operation of micro-irrigation systems, with head loss and filtration efficiency serving as the two key indicators for evaluating performance. In this study, we used a pre-pump filter-pontoon mesh rotary filter as the research object and conducted physical model tests under the flow rate (798–1050 L1 h−1), sand content (0.5–2.5 g1 L−1), and aperture of the filter screen (0.125–0.180 mm). We then adopted range analysis, variance analysis (ANOVA), dimensional analysis, and the multiple linear regression (MLR) method to analyze the results. The results showed that the order of factors affecting the head loss of the assessment indices, from large to small, was as follows: flow rate, sand content, and aperture of the filter screen. The order of factors affecting the filtration efficiency of the assessment indices from large to small was as follows: sand content, flow rate, and aperture of the filter screen. Predictive models for head loss and filtration efficiency were developed, with coefficients of determination R2 of 0.969 and 0.954, and root mean square error (RMSE) values of 0.1041 and 0.0183. The model exhibited high accuracy and could be used to predict the head loss and filtration efficiency of the pontoon mesh rotary filter. In the test range, the optimal working condition of this filter was a flow rate of 930 L1 h−1, sand content of 2.0 g1 L−1, and 0.150 mm aperture of the filter screen. In addition, the head loss under this condition was 0.281 m, and the filtration efficiency was 84.01%. These results could serve as a reference for the further optimization and application of the pontoon mesh rotary filter, while also enriching the hydraulic performance and filtration performance of the pre-pump filter.

Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River

Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate.

Evaluation of zonulin levels in patients with migraine

BackgroundZonulin regulates permeability in blood–brain and intestinal barriers. The pathophysiology of migraine is based on the effect of neurogenic inflammation. The aim of the current investigation was to examine the serum zonulin level in individuals suffering from migraine.MethodsThe sample comprised 40 individuals who had migraine and 40 controls. Disease duration, attack duration, attack frequency, Visual Analog Scale (VAS) scores, and comorbidities were available for the migraine group. Serum zonulin levels were evaluated by using the ELISA method.ResultsThere were no statistically significant differences between the two groups concerning age or gender (p > 0.05). The zonulin value of patients with migraine was higher when compared to the controls, indicating a significant difference (p = 0.037; p < 0.05). The zonulin level did not correlate with disease duration, attack duration, VAS score, or attack frequency (p > 0.05). The receiver operating characteristic curve analysis of zonulin revealed a cut-off value of 30.58 and above, at which it had 52.50% sensitivity, 77.5% specificity, 70% positive predictive value, and 62% a negative predictive value. The area under the curve was 63.6%, and the standard error value was 6.3%. The analysis also showed a statistically significant correlation between migraine diagnosis and a zonulin level of 30.58 (p = 0.006; p < 0.01).ConclusionsElevated zonulin levels in patients with migraine support the disruption of the intestinal barrier and neuroinflammation in these patients. The zonulin level may be a predictive biomarker of migraine. Multicenter, randomized trials are needed to evaluate treatments for intestinal permeability and zonulin levels in migraine patients.

Commercial vehicle state estimation based on Interactive Multi-Model Square Root Cubature Kalman Filtering

Abstract Accurate and reliable vehicle state information is essential for ensuring vehicle safety and stability. While Interactive Multiple Model Square Root Cubature Kalman Filtering (IMM-SRCKF) has shown promise in state estimation, this work specifically addresses the unique challenges of achieving high-accuracy and real-time state estimation for commercial vehicles within complex driving scenarios. A nonlinear three-degree-of-freedom vehicle dynamic model, encompassing longitudinal, lateral, and yaw motions, was developed as a foundation. State space and observation equations were then derived. The IMM-SRCKF algorithm was strategically utilized and adapted to enhance the selection of process and measurement noise covariance matrices, effectively integrating multiple model strategies with the inherent advantages of square root Kalman filtering. This approach enables real-time dynamic adjustment of each sub-model's weights. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that the proposed IMM-SRCKF method offers significant improvements over traditional techniques like the Extended Kalman Filter (EKF), Cubature Kalman Filter (CKF), and Square Root Cubature Kalman Filter (SRCKF), particularly in demanding double lane change and serpentining maneuvers. The notably lower Normalized Root Mean Square Error (NRMSE) values confirm the substantial enhancements in both accuracy and stability for commercial vehicle state estimation.

Colorimetry in food analysis using digital imaging system

AbstractA non‐destructive, user‐friendly, portable colour measurement system was developed using the MATLAB interface that gives acceptable results with any imaging sensor. Digital images captured by the imaging sensor were used to obtain quantitative colour information in three formats, namely, RGB, XYZ and CIELAB. The zoom‐in feature is useful for obtaining detailed colour features of the image, while cropping of the region of interest aids in reducing the image matrix and, ultimately, the computational power required. Quantitative information on the colour value of the product (both average and region to region) can be obtained in all three formats (i.e., RGB, XYZ and CIELAB). It also gives the three closest Royal Horticultural Society (RHS) values of the product. The accuracy of the measurement system was first validated against a standard colour chart then it was tested using three image sensors of different configurations. Coefficient of determination (R2) and root mean square error values of 0.983 and 3.391 for L*, 0.994 and 1.851 for a* and 0.996 and 2.042 for b*, respectively, were obtained during performance evaluation. Similarly, when different imaging sensors were used for colour determination, the colour values showed no significant variation for all tested samples (i.e., various fruits and vegetables of different chroma/hues).

Prediction Of Cigarette And Tobacco Price Index In Tangerang City Using Ses And Double Linear Exponential Smoothing

The cigarette and tobacco price index is a crucial indicator that reflects changes in prices and demand in the tobacco market. Accurate predictions of this index are essential for the government and industry players in planning policies and business strategies. This study aims to forecast the cigarette and tobacco price index in Banten Province using the Single Exponential Smoothing (SES) and Double Linear Exponential Smoothing (DES) methods. The data used in this research comprises monthly cigarette and tobacco price index data from January 2021 to December 2023. SES and DES models are applied for prediction, and their results are evaluated using performance indicators such as Mean Absolute Percentage Error (MAPE). The research findings indicate that both methods are highly effective in predicting the cigarette and tobacco price index, with the SES method providing slightly more accurate predictions than the DES method. The MAPE error value for the SES method is 0.51%, while the DES method has a MAPE error value of 0.65%. These results are expected to contribute to policymakers and industry players in understanding price trends and making more informative decisions.

Signal Positioning of Lightning Detection and Warning System Combining Direction of Arrival Algorithm and Capon Algorithm

Aiming at the poor warning effect and slightly low precision of signal positioning in the traditional lightning detection and warning system, the research utilizes the artificial intelligence approach to signal positioning in the lightning detection and warning system for performance improvement. The study first digitized the lightning signal using the arrival direction algorithm and then used the Capon algorithm based on the digitized processing to reduce the interference and improve the accuracy of lightning positioning. The results indicated that the root mean square error value and positioning angle error of lightning warning signal positioning data processing by hybrid algorithm were 6.72% and 5.93%, respectively. Meanwhile, the percentage of detection efficiency and real time was 96.36% and 95.16%, respectively, and the anti-interference ability was 94.02%. Moreover, the average value of time-consuming lightning warning positioning and the positioning error were 2.39 s and 2.69%, respectively. Moreover, the performance of all the comparison indexes was better than that of the comparison methods. This indicates that the method not only improves the precision of lightning signal positioning but also enhances the stability and real-time performance of the system. It has significant application potential in the field of lightning detection and warning and can effectively improve the precision and timeliness of lightning warning.

Multiple Regression Analysis and Non-Dominated Sorting Genetic Algorithm II Optimization of Machining Carbon-Fiber-Reinforced Polyethylene Terephthalate Glycol Parts Fabricated via Additive Manufacturing Under Dry and Lubricated Conditions

The present research deals with the processing of the additively manufactured Carbon-Fiber-Reinforced Polymer (CFRP) under dry and lubricated cutting conditions, focusing on the generated surface roughness. The cutting speed, feed, and depth of cut were selected as the continuous variables. A comparison between the generated surface roughness of the dry and the lubricated cuts revealed that the presence of coolant contributed towards reducing surface roughness by more than 20% in most cases. Next, a regression analysis was performed with the obtained measurements, yielding a robust prediction model, with the determination coefficient R2 being equal to 94.65%. It was determined that feed and the corresponding interactions contributed more than 45% to the model’s R2, followed by the depth of cut and the machining condition. In addition, the cutting speed was the variable with the least effect on the response. The Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) was employed to identify the front of optimal solutions that consider both minimizing surface roughness and maximizing Material Removal Rate (MRR). Finally, a set of extra experiments proved the validity of the model by exhibiting relative error values, between the measured and predicted roughness, below 10%.

A hybrid Prairie INFO fission naked algorithm with stagnation mechanism for the parametric estimation of solar photovoltaic systems

This paper presents a study to enhance the performance of a recently introduced naked mole-rat algorithm (NMRA), by local optima avoidance, and better exploration as well as exploitation properties. A new set of algorithms, namely Prairie dog optimization algorithm, INFO, and Fission fusion optimization algorithm (FuFiO) are included in the fundamental framework of NMRA to enhance the exploration operation. The proposed algorithm is a hybrid algorithm based on four algorithms: Prairie Dog, INFO, Fission Fusion and Naked mole-rat (PIFN) algorithm. Five new mutation operators/inertia weights are exploited to make the algorithm self-adaptive in nature. Apart from that, a new stagnation phase is added for local optima avoidance. The proposed algorithm is tested for variable population, dimension size, and efficient set of parameters is analysed to make the algorithm self-adaptive in nature. Friedman as well as Wilcoxon rank-sum tests are performed to determine the effectiveness of the PIFN algorithm. On the basis of a comparison of outcomes, the PIFN algorithm is more effective and robust than the other optimization techniques evaluated by prior researchers to address standard benchmark functions (classical benchmarks, CEC 2017, and CEC-2019) and complex engineering design challenges. Furthermore, the effectiveness as well as reliability of the PIFN algorithm is demonstrated by testing using various PV modules, namely the RTC France Solar Cell (SDM, and DDM), Photowatt-PWP201, STM6- 40/36, and STP6-120/36 module. The results obtained from the PIFN algorithm are compared with various MH algorithms reported in the existing literature. The PIFN algorithm achieved the lowest root-mean-square error value, for RTC France Solar Cell (SDM) is 7.72E−04, RTC France Solar Cell (DDM) is 7.59E−04, STP6-120/36 module is 1.44E−02, STM6-40/36 module is 1.723E−03, and Photowatt-PWP201 module is 2.06E−03, respectively. In order to enhance the accuracy of the obtained results of parameter estimation of solar photovoltaic systems, we integrated the Newton-Raphson approach with the PIFN algorithm. Experimental and statistical results further prove the significance of the PIFN algorithm with respect to other algorithms.

Early Prediction of the Remaining Useful Life of Lithium‐Ion Cells Using Ensemble and Non‐Ensemble Algorithms

ABSTRACTLithium‐ion cells have become an important part of our daily lives. They are used to power mobile phones, laptops and more recently electric vehicles (both two‐ and four‐wheelers). The chemical behavior of the cells is rather complex and non‐linear. For reliable and sustainable use of the cells for practical applications, it is imperative to predict the precise pace at which their capacity will degrade. More importantly, the lifetime of the cells must be predicted at an early stage, which would accelerate development and design optimization of the cells. However, most of the existing methods cannot predict the lifetime at an early stage, since there is a weak correlation between the cell capacity and lifetime. In this study for accurate forecasting of the battery lifetime, the patterns of the parameters such as cell current, voltage, temperature, charging time, internal resistance, and capacity were examined during charging and discharging cycle of the cell. Twelve manually crafted features were prepared from these parameters. The dataset for the features was created using the raw data of the first 100 cycles of 124 cells. Six ensemble and non‐ensemble machine learning algorithms, namely, multiple linear regression (MLR), decision tree, support vector machine (SVM), gradient boosting machine (GBM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), were trained with the features for predicting the life‐cycle of the cells. The R2 and root mean squared error (RMSE) values of MLR, decision tree, SVM, GBM, LGBM, and XGBoost were found to be 0.72 and 201, 0.83 and 155, 0.85 and 146, 0.92 and 100, 0.9 and 112, and 0.94 and 95, respectively. The prediction accuracy of lithium‐ion cell life‐time was found to be the best with the XGBoost algorithm. This shows that only first 100 cycles are required foraccurately predicting the number of cycles the lithium‐ion cell can work for. Lastly, the results of the study were compared with the available studies in the literature. Three studies were chosen, and the RMSE of the method proposed in this study was found to be higher than the three studies by 43, 17, and 20. Therefore, the proposed method is a suitable option for predicting the lifetime of lithium‐ion cells during the early stages of its development.

Predictive analysis of COVID-19 occurrence and vaccination impacts across the 50 US states

ObjectiveThis study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities. The proposed model was evaluated using external validation to ensure optimal protection of vaccinated populations, distinguishing between males and females. MethodsThe data from the Centers for Disease Control and Prevention (CDC) in the US, collected between 2021 and 2023, were preprocessed through merging and imputation. A deep learning long short-term memory (LSTM) model was developed to analyze the effectiveness of vaccination in predicting COVID-19 cases and fatalities. The model, which was validated internally and externally, examined the impact of vaccination according to sex. The performance was assessed against current state-of-the-art models, with the LSTM model exhibiting lower root mean square error (RMSE) values. ResultsWe performed intra-, inter-, and external-validation analyses. First, one- and two-dose vaccinations significantly reduced the number of COVID-19 cases and mortality in highly affected states. Second, in the inter-model analysis, the LSTM outperformed the autoregressive integrated moving average (ARIMA) model in predicting cases and deaths, yielding superior results for Texas, California, and Florida. Third, with external validation, our LSTM model effectively predicted vaccination impacts regardless of sex. ConclusionsOur study demonstrates the effectiveness of COVID-19 vaccination, showing that full vaccination significantly reduced the number of confirmed cases and deaths, influencing future public health policies.