Mean Absolute Percentage Deviation Research Articles

Visualising the strength development of FICP-treated sand using impedance spectroscopy

Fungal Induced Calcium Carbonate Precipitation (FICP) is a novel method used in geotechnical engineering that enhances the engineering properties of sand by using the potential of fungal activity. This research is the first attempt to monitor the strength of FICP treated sand using embedded Piezoelectric (PZT) patch based Electromechanical Impedance (EMI) spectroscopy. In the past, the strength of such treated sand has been determined through the destructive methods like Unconfined Compressive Strength (UCS) test. In this study, the sand is mixed with the filamentous fungus Aspergillus Niger and the cementation solution (urea and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{C}\ ext{a}\ ext{C}\ ext{l}}_{2}$$\\end{document} in the ratio of 1:1) is injected after every 24 h. Results recorded from the cost-effective EVAL AD5933 chip indicate that the shifting of frequency impedance signals in each phase is in good alignment with UCS and calcium carbonate content (CCC). Following the 28-day treatment period, the treated sand achieves a maximum UCS of 3.93 MPa, accompanied by a CCC of 15.19%. In order to correlate EMI signals with treatment cycles, UCS, and CCC, various multi linear regression (MLR) equations for statistical metrics like root mean square deviation (RMSD), mean absolute percentage deviation (MAPD), and correlation coefficient deviation (CCD) are developed. Additionally, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analyses have been conducted to observe the success of the FICP process in the sand.

Characterizing Mechanical Changes in the Biceps Brachii Muscle in Mild Facioscapulohumeral Muscular Dystrophy Using Shear Wave Elastography.

There is no general consensus on evaluating disease progression in facioscapulohumeral muscular dystrophy (FSHD). Recently, shear wave elastography (SWE) has been proposed as a noninvasive diagnostic tool to assess muscle stiffness in vivo. Therefore, this study aimed to characterize biceps brachii (BB) muscle mechanics in mild-FSHD patients using SWE. Eight patients with mild FSHD, the BB were assessed using SWE, surface electromyography (sEMG), elbow moment measurements during rest, maximum voluntary contraction (MVC), and isometric ramp contractions at 25%, 50%, and 75% MVC across five elbow positions (60°, 90°, 120°, 150°, and 180° flexion). The mean absolute percentage deviation (MAPD) was analyzed as a measure of force control during ramp contractions. The shear elastic modulus of the BB in FSHD patients increased from flexed to extended elbow positions (e.g., p < 0.001 at 25% MVC) and with increasing contraction intensity (e.g., p < 0.001 at 60°). MAPD was highly variable, indicating significant deviation from target values during ramp contractions. SWE in mild FSHD is influenced by contraction level and joint angle, similar to findings of previous studies in healthy subjects. Moreover, altered force control could relate to the subjective muscle weakness reported by patients with dystrophies.

Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.

Binary phase behavior of select organosulfur compounds in supercritical carbon dioxide: A Monte Carlo molecular simulation study.

Pressure-composition binary phase diagrams were determined for methanethiol, dimethyl sulfide, thiophene, benzothiophene, or dibenzothiophene with carbon dioxide at temperatures from 363 to 453K and pressures from 2 to 20 MPa. Utilizing Gibbs ensemble Monte Carlo molecular simulation, phase coexistence compositions were determined, along with the impact of 4% water cosolvent on select results. Solution structure as a function of pressure and temperature is characterized via radial distribution functions. Comparison to available experimental composition data gives overall mean absolute percentage deviations of 2.2% for thiophene, 37% for methanethiol, and 99% for benzothiophene. Solubilities in a CO2-rich phase are calculated to be sufficient to allow extraction and detection of the compounds studied here via supercritical fluid chromatography and mass spectrometry as a possible analysis approach for future Mars rover missions.

Monitoring of compressive strength gain in mass concrete using embedded piezoelectric transducers

AbstractThis study extended the electromechanical impedance (EMI) technique to monitor the 28‐day age of strength gain in mass concrete, although it has been validated in strength monitoring of a lab‐scaled concrete specimen. Embedded piezoelectric (PZT) transducer, namely, aluminum embedded PZT (AEP), that was wrapped by two sandwich aluminum pastes was proposed for EMI monitoring. The workability of the AEP was first verified via finite element analysis, where the effect of hydration heat on the EMI signature of the AEP was evaluated via numerical modeling and prior thermal test. In the experiment, totally four AEP transducers arranged at different loci were applied to monitor strength gain in a mass concrete specimen. As a comparison, the maturity method was also performed to estimate the strength of the specimen. Characteristics of EMI signature and its statistical indices including root mean square deviation (RMSD) and mean absolute percentage deviation (MAPD) were analyzed and correlated to strength development in mass concrete. Monitoring results indicated that the AEP transducers were capable of identifying the strength gain of mass concrete. The logarithmic function between the RMSD/MAPD index values and compressive strength perfectly predicted the strength development, which could be further employed for real‐life and in situ applications.

A Modified Model of Micro-Scale Gas Seepage in Rock Porous Media

It is of significance to study the micro-scale gas seepage mechanism in rock porous media due to shale gas exploitation and prevention of tunnel gas explosion. However, current microscale seepage models have problems, such as poor accuracy or small computational domain. In this work, a representative volume element scale microscopic seepage model with high precision and large computational domain is established using the lattice Boltzamnn method. In the representative volume element scale model, micro-scale effect is considered by the Beskok-Karniadakis correction relationship. Then the accuracy of the representative volume element scale model is improved by proposing an equivalent resistance correction coefficient. The correction coefficient value is obtained by comparing the calculated result for the flow rate at a representative volume element scale and more precise pore scale. For example, when the porosity is 0.9 and the dimensionless specific surface area is 30, the corrected model can reduce the average deviation from 34.38 to 0.14%. Finally, to expand the applicability of the proposed model, a correlation for the correction coefficient is introduced as a function of the Knudsen number, porosity of porous media, and dimensionless specific surface area. The mean absolute percentage deviation of the correction coefficient correlation is 9.25%.

A hybrid algorithm for estimating total nitrogen from a large eutrophic plateau lake using Orbita hyperspectral (OHS) satellite imagery

Total nitrogen concentration (CTN) enrichment is the primary cause of natural water eutrophication. Accurately estimating CTN and its spatiotemporal dynamics is crucial for formulating monitoring and control measures to alleviate lake eutrophication. A hybrid model was proposed for estimating CTN in optically complex inland waters by incorporating the relationship between CTN and water optical active components for Zhuhai-1 Orbita hyperspectral (OHS) imagery. Compared with other semi-analytical algorithms, the re-adjusted reference wavelength QAA716 shows the best performance in aph(λ) retrieval. The hybrid model for CTN estimation achieves an root mean square deviation (RMSD) of 0.20 mg/L, a mean absolute percentage deviation (MAPD) of 6.96 %, and a unbiased mean absolute percentage deviation (UMAPD) of 6.96 %, with aph(569) accuracy exerting the greatest influence. Ground-satellite synchronous validation demonstrates robust performance, with an RMSD of 0.28 mg/L, a MAPD of 14.49 %, and a UMAPD of 14.92 %. The hybrid model was applied to OHS observations of Lake Dianchi from April 2019 to September 2021. The analysis revealed a generally decreasing trend in CTN during this timeframe. The above results demonstrate that the robustness and applicability of the proposed CTN hybrid model for inland waters with complex optical properties. Furthermore, satellite-based data products provide valuable information for formulating lake management strategies.

An Electromechanical Impedance-Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

The multiple intersecting stiffeners on the chemical milling stiffened panel (CMSP) limit the application of active health monitoring methods on it. An imaging algorithm based on electromechanical impedance (EMI) and probability-weighting is proposed to achieve quantitative evaluation and localization of the damage on CMSP. The proposed algorithm compensates for the difference in sensor performance with coefficients and there is no need to determine the key parameters of the algorithm through prior experiments. In the paper, the applicability of ultrasonic guided wave (GW) and EMI on CMSP was first studied through the finite element method. Based on EMI and the mean absolute percentage deviation (MAPD), the selected damage indicator (DI), a probability-weighted damage imaging algorithm are proposed and experimentally verified. The results indicate that due to the reflection and attenuation effects of stiffeners on GW, the signal characteristics of damage scattering waves are contaminated, making it difficult to quantitatively characterize the damage from GW signals through DIs. MAPD is positively correlated with the damage degree and has consistency in characterizing the signal of different PZTs under the same working condition. The feasibility and accuracy of the proposed algorithm are verified through experiments which show a strong engineering application capability.

Performance evaluation of advanced machine learning methodologies in simulating hydrogen chloride (HCl) absorption by deep eutectic solvents

This work aims to construct the physical-based machine learning (ML) approach for estimating the hydrogen chloride (HCl) solubility in eleven different deep eutectic solvents (DESs). All DES-HCl equilibrium records versus temperatures and pressures are gathered from the literature to ensure ML universality. Then, a well-known feature selection technique was applied to identify the physically meaningful descriptors for the hydrogen bond acceptors (HBA) and hydrogen bond donors (HBD). Afterward, temperature, pressure, selected meaningful physical descriptors, and HBA/HBD molar concentrations are utilized to design ML tools to compute HCl solubility in DESs. Diverse MLs, including extreme learning machines, convolutional neural networks, long short-term memory, cascade neural networks, and extreme gradient boosting (XGBoost), are tested, and their accuracy is compared to select the best candidate. The XGBoost model predictions for HCl solubility in DESs are in acceptable agreement with the literature records. This model precisely simulates 417 DES-HCl equilibrium records in the temperature range of 293–348 K and pressures of up to 127 kPa with a correlation coefficient of 0.99621, mean absolute percentage deviation of 3.04, mean absolute error of 0.448, and root mean square error of 0.729. Results of the designed XGBoost model prove that acetamide-ethylene glycol (1:1), which can averagely dissolve up to 32.47 wt% (weight percent) of HCl, is the best DES for the HCl capture and recovery. After approving the XGBoost model reliability with different visual and numerical inspections, the Leverage method also justified its validity domain by more than 97.6%.

Damage localization on reinforced concrete slab structure using electromechanical impedance technique and probability-weighted imaging algorithm

Damage localization plays a key role in structural damage identification and health monitoring. This paper proposed a new damage localization approach using electromechanical impedance/admittance (EMI/EMA) technique integrated with probability-weighted imaging algorithm based on Gaussian distribution. The proposed approach comprises five steps namely measurement of the EMA signature from an array of piezoelectric ceramic lead zirconate titanate (PZT) transducers, computation of statistic damage indicator, establishment of regression model for the indicator, damage visualization via Gaussian distribution and final damage localization. Experimental validation of the approach was conducted on a reinforced concrete (RC) slab with artificial crack damages detected by using an array of surface-bonded PZT patches. Comparative studies on localization accuracy were performed by utilizing root mean square deviation (RMSD), baseline-changeable RMSD namely RMSDk, mean absolute percentage deviation (MAPD) and correlation coefficient (CC). Experimental results demonstrated that the proposed approach could achieve precise localization of concrete crack with error as small as 0 mm, and that using RMSDk index exhibited the highest localization accuracy in average. The proposed approach promoted the capability of the EMI technique in characterization of concrete structural damages.

Thermal−hydraulic characteristics of R32 and R410A condensation in plate heat exchangers with 1 mm chevron depth

Compact brazed plate heat exchangers (BPHEs) with low chevron depths are widely used in new heat pumps and air-conditioners for reducing inventory and improving efficiency. This study analyses the complete condensation of R410A and R32 inside BPHEs with a chevron depth of 1 mm. The effects of the mass flux and chevron patterns (V-shaped and W-shaped) on the heat transfer coefficient (HTC) and frictional pressure drop are investigated. The HTC of R32 is 30–50 % higher than that of R410A, which has a 10–20 % higher friction factor at high mass fluxes and vice versa at low mass fluxes. The HTC of R410A monotonically increases with the mass flux, whereas that of R32 does not. The W-shaped pattern results in 30 % and 15 % higher pressure drops compared with the V-shaped pattern for R410A and R32, respectively. The friction factor of R32 decreases more sharply than that of R410A as the mass flux increases, particularly at low mass fluxes. The correlations developed by Longo et al. and Zhang et al. show the best agreement with the experimental data for the HTCs of R32 and R410A, respectively, with mean absolute percentage deviations (MAPDs) of 15 %. The friction factor correlation of Zhang et al. accurately predicts the pressure drop of R32 for the W-shaped and V-shaped patterns. In addition, it accurately predicts the pressure drop of R410A for the W-shaped pattern, with an MAPD of less than 10 %, but over-estimates it by 37 % for the V-shaped pattern.

Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners

Terrestrial LiDAR scanning (TLS) has the potential to revolutionize forestry by enabling the precise estimation of aboveground biomass, vital for forest carbon management. This study addresses the lack of comprehensive benchmarking for leaf-filtering algorithms used in TLS data processing and evaluates four widely recognized geometry-based leaf-filtering algorithms (LeWoS, TLSeparation, CANUPO, and a novel random forest model) across openly accessible TLS datasets from diverse global locations. Multiple evaluation dimensions are considered, including pointwise classification accuracy, volume comparisons using a quantitative structure model applied to wood points, computational efficiency, and visual validation. The random forest model outperformed the other algorithms in pointwise classification accuracy (overall accuracy = 0.95 ± 0.04), volume comparison (R-squared = 0.96, slope value of 0.98 compared to destructive volume), and resilience to reduced point cloud density. In contrast, TLSeparation exhibits the lowest pointwise classification accuracy (overall accuracy = 0.81 ± 0.10), while LeWoS struggles with volume comparisons (mean absolute percentage deviation ranging from 32.14 ± 29.45% to 49.14 ± 25.06%) and point cloud density variations. All algorithms show decreased performance as data density decreases. LeWoS is the fastest in terms of processing time. This study provides valuable insights for researchers to choose appropriate leaf-filtering algorithms based on their research objectives and forest conditions. It also hints at future possibilities for improved algorithm design, potentially combining radiometry and geometry to enhance forest parameter estimation accuracy.

Monitoring of crack repair in concrete using spherical smart aggregates based on electromechanical impedance (EMI) technique

Cracks will inevitably occur in concrete structures or members during the construction process and service life due to aging, environmental factors, external loads, etc. To improve the strength and stability of the cracked concrete structures, many methods have been proposed to repair the cracks. However, the monitoring of the repairing process and repair quality has not been fully studied. The previous studies have proved that the spherical smart aggregates (SSAs) based on the electromechanical impedance (EMI) technique have outperformed the traditional smart aggregates (SAs) based on the EMI technique in structural health monitoring of civil structures, however, SSAs have not been applied to the monitoring of the concrete crack repair. In this work, the monitoring of the concrete crack repair using the SSAs based on the EMI technique was explored. A total of eight concrete specimens were prepared, and cracks in the concrete specimens were simulated by manually cutting under laboratory conditions. According to the principle of grouting method, two repair agents including cement paste and cement mortar were used to repair the cracks. The impedance signals of 28 d were measured, and three quantitative indicators, namely root mean square deviation, mean absolute percentage deviation, and correlation coefficient deviation were used to evaluate the quality of the concrete repair effect. The results indicate that the SSAs show excellent sensitivity and stability over the traditional SAs. In addition, the normalized values of the quantitative indicators were analyzed to distinguish the types of repair agents. A mathematical expression of exponential function was also proposed by fitting the experimental data to quantitatively evaluate and predict the repair effect of concrete cracks. Further, the influences of temperature, humidity, crack depth and width on the experimental results were analyzed and discussed. The numerical simulation was also presented to validate the reasonability of the experimental results.

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

Electric load forecasting is a vital task for energy management and policy-making. However, it is also a challenging problem due to the complex and dynamic nature of electric load data. In this paper, a novel technique, called LSV/MOPA, has been proposed for electric load forecasting. The technique is a hybrid model that combines the advantages of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), two powerful artificial intelligence algorithms. The hybrid model is further optimized by a newly Modified Orca Predation Algorithm (MOPA), which enhances the forecasting accuracy and efficiency. The LSV/MOPA model has been applied to historical electric load data from South Korea, covering four regions and 20 years. The LSV/MOPA model has been compared with other state-of-the-art forecasting techniques, including SVR/FFA, LSTM/BO, LSTM-SVR, and CNN-LSTM. The results show that the LSV/MOPA model with minimum average mean absolute percentage deviation error, including 365 in northern region, 12.8 in southern region, 8.6 in central region, and 30.8 in eastern region, provides the best fitting and outperforms the other techniques in terms of the Mean Absolute Percentage Deviation (MAPD) index, achieving lower values for all regions and years. The LSV/MOPA model also exhibits faster convergence and better generalization than the other techniques. This study demonstrates the effectiveness and superiority of the LSV/MOPA model for electric load forecasting and suggests its potential applications in other sectors where accurate forecasting is crucial.

Evaluation of ground conductor corrosion based on EMI technique with asymmetric sensor layout

ABSTRACT The ground conductor is prone to corrosion, which affects its ability to diverge current and the normal operation of the power system. The traditional detection method can’t detect its corrosion degree intuitively. This paper proposes a novel method based on Electromechanical impedance (EMI) technique to evaluate the corrosion degree of the grounding conductor. Firstly, based on the analysis of corrosion of metals. The change of ground conductor radius Δr is used as the corrosion parameter. A linear model of peak frequency Δf m and corrosion parameter Δr in asymmetric layout is established by analysing EMI and conductor mechanical admittance. Secondly, the EMI coupling models with different sensor layouts were built on the finite element software. It is verified that the asymmetric sensor layout effectively improves the sensitivity to corrosion. The linear model greatly improves the assessment accuracy compared with the Mean Absolute Percentage Deviation (MAPD) and Root Mean Square Deviation (RMSD) damage index. Thirdly, the proposed method is verified in a real grounding conductor corrosion experiment. The method based on the EMI technique with asymmetric sensor layout can evaluate the corrosion of the grounding conductor well.

Evaluating the capabilities of China's new satellite HJ-2 for monitoring chlorophyll a concentration in eutrophic lakes

HJ-2 (Huanjing-2) was launched in September 2020. It has a charge-coupled device (CCD) that offers a spatial resolution of 16 m and a revisiting time of two days, making it an excellent option for environmental and disaster monitoring. However, more evaluation is needed for the ability of CCD to monitor water quality. As chlorophyll a concentration (CChla) is the main indicator for water quality evaluation, in this study, the performance of the CCD sensor to monitor CChla has been verified, with a particular focus on typical eutrophic lakes. The essential findings can be summarized as follows: (1) the CCD sensor has a signal-to-noise ratio (SNR) greater than 400 in all bands except the green band, indicating that it meets the requirements for water quality monitoring; (2) the semi-empirical estimation model of CChla derived from HJ-2 CCD has a mean absolute deviation (MAD) of 8.18 μg/L, root mean square deviation (RMSD) of 10.66 μg/L and mean absolute percentage deviation (MAPD) of 20.93 %; (3) the higher temporal and spatial resolution of the CCD data enables effective monitoring of water quality parameters characterized by high spatiotemporal variations; (4) HJ-2 CCD exhibits strong consistency with Sentinel-2 MSI and Sentinel-3 OLCI across various bands, with most bands showing correlation coefficients exceeding 0.8 and a MAPD below 30 %. This study reveals the significant potential and practical value of HJ-2 CCD data for monitoring and assessing water quality, providing important information for the design and development of next-generation satellites.

Comprehensive kinetic modeling of the Fischer Tropsch synthesis over the Ru–Co@C(Z-d)@void@CeO2 catalyst based on a molecular dynamics evaluated mechanism

Fischer Tropsch synthesis (FTS) is a promising catalytic solution to achieve ultra-clean fuel in which catalyst has a significant role. Even though several kinetic models have been rendered for it, representing a new one based on a meticulous mechanism is noteworthy, particularly when employing a novel catalyst. Hence, in this work, molecular dynamic (MD) simulation is utilized to identify the appropriate pathway from the viewpoint of minimum energy of the elementary reactions. In this regard, we consider a majority of plausible FTS elementary reactions over Ru–CO@C(Z-d)@void@CeO2 (cobalt carbon-MOF based plus ruthenium and ceria with porous structure) catalyst, and by employing MD computations forward and reverse activation energy barrier values for each reaction are evaluated. Then, based on the minimum energy pathway (MEP), the appropriate mechanism is derived, which is inferred from the H-assisted CO dissociation pathway for the beginning step. Subsequently, a kinetic model is developed based on this mechanism, and the model parameters are correlated and optimized employing the genetic and Levenberg-Marquardt algorithms, respectively. In addition, we use various statistical methods to assess the significance of the model. For instance, the Mean Absolute Percentage Deviation (MAPD) of the correlated values from the experimental value is 4.46%. Additionally, the relative residual percentage (RR%) plots illustrate that 99% of the correlated data have less than a 10% deviation.

Towards resilient operation of photovoltaic-thermal collector with incorporated organic phase change material: Numerical and experimental investigation

The resilient operation of the novel free-standing photovoltaic-thermal (PVT) collector with an integrated phase change material (PCM) in variable weather conditions was examined by developing the computational fluid dynamics (CFD) 3D model to perform numerical analysis of heat transfer. The developed transient multi-parameter numerical model was directly compared with experimental measurements for the case of variable cloudiness, unstable ambient temperature, and fluctuating wind. Due to volatile weather conditions, the numerical analysis included variable time-dependent boundary conditions implemented using user-defined functions (UDF). The investigation considered a novel multi-parameter numerical approach, together with novel experimental data for unconventional organic PCM. Specifically, the thermal characteristics of unconventional organic PCM, i.e., pork fat, were experimentally determined with the differential scanning calorimetry (DSC) method and embedded in the numerical model. It was found that melting of the crystal phase of pork fat PCM starts at 8.3 °C and ends at 45.2 °C with a latent heat of melting being 45.4 Jg−1. The numerical model was successfully validated with experimental data. The mean absolute percentage deviation of the operating temperature numerical forecast compared with the experiment was from about 2.9% to 4.9%, depending on the considered domain. The findings of this investigation can be used to improve the existing designs of PVT-PCM collectors. Furthermore, a validated numerical model can be used to investigate the impact of various operating parameters on the system performance to enhance the resilience aspect in the early development phase of novel designs.

Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003–2019

The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) together form one of the largest marginal sea systems in the world, including enclosed and semi-enclosed ocean margins and a wide continental shelf influenced by the Changjiang River and the strong western boundary current (Kuroshio). Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML-ECS). MeSAA-ML-ECS introduced semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO2 achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE = 20 μatm) and mean absolute percentage deviation (APD = 4.12 %) for independent in situ validation dataset. During 2003–2019, the annual average CO2 sinks in the BS, YS, ECS, and entire study area were 0.16 ± 0.26, 3.85 ± 0.68, 14.80 ± 3.09, and 18.81 ± 3.81 Tg C/yr, respectively. Under continuously increasing atmospheric CO2 concentration, the BS changed from a weak source to a weak sink, the YS experienced interannual fluctuations but did not show significant trend, while the ECS acted as a strong sink with CO2 absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO2 uptake in the entire study area increased by 85 % in 17 years. For the first time, we present the most refined variation in the satellite-derived pCO2 and air-sea CO2 flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.

Forecasting Model Accuracy Assessment in a Bottled Water Supply Chain

This study presents an accuracy assessment of various classical time series forecasting methods to determine the most accurate forecasting method for predicting demand of the 50cl product of a bottled water supply chain. The classical time series forecasting methods compared are the moving average, weighted moving average, exponential smoothing, adjusted exponential smoothing, linear trend line, Holt’s model, and Winter’s model. The Mean Absolute Percent Deviation (MAPD) value was determined for the various forecasting methods to find the forecasting method with the least MAPD and hence the highest forecasting accuracy. The tracking signal measure was also used to determine the forecasting models which were biased. The results showed that though the weighted moving average method began to underforecast demand in the final period, it had the lowest MAPD value of 2.25%, making it the forecasting method with the highest accuracy for predicting the 50cl bottled water demand. On the other hand, though the exponential smoothing forecasting method had the highest MAPD value of 3.43% and least accuracy for predicting the bottled water demand, its forecasts were consistently within the tracking signal control limits. This study will aid supply chain analysts in implementing accuracy assessment of classical time series forecasting models.