Root Mean Square Research Articles

Is Greener Better? Quantifying the Impact of a Nature Walk on Stress Reduction Using HRV and Saliva Cortisol Biomarkers

The physiological impact of walking in nature was quantified via continuous heart rate variability (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N = 17 participants who walked “The Green Road” at Walter Reed National Military Medical Center in Bethesda, Maryland. For N = 15 of the participants, HRV analysis revealed two main groups: group one individuals had a 104% increase (mean) in the root mean square standard deviation (RMSSD) and a 47% increase (mean) in the standard deviation of NN values (SDNN), indicating an overall reduction in physiological stress from walking the Green Road, and group two individuals had a decrease (mean) of 42% and 31% in these respective HRV metrics, signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturbance were more robust for the Green Road than for a comparable urban road corridor and showed that a higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants, and it showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53%, on average, when compared with 37% of walking along an urban road. It was also observed that the order in which individuals walked the Green Road and urban road also impacted their cortisol responses, with those walking the urban road before the Green Road showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the Green Road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.

Effect of temperature on transport index of fish oil-based drilling mud in high pressure high temperature well: a hybrid model approach

Diesel Oil-based drilling mud used in the oil and gas industry causes severe environmental and public health issues due to its high toxicity, especially in high-pressure, high-temperature (HPHT) wells. Therefore, easily biodegradable synthetic oil-based mud with high drilling performance is essential. Fish oil is a bio-oil known for its non-toxicity, high lubricity, thermal stability, and biodegradability. Effective hole cleaning is crucial for drilling performance and is ensured by the rheological properties of drilling mud. While rheological parameter prediction can be performed using an artificial neural network (ANN) based model, such models often fail to provide accurate predictions for unseen data. In contrast, empirical models are more robust and facilitate generalization. This study aims at assessment of the suitability of invert emulsion fish oil-based drilling mud (IEFOBDM) in HPHT wells through evaluation of hole cleaning performance (HCP) using a hybrid approach that combines an ANN with nonlinear regression (NLR) for temperatures ranging from 40 °C to 80 °C.Firstly, an ANN model was developed considering mud weight, temperature, and shear rate. Secondly, the NLR model was used in the prediction of rheological parameters, namely, Yield point (YP) and Plastic viscosity (PV), based on the predicted shear stress from the ANN model. Finally, the performance metrics of the ANN-NLR rheological model were evaluated using the root mean square error (RMSE) and correlation coefficient (R2) and comparison made with the Single ANN rheological model. The results showed the outperformance of ANN-NLR model over the ANN model for rheological parameter prediction, with R2 values of (0.99) for YP and (1) for PV. The predicted PV and YP values were then used for evaluation of transport index (TI) for HCP analysis. The effect of temperature on TI analysis showed this with increase in temperature, TI also increased, indicating the proposed mud providing good HCP under HPHT conditions.

Self-Care and Quality of Life of Ostomy Patients: A Structural Equation Modeling Analysis

Background. Previous research has shown that patients with ostomy frequently exhibit a low health-related quality of life (HRQoL). Self-care is a key element that influences their HRQoL. However, the evidence regarding the relationship between these two constructs in patients with ostomy is still not clear. Materials and Methods. This was a secondary analysis of an Italian multicenter, observational, longitudinal study. Participants were recruited from seven outpatient ostomy care clinics in central and northern Italy. The Stoma-Specific Quality of Life Questionnaire (Stoma QoL) and the Ostomy Self-Care Index (OSCI) were administered to the participants. The relationship between self-care and HRQoL was analyzed using structural equation modeling. Results. A total of 521 patients were enrolled. Our results emphasized that self-care is a predictor of HRQoL among people with ostomy. Self-care maintenance and self-care monitoring had a positive effect on HRQoL (self-care maintenance: β = 0.506, p < 0.001; self-care monitoring: β = 0.303, p < 0.001). The model exhibited acceptable fit indices: χ2 (151, n= 521) = 516.447, p ≤ 0.001, comparative fit index (CFI) = 0.97, Tucker–Lewis index (TLI) = 0.96, root mean square error of approximation (RMSEA) = 0.068 (90% CI, 0.062–0.075), p < 0.001, and standardized root mean square residual (SRMR) = 0.038. Conclusions. The structural equation model tested the causal relationship between self-care and HRQoL in people with a stoma, demonstrating for the first time that inadequate self-care in patients with ostomy may lead to lower HRQoL. Thus, to enhance HRQoL, interventions should be designed to improve self-care behaviors. Future research should investigate potential mediating variables in the relationship between self-care and HRQoL.

The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis

Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic and underdiagnosed; consequently, there is a demand for simple, non-invasive diagnostic tools. In this study, we developed a method to quantify liver steatosis based on miRNAs, present in liver and serum, that correlate with liver fat. The miRNAs were analyzed by miRNAseq in liver samples from two cohorts of patients with a precise quantification of liver steatosis. Common miRNAs showing correlation with liver steatosis were validated by RT-qPCR in paired liver and serum samples. Multivariate models were built using partial least squares (PLS) regression to predict the percentage of liver steatosis from serum miRNA levels. Leave-one-out cross validation and external validation were used for model selection and to estimate predictive performance. The miRNAseq results disclosed (a) 144 miRNAs correlating with triglycerides in a set of liver biobank samples (n = 20); and (b) 124 and 102 miRNAs correlating with steatosis by biopsy digital image and MRI analyses, respectively, in liver samples from morbidly obese patients (n = 24). However, only 35 miRNAs were common in both sets of samples. RT-qPCR allowed to validate the correlation of 10 miRNAs in paired liver and serum samples. The development of PLS models to quantitatively predict steatosis demonstrated that the combination of serum miR-145-3p, 122-5p, 143-3p, 500a-5p, and 182-5p provided the lowest root mean square error of cross validation (RMSECV = 1.1, p-value = 0.005). External validation of this model with a cohort of mixed MASLD patients (n = 25) showed a root mean squared error of prediction (RMSEP) of 5.3. In conclusion, it is possible to predict the percentage of hepatic steatosis with a low error rate by quantifying the serum level of five miRNAs using a cost-effective and easy-to-implement RT-qPCR method.

Development of the Ward Nurses' Perspective-taking of the Staff Receiving Discharged Patients Scale: An observational study of ward nurses.

Discharge planning involves coordinating between different care settings. Failed coordination can lead to delayed care at the facilities receiving discharged patients. Nurses who implement discharge planning must consider the circumstances of the staff receiving the discharged patients. This study aims to develop a nurses' perspective-taking scale for measuring the cognitive process of imagining a staff's situation when receiving discharged patients. An online survey was conducted from September to November 2021 with nurses in Japanese acute care hospitals, using a 20-item questionnaire based on interviews and a literature review. Item reduction was conducted based on inter-item correlations, item-total correlations, and exploratory factor analysis. Confirmatory factor analysis was performed. For the exploratory and confirmatory factor analysis, the samples were randomly split in half and tested. Cronbach's alpha, intraclass correlation coefficients, and correlation coefficients were calculated using both self-developed and pre-existing measures. The questionnaire was distributed to 1,289 individuals, and 416 valid responses were obtained. Item reduction and exploratory factor analysis resulted in a 10-item scale with two subscales, "imagine-other" (six items), which is imagining the other person's situation, and "imagine-self" (four items), which is imagining oneself in the other person's situation. The goodness-of-fit indices were as follows: comparative fit index, 0.95; root mean square error of approximation, 0.08; and standardized root mean squared residual, 0.06. Correlation coefficients with existing scales were 0.38, 0.57, and -0.33. Cronbach's alpha was 0.89, and the intraclass correlation coefficient was 0.54. The newly developed scale proved to be reliable, valid, and suitable for use. This scale can measure the degree of perspective-taking by nurses, which can improve collaboration between facilities and the effectiveness of discharge planning.

An improved multi-objective artificial physics optimization algorithm based on R2 indicator and its application

Artificial Physics Optimization (APO) algorithms in solving constrained multi-objective problems often encounter challenges such as uneven population distribution and imbalances between global and local search capabilities. To address these issues, we propose the Constrained Rank Multi-Objective Artificial Physical Optimization based on the R2 indicator (R2-ICRMOAPO) algorithm. This algorithm integrates non-dominated sorting with the R2 indicator and updates the external storage set using the contribution value derived from the R2 indicator formula, ensuring both set distribution and convergence. It also dynamically adjusts inertia weights and gravitational factors to enhance its global and local search capabilities. To evaluate the performance of the R2-ICRMOAPO algorithm, we compared it with four other multi-objective optimization algorithms using standard test functions. The results indicate that it demonstrates superior distribution and optimization performance. Furthermore, we applied it to optimize the parameters of a hydro-pneumatic suspension system. The experimental results show that it can reduce the root mean square value of car body vertical acceleration by approximately 21.4% and the root mean square value of dynamic tire load by about 19.6%. This reduction effectively enhances vehicle smoothness within a reasonable range. Consequently, these results confirm the feasibility of the R2-ICRMOAPO algorithm to solve practical problems.

Predicting the unseen: A shoreline shift analysis and prediction along Mayo Bay, Davao Oriental

45%-60% of the world’s population resides in shoreline areas. Shoreline regions are one of the most vulnerable areas to the effects of global warming. Positions of shorelines are challenging to predict, but the trend of accretion and erosion can be determined using statistical and geospatial techniques. Mati City is a major tourist destination for white sand beaches and pristine waters. Shorelines along Mayo Bay are a source of income for the local community. However, Mayo Bay has been subjected to shifts due to erosion. This study aims to determine the trend of the shoreline shift in Mayo Bay from 2013 to 2023. Landsat 8 OLI satellite images are used in this study. Results reveal that most shorelines experienced erosion, with 97.26% erosion transects. The shoreline length has slightly increased by 0.08% from 2013 to 2023 and is predicted to increase by 3.57% in 2063 and 11.51% by 2100. Barangay Lucatan shows the highest shoreline expansion, while Barangays Bobon and Dahican exhibit the most erosion, with mean rates of -27.15 m/year and -23.60 m/year, respectively. With a classification accuracy of 89% and Root Mean Square Error (RMSE) of 0.05, the study provides a reliable basis for Mayo Bay’s shoreline management. The findings will inform erosion mitigation efforts and guide sustainable coastal management plans for at-risk areas.

Structural damage detection using a sensitivity-based model updating approach with autocorrelation function data

ABSTRACT This study presents a novel sensitivity equation for estimating structural parameters using autocorrelation function (ACF) data of dynamic responses. The proposed method reduces the need for extensive excitation frequencies, making structural parameter identification more practical and cost-effective, especially for large-scale structures. Furthermore, due to the quadratic relationship between ACF and the structural dynamic response, this approach exhibited greater sensitivity to damage than time-history-based methods. The method’s effectiveness was evaluated using simulated data from truss and offshore jacket platform models. Incomplete measurements were addressed by using partially measured structural characteristics without model reduction and data expansion processes. Measurement and modelling errors were simulated by adding uniformly distributed random error into the damaged structure data. The low values of the coefficient of variation indicated the method’s robustness against these errors. The accuracy of the method was confirmed through low root mean square error (RMSE) values and high variance accounted for (VAF) values.

Machine learning-driven prediction of EBNA1 inhibitors against Epstein–Barr virus in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC), particularly prevalent in regions such as Malaysia, is a significant health concern often linked to Epstein-Barr virus (EBV) infection. The EBV nuclear antigen 1 (EBNA1), crucial for EBV survival and NPC tumorigenicity, has emerged as a potential therapeutic target for EBV-positive NPC. In this study, we utilized quantitative structure-activity relationship (QSAR) models to predict potential inhibitors of EBNA1. These models were developed based on the molecular fingerprints of known EBNA1 inhibitors, using both classification and regression approaches. Our QSAR classification models demonstrated consistently high precision, recall, F1 score, and accuracy scores across the training set. The top-performing models, constructed using logistic regression algorithms, achieved perfect precision scores of 1.000 in the test set evaluation. These models’ recall, F1 score, and accuracy scores were 0.571, 0.727, and 0.667, respectively. On the other hand, the best-performing model among the regression models was built using the sequential minimal optimization regression algorithm, achieving a correlation coefficient of 0.703. The mean absolute error and root mean square error of this QSAR regression model were 0.173 and 0.217, respectively, whereas the relative absolute error was 0.689. We screened the enamine advanced compound library using this regression model to predict compounds with potential EBNA1 inhibitory effects. This led to the identification of the top 10 compounds with the most promising predicted EBNA1 inhibitory properties.

Mapping of the health of the Nation Outcomes Scale (HoNOS) and Positive and Negative Symptom Scale (PANSS) to the EQ-5D-3L in psychosis patients using the PHAMOUS study

ABSTRACT Objectives When health outcomes relevant for economic evaluations are unavailable, algorithms can be developed to map utilities using available clinical outcome measures. This study aims to develop two mapping algorithms estimating EuroQol-5 dimension-3 level (EQ-5D-3 L) utilities using the clinician-rated Health of the Nation Outcome Scores (HoNOS) and Positive and Negative Syndrome Scale (PANNS). Methods A dataset with 2,029 observations of patients with psychotic disorders included EQ-5D-3 L, HoNOS, PANSS item scores, and demographics. Correlations between instruments were evaluated. Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) algorithms with various predictor variable sets were applied. Model performance was cross-validated using R-squared and Root Mean Square Error (RMSE). Results High ceiling effects were observed for EQ-5D-3 L, with weak to moderate negative correlations between EQ-5D and HoNOS (r = -0.34) and PANSS (r = -0.27). Overall, LASSO models outperformed RF models, with individual item models performing best for the HoNOS and PANSS, with the best observed RMSEs of 0.241 and 0.231, respectively. Conclusions The HoNOS and PANSS could be mapped onto EQ-5D-3 L utilities but lack accuracy for individual patient predictions. However, in the absence of alternatives, they could adequately predict population-based utility score differences for health economic evaluations.

Enhancing Long-Term Robustness of Inter-Space Laser Links in Space Gravitational Wave Detection: An Adaptive Weight Optimization Method for Multi-Attitude Sensors Data Fusion

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively.

Graph-based machine learning model for weight prediction in protein–protein networks

Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein–protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The development of high-throughput experimental technologies allows for the generation of numerous data which permits increasing the sophistication of PPI models. However, despite significant progress, current PPI networks remain incomplete. Discovering missing interactions through experimental techniques can be costly, time-consuming, and challenging. Therefore, computational approaches have emerged as valuable tools for predicting missing interactions. In PPI networks, a graph is usually used to model the interactions between proteins. An edge between two proteins indicates a known interaction, while the absence of an edge means the interaction is not known or missed. However, this binary representation overlooks the reliability of known interactions when predicting new ones. To address this challenge, we propose a novel approach for link prediction in weighted protein–protein networks, where interaction weights denote confidence scores. By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. Our model significantly improves prediction accuracy, surpassing traditional approaches in terms of Mean Absolute Error, Mean Relative Absolute Error, and Root Mean Square Error. Our proposed approach holds the potential for improved accuracy in predicting PPIs, which is crucial for better understanding the underlying biological processes.

The research on the applicability of different typhoon wind fields in the simulation of typhoon waves in China’s coastal waters

Typhoon waves possess significant destructive potential, and their numerical simulation relies on accurate sea surface wind fields. An evaluation of different combinations of the radial air pressure distribution coefficient B and the radius of maximum wind speed (Rmax) in the Holland wind field (HWF) model was conducted to determine the optimal configuration. The HWF and the ERA5 wind field (EWF) were used as input wind fields to drive the typhoon wave model for China’s coastal waters. Validation results indicated that neither wind field accurately reflected real conditions; therefore, a hybrid wind field (HBWF) was created by combining HWF and EWF using weighting coefficients that vary with the radius of wind speed to enhance accuracy. Simulation results showed that the HBWF improved the accuracy of significant wave heights (SWHs), with a mean relative error of 25.29%, compared to 32.48% for HWF and 27.94% for EWF. Additionally, HBWF also demonstrated the best performance in terms of root mean square error (RMSE) and consistency index. Overall, the HBWF enhances the simulation accuracy of typhoon waves in China's coastal waters.

Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Abstract With the aim of enhancing the accuracy of current models for forecasting vegetable prices and improving market structures, this study focuses on the prices of bell peppers at the Nanhuanqiao Market in Suzhou. In this paper, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model for vegetable price forecasting based on Principal Component Analysis (PCA) and Attention Mechanism (ATT). Initially, we utilized the Pearson correlation coefficient to filter out the factors impacting prices. Then, we applied PCA to reduce dimensionality, extracting key price features. Next, we captured local sequence patterns with CNN, while handling time-series features with GRU. Finally, these outputs were integrated via ATT to generate the final prediction. Our results indicate that the hybrid CNN-GRU model, enhanced by PCA and ATT, achieved a Root Mean Square Error (RMSE) as low as 0.16. This performance is 11.11%, 11.11%, and 15.79% better than that of the PCA-CNN, PCA-GRU, and CNN-GRU-ATT models, respectively. Furthermore, in order to prove the effectiveness of our proposed model, the proposed model is compared with the state-of-the-art models and classical machine learning algorithms under the same dataset, the results indicate that our proposed hybrid deep learning model based on PCA and ATT shows the best performance. Consequently, our model offers a valuable reference for vegetable price prediction.

Systematic researches on the ground state properties of the medium-mass neutron-rich nuclei

Abstract The recently developed relativistic-mean-field in complex momentum representation with the functional NL3* was used to explore the exotic properties of neutron-rich Pd, Cd, Te, and Xe isotopes. The results were compared with those obtained using the relativistic Hartree-Bogoliubov (RHB) calculations, and available experimental data. The single-particle levels were obtained for the bound and resonant states. The two neutron separation energies S2n and root mean square (rms) radii, agree with the experimental data. It is shown that a halo structure in the extremely neutron-rich 164-180Te and 164-182Xe, as well as a thick neutron skin in the extremely neutron-rich Pd and Cd isotopes. From the numbers of neutrons (Nλ) and (N0 ) occupying the levels above the Fermi surface and the zero-potential energy level, it was found that pairing correlations play an important role in the formation of halo phenomena. These findings are further supported by investigating S2n, rms radii, occupation probabilities, contributions of single-particle levels to the neutron rms radii, and density distributions. The neutron rms radii increased sharply, obviously deviating from the traditional rule r ∝A1/3, and the density distributions were very diffuse. Finally, the contributions of different single-particle levels to the total neutron density and wavefunction were discussed. It was found that the sudden increase in the neutron rms radii and diffuse density distributions mainly come from the resonant levels with a lower orbital angular momentum near the continuum threshold.

A novel hybrid model for long-term water quality prediction with the ‘decomposition–inputs–prediction’ hierarchical optimization framework

ABSTRACT Accurate, stable, and long-term water quality predictions are essential for water pollution warning and efficient water environment management. In this study, a hierarchical water quality prediction (HWQP) model was developed based on ‘data decomposition–predictor screening–efficient prediction’ via wavelet decomposition, Spearman correlation analysis, and long short-term memory network, respectively. The observed data from 14 stations in the Huaihe River–Hongze Lake system, including ammonia nitrogen (AN) and chemical oxygen demand (COD), were used to make long-term water quality predictions. The results suggested that, compared to existing water quality prediction models, the HWQP model has higher accuracy, with the root mean square errors of 6 and 17% for simulating AN and COD, respectively. The AN and COD concentrations will range from 0 to 1 mg/l and from 3 to 5 mg/l at 12 stations, respectively, and the COD concentrations will exceed the water quality target at Stations 4 and 5. The established model has great potential to address the challenges associated with the water environment.

Nondestructive Identification of Internal Potato Defects Using Visible and Short-Wavelength Near-Infrared Spectral Analysis

Potatoes are a staple food crop consumed worldwide, with their significance extending from household kitchens to large-scale food processing industries. Their market value and quality are often compromised by various internal defects such as pythium, bruising, internal browning, hollow heart, gangrene, blackheart, internal sprouting, and dry rot. This study aimed to classify internal-based defects and investigate the quantification of internal defective areas in potatoes using visible and short-wavelength near-infrared spectroscopy. The acquisition of the spectral data of potato tubers was performed using a spectrometer with a wavelength range of 400–1100 nm. The classification of internal-based defects was performed using partial least squares discriminant analysis (PLS-DA), while the quantification of the internal defective area was based on partial least squares regression (PLSR). The PLS-DA double cross-validation accuracy for the distinction between non-defective and all internally defective potatoes was 90.78%. The double cross-validation classification accuracy achieved for pythium, bruising, and non-defective categories was 91.03%. The internal defective area model based on PLSR achieved a correlation coefficient of double cross-validation of 0.91 and a root mean square error of double cross-validation of 0.85 cm2. This study makes a valuable contribution to advancing nondestructive techniques for evaluating internal defects in potatoes.

Adaptive Remaining Capacity Estimator of Lithium-Ion Battery Using Genetic Algorithm-Tuned Random Forest Regressor Under Dynamic Thermal and Operational Environments

The increasing interests and recent advancements in artificial intelligence and machine learning have significantly accelerated the development of novel techniques for the state estimation of batteries in electrified vehicles’ battery management systems (BMSs). Determining the remaining capacity among the several BMS states is crucial for ensuring the safe and stable functioning of an electric vehicle. This paper proposes an adaptive estimator for the remaining capacity of lithium-ion batteries, leveraging a Genetic Algorithm (GA)-tuned random forest (RF) regressor. The estimator is designed to function effectively under varying thermal conditions. The optimization of critical parameters, namely, the number of estimators (n-estimators) and the minimum number of samples per leaf (min-samples-leaf), is a focal point of this study to enhance model accuracy and robustness. The model effectively captures the battery’s dynamic behavior and inherent non-linearity. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) achieved during testing demonstrate promising accuracy and superior prediction. The results demonstrated significant improvements in state of charge (SOC) estimation accuracy. The proposed GA-optimized RF model achieved an MAE of 0.0026 at 25 °C and 0.0102 at −20 °C, showing a 41.37% to 50% reduction in the MAE compared to traditional random forest models without GA optimization. The RMSE was also reduced by 18.57% to 31.01% across the tested temperature range. These improvements highlight the model’s ability to accurately estimate the SOC in varying thermal conditions.

Time Series Perspective on the Sustainability of the South African Food and Beverage Sector

This study aims to quantify and graphically illustrate the negative lingering effect that the COVID-19 pandemic had on the sales in South African Rands (ZAR) of the food and beverage sector using the time series seasonal autoregressive integrated moving average with exogenous components (SARIMAX) intervention model. The SARIMAX2, 1, 00, 1, 212 intervention model provided the best fit, supported by the lowest values of the model selection and error metrics (Akaike’s information criterion, Bayesian information criterion, and root mean square error). The total estimated loss of sales in the 52 months during the intervention period (March 2020 to June 2024) amounts to ZAR 130,579 million. The most affected months were April 2020 and May 2020 with estimated losses of ZAR 7719 million and ZAR 7633 million, respectively. The findings of this study align with the Statistics South Africa® statistical report based on empirical estimation without any model fitting, thus highlighting the effectiveness of the SARIMAX intervention model in quantifying the effects of the pandemic. The lingering negative impact of the COVID-19 pandemic still continues to threaten the sustainability of the South African food and beverage sector, violating the United Nations’ Sustainable Development Goal, Number 2, which is to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture.

Structural Design and Optimization on Three‐Row J‐Type Air Cooling Channel of Pouch LiB Module

To enhance the cooling efficiency of pouch lithium‐ion battery modules, a three‐row J‐type air cooling channel structure is proposed, utilizing the previously developed multiple inlet/outlet air cooling frames. The influence of the location and number of inlets and outlets on heat dissipation performance is investigated through six air cooling channel schemes, providing a baseline for the multiobjective structural optimization of the proposed structure. Three important structural parameters, , , and l, of its air convergence and divergence plenums are selected as the design variables to improve the airflow uniformity in branch channels and minimize the pressure drop between inlets and outlets. The final design exhibits superior heat dissipation performance compared to baseline. It is noted that the airflow root mean square error in branch channels is reduced by 75.64%, while the pressure drop is increased by 19.74%. These are the critical factors for ensuring the heat dissipation performance of battery module. The maximum temperature and the maximum temperature difference of battery module are reduced by 5.29% and 23.91%, respectively, which help maintain its working temperature within a reasonable range.