Exploration of the prediction and generation patterns of heterocyclic aromatic amines in roast beef based on Genetic Algorithm combined with Support Vector Regression.

Heterocyclic aromatic amines (HAAs) are harmful byproducts in food heating. Therefore, exploring the prediction and generation patterns of HAAs is of great significance. In this study, genetic algorithm (GA) and support vector regression (SVR) are used to establish a prediction model of HAAs based on heating conditions, reveal the influence of heating temperature and time on the precursor and formation of HAAs in roast beef, and study the formation rules of HAAs under different processing conditions. Principal component analysis (PCA) showed that the effect on HAAs generation increases with the increase of heating temperature and time. The GA-SVR model exhibited near-zero absolute errors and regression correlation coefficients (R) close to 1 when predicting HAAs contents. The GA-SVR model can be applied for real-time monitoring of HAAs in grilled beef, providing technical support for controlling hazardous substances and intelligent processing of heat-processed meat products.

Hybrid machine learning approach integrating GMDH and SVR for heavy metal concentration prediction in dust samples.

In agricultural regions prone to dust storms, heavy metal contamination of soil and crops from airborne particulates poses significant risks to food safety and public health. This study has assessed the potential of machine learning models for predicting concentrations of toxic heavy metals like arsenic, chromium, and lead in dust from the agricultural Sistan region of southeastern Iran. This region experiences frequent dust storms mobilizing particulates from local dried lakes onto agricultural lands. The metals including nickel, copper, magnesium, cobalt, zinc, chromium, arsenic, and lead were measured in summer dust samples during 2012-2018 across 15 stations. Two hybrid models were developed combining group method of data handling (GMDH) and support vector regression (SVR) machine learning with harmony search optimization (H) so as to predict toxic metals arsenic, chromium, and lead using nickel, copper, magnesium, cobalt, and zinc inputs. Standard error maps were uncertainty higher in southern and western areas, and they are most impacted by dust storms. Results demonstrated that the hybrid GMDH + H and SVR + H models improved the accuracy of individual GMDH and SVR models in predicting heavy metals. The GMDH + H model performed the best for the lead with an agreement index (d-index) of 0.98, root mean square error (RMSE) of 2.87 ppm, normalized RMSE (NRMSE) of 0.12, and coefficient of determination (RR) of 0.96. The SVR + H model showed the highest accuracy for arsenic and chromium, obtaining d-index 0.96, RMSE 0.47 ppm, NRMSE 0.09, and RR 0.92 for arsenic, and d-index 0.96, RMSE 11.24 ppm, NRMSE 0.16, and RR 0.93 for chromium. Taylor's diagram and heatmap analysis confirmed the superiority of the hybrid techniques. This work demonstrates the utility of state-of-the-art computing for addressing complex environmental health challenges.

Prediction of protein content in paddy rice (Oryza sativa L.) combining near-infrared spectroscopy and deep-learning algorithm.

Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice's protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, Rp 2 = 0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with Rp 2 = 0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice.

Accelerated Fatigue Test for Electric Vehicle Reducer Based on the SVR-FDS Method.

The reducer serves as a pivotal component within the power transmission system of electric vehicles. On one hand, it bears the torque load within the power transmission system. On the other hand, it also endures the vibration load transmitted from other vehicle components. Over extended periods, these dynamic loads can cause fatigue damage to the reducer. Therefore, the reliability and durability of the reducer during use are very important for electric vehicles. In order to save time and economic costs, the durability of the reducer is often evaluated through accelerated fatigue testing. However, traditional approaches to accelerated fatigue tests typically only consider the time-domain characteristics of the load, which limits precision and reliability. In this study, an accelerated fatigue test method for electric vehicle reducers based on the SVR-FDS method is proposed to enhance the testing process and ensure the reliability of the results. By utilizing the support vector regression (SVR) model in conjunction with the fatigue damage spectrum (FDS) approach, this method offers a more accurate and efficient way to evaluate the durability of reducers. It has been proved that this method significantly reduces the testing period while maintaining the necessary level of test reliability. The accelerated fatigue test based on the SVR-FDS method represents a valuable approach for assessing the durability of electric vehicle reducers and offering insights into their long-term performance.

Impact of HCV eradication by directly acting antivirals on glycemic indices in chronic hepatitis C patients -a nationwide Taiwan HCV registry.

BACKGROUND/AIMS: Hepatitis C virus (HCV) eradication using antiviral agents augments the metabolic profile. Changes in glycated hemoglobin (HbA1c) levels in chronic hepatitis C patients who receive glecaprevir/pibrentasvir (GLE/PIB) remain elusive. METHODS: Data from 2417 patients treated with GLE/PIB from the Taiwan HCV Registry were analyzed, and pretreatment HbA1c levels were compared with 3-months after the-end-of treatment levels. A sustained virological response (SVR) was defined as undetectable HCV RNA at 12 weeks after the end of treatment. A significant change in HbA1c level was defined as the 75th percentile of the change in the HbA1c level before and after treatment (decrement >0.2%). RESULTS: Serum HbA1c levels decreased significantly (6.0 vs 5.9%, P < 0.001). Post-treatment HbA1c levels decreased in all subgroups, except in non-SVR patients (5.7 vs 5.7%, P = 0.79). Compared to patients without significant HbA1c improvement (decrement >0.2%), those with HbA1c improvement were older (60.2 vs 58.6 years, P < 0.001), had higher serum creatinine levels (1.9 vs 1.6 mg/dL, P < 0.001), triglycerides (129.8 vs 106.2 mg/dL, P < 0.001), fasting glucose (135.8 vs 104.0 mg/dL, P < 0.001), and pretreatment HbA1c (7.1 vs 5.7%, P < 0.001) and had a higher proportion of male sex (57.9% vs 50.9%, P = 0.003), diabetes (84.3 vs 16.8%, P < 0.001), more advanced stages of chronic kidney disease (CKD) (15.7 vs 11.1 %, P < 0.001), anti-diabetic medication use (47.3 vs 16.4%, P < 0.001) and fatty liver (49.6 vs 38.3 %, P < 0.001). Multivariate analysis revealed that the factors associated with significant HbA1c improvement were age (odds ratio [OR]/95% confidence intervals [CI]: 1.01/1.00-1.02, P = 0.01), HbA1c level (OR/CI: 2.83/2.48-3.24, P < 0.001) and advanced CKD stages (OR/CI: 1.16/1.05-1.28, P = 0.004). If the HbA1c variable was not considered, the factors associated with significant HbA1c improvement included alanine aminotransferase level (OR/CI, 1.002/1.000-1.004, P = 0.01), fasting glucose level (OR/CI: 1.010/1.006-1.013, P < 0.001), and diabetes (OR/CI: 3.35/2.52-4.45, P < 0.001). CONCLUSIONS: The HbA1c levels improved shortly after HCV eradication using GLE/PIB. The improvement in glycemic control can be generalized to all subpopulations, particularly in patients with a higher baseline HbA1c level or diabetes.

Hepatitis C epidemiology and treatment outcomes in Italy: Impact of the DAA era and the COVID-19 pandemic.

HCV infection poses a global health threat, with significant morbidity and mortality. This study examines HCV trends in a large Italian region from 2015 to 2022, considering demographic changes, evolving clinical profiles, treatment regimens and outcomes, including the impact of the COVID-19 pandemic. This multicentre retrospective study analysed demographics, clinical histories and risk factors in 6882 HCV patients. The study spanned before and after the direct-acting antiviral (DAA) era, and the COVID-19 period, focusing on treatment outcomes (SVR12, non-SVR12 and patients lost to follow-up). Statistical methods included ANOVA, multinomial logistic regression, Kruskal-Wallis test and chi-square analysis, and were conducted adhering to the intention-to-treat (ITT) principle. The cohort, mainly Italian males (average age 58.88), showed Genotype 1 dominance (56.6%) and a high SVR12 rate (97.5%). The pandemic increased follow-up losses, yet SVR12 rates remained stable, influenced by factors like age, gender, cirrhosis and comorbidities. Despite COVID-19 challenges, the region sustained high SVR12 rates in HCV care, emphasising the importance of sustained efforts in HCV care. Continuous screening and targeted interventions in high-risk populations are crucial for achieving WHO elimination targets. The study highlights the resilience of HCV care during the pandemic and provides insights for future public health strategies.

High serum gamma-glutamyltransferase level after hepatitis C virus elimination is a risk factor for the development of hepatocellular carcinoma.

AIM: Gamma-glutamyltransferase (GGT) is known as an oxidative stress marker, induced by alcohol consumption and metabolic disorders, and is reported as a predictor of hepatocellular carcinoma (HCC) development after hepatitis C virus (HCV) elimination. However, it is not clear whether GGT serves simply as a surrogate marker for overlapping metabolic diseases or reflects HCV-specific carcinogenicity. We investigated the association between GGT and hepatocarcinogenesis after achieving a sustained viral response (SVR), accounting for drinking habits or diabetes, and examined predisposing factors associated with GGT levels after SVR. METHODS: This is a prospective, multicenter, and observational study using the database of 1001 patients after HCV eradication with direct-acting antiviral agents. The association of GGT at SVR with cumulative HCC development was examined in a multivariate analysis using Cox proportional hazard models after adjustment for covariates including alcohol and diabetes. The association between oxidative stress markers or genetic factors and GGT levels was analyzed. RESULTS: High GGT levels at SVR were associated with HCC development (HR] 2.38, 95% CI 1.10-5.17). This association was also significant when restricted to patients without alcohol consumption or diabetes (HR 8.38, 95% CI 2.87-24.47). GGT levels were correlated with serum growth differentiation factor 15 levels, a marker of mitochondrial dysfunction. Single-nucleotide polymorphisms of ZNF827 and GDF15 were associated with high GGT levels. CONCLUSIONS: High GGT levels at SVR were associated with HCC development after accounting for alcohol consumption and diabetes. GGT levels are influenced by genetic predisposition and may reflect mitochondrial dysfunction after HCV eradication.

Cortical neurite microstructural correlates of time perception in healthy older adults.

The human experience is significantly impacted by timing as it structures how information is processed. Nevertheless, the neurological foundation of time perception remains largely unresolved. Understanding cortical microstructure related to timing is crucial for gaining insight into healthy aging and recognizing structural alterations that are typical of neurodegenerative diseases associated with age. Given the importance, this study aimed to determine the brain regions that are accountable for predicting time perception in older adults using microstructural measures of the brain. In this study, elderly healthy adults performed the Time-Wall Estimation task to measure time perception through average error time. We used support vector regression (SVR) analyses to predict the average error time using cortical neurite microstructures derived from orientation dispersion and density imaging based on multi-shell diffusion magnetic resonance imaging (dMRI). We found significant correlations between observed and predicted average error times for neurite arborization (ODI) and free water (FISO). Neurite arborization and free water properties in specific regions in the medial and lateral prefrontal, superior parietal, and medial and lateral temporal lobes were among the most significant predictors of timing ability in older adults. Further, our results revealed that greater branching along with lower free water in cortical structures result in shorter average error times. Future studies should assess whether these same networks are contributing to time perception in older adults with mild cognitive impairment (MCI) and whether degeneration of these networks contribute to early diagnosis or detection of dementia.

Dynamic change of MASLD in chronic hepatitis C patients after viral eradication: A nationwide registry study in Taiwan.

Background/Aims: Steatotic liver disease (SLD) is a common manifestation in chronic hepatitis C (CHC). Metabolic alterations in CHC are associated with metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to elucidate whether hepatitis C virus (HCV) eradication mitigates MASLD occurrence or resolution. Methods: We enrolled 5,840 CHC patients whose HCV was eradicated by direct-acting antivirals in a nationwide HCV registry. MASLD and the associated cardiometabolic risk factors (CMRFs) were evaluated at baseline and 6 months after HCV cure. Results: There were 2,147 (36.8%) patients with SLD, and 1,986 (34.0%) of them met the MASLD criteria before treatment. After treatment, HbA1C (6.0% vs. 5.9%, P<0.001) and BMI (24.8 kg/m2 vs. 24.7 kg/m2, P<0.001) decreased, whereas HDL-C (49.1 mg/dL vs. 51.9 mg/dL, P<0.001) and triglycerides (102.8 mg/dL vs. 111.9 mg/dL, P<0.001) increased significantly. The proportion of patients with SLD was 37.5% after HCV eradication, which did not change significantly compared with the pretreatment status. The percentage of the patients who had post-treatment MASLD was 34.8%, which did not differ significantly from the pretreatment status (P=0.17). Body mass index (BMI) (odds ratio [OR]/95% confidence intervals [CI]: 0.89/0.85-0.92, P<0.001) was the only factor associated with MASLD resolution. In contrast, unfavorable CMRFs, including BMI (OR/CI: 1.10/1.06-1.14, P<0.001) and HbA1c (OR/CI: 1.19/1.04-1.35, P=0.01), were independently associated with MASLD development after HCV cure. Conclusions: HCV eradication mitigates MASLD in CHC patients. CMRF surveillance is mandatory for CHC patients with metabolic alterations, which are altered after HCV eradication and predict the evolution of MASLD.

Effect of self-efficacy among persons who inject drugs treated with direct-acting antivirals on Hepatitis C treatment initiation, duration, completion, adherence, and cure.

BACKGROUND: Self-efficacy, a patient-level factor, has been shown to facilitate patient engagement in treatment and optimize treatment-related outcomes in various health contexts. Research on interventions supporting hepatitis C virus (HCV) direct-acting antiviral (DAA) treatment uptake and adherence among persons who inject drugs (PWID) is needed, but whether self-efficacy factors influence DAA treatment cascade outcomes in this population has been less studied. METHODS: Using the HERO study data, we analyzed a subset of participants with any general health self-efficacy data (n=708) measured at baseline and end-of-treatment time points using a 5-items instrument (facets: 'goal setting', 'goal attainment', 'having a positive effect', 'being in control', and 'working to improve'). The cascade outcomes included DAA treatment initiation, duration, adherence, completion, and sustained virologic response (SVR). The effect of baseline and change (Δ) scores for composite and item-level self-efficacy on the cascade outcomes was assessed using logistic regression and generalized linear models. RESULTS: Higher baseline composite self-efficacy [adjusted odds ratio (95 % confidence interval) =1.57 (1.07, 2.29)], 'goal attainment' [1.31 (1.03, 1.67)] and 'having a positive effect' [1.33 (1.03, 1.74)] were associated with greater likelihood of treatment initiation. 'Δ Goal attainment' was significantly associated with SVR [1.63 (1.04, 2.53)]. 'Δ Being in control' and 'Δ working to improve' were associated with treatment adherence and duration, respectively. CONCLUSIONS: General health self-efficacy positively influences DAA treatment initiation among PWID. 'Goal attainment' facilitates the achievement of DAA treatment-related outcomes. Further studies should assess the effect of self-efficacy related to performing healthcare tasks specific to DAAs on the treatment-related outcomes.

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm.

This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic perturbation and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance the convergence accuracy and speed of the optimization algorithm. The study assesses the SCMSSA algorithm's performance against other optimization algorithms using six test functions to show the efficacy of the enhancement strategies. Furthermore, its efficacy in improving Support Vector Regression (SVR) models for CO2 prediction is assessed. The results reveal that the SVR-SCMSSA hybrid model surpasses other hybrid models and standard SVR in terms of training and prediction accuracy by obtaining 95 % accuracy. Its swift convergence, precision, and resistance to local optima position make it an excellent choice for addressing complex problems such as CO2 prediction, with critical implications for sustainability efforts. Moreover, feature importance analysis by SVR-SCMSSA offers valuable insights into the key contributors to CO2 prediction in the dataset, emphasizing the significance and impact of factors such as fossil fuel, Biomass, and Wood as major contributors to CO2 emission. The research suggests the adoption of the SVR-SCMSSA hybrid model for more accurate and reliable CO2 prediction to researchers and policymakers, which is essential for environmental sustainability and climate change mitigation.

Prediction and reliability analysis of shear strength of RC deep beams.

This study explores machine learning (ML) capabilities for predicting the shear strength of reinforced concrete deep beams (RCDBs). For this purpose, eight typical machine-learning models, i.e., symbolic regression (SR), XGBoost (XGB), CatBoost (CATB), random forest (RF), LightGBM, support vector regression (SVR), artificial neural networks (ANN), and Gaussian process regression (GPR) models, are selected and compared based on a database of 840 samples with 14 input features. The hyperparameter tuning of the introduced ML models is performed using the Bayesian optimization (BO) technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R2 = 0.997 and 0.947 in the training and testing sets, respectively). In addition, simple and practical design expressions for RCDBs have been proposed based on the SR model with a physical meaning and acceptable accuracy (an average prediction-to-test ratio of 0.935 and a standard deviation of 0.198). Meanwhile, the shear strength predicted by ML models was then compared with classical mechanics-driven shear models, including two prominent practice codes (i.e., ACI318, EC2) and two previous mechanical models, which indicated that the ML approach is highly reliable and accurate over conventional methods. In addition, a reliability-based design was conducted on two ML models, and their reliability results were compared with those of two code standards. The findings revealed that the ML models demonstrate higher reliability compared to code standards.

Efficacy of Sofosbuvir and Velpatasvir Combination in the Treatment of Hepatitis C Virus (HCV) in Chronic Kidney Disease (CKD) Patients.

Background Hepatitis C virus (HCV) infection is still common in patients with chronic renal failure, even those on maintenance dialysis. A bidirectional association exists between HCV infection and chronic renal disease. Objective To assess the efficacy of sofosbuvir and velpatasvir combination in the treatment of chronic HCV in chronic kidney disease (CKD) patients. Methodology This descriptive, cross-sectional study was undertaken at the departments of Gastroenterology and Nephrology Lady Reading Hospital, Peshawar, from April 7, 2021, to October 7, 2021. Patients with chronic HCV and chronic renal disease at stage 4 or 5 were included while patients with decompensated cirrhosis liver, hepatoma, hepatitis B virus/HCV (HBV/HCV) coinfection, and post liver transplant patients were excluded. HCV infection was diagnosed based on detectable HCV ribonucleic acid (HCV RNA) by PCR (polymerase chain reaction). In contrast, CKD was diagnosed based on the Kidney Disease Improving Global Outcomes (KDIGO) criteria for CKD. Sofosbuvir 400 mg orally daily and velpatasvir 100 mg orally with meals were given daily for 12 weeks. Effectiveness was defined as negative HCV RNA by PCR 12 weeks after treatment completion called sustained virological response rate 12 weeks after treatment completion (SVR12). Results A total of 73 patients including 67 (91.78%) males and six (8.22%) females between the ages of 20 years and 70 years were included in this study. The mean age of the participants was 48.77±8.0 years. Twelve weeks after the treatment completion, 69 (94.52%) had negative HCV RNA, whereas four (5.48%) patients had detectable HCV RNA. Conclusion It can be concluded from our study that a fixed-dose combination of sofosbuvir 400 mg and velpatasvir 100 mg is quite effective and recommended for treating chronic hepatitis C infection in patients with chronic renal disease in our local setup.

Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches.

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length-slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.

Cefixime removal via WO3/Co-ZIF nanocomposite using machine learning methods.

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L-1, 3.13, 117.65 min, and 0.19 g L-1) and (5 mg L-1, 3, 120 min, and 0.19 g L-1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.

Evaluation of alcohol use behavior among patients cured through HCV elimination program in Georgia.

OBJECTIVE: The objective of the study was to understand the role of self-reported drinking behavior on liver health after achieving sustained viral response (SVR) among HCV patients. RESULTS: The study was conducted in HCV treatment provider clinics in three cities in Georgia: Tbilisi, Batumi, and Telavi. Face-to-face interviews were conducted using a questionnaire developed specifically for this study. 9.5% considered themselves heavy drinkers, while 94.2% were aware that heavy alcohol consumption can progress liver fibrosis. During treatment, 97.8% abstained from alcohol, while 76.6% reported resuming drinking after achieving SVR. Additionally, 52.1% believed that moderate alcohol intake is normal for individuals with low fibrosis scores. Liver fibrosis improvement was more prevalent among individuals who abstained from alcohol after HCV diagnosis (85.4% vs. 71.4%, p < 0.01) and after achieving SVR (87.5% vs. 74.7% of those who resumed drinking after achieving SVR, p < 0.02). In conclusion, the majority of HCV patients abstain from alcohol during treatment but resume drinking after achieving SVR. Those who abstain from alcohol intake after HCV cure have a higher chance of liver fibrosis improvement.

The Potential of Multi-Task Learning in CFDST Design: Load-Bearing Capacity Design with Three MTL Models.

Concrete-filled double steel tubes (CFDSTs) are a load-bearing structure of composite materials. By combining concrete and steel pipes in a nested structure, the performance of the column will be greatly improved. The performance of CFDSTs is closely related to their design. However, existing codes for CFDST design often focus on how to verify the reliability of a design, but specific design parameters cannot be directly provided. As a machine learning technique that can simultaneously learn multiple related tasks, multi-task learning (MTL) has great potential in the structural design of CFDSTs. Based on 227 uniaxial compression cases of CFDSTs collected from the literature, this paper utilized three multi-task models (multi-task Lasso, VSTG, and MLS-SVR) separately to provide multiple parameters for CFDST design. To evaluate the accuracy of models, four statistical indicators were adopted (R2, RMSE, RRMSE, and ρ). The experimental results indicated that there was a non-linear relationship among the parameters of CFDSTs. Nevertheless, MLS-SVR was still able to provide an accurate set of design parameters. The coefficient matrices of two linear models, multi-task Lasso and VSTG, revealed the potential connection among CFDST parameters. The latent-task matrix V in VSTG divided the prediction tasks of inner tube diameter, thickness, strength, and concrete strength into three groups. In addition, the limitations of this study and future work are also summarized. This paper provides new ideas for the design of CFDSTs and the study of related codes.

Assimilation of PSO and SVR into an improved ARIMA model for monthly precipitation forecasting.

Precipitation due to its complex nature requires a comprehensive model for forecasting purposes and the efficiency of improved ARIMA (IARIMA) forecasts has been proved relative to the conventional models. This study used two procedures in the structure of IARIMA to obtain accurate monthly precipitation forecasts in four stations located in northern Iran; Bandar Anzali, Rasht, Ramsar, and Babolsar. The first procedure applied support vector regression (SVR) for modeling the statistical characteristics and monthly precipitation of each class, IARIMA-SVR, which improved the evaluation metrics so that the decrease of Theil's coefficient and average relative variance in all stations was 21.14% and 17.06%, respectively. Two approaches are defined in the second procedure which includes a forecast combination (C) scheme, IARIMA-C-particle swarm optimization (PSO), and artificial intelligence technique. Generally, most of the time, IARIMA-C-PSO relative to the other approach, exhibited acceptable results and the accuracy improvement was greater than zero at all stations. Comparing the two procedures, it is found that the capability of IARIMA-C-PSO is higher concerning the IARIMA-SVR, so the decrease in the normalized mean squared error value from IARIMA to IARIMA-SVR and IARIMA-C-PSO is 36.72% and 39.92%, respectively for all stations. The residual predictive deviation (RPD) of IARIMA-C-PSO for all stations is greater than 2, which indicates the high performance of the model. With a comprehensive investigation, the performance of Bandar Anzali station is better than the other stations. By developing an improved ARIMA model, one can achieve a high performance in structure identifying and forecasting of monthly time series which is one of the issues of interest and importance.

Smart cost estimation: Empirical case for extra-high voltage transmission towers.

Nowadays, electricity has become an integral part of human lives. Most of our daily appliances, tools, and personal belongings are inseparable from electricity. To ensure a proper electricity distribution with an efficient transfer capability, Extra-High Voltage (EHV) transmission towers are needed. To design such a structure, it is of utmost importance to account for the cost of said tower. However, the process to estimate the cost of EHV transmission towers is both time-consuming and strenuous on human labor since a lot of consideration have to be taken. To overcome this, an imperative requirement exists for a prompt, precise, and automated tool to replace the existing manual cost estimation method. This research endeavor aims to craft a tool using support vector regression (SVR) with the capacity to prognosticate construction expenses for projects involving EHV transmission towers. The exploration of pertinent literature has enabled us to amass historical data and delineate the attributes essential for estimating costs linked to EHV transmission tower construction. The investigation delves into a comprehensive dataset spanning the past decade in Taiwan. Within this timeframe, 317 EHV transmission towers were erected between 2009 and 2019. However, 79 of these instances are excluded due to incomplete information, thereby yielding 238 viable datasets (comprising 75 % of the overall total) to underpin the development of the SVR model. By configuring the parameters to C = 0.2 and γ = 0.1, followed by 5-fold cross-validation, the resultant SVR model attains a remarkable prediction accuracy of 97.91 %, on average. As a result, the proposed SVR-based model can effectively and accurately predict the cost of constructing an EHV transmission tower project and reduce the time spent on estimation, thus contributing to the enhancement of the resilience and robustness of the transmission network system.