Acceptable Error Research Articles

Environmentally Friendly Synthesis of Gelatin Hydrogel Nanoparticles for Gastric Cancer Treatment, Bisphenol A Sensing and Nursing Applications: Fabrication, Characterization and ANN Modeling

This study presents a dual application approach for the environmentally friendly synthesis of gelatin hydrogel nanoparticles with potential applications in gastric cancer treatment, bisphenol A (BPA) sensing, and nursing. Gelatin hydrogel nanoparticles were synthesized using a green and freeze-drying method, avoiding the use of toxic chemicals and solvents. The nanoparticles showed excellent biocompatibility and promising potential for drug delivery system (DDS) in gastric cancer treatment. The controlled release of anticancer drugs from the gelatin nanoparticles was showed, highlighting their potential in targeted therapy. Additionally, the gelatin hydrogel nanoparticles were explored for BPA sensing. BPA is a widely used chemical known for its adverse effects on human health. The gelatin nanoparticles showed high selectivity and sensitivity towards BPA detection, making them suitable for environmental monitoring and health applications using scanning electron microscope (SEM). Also, in this study, an artificial neural network (ANN) was used to estimate the release of docetaxel (%) at 72 hours, the release of paclitaxel (%) at 72 hours, tensile strength with sample (wt%), and porosity (%) in broader ranges than the experimental samples. The environmentally friendly synthesis of gelatin hydrogel nanoparticles presented in this study offers a versatile platform with dual applications in gastric cancer treatment and sensing of harmful chemicals. The obtained results show the potential of these nanoparticles for innovative therapeutic and diagnostic strategies in healthcare and environmental monitoring. The study showed the development of sustainable and multifunctional nanomaterials for various biomedical applications. The modeling of the neural network predictions shows that increasing the sample (wt%) and porosity (%) leads to an increase in the release of docetaxel (%) at 72 hours, the release of paclitaxel (%) at 72 hours, and tensile strength. As porosity decreases, the release of docetaxel increases, and the release of paclitaxel and tensile strength also increase. Additionally, the prediction errors of the ANN in this study were evaluated using linear regression, showing acceptable error rates compared to the target results obtained from the experimental tests.

Numerical Simulation and Artificial Neural Network Modeling of Cavitation on 2D Tandem Hydrofoils Arrangement

Abstract In the current research, we used a parametric approach to investigate cavitation with tandem-arranged Clark Y-11.7% hydrofoils. We simulated the tandem-arranged hydrofoil system using ANSYS FLUENT with the Zwart cavitation model combined with the Transition SST turbulence model. To substantiate the accuracy and reliability of the numerical model, computations were performed for an individual Clark Y-11.7% hydrofoil and compared to experimental results. The following parameters were studied in tandem arrangement: cavitation number(σ), horizontal direction spacing ratio between two hydrofoils(x), vertical direction spacing ratio between two hydrofoils(y), angle of attack (AOA) of the upstream hydrofoil (α 1), and angle of the downstream hydrofoil relative to the upstream hydrofoil (α 2). The lift coefficient (C L) was used to estimate due to the acceptable error. An optimal artificial neural network (ANN) model was trained based on the five-parameter combination from numerical simulations. The model’s weights and biases were presented, providing a prediction method for hydrofoils in tandem arrangement with cavitation. A global sensitivity analysis based on the trained model is implemented, which reveals the AOA of the downstream hydrofoil (α 2), the horizontal spacing ratio (x), and the AOA of the upstream hydrofoil (α 1) have a main effect on the performance of the tandem arrangement.

Evaluation of the Colistin HiMIC Plate Kit for Colistin Susceptibility Testing of Klebsiella pneumoniae.

Colistin HiMIC Plate Kit (HiMedia Laboratories), a new commercial broth microdilution (BMD) test for colistin susceptibility testing was evaluated. BMD according to ISO standard 20776-1 (2019) with two-fold dilutions from 128 to 0.125 mg/L was used as a reference method. The colistin reference MICs (minimal inhibitory concentration) ranged from 0,25 to 128 mg/L with 15 (20.5%; 15/73) isolates having colistin reference MICs close to the current EUCAST breakpoint (MICs of 2, 4, and 8 mg/L). The study assessed the compliance of a commercial kit with the CLSI criteria, including categorical agreement (CA) and essential agreement (EA ≥90%), very major error (VME rate) <3%, and major error (ME) rate <3%. On 73 carbapenemase-producing Klebsiella pneumoniae isolates Colistin HiMICTM Plate Kit showed CA and EA of 100% (73/73; 95% CI 0.97-1.00) and 82.2% (60/73; 95% CI: 0.72-0.90), respectively. No ME (false-resistant results) and VME (false-susceptible results) were detected. Kit showed acceptable CA, ME, and VME error parameters, whereas the EA did not meet the ≥90% threshold. Laboratories must check for possible limitations of commercial kits before they can be used for colistin susceptibility testing.

Numerical Solutions of Some Weak Singular Nonlinear Integral Equations of The First Type Using The Spectral Collocation Method

In this paper, a collocation-spectral approximation is proposed for weakly nonlinear and neutral singular Volterra integral-differential equations with rough solutions. We used some appropriate transformations to transform the equation of the equation into a new equation, so that the solution of the new equation has a better order (smoothness) and Jacobi's orthogonal polynomial theory can be easily used. Under appropriate assumptions on the nonlinear part, we were able to perform an acceptable error analysis on the soft and the weighted soft. To obtain a numerical approximation, some numerical examples (linear and non-linear) with uneven solutions are considered and numerical results are also presented. Also, a comparison between the proposed method and some existing numerical methods is also provided.

Unveiling the hidden narratives: investigating gender, identity, and activism in contemporary dance performances within the realm of social arts

Objectives: This study shows how contemporary dance serves as a dynamic platform for challenging and redefining notions of gender and identity. By analyzing a variety of contemporary dance performances, the research aims to reveal the underlying narratives that reflect and contest societal norms. Through a detailed examination of choreographic choices, movement vocabulary, and thematic elements, the study highlights how these components contribute to the deconstruction of traditional gender constructs. Methods: Additionally, the research investigates the role of contemporary dance as a form of activism, focusing on how performers use their craft to entertain, raise awareness, and provoke dialogue on social issues. An artificial neural network (ANN) was employed to estimate the relationships between gender fluidity, the challenge of social norms, and their effects on transformation potential, activist participation, and artistic expression. Results: The results indicate that increases in both gender fluidity and thechallenge to social norms significantly enhance transformation potential, activist participation, and artistic expression. The accuracy of the ANN's predictions was validated through linear regression analysis, demonstrating acceptable error margins compared to experimental results. Conclusions: This study ultimately underscores the transformative power of contemporary dance in shaping social discourse and advocacy.

WebMRT: An online tool to predict summertime mean radiant temperature using machine learning

Mean Radiant Temperature (Tmrt) is the most critical atmospheric variable influencing outdoor human thermal exposure and comfort in hot, dry environments. However, accurately quantifying Tmrt requires time-consuming field measurements with expensive equipment or complex, resource-intensive computations. We introduce WebMRT, an online tool to predict Tmrt using a data-driven approach. It features an intuitive interface using air temperature, shading status, and built environment features as predictors of Tmrt for a user-selected summer day, time, and location. Utilizing a tree-based ensemble model, WebMRT is trained on state-of-the-art human-biometeorological data collected by MaRTy using LightGBM after evaluating its performance against several candidate machine learning regressors. Feature engineering was applied to the day and time input, and two additional temporal features were derived: ‘Solar Altitude’ and ‘Minutes-from-Sunrise’. These inputs are integrated into the user interface, emphasizing simplicity and easy access for users at the frontend. After training the regressor on MaRTy datasets and employing k-fold cross-validation with ten folds, the model demonstrated strong predictive power (R2=0.92) with acceptable error (RMSE=3.43, MAPE=5.33) and bias (MBE=0.20). WebMRT also features optional fisheye photo uploads, processed using transfer learning techniques for image segmentation, further enhancing the tool's predictive accuracy, user experience, and applications towards climate action decision-making processes.

A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices

Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck, although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67±7.64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.67\\pm 7.64$$\\end{document}% within the clinically acceptable ± 5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate value accuracy within the clinically appropriate ± 3 BrPM of 94.94±3.56\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$94.94\\pm 3.56$$\\end{document}% with reference to the guiding visual metronome, and 88.4±7.63\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88.4\\pm 7.63$$\\end{document}% with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the advantages of the neck as a multi-modal physiological monitoring location.

Assessing the equivalent spring modelling method of CFS elements encased in ultra-lightweight concrete

An efficient finite element approach was recently developed to analyse encased cold-formed steel (CFS) structures. This new technique replaced encasing material with unidirectional springs, analogous to the Winkler foundation concept, to shorten the analysis time while ensuring accuracy and reliability in predicting the structural behaviour of encased CFS components. In this paper, the validity, and limitations of the simplified spring model to represent outstanding plates were assessed. The investigation demonstrated that the simplified spring model could effectively predict the ultimate load for a wide range of ultra-lightweight concrete moduli (50-250 MPa) with an acceptable error. The analysis indicated that plate elements initially in cross-section class 4 without encasing material become at least class 3, or better as a consequence of encasing. Previously reported experiments were used to evaluate the performance of the ESM. The analysis demonstrated that the ESM can accurately predict the local failure ultimate load of encased CFS sections with an acceptable error percent and significantly less computational effort than a 3D solid model.

A Filterless Optical Generation Scheme of Frequency 32-Tupling Millimeter-Wave Signal Based on Cascaded Polarization Modulators

ABSTRACT In the work, a new method for generating a frequency 32-tupling millimeter-wave (mm-wave) signal with higher quality only using four polarization modulators (PolM) is proposed. To generate a pure 32-tupling mm-wave signal, it is necessary to beat ± 16th-order optical sidebands beat frequency. Mathematical calculations and simulation results both show that the scheme can obtain ± 16th-order harmonics, thereby generating frequency 32-tupling mm-wave signals. The optical sideband suppression ratio (OSSR) and radio frequency spurious suppression ratio (RFSSR) can reach 53.74 dB and 47.59 dB, respectively. Besides, the parameters that will affect OSSR and RFSSR are also analyzed and acceptable error ranges are provided.

A study on the precision of voxel- and surface-based mandibular superimposition.

To apply the more accurate technique for mandibular superimposition and provide a valuable reference for the assessment of mandibular tooth movement and condylar remodeling before and after orthodontic treatment. This retrospective study involved 38 adult patients who underwent two cone beam computer tomography (CBCT) scans at different stages of treatment at Fujian Provincial People's Hospital between September, 2020 and December, 2022. The software Dolphin was used for mandible segmentation, enabling voxel-based mandibular superimposition with the mandibular ramus as the reference region. The Geomagic Wrap software was employed to process surface-based mandibular superimposition with the mandibular ramus as a reference. Additionally, the voxel and surface-based methods were compared for precision, with the mandibular ramus being the reference. After voxel-based mandibular superimposition using the mandibular ramus as a reference, with all measurement errors (< 0.20 mm). In contrast, the results of surface-based mandibular superimposition with the same reference, and the measurement errors were all less than 0.10 mm. The Wilcoxon signed-rank test revealed statistically significant differences between AS1 and BS1, AS2 and BS2, AS3 and BS3, and AS4 and BS4 (all r< 0.05). Moreover, the absolute mean distances of AS1-AS4 were all greater than those of BS1-BS4. All mandibular superimposition procedures, including the voxel- and surface-based ones using the mandibular ramus as a reference, have acceptable surface errors (< 0.20 mm), indicating the good reliability of these techniques. Under the specified conditions, surface-based mandibular superimposition appears to yield a higher degree of precision compared with the voxel-based technique.

O31 VALIDATION ASSESSMENT OF A CUFF-BASED CENTRAL BLOOD PRESSURE DEVICE ACCORDING TO ARTERY SOCIETY RECOMMENDATIONS

Background and objective. Uscom BP+ is a cuff-based blood pressure (BP) device designed to non-invasively estimate central BP as distinct from conventional brachial BP. This study aimed to assess the accuracy of the Uscom BP+ device compared to invasively measured BP. Methods. Automated non-invasive cuff central BP (using the Uscom BP+ device) and invasive central aortic BP (using fluid-filled catheters) were recorded simultaneously in 191 participants (65% male, aged 66±11 years) receiving coronary angiography at three independent research sites (Hobart, Australia, Perugia, Italy and Wroclaw, Poland). Validation assessment procedures were undertaken according to the Artery Society recommendations and with a minimal acceptable error of ≤5±≤8 mmHg as pass criteria. Results. Using the device default calibration technique (brachial cuff systolic [SBP] and diastolic BP [DBP]), cuff central SBP underestimated invasive central SBP [mean(SD) difference: -10.2(11.2) mmHg] and cuff central DBP overestimated invasive central DBP [mean(SD) difference: 9.7(8.6) mmHg]. When calibrating by cuff mean arterial pressure and DBP in a sub-group (n=140), SBP accuracy was improved, but variability remained high [mean(SD) difference: -6.4(13.8) mmHg, p=0.002 vs default calibration], whereas DBP accuracy and variability remained similar [mean(SD) difference: 9.0(8.4) mmHg, p=0.23 vs default calibration]. Conclusions. The Uscom BP+ cuff device does not pass the Artery Society accuracy criteria compared with invasively measured central BP.

Economical Experimental Device for Evaluating Thermal Conductivity in Construction Materials under Limited Research Funding

African scientific research faces formidable challenges, particularly with limited access to state-of-the-art measurement instruments. The high cost associated with these devices presents a significant barrier for regional research laboratories, impeding their ability to conduct sophisticated experiments and gather precise data. This predicament not only hampers the individual laboratories but also has broader implications for the African scientific community and the advancement of knowledge in developing nations—the financial cost barrier considerably impacts the research quality of these laboratories. Reflection on technical and economical solutions needs to be quickly found to help these countries advance their research. In civil engineering, the thermal conductivity property is the most important measurement for characterizing heat transfer in construction materials. Existing devices (i.e., conductometers) in a laboratory are expensive (approximately EUR 30,000) and unavailable for some African laboratories. This study proposes a new and affordable device to evaluate thermal conductivity in construction materials. The method involves establishing a thermal flux between a heat source (from the Joule effect provided by steel wool where a current is circulating) and a cold source (generated by ice cubes) under steady-state conditions. The development of the cylindrical prototype is based on the comparative flux-meter method outlined in the measuring protocol of the ASTM E1225 standard document. Experiments were conducted on four distinct materials (polystyrene, wood, agglomerated wood, and rigid foam). The results indicate a correct correlation between the experimental values obtained from the newly developed prototype and the reference values found in the literature. For example, concerning the experimental polystyrene study, the detailed case analysis reveals a good correlation, with a deviation of only 4.88%. The percent error found falls within the acceptable range indicated by the standard recommendations of the ASTM E1225 standard, i.e., within 5% acceptable error.

Intra-Urban Induced Heating Assessment in Kuwait’s Desert Metropolis Using Explainable Machine Learning

Intra-urban induced heating (IUIH) in hot desert cities exhibits distinct patterns and complex diurnal interactions with built environment features, differing significantly from those in temperate areas and remains not fully understood. Understanding how various built environment features contribute to intra-urban thermal variability is an essential first step in developing sub-diurnal targeted heat mitigation strategies. This study presents a data-driven examination of IUIH dynamics in Kuwait’s desert metropolis. Near-surface air temperature observations were collected using high-resolution loop-type traverses at selected hours during a representative summer day to determine IUIH variability. The diurnal impacts of built environment features were modeled using an ensemble learning approach and interpreted with SHapley Additive exPlanations. Among several candidate machine learning regressors evaluated, Random Forest demonstrated strong predictive power (R2 = 0.954) with acceptable error (RMSE = 0.096, MAPE = 0.001) and least bias (MBE = 0.008). The study’s significance lies in its assessment framework that emphasizes explainability of sub-diurnal dynamics, offering detailed insights that challenge traditional assumptions and inform both immediate local climate interventions and strategic urban planning. The findings reveal that simple day-evening comparatives might overlook nuanced sub-diurnal dynamics, such as potential irrigation-induced warming by shrubs observed at mid-day and the complex trade-offs between radiative and transpirative processes by trees in the afternoon and evening. Additionally, the study identifies cooling effects associated with natural land cover, presenting a critical optimization challenge between compact and open urban forms to effectively modulate near-surface air temperatures.

Hybrid nanocomposites impact on heat transfer efficiency and pressure drop in turbulent flow systems: application of numerical and machine learning insights

This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.

The influence of the location of arable land incorporating its roughness on the optimal time when the land reaches its mean diurnal blue-sky albedo value

This work is a supplement the article “Influence of arable land location incorporating its roughness on blue-sky albedo variation” [J Quant Spectrosc Radiat Transf. 296 (2023) 108,440]. It quantifies the influence of the location (expressed by latitude) of bare arable land on a global scale and its roughness on the optimal time (To) of land observation with acceptable errors to determine its mean diurnal blue-sky albedo (αd). The article also discusses how the risk of making these errors changes with the change in land latitude. The study focuses on the influence of the location of one soil unit, Cambisol, common to all latitudes in both hemispheres. This is the same unit whose albedo variation was analyzed in the supplementary article mentioned above.

An innovative deformation coordination method for analyzing distortion effects on box girders

A deformation coordination method is proposed in this study to account for the distortion effects on a box girder. The differential equation for distortion in vertical web box girders is derived based on the deformation coordination condition of the distortion angle, considering both external loads and internal forces. Subsequently, a comparative analysis is conducted to explore the similarities and differences between the differential equations derived from the proposed deformation coordination method, the plate element analysis method and the total potential energy variation method. The accuracy of the proposed approach is verified through bench-scale tests and numerical simulations. The findings indicate that the derived governing distortion differential equation and distortion attenuation coefficients in the proposed method align with those obtained from the plate element analysis method and the total potential energy variational method, which enhances the applicability to allow for the distortion equations to be obtained simply by calculating the distortion displacements. The analytical findings regarding the distortion warping normal stresses on the cross-sections of the box girders demonstrate favorable correspondence with the experimental results, displaying an acceptable error ranging from − 0.3% to 5.4%. Moreover, the peak of distortion warping normal stresses on the mid-span cross-section increases with higher span-to-depth ratios and height-to-thickness ratios of the web. Consequently, augmenting the thickness of the box wall proves to be an effective means of reducing the distortion effect in box girders.

An Empirical Spatiotemporal Input Model for Regional Seismic Ground Motions

The development of the economy and technology has attracted greater attention to regional seismic resistance, a key technology for which is the simulation of regional seismic ground motions. This study proposes a simple and highly operable method for the generation of regional seismic ground motions, which includes five steps. First, a seismic ground motion recorded at a seismic station is selected as the original ground motion. The original ground motion is then decomposed into eight sub-signals by wavelet packet transform (WPT). Next, the eight sub-signals are adjusted by the frequency attenuation model. The generated ground motion is then amplitude modulated by the peak ground acceleration (PGA) attenuation model. The frequency attenuation model and the PGA attenuation model are obtained by fitting seismic data from the 1999 Chi-Chi earthquake. Finally, the propagation of the ground motion is considered. The proposed method can provide a time history of seismic ground motions for any point in a region. To investigate the rationality and applicability of the proposed method, the characteristics of the generated seismic ground motions are compared with those of true seismic ground motions. The average error of the PGA of the generated ground motions and the true ground motions is 0.072[Formula: see text][Formula: see text]; thus, the proposed method is suitable for use in small-scale regions. Furthermore, 4-story and 15-story structures are used for structural response analysis. The story drift ratio error between the generated ground and true ground motions is found to be within an acceptable error range. The proposed method provides a new way to generate ground motions for regional seismic investigation.

Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

Abstract Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

Comparison of biuret and refractometery method for serum total protein measurements in cattle and goat

ObjectivesThe biuret method is frequently used to determine serum total protein. On the other hand refractometer, a quicker and less expensive option, is available to determine serum total protein. However, there is no study conducted in Ethiopia to compare serum total protein measurement in veterinary settings. Therefore, this study was conducted to compare the serum total protein concentration measurement in cattle and goats obtained by the biuret method and refractometer.ResultsSerum samples from 60 cattle and 60 goats were assayed by both methods and data were analyzed with a paired t-test, Pearson’s correlation, and Bland-Altman plots. There was a strong positive correlation between the total protein values determined with the refractometer and the biuret method in cattle (r = 0.93) and goats (r = 0.97). There were no significant differences (p > 0.05) in the protein values measured with the refractometer and those evaluated with the biuret method in both species. Bland-Altman plots showed that biases indicating the analytic and user error were 8.33% in both species which is below the acceptable total error (< 10%). Thus, refractometer can be used in place of biuret method since it is valid enough to measure serum total protein in cattle and goats.

Cross-concentration calibration of low-cost sensors for effective dust monitoring at construction sites

Building activities commonly generate substantial amounts of construction dust, adversely affecting the nearby environment and public health. Construction workers, in particular, face significant health hazards due to their prolonged exposure to elevated levels of this dust. Traditional method of monitoring individual exposure to construction dust, such as gravimetric samplers or high-end analytical instruments, are often expensive, cumbersome, and not suitable for real-time, widespread deployment. This study employs the low-cost sensors (PMS A003-G10) to measure dust concentrations in varied environments: first low, then high, and then once again low concentrations. In the first low-concentration environment, the G10 sensors showed strong correlation (R2 > 0.81) and acceptable error (RMSE<13.6 μg/m3). However, in high-concentration environment, the G10 sensor faced range limitation issues, yet maintained good correlation. Post high-concentration exposure, the G10 sensor exhibited increased NRMSE and MAPE, indicating adverse impacts on its measurement capability. To enhance the G10's performance in high concentrations, temperature and humidity were used as calibration factors. Four machine learning algorithms (MLR, RF, KNN, and XGBoost) were compared, with XGBoost demonstrating superior calibration (R2 > 0.96, RMSE<117.1 μg/m3). The model's generalizability was validated by integrating data from both low and high-concentration environments into the XGBoost training. Subsequent application to the second low-concentration dataset post high-concentration exposure assessed the model's generalizability and applicability. This study demonstrates that with appropriate calibration, low-cost sensors can effectively monitor individual exposure to construction dust across diverse concentration levels.