Multiple Regression Prediction Model Research Articles

Assessment of black rot (Xanthomonas campestris) of cabbage (Brassica oleracea) in East Khasi Hills, Meghalaya

Cabbage (Brassica oleracea L. var. capitata) holds significant economic importance in Meghalaya, thriving in its high altitude and cool climate. East Khasi Hills (EKH) district, where cabbage cultivation remains viable throughout the year, contributes 70% of the state's total cabbage production. A severe outbreak of cabbage black rot, caused by Xanthomonas campestris p.v. campestris, has raised concerns in this region. To comprehensively understand the outbreak and assess its economic impact, an extensive disease survey was conducted across 6 districts of Meghalaya, with a particular focus in the EKH during 2021–22 and 2022–23. The survey was on different cruciferous vegetables such as cabbage, broccoli (Brassica oleracea L. var. italica), cauliflower (Brassica oleracea L. var. botrytis), radish (Raphanus sativus L.), mustard (Brassica juncea L.), and knol khol (Brassica oleracea L. var. gongylodes). It was found that on an average, Meghalaya experiences a disease incidence (DI) of a 52.7% and per cent disease index (PDI) of 30.9% for black rot in cabbage. However, in the EKH, highest disease incidence (69.98%) and PDI (43.38%) of black rot was recorded in cabbage, that surpassed knol khol and cauliflower. Highest DI and severity were observed during monsoon season. Further analysis revealed significant correlations (r) between black rot incidence and severity in cabbage with temperature (0.91), humidity (0.87) and rainfall (0.88). Additionally, multiple regression predictive model performed using weather parameters and DI led to the prediction of PDI with accuracy of 95% in EKH.

Prediction of Rock's Brittleness and Dynamic Properties Utilizing Effective Artificial Intelligence Approaches

This research aims to determine the brittleness index (BI) and engineering properties of limestone specimens. In addition, this study evaluates the effect of moisture on the developed models to predict the BI and shear wave velocity (Vs), based on the point load index (Is50), dry and saturated tensile strength (Ts-d and Ts-s), and porosity. Gaussian process regression (GPR), multilayer feed-forward neural network (MFFNN), and multiple linear regression (MLR) predictive models were utilized. Microscopic examination of the limestone specimens revealed that calcite is the predominant mineral. It was observed that samples with higher calcite content exhibited greater brittleness and strength properties while having lower porosity. The results obtained from the MLR analysis demonstrated that it is possible to accurately forecast the brittleness index (BI), as well as the dry and saturated shear wave velocities (Vs-d and Vs-s) at the specific sites under investigation. The moisture effect on developed models showed that Vs prediction in dry conditions (Vs-d) was less accurate compared to the saturated conditions (Vs-s). Conversely, the relationships developed for estimating the BI in dry conditions exhibited higher accuracy. The analysis of all model assumptions using MLR indicated that the models could be reliably utilized. However, the MFFNN and GPR methods were found to be more conservative in estimating these properties. Moreover, the study identified the best transfer function and training algorithm for predicting Vs and BI. The evaluation metrics, such as R2 and RMSE revealed that GPR demonstrated higher precision compared to MFFNN and MLR.

Data-Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network.

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects.

Development and validation of a novel risk classification tool for predicting long length of stay in NICU blood transfusion infants

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Vibration prediction and analysis of the main beam of the TBM based on a multiple linear regression model

The vibration of tunnel boring machine (TBM) is very difficult to monitor on sites, and related research on prediction methods is rare. Based on the field tunnelling test of a TBM in the Xinjiang Ehe project, the vibration information of the main beam of the TBM under different surrounding rock conditions is collected. The relationships among the tunnelling parameters, surrounding rock parameters and vibration parameters were studied. The results show that the penetration, cutter head speed, torque and thrust are important parameters affecting TBM vibration. In addition, the field penetration index and cutter head driving power index are significantly related to the root mean square of acceleration. Based on this, a multiple regression prediction model of TBM vibration is established. The model was verified and analysed via field projects, and the relative prediction error was less than 12%. This method can be used to predict the vibration of a TBM in real time through characteristic parameters without the use of a traditional monitoring system. This approach is highly important for determining the status of TBM equipment in real time.

Modeling and Detection Scheme for Zero-Dynamics Attack on Wind Power System

In a wind farm, wind power system is key to maintaining continuous generation of point energy. Since Zero-Dynamics attack poses a major risk to future wind farms, investigation on the effect of Zero-Dynamics attack against wind power systems has both theoretical and practical importance. In this paper, we consider a scenario in which a wind power system is subject to Zero-Dynamics attack in power generation chain. Firstly, we transform a semi-direct D-PMSG into an equivalent circuit to establish mathematical model of wind power system under Zero-Dynamics attack. Then, we analyze and prove the advantages and limitations of Zero-Dynamics attack by divide the equation of state into a stable part and an unstable part with the help of system relative degree ρ. In addition, to better achieve desired attack effect, adjustments are made for Zero-Dynamics attack based on its limitations. Finally, we combine an Multiple Linear Regression (MRL) predictive control model with Byrnes-Isidori normal form to protect wind power system. The theoretical results were verified on designed small wind power system.

Research on energy-saving strategies in the architectural design of the Eastern Railway in the era of artificial intelligence

Abstract This paper takes the air conditioning energy consumption in railroad building design as the main object of analysis and uses an orthogonal experimental design method to study the influencing factors and effects of energy consumption in the Middle East railroad construction design. Stochastic working condition simulation is carried out for the typical models of passenger stations in five climate zones to obtain the data set of the passenger station energy consumption model, and the multiple linear regression prediction models of air conditioning energy consumption in different regions are established. Combined with the hour-by-hour average outdoor temperature, hour-by-hour passenger flow and lighting and related equipment data in the waiting hall of a city in July, the air-conditioning cold load of railroad passenger stations is simulated and analyzed. Explore the implementation effect of energy-saving strategies for railroad passenger station air-conditioning system, and for the traditional PID control strategy, put forward the control strategy based on model prediction, analyze the change of centralized air-conditioning system cold load and the change of the actual performance coefficient of the chiller unit under different control strategies. The data show that compared with the traditional PID control, the model prediction control reduces the total energy consumption by 0.2063, which realizes the energy-saving optimization of the air conditioning system for railroad building design.

Evaluation of the Phytoremediation Potential of the Sinapis alba Plant Using Extractable Metal Concentrations.

Testing the feasibility of soil phytoremediation requires the development of models applicable on a large scale. Phytoremediation mechanisms include advanced rhizosphere biodegradation, phytoaccumulation, phytodegradation, and phytostabilization. The aim of this study was to evaluate the phytoremediation potential of the Sinapis alba. Identification of the factors influencing the extraction process of metals from contaminated soils in a laboratory system suitable for evaluating the phytoavailability of these metals in three solutions (M1-CaCl2, M2-DTPA, and M3-EDTA) included the following: distribution of metals in solution (Kd), soil properties and mobile fractions (SOC, CEC, pH), response surface methodology (RSM), and principal component analysis (PCA). The evaluation of the phytoremediation potential of the Sinapis alba plant was assessed using bioaccumulation coefficients (BACs). The accumulation of heavy metals in plants corresponds to the concentrations and soluble fractions of metals in the soil. Understanding the extractable metal fractions and the availability of metals in the soil is important for soil management. Extractable soluble fractions may be more advantageous in total metal content as a predictor of bioconcentrations of metals in plants. In this study, the amount of metal available in the most suitable extractors was used to predict the absorption of metals in the Sinapis alba plant. Multiple regression prediction models have been developed for estimating the amounts of As and Cd in plant organs. The performance of the predictive models generated based on the experimental data was evaluated by the adjusted coefficient of determination (aR2), model efficiency (RMSE), Durbin-Watson (DW) test, and Shapiro-Wilk (SW) test. The accumulation of the analyzed metals followed the pattern Root > Pods > Leaves > Seeds, stems > Flowers for As and Leaves > Root > Stem > Pods > Seeds > Flowers for Cd in soil contaminated with different metal concentrations. The obtained results showed a phytoremediation potential of the Sinapis alba plant.

Diagenesis and Diagenetic Mineral Control on Reservoir Quality of Tight Sandstones in the Permian He8 Member, Southern Ningwu Basin

The sandstone reservoirs of the He8 member within the Lower Permian Shihezi Formation are important targets for oil and gas exploration in the southern Ningwu Basin. This study utilized thin-section identification, scanning electron microscopy, and X-ray diffraction analysis to examine the petrological features and reservoir characteristics, and evaluate the impact of the mineral composition and diagenesis type on the porosity of the sandstone reservoir. Additionally, a multiple linear regression prediction model was developed to predict the distribution of promising sandstone reservoirs in the study area. The results of the analysis revealed that the sandstone of the He8 member is mainly composed of feldspathic lithic sandstone, followed by lithic sandstone. The main reservoir type is characterized by secondarily dissolved pores and micropores within kaolinite aggregates. The low porosity (ranging from 0.2% to 10.7%) and permeability indicate that the He8 member is a tight sandstone reservoir. This reservoir has undergone compaction, cementation, and dissolution diagenesis, and is presently in the stage of mesodiagenesis B. The rigid framework of quartz, the dissolution of feldspar grains, and the intergranular pores of kaolinite are significant contributors to reservoir quality and the main drivers of porosity. In this study, a multivariate linear regression model was developed based on the mineral content of quartz, feldspar, carbonate minerals, kaolinite, smectite, and rock fragments, which accurately predicts the porosity of the studied reservoirs. Based on this model, it was predicted that the north of the Jingle South sub-depression contains a favorable reservoir space in the tight sandstone reservoir of the He8 member. The findings of this study hold significant reference value in the quantitative characterization of tight sandstone reservoirs with similar depositional and diagenetic characteristics, and improving the prediction effect of favorable reservoirs.

Identifying Postural Instability in Children with Cerebral Palsy Using a Predictive Model: A Longitudinal Multicenter Study.

Insufficient postural control and trunk instability are serious concerns in children with cerebral palsy (CP). We implemented a predictive model to identify factors associated with postural impairments such as spastic or hypotonic truncal tone (TT) in children with CP. We conducted a longitudinal, double-blinded, multicenter, descriptive study of 102 teenagers with CP with cognitive impairment and severe motor disorders with and without truncal tone impairments treated in two specialized hospitals (60 inpatients and 42 outpatients; 60 males, mean age 16.5 ± 1.2 years, range 12 to 18 yrs). Clinical and functional data were collected between 2006 and 2021. TT-PredictMed, a multiple logistic regression prediction model, was developed to identify factors associated with hypotonic or spastic TT following the guidelines of "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis". Predictors of hypotonic TT were hip dysplasia (p = 0.01), type of etiology (postnatal > perinatal > prenatal causes; p = 0.05), male gender, and poor manual (p = 0.01) and gross motor function (p = 0.05). Predictors of spastic TT were neuromuscular scoliosis (p = 0.03), type of etiology (prenatal > perinatal > postnatal causes; p < 0.001), spasticity (quadri/triplegia > diplegia > hemiplegia; p = 0.05), presence of dystonia (p = 0.001), and epilepsy (refractory > controlled, p = 0.009). The predictive model's average accuracy, sensitivity, and specificity reached 82%. The model's accuracy aligns with recent studies on applying machine learning models in the clinical field.

Implementing an outpatient surgical management in moderated-high risk patients with groin hernia repair.

There is a growing trend to expand Ambulatory Surgery (AS) criteria in abdominal wall surgery. No Admission (NOADS) circuit. The present study aimed to assess the impact of classification criteria on postoperative results and hospital stays in a NOADS versus a conventional admission circuit to throw some light on surgical circuit inclusion. A retrospective analysis of a prospective;y maintained database was performed comparing groin hernia's interventions in a NOADS vs Admission circuit in our center in 2018-2021. A multiple regression predictive model followed by a retrospective retest were dessigned to assess the impact of each criterion on hospital stay. In total, 743 patients were included, 399 in the Admission circuit (ADC) and 344 in NOADS circuit (NOADS). There were no statistical differences in complication or readmission rates (p = 0.343 and p = 0.563), nevertheless, a shorter hospital stay was observed in NOADS (p = 0.000). A hierarchical multiple regression predictive model proposed two opposite scenarios. The best scenario, not likely to need admission, was a female patient operated via the laparoscopic approach of a unilateral primary hernia (Estimated Postoperative Stay: 0.049days). The worst scenario, likely to need admission, was a male patient operated via the open approach of a bilateral and recurrent hernia (Estimated Postoperative Stay: 1.505 Days). Groin hernia patients could safely benefit from a No Admission (NOADS) circuit. Our model could be useful for surgical circuit decision-making, especially for best/worst scenarios.

Study on the Drying Process and the Influencing Factors of Desiccation Cracking of Cohesive Soda Saline-Alkali Soil in the Songnen Plain, China

The surface of cohesive soda saline-alkali soil in the Songnen Plain of China exhibits obvious desiccation cracking phenomenon during water evaporation. Quantitative research on the process and influencing factors of desiccation cracking are significant for determining the mechanical properties of salt-affected soil, improving the salinization status and the local ecological environment. This study aims to conduct laboratory-controlled cracking experiments on soda saline-alkali soils, and to regularly measure the soil moisture and the cracking status of 40 soil samples. After processing the crack images uniformly, crack characteristics including crack length, crack area, contrast texture feature, and box-counting fractal dimension are extracted. The results indicate that the decrease in soil moisture can be divided into three stages such as fast evaporation stage, slow evaporation stage and stable evaporation stage, and that the evaporation rate shows a linear negative correlation with soil salinity. The measurements also indicate that crack length rapidly increases and reaches stability with a decrease in water content compared with the crack area. The high correlation between different crack characteristics and the soil total salinity (correlation coefficient from 0.44 to 0.95) shows that salt content is the dominant factor affecting the desiccation cracking of soda saline-alkali soil. A multiple linear regression prediction model was finally established based on crack characteristics for different salinity parameters with high prediction accuracy for Na+, EC and total salinity (R2 &gt; 0.91 and RPD &gt; 2.5) and also certain prediction accuracy for Cl− and CO32− (R2 &gt; 0.66 and RPD &gt; 1.5).

Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study.

Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as "chickenpox," "chickenpox treatment," "treatment of chickenpox," "chickenpox symptoms," and "chickenpox virus," trend consistently. Some BDI search terms, such as "chickenpox pictures," "symptoms of chickenpox," "chickenpox vaccine," and "is chickenpox vaccine necessary," appeared earlier than the trend of "chickenpox virus." The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems.

Geochemical Characteristics of Cd in Different Parent Soils in Karst Area and Prediction of Cd Content in Maize

The naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil physicochemical properties, soil Cd content, and bioavailability of different parent materials in the karst area, there are certain limitations in using the total soil Cd content to classify the environmental quality of cultivated land. In this study, surface soil and maize samples of eluvium and alluvial parent material in typical karst areas were collected systematically; the contents of maize Cd, soil Cd, pH, and oxides were analyzed, the Cd geochemical characteristics of different parent soils and the influencing factors of their bioavailability were revealed, and scientific and effective arable land use zoning suggestions based on the prediction model were suggested. The results showed that the physicochemical properties of different parent material soils in the karst area were obviously different. The alluvial parent material soil had low Cd content but high bioavailability, and the maize Cd exceeding rate was high. The maize Cd bioaccumulation factor was significantly negatively correlated with soil CaO, pH, Mn, and TC, and the correlation coefficients were -0.385, -0.620, -0.484, and -0.384, respectively. Compared with the multiple linear regression prediction model, using the random forest model to predict the maize Cd enrichment coefficient had higher accuracy and precision. Furthermore, a new scheme for the safe utilization of cultivated land at the plot scale based on soil Cd and predicted crop Cd content was proposed in this study, making full use of arable land resources to ensure crop safety.

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere.The purpose of this study was to create the best O3model for forecasting O3concentrations in the industrial area and to determine the variables that affect O3concentrations.Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3concentration were developed.MLR3had the highest correlation coefficient of 0.792 during development as compared to models MLR1and MLR2. MLR2was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models.The establishment of an O3prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions.

Influence of Cooling and Lubrication Parameters on Robot Bone Grinding Temperature and Prediction Modeling

In the process of robot bone grinding, a large amount of heat is generated, which will cause mechanical and thermal damage to bone tissues and nerves. It is necessary to study the influence of cooling and lubrication parameters on the robot bone grinding temperature and establish the prediction models among them. The FE model of single abrasive bone grinding was established to explore the influence of cooling and lubrication parameters on the bone grinding temperature. Response surface design of experiment was carried out to obtain the simulation results, and Design-Expert was used to establish a multiple regression prediction models of grinding temperature under the condition of different cooling and lubrication. Through the variance and response surface comparative analysis of the prediction model, the influence rules of the bone grinding parameters and the cooling and lubrication parameters on the bone grinding temperature was obtained. A robot bone grinding experiment was performed to prove the accuracy of FE simulation and prediction model. The research results show that the relationship between grinding temperature and cooling lubrication parameters obtained by FE simulation, RSM prediction and experiment verification is consistent, and the simulation model and prediction model of cooling and lubrication parameters are proven to be correct and effective. The influence rules and prediction effects obtained in this study will provide a reasonable scheme for doctors to implement robot bone grinding with high efficiency and low damage, and establish the theoretical basis for the effective control of robot bone grinding force thermal damage.

Influencing factors and prediction of net carbon sink in the primary sector of the coastal city in China.

To achieve the goal of urban carbon dioxide emission reduction, how to increase carbon sequestration has become a top priority. The biological sink is mainly divided into green carbon sink and blue carbon sink. Coastal cities have two kinds of carbon sinks. There, the study of carbon sinks in coastal cities is the primary choice to cope with climate change. Therefore, this study chooses coastal cities with primary industries including agriculture, fishery, and forestry as the study subjects. The LMDI (Log-Mean Divisia Index) method and multiple regression prediction models were used to explore the low-carbon countermeasures which increase urban net carbon sink from the perspective of influencing factors and future potential. The study found that the average output value of employees in the primary industry is the main driving factor, and the change in the purchasing power of unit carbon sinks and the change in the proportion of employees in the primary industry have inhibited the increase in net carbon sinks. Projections based on the primary industry's output and afforestation area as independent variables show an overall upward trend in net carbon sinks, reaching 15.70 million tons of net carbon sinks in 2060, offsetting 10-20% of total carbon emissions in the same year. Based on the calculation results, this paper puts forward some corresponding countermeasures to increase carbon sinks. This paper provides a theoretical reference for the low-carbon development of coastal cities in China, and the strategies can be also expanded to other cities with similar resources around the world.

Road traffic accident prediction for mixed traffic flow using artificial neural network

Transport scenariosin developingcountries arefundamentally different fromthose indevelopedcountries. The latterconsists primarilyof passenger cars and can beadequately describedas“homogeneous” traffic,butthe formerconsistsof vehicle types withdifferentstatic and dynamiccharacteristics thatoccupy the same right ofway. Vehiclemovementis asynchronous. Fewstudies have attempted tounderstand the characteristics of mixed traffic. Thisarticleexplores the sharing attributes and influencing causes of traffic accidents inamixed trafficarea. A predictability model isemployed todescribethe connection between highwaydisastersand appropriate constraints such astraffic capacity, road provisions,and atmosphereissues. In this paper,the comparison has been done between the Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) predictive models. The study has been conducted at Pimpri Chinchwad Muncipal Corporation (PCMC) region of Pune, Maharashtra, India. For this work, nine years data has been used ranging from the year 2011 to 2019. Results revels that, maximum numbers of accidents were occurred in clear weather condition. Distinctive accidents were occurred due to overloaded vehicle conditions. Also it has been found that the less number of female drivers are responsible for accident. Forecasting model using ANN presents outstanding precision. In this study, additional prominence has been givento the real constraints which are accountable for accident cause in heterogeneous traffic flow.

Predictive model for the spreadability of cosmetic formulations based on large amplitude oscillatory shear (LAOS) and machine learning

Inspired by the analogy between the application process of cosmetics and large amplitude oscillatory shear (LAOS), we suggest a novel predictive model for the spreadability of cosmetic formulations via LAOS analysis and machine learning techniques. Rheological measurements of cosmetic formulations, including the transient elastic and viscous moduli from the sequence of physical process (SPP) analysis, were selected as features for the predictive models, and the spreadability of each formulation that is quantitatively rated by trained panels was set up as the target variable. First, multiple linear regression prediction models are derived, and it was shown that the LAOS-SPP parameters were more effective features than other rheological parameters that were conventionally related to spreadability of cosmetics. Additionally, a non-linear prediction model was developed based on the random forest regressor algorithm, considering the possibility of the nonlinear correlation between rheological measurements and spreadability. The random forest regressor model showed better performance than the linear regression model, and the LAOS-SPP parameters were found to be more effective features for the random forest regressor model as in the multiple linear regression model. The correlation between the LAOS-SPP parameters and the spreadability is interpreted in terms of the rheological transition during rubbing process of cosmetics. Our findings indicate the importance of the nonlinear rheological behavior in the texture perception mechanism of cosmetics, and how rheological measurements can be combined with machine learning techniques to solve complicated problems.