Coefficient Of Determination Research Articles

Treatment of Reactive Orange 16 Dye-Bearing Wastewater by Electro-Fenton Process with Stainless-Steel Electrodes: Statistical Optimization and Operational Analysis

Abstract: In the current work, the Electro-Fenton (EF) based Reactive Orange 16 (RO16) dye treatment was studied and compared with central composite (CC) and Taguchi design (TD) statistical optimization tools. Color removal (RC) and COD decay (RCOD) were chosen responses for the effect of pH (A), electrolysis time (B), initial dye concentration (C), and current density (D). The facecentred CC design and L16 orthogonal array were used in the experimental procedures. At optimal conditions, the coefficient of determination (R2) values of 0.99 for CC and 0.97 for TD suggest statistical significance and good model agreement. The results of the ANOVA and Prob. > F values supported the model’s successful experimental data fitting. Taguchi method was found as an appropriate methodology for parameter percentage contributions with fewer experimental runs. Moreover, the S/N ratio charts proved to be a successful CC design replacement. The current density and pH were found to be the most important factors for the EF process. A higher biodegradability (BOD5/COD) and minimum iron concentration (0.45 mg/L) in the effluent sludge demonstrated good environmental disposal suitability. In the last, the effect of various inhibitors/scavengers (SO4 −2, PO4 −3, EDTA, etc.) on the EF process performance was also carried out.

Assessing the Influence of Sugarcane Bagasse Ash for the Production of Eco-friendly Concrete: Experimental and Machine Learning Approaches

The innovative utilization of sugarcane bagasse ash (SCBA) in producing lightweight concrete (LWC) needs to be explored experimentally and quantification with machine learning approaches. Therefore, this study aims to investigate fresh, mechanical and microstructural characteristics and machine learning modeling of incorporating SCBA as a partial substitute of cement. In this investigation, OPC was replaced partially by the weight of SCBA in the proportion of 0%, 5%, 10%, 15%, and 20%. The influences of SCBA on the fresh characteristics of concrete were investigated through tests of the slump, compaction factor, Kelly ball, k-slump, density, and air content. In addition, for hardened properties compressive, splitting tensile, flexural strength, and static modulus of elasticity were tested at 7 and 28 days. Furthermore, a microstructural characteristic was carried out for varying content of SCBA. Different machine learning (ML) predictive models were performed utilizing artificial neural network (ANN) and random forest (RF), to predict the fresh and mechanical attributes of the LWC. It was noticed that the workability value of fresh concrete was enhanced almost by 40% to 50% with the incorporation of SCBA content. The hardened test results revealed that a mixture having SCBA up to 10% content SCBA resulted in nearly 10% to 30% better performance to satisfy the requirement of LWC. Regarding the sustainability assessment, the less embodied CO2 (eCO2) is produced in SCBA mixes, whereas it is also economically beneficial to produce SCBA mixes. Models' efficacy was evaluated including mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). The maximum R2 and minimum RMSE came across for RF (0.989 and 1.393, respectively) which provides the well estimation of the model.

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

Quality and customer loyalty in the port services of Manzanillo

The customs industry, particularly within the Mexican context, plays a significant role both domestically and internationally, owing to its contribution to the global economy. With the expansion and standardization of customs clearance services, there has emerged a necessity for providers of these services to seek customer loyalty. Grounded in the principles of quality, the study examines the elements of the SERVQUAL model: tangibles, reliability, responsiveness, assurance, and empathy. Goals:The aim of the study was to extend the SERVQUAL model by incorporating customer attitude as an additional component within the three primary dimensions for measuring service quality. Methodology: A survey was employed to gather data from 100 customs brokerage firms through simple random sampling. Given the utilization of structural equation modeling (SEM) methodology to assess the theoretical model, Partial Least Squares (PLS) analysis technique was applied using Smart PLS 3.3.0 software. The findings indicated that the extended model had a significant impact on both customer satisfaction and loyalty within the customs facilities of Manzanillo, Colima. The coefficients of determination were 61.5% for customer satisfaction and 54% for customer loyalty, with a root mean square error of approximation (SRMR) of 0.065, confirming the model's predictive validity. Contributions: Customs facilities should focus their strategies on addressing issues that may be impacting service quality, particularly concerning sensitivity, empathy, and reliability factors, as these have a moderate level of influence on quality determination, according to customs agencies' perceptions. Regarding the inherent limitations of online surveys and their application in specialized groups, it is crucial to expand the research to include other clients of the customs facility.

A comparison between geomembrane-sand tests and machine learning predictions

The interaction between soils and geosynthetics plays an important role in the applications of these materials for reinforcement in geotechnical engineering. The complexities of soil-geosynthetic interactions vary depending on the type and properties of both the geosynthetic and the soil. This paper introduces a machine learning approach, specifically a random forest algorithm, for predicting interface friction angles. The dataset comprises 495 interfaces involving geomembranes and sand, with 14 influencing parameters recorded for each interface, influencing the shear strength outcome. In the analysis, Pearson's correlation coefficient is employed to measure the linear interdependence between each pair of input-input and input-output variables. Following the linear regression analysis, an optimized random forest is utilized to project the interface friction angle. The random forest algorithm divides the selected data into training and testing sets, and only 3% of the training set and 6% of the testing set exceed ±5° from the actual records. The coefficient of determination (R2) indicates strong agreement between the predicted and laboratory study friction angles, with R2 = 0.93 for the training set and R2 = 0.92 for the testing set. Consequently, the random forest algorithm demonstrates effectiveness in predicting interface friction angles.

Time in range prediction using the experimental mobile application in type 1 diabetes

BACKGROUND: Time in range (TIR) is a promising indicator of glycemic control used for evaluation of continuous glucose monitoring (CGM) for patients with diabetes mellitus (DM). The current problem is the assessment and prediction of TIR for patients who use self-monitoring of blood glucose (SМBG) corresponding low CGM availability for the majority of diabetic patients.AIM: To develop a predictive model of TIR for patients with T1DM based on data of the experimental mobile application.MATERIALS AND METHODS: An analysis of 1253 professional CGM profiles of patients with T1DM was performed. On the base of included records, TIR(CGM) was calculated and training models of 7-point SMBG profiles were generated. SMBG profiles’re loaded into the developed experimental mobile application that calculated standard glycemic control parameters. The dataset was divided into main and test samples (80 and 20%). For the main sample, the following methods’re used to develop predictive models: simple linear regression (SLR), multiple linear regression (MLR), artificial neural network (ANN). The effectiveness of the developed models was assessed on the test sample with the calculation of the mean absolute error (MAE), the root mean square error (RMSE).RESULTS: The 568 CGM profiles’re included in the study. TIR in the main group (n=454) — 45 [33; 65]%, in the test group (n=114) — 43 [33; 58]%. The most significant predictors of the regression models were the derived TIR (dTIR), p<0,001; derived time below range level 1 (dTBR1), p<0,001; standard deviation of blood glucose (SD), p=0,007. Determination coefficient for SLR (predictor: dTIR) — 0,844; for MLR (predictors: dTIR, dTBR1, SD) — 0,907. ANN multilayer perceptron models with two and one hidden layers’re developed, with the RMSE on the validation set 4,617 and 6,639%, respectively. The results of the forecast efficiency on the test sample were: dTIR: MAE — 6,82%, RMSE — 8,60%; SLR: MAE — 5,66%, RMSE — 7,34%; MLR: MAE — 4,18%, RMSE — 5,28%; ANN (2 layers): MAE — 4,14%, RMSE — 5,19%; ANN (1 layer): MAE — 4,44%, RMSE — 5,52%.CONCLUSION: ANN with two hidden layers and MLR demonstrated the best ability for TIR prediction. Further studies are required for clinical validation of developed prognostic models.

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

Multifractal Characteristics and Displacement Prediction of Deformation on Tunnel Portal Slope of Shallow Buried Tunnel Adjacent to Important Structures

The tunnel portal section is often in extremely weak and fragmented strata, and the deformation of the portal side and slope will affect the stability of the surrounding rock and the tunnel-supporting structure. However, the deformation characteristics and displacement development patterns of slopes in the tunnel portal section are not clear. In this paper, the multifractal characteristics and displacement prediction of the deformation sequence of the tunnel portal slope at of a weak and water-rich shallow buried tunnel adjacent to an important structure are studied in depth. Combined with the deformation characteristics of the tunnel portal slope, a suitable slope monitoring and measurement scheme is designed to analyze the deformation pattern of the tunnel portal slope. Based on the multifractal detrended fluctuation analysis (MF-DFA) method, the multifractal characteristics of the deformation monitoring sequences at each monitoring point of the tunnel portal slope are analyzed. The multifractal characteristics of displacement sequences at different monitoring points of the tunnel portal slope are consistent with the actual monitoring results. Furthermore, the Long Short-Term Memory (LSTM) model is optimized using the Particle Swarm Optimization (PSO) algorithm to predict the deformation of the tunnel portal slope. The results show that the maximum mean square error (MSE) of the horizontal displacement test set prediction results is 0.142, and the coefficient of determination (R2) is higher than 91%. The maximum value of MSE for vertical displacement test set prediction is 0.069, and the R2 are higher than 91%. The study shows that the performance of the PSO-LSTM prediction model can meet the requirements for predicting the displacement of the tunnel portal slope. Based on the MF-DFA method and PSO-LSTM prediction model, the fluctuation characteristics of the displacement value of the tunnel portal section can be accurately identified and the displacement development pattern can be effectively predicted. The conclusions of the study are of great practical significance for the safe construction of the tunnel portal section.

Comparison of urban physical environments and thermal properties extracted from unmanned aerial vehicle images and ENVI-met model simulations

Identifying the thermal properties of urban areas and integrating these properties into spatial planning is essential for sustainable development. Continuous and efficient improvements of urban thermal environments require novel studies, which are limited to date. This study addresses this gap by presenting a comparison of the physical environments and thermal properties of an urban area (Changwon National University, Gyeongsangnam-do, South Korea) based on unmanned aerial vehicle (UAV) images and ENVI-met model simulations. We predicted the thermal environment of the study area using UAV images and explored the potential application of UAVs in spatial planning. The comparison revealed that the coefficient of determination (R2) between mean radiant temperature (Tmrt) values simulated by the ENVI-met model and predicted from UAV images was 0.168–0.320. Significant differences were observed in shaded areas and vegetated regions with tall trees, where UAVs encountered difficulties in collecting accurate information due to obstructed visibility. The discrepancies between the simulated and predicted Tmrt values highlight the limitations of UAVs in capturing data in shaded and densely vegetated areas, as well as disparities in the physical environments determined by the two methods. Despite these limitations, UAVs can be valuable tools for urban and environmental planning. Future advancements addressing these limitations may enable UAVs to efficiently diagnose urban thermal environments and guide improvement efforts.

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk.

To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the analysis of comorbidities. In this cross-sectional study, we have used data from 11,028 anonymized records of patients admitted to a high-complexity hospital in Bogota, Colombia between 2017-2019 with a primary diagnosis of Diabetes. These cases were classified according to Charlson's comorbidity index in several risk categories. The main variables analyzed in this study are hospitalization costs (which include pharmaceutical and non-pharmaceutical expenditures), age, gender, length of stay, medicines and services consumed, and comorbidities assessed by the Charlson's index. The model's dependent variable is expenditure (composed of pharmaceutical and non-pharmaceutical expenditures). Based on these variables, different machine learning models (Multivariate linear regression, Lasso model, and Neural Networks) were used to estimate the pharmaceutical and non-pharmaceutical expenditures associated with the clinical risk classification. To evaluate the performance of these models, different metrics were used: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). The results indicate that the Neural Networks model performed better in terms of accuracy in predicting pharmaceutical and non-pharmaceutical expenditures considering the clinical risk based on Charlson's comorbidity index. A deeper understanding and experimentation with Neural Networks can improve these preliminary results, therefore we can also conclude that the main variables used and those that were proposed can be used as predictors for the medical expenditures of patients with diabetes type-II. With the increase of technology elements and tools, it is possible to build models that allow decision-makers in hospitals to improve the resource planning process given the accuracy obtained with the different models tested.

Comparing the performance of machine learning models for predicting the compressive strength of concrete

This work aimed to investigate and compare the performance of different machine learning models in predicting the compressive strength of concrete using a data set of 1234 compressive strength values. The predictive variables were selected based on their relevance using the SelectKBest method, resulting in an analysis of eight and six predictive variables. The evaluation was conducted through linear correlation studies via simple linear regression and non-linear correlation studies using support vector regression (SVR), random forest (RF), gradient boosting (GB), and artificial neural networks (ANN). The results showed a coefficient of determination (R2) = 0.897 and a root mean square error (RMSE) = 6.535 MPa for SVR, R2 = 0.885 and RMSE = 5.437 MPa for GB, R2 = 0.868 and RMSE = 5.859 MPa for GB and R2 = 0.894 and RMSE = 5.192 MPa for ANN, all for test set and eight predictor variables. The comparison between the machine learning methods revealed significant differences. For instance, ANN stood out with a higher R2 value, demonstrating its remarkable ability to explain the variability in the data. ANN also showed the lowest RMSE value, indicating notable accuracy in the predictions. Although ANN has demonstrated higher performance, GB shows a closer performance, which no differences from a practical application. The choice between these approaches depends on considerations regarding the balance between explainability and accuracy. While GB provides a more in-depth understanding of the relationship between variables, ANN stands out for the accuracy of its predictions.

Simultaneous Analysis of Thirteen Compounds in Yeokwisan Using High-Performance Liquid Chromatography-Photodiode Array Detection and Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry and Their Antioxidant Effects.

Yeokwisan (YWS) is an herbal medicine prescription consisting of six oriental herbal medicines, developed to treat reflux esophagitis. We focused on developing an analytical method capable of simultaneously quantifying 13 compounds in YWS samples using high-performance liquid chromatography-photodiode array detection (HPLC-PDA) and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and exploring their antioxidant effects. All compounds examined in both analytical systems were chromatographically separated on a SunFireTM C18 (4.6 × 250 mm, 5 μm) column and an Acquity UPLC BEH C18 (2.1 × 100 mm, 1.7 μm) column using gradient elution of a water-acetonitrile mobile phase. Antioxidant effects were evaluated based on radical scavenging activity (DPPH and ABTS tests) and ferrous ion chelating activity. In two analytical methods, the coefficient of determination of the regression equation was ≥0.9965, the recovery range was 81.11-108.21% (relative standard deviation (RSD) ≤ 9.33%), and the precision was RSD ≤ 11.10%. Application of the optimized analysis conditions gave quantitative analysis results for YWS samples of 0.02-100.36 mg/g. Evaluation of the antioxidant effects revealed that baicalein and baicalin exhibit significant antioxidant activity, suggesting that they play an important role in the antioxidant effects of YWS.

AI based optimal analysis of electro-osmotic peristaltic motion of non-Newtonian fluid with chemical reaction using artificial neural networks and response surface methodology

Purpose The present work is devoted to investigating the sensitivity analysis of the electroosmotic peristaltic motion of non-Newtonian Casson fluid with the effect of the chemical reaction and magnetohydrodynamics through the porous medium. The main focus is on flow efficiency quantities such as pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall. This initiative is to bridge the existing gap in the available literature. Design/methodology/approach The governing equations of the problem are mathematically formulated and subsequently simplified for sensitivity analysis under the assumptions of a long wavelength and a small Reynolds number. The simplified equations take the form of coupled nonlinear differential equations, which are solved using the built-in Matlab routine bvp4c. The response surface methodology and artificial neural networks are used to develop the empirical model for pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall. Findings The empirical model demonstrates an excellent fit with a coefficient of determination reaching 100% for responses, frictional forces on the upper wall and frictional forces on the lower wall and 99.99% for response, for pressure rise per wavelength. It is revealed through the sensitivity analysis that pressure rise per wavelength, frictional forces on the upper wall and frictional forces on the lower wall are most sensitive to the permeability parameter at all levels. Originality/value The objective of this study is to use artificial neural networks simulation and analyze the sensitivity of electroosmotic peristaltic motion of non-Newtonian fluid with the effect of chemical reaction.

Kinetic modeling for upgrading of heavy crude oil via thermal cracking in porous media: SARA fractions and gas compositions

Pyrolysis is regarded as the primary reaction for heavy crude oil upgrading during ISC process, and gas compositions produced by this reaction is also gaining attention. It is critical to establish the kinetic model to predict all products. The kinetic of heavy oil upgrading was studied and all experiments were carried out in an autoclave at 350–450 °C, 0.5 MPa, and 1–10 h. An improved kinetic including SARA fractions, coke, and gas compositions was proposed be more detailed description of the reaction process. Smaller SSE and bigger coefficients of determination (>0.99) of parity plots suggested the accuracy of calculated parameters of the improved kinetic model. The sensitivity analysis results demonstrate that the obtained kinetic parameters represent the optimal values. Gas prediction, a neglected aspect in previous studies, was also addressed, further enhancing the model’s utility.

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

The inhibitory activity of peptides on angiotensin-converting enzyme (ACE) is a measure of their antihypertensive potential. Quantitative structure–activity relationship (QSAR) models obtained based on the analysis of sequences of amino acids are suggested. The average determination coefficient for the active training sets is 0.36 ± 0.07. The average determination coefficient for validation sets is 0.79 ± 0.02. The paradoxical situation is caused by applying the vector of ideality of correlation, which improves the statistical quality of a model for the calibration and validation sets but is detrimental to the statistical quality of models for the training sets.

Accuracy improvement of a multi-ring beam structured inner surface measurement: via novel calibration methodology and light source optimization

Parts with a large depth-to-diameter ratio play a critical role in the military, aerospace, and automotive industries. However, accurately measuring their inner surface profile remains challenging owing to the lack of adequate and accurate sensors. We developed a multi-ring structured light system to obtain three-dimensional data of inner contours, such as inner diameters, which are crucial for ensuring component performance and safety. In this study, we proposed three simple yet effective techniques to improve the multi-ring beam structured measurement system. First, we designed a distortion correction method to calibrate the imaging system. Second, a two-step calibration approach was used to calibrate the multi-ring projection. Meanwhile, we benchmarked the effects of different light sources on image speckles. The calibration results demonstrated that the coefficient of determination (R-2) used for line fitting exceeded 0.999. Moreover, the measurement experimental results show that the uncertainty of less than 10 µm and the smallest measurable pipe inner diameter can reach 15 mm, demonstrating that our methods are promising for improving the accuracy of structured light optical sensing systems. This system satisfies the measurement requirements and can be immediately utilized to meet the high demand for inner contour measurements in industrial applications.

Length-weight relationship, condition factor, and diet analysis of thresher sharks (Family: Alopiidae) along the southern coast of India

Sharks are fascinating groups of elasmobranchs that are consistently threatened by commercial tuna fishing operations, as they share a common habitat with most scombrids. This raises ecological concerns to explore the vulnerability of the species in its prevailing ecosystem. The length-weight relationship of three known species of thresher sharks viz, the common thresher (Alopias vulpinus), bigeye thresher (A. superciliosus), and pelagic thresher (A. pelagicus) were estimated based on 771 specimens landed as bycatches from the commercial tuna gillnetter and multi-gear utilizing boats (MGUB) off Bay of Bengal between June 2022 and November 2023. The fork length (FL) ranged from a minimum of 51 cm to a maximum of 288 cm. The dominant fork length class for A. vulpinus, A. superciliosus, and A. pelagicus were 101–120, 101–140 cm, and 61–80 cm respectively. The estimated slope (b) values in the range between 2.48 and 2.66 reveal negative allometry for all three species. The coefficient of determination was highly significant with r2 values of 0.98–0.99. The male-to-female ratio for A. vulpinus (1.03:1) and A. superciliosus (1.07:1), were male-biased except for A. pelagicus, (1:1). We also present data on the foraging components of the bigeye thresher shark that typically comprised teleosts and cephalopods, with occasional crustacean prey. Biological data collection and constant surveillance would help better understand the population structure of this iconic marine species and preserve them from overexploitation.

Evaluating Plasmodium falciparum automatic detection and parasitemia estimation: A comparative study on thin blood smear images.

Malaria is a deadly disease that is transmitted through mosquito bites. Microscopists use a microscope to examine thin blood smears at high magnification (1000x) to identify parasites in red blood cells (RBCs). Estimating parasitemia is essential in determining the severity of the Plasmodium falciparum infection and guiding treatment. However, this process is time-consuming, labor-intensive, and subject to variation, which can directly affect patient outcomes. In this retrospective study, we compared three methods for measuring parasitemia from a collection of anonymized thin blood smears of patients with Plasmodium falciparum obtained from the Clinical Department of Parasitology-Mycology, National Reference Center (NRC) for Malaria in Paris, France. We first analyzed the impact of the number of field images on parasitemia count using our framework, MALARIS, which features a top-classifier convolutional neural network (CNN). Additionally, we studied the variation between different microscopists using two manual techniques to demonstrate the need for a reliable and reproducible automated system. Finally, we included thin blood smear images from an additional 102 patients to compare the performance and correlation of our system with manual microscopy and flow cytometry. Our results showed strong correlations between the three methods, with a coefficient of determination between 0.87 and 0.92.