•A novel QSPR model for predicting the DCN of hydrocarbon species and mixtures is reported.•The QSPR model used a combination of molecular descriptors with an overall low degeneracy.•DCNs of aviation ...fuels were predicted using the QSPR model and GCxGC analyses.•DCN prediction for jet fuels were in good agreement with IQT measurements.
This study presents a computational methodology for determining the Derived Cetane Number (DCN) of practical aviation fuels. The proposed approach integrates a novel Quantitative Structure-Property Relationship (QSPR) model designed to predict DCN for hydrocarbon species and mixtures with fuel composition analysis obtained through Two-Dimensional Gas Chromatography (GCxGC). The QSPR model used 20 independent variables computed based on selected hydrocarbon molecular descriptors, including functional groups and distance-based topological indexes. The multivariate regression analysis was used to train the QSPR model based on a dataset consisting of 48 individual hydrocarbon species and 157 surrogate mixtures. The model demonstrated robust predictive capabilities with high coefficients of determination (R2) of 0.96 on the training dataset and 0.94 on the independent testing dataset. The latter consisted of 43 surrogate mixtures formulated both in-house and sourced from archived literature. The application of the QSPR model for practical jet fuels involves specifying the detailed jet fuel compositions using GCxGC analysis and a randomization algorithm based on a database featuring over 17,000 distinct hydrocarbon species. The overall model's performance on practical jet fuels aligns closely with its performance on the training and testing datasets, affirming its practical utility. To enhance prediction accuracy of the proposed computational approach for practical jet fuels, density was explored as a potential constraining property to narrow randomization results with those detailed composition having a density similar to the actual fuel. In this regard, a novel relationship was established to predict fuel densities based on compositional characteristics. Despite the promising results in density prediction, this study indicates that density alone is insufficient to effectively constrain randomized compositions for significantly improved DCN predictions, and thus, further development is required.
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In recent years, the use of chemometric tools together with analytical techniques has been increasing in forensics due to their useful role in extracting additional reliable and objective ...information, which can be of great help in finding and understanding the connections between the forensic evidence and the suspects involved in complex crimes. Although there are various reviews available that deal with discussing the most widely used chemometric methods and their applications in forensics, most of them focus on multivariate classification and discrimination methods, overlooking multivariate regression methods. The present review aims to prove the powerful applicability of multivariate regression methods in one of the most challenging fields of forensics – forensic dating. Based on the authors’ own experience and recent research studies, the review has been structured in three sections, covering the benefits and drawbacks of using multivariate regression methods in forensic dating and how to overcome these drawbacks, including also a methodological proposal on how to make optimal use of these methods. This review intends to provide a solid ground for emphasising and encouraging the application of chemometric-based methodologies in the coming years, in order to provide solutions to those challenges posed by forensic dating which cannot be solve by conventional methodologies.
•Emphasis on the potential of chemometric-based methodologies applied to forensic dating.•Strategies to overcome the drawbacks of data pre-processing and (O)PLSR modelling in forensic dating.•Methodological proposal for the optimal application of multivariate regression methods to forensic dating.
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Disaster vulnerability is not just a condition but a consequence of the interactions of natural and social factors. From this viewpoint, it is imperative to consider both natural and social factors ...when planning effective disaster risk reduction strategies. This study sought to determine factors that predict disaster vulnerability. Specifically, this research determined the associations between several socio-ecological dimensions (e.g., environmental, social, and economic profile), coping capacity, disaster preparedness, and disaster vulnerability of selected households in Dumaguete city. Using snowball sampling, a descriptive correlational design was used to collect data from self-report surveys of non-probable samples of 150 households from different disaster-prone barangays. Frequency count, weighted mean, and standard deviation were used to describe the data, and a multiple linear regression analysis was done to test the study's hypotheses. Findings show that the respondents manifested moderate levels of coping capacity and disaster preparedness while having high levels of disaster vulnerability. The study revealed that disaster preparedness and coping capacity were negatively correlated to disaster vulnerability. Aside from coping capacity and disaster preparedness, environmental, social, and economic profiles were also noted to predict disaster vulnerability significantly. Environmental profile was the most vital determinant of the participants’ perceived disaster vulnerability. To better prepare for disasters, it is suggested that city disaster risk reduction strategies focus on helping vulnerable barangays develop better adaptive capacities and manage environmental hazards. This could include providing training on essential hazard management and creating income-generating opportunities to offset the adverse effects of disasters.
and aims: To prevent the spread of coronavirus disease (COVID19) total lockdown is in place in India from March 24, 2020 for 21 days. In this study, we aim to assess the impact of the duration of the ...lockdown on glycaemic control and diabetes-related complications.
A systematic search was conducted using Cochrane library. A simulation model was created using glycemic data from previous disasters (taken as similar in impact to current lockdown) taking baseline HBA1c and diabetes-related complications data from India-specific database. A multivariate regression analysis was conducted to analyse the relationship between the duration of lockdown and glycaemic targets & diabetes-related complications.
The predictive model was extremely robust (R2 = 0.99) and predicted outcomes for period of lockdown up to 90 days. The predicted increment in HBA1c from baseline at the end of 30 days and 45 days lockdown was projected as 2.26% & 3.68% respectively. Similarly, the annual predicted percentage increase in complication rates at the end of 30-day lockdown was 2.8% for non-proliferative diabetic retinopathy, 2.9% for proliferative diabetic retinopathy, 1.5% for retinal photocoagulation, 9.3% for microalbuminuria, 14.2% for proteinuria, 2.9% for peripheral neuropathy, 10.5% for lower extremity amputation, 0.9% for myocardial infarction, 0.5% for stroke and 0.5% for infections.
The duration of lockdown is directly proportional to the worsening of glycaemic control and diabetes-related complications. Such increase in diabetes-related complications will put additional load on overburdened healthcare system, and also increase COVID19 infections in patients with such uncontrolled glycemia.
•A lockdown of India in an attempt to preventing the spread of the virus may adversely impact both diabetes control and associated complications by putting a restriction on the available resources.•This is probably the first study to highlight that there is an increment in both the HBA1c as well as diabetes-related complications with increasing duration of a lockdown.•Annualized rates of HBA1c increment and diabete-related complication are projected to increase in proportion to the duration of lockdown (in days).
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•Imaging spectroscopy technology was used to map properties of soil fertility in intact paddy profiles.•SVMR, PLSR, BPNN models coupled with BOSS were compared.•Imaging spectroscopy technology with ...BOSS-SVMR is recommended for elemental mapping.•The long-term (approximately 1000 years) evolution characteristics of paddy soil fertility were analyzed.•Long-term rice cultivation was beneficial to the improvement of surface soil fertility.
It is essential for future security and soil sustainability to understand how soil fertility evolves under long-term anthropogenic influences. This study investigated the feasibility of laboratory-based imaging spectroscopy (IS) techniques to predict fertility properties (soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), pH) of soil profiles at different ages of rice planting. Three methods, support vector machine (SVM), partial least square regression (PLSR) and back propagation neural network (BPNN), were used to calibrate the models. The prediction performances of full-spectrum multivariate models and variable-selected (i.e., the bootstrapping soft shrinkage (BOSS) algorithm) optimization models were compared. The best models were used to map the fertility properties for every profile and the synthetic index for soil fertility of profiles were computed and used to quantify the soil fertility. And then the evolution of soil fertility was analyzed on a millennium scale. The results showed that the five fertility properties could be predicted and mapped for all profiles, and the BOSS-SVMR models performed better than other models. According to the independent validation results, SOM, TN, and pH were predicted well (R2≥ 0.82, RPIQ ≥ 3.37), and the prediction performance for TP and TK was acceptable (0.50 ≤ R2< 0.66, 2.02 ≤ RPIQ < 2.70). The vertical distribution of soil fertility at different rice-planting ages showed that the soil fertility in the surface layer (0–30 cm) was high and decreased gradually with depth, tending to be stable in the deeper layer (30–100 cm). Rice planting significantly improved surface soil fertility. The linear function fit the changes in soil fertility with planting age better than the power function, indicating that the soil fertility in this area may have a high capacity for improvement. It was concluded that IS technology could be applied to high-resolution mapping of fertility properties in the intact paddy soil profiles. These results can offer new insights on soil fertility evolution and future soil sustainability under long-term anthropogenic influences.
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STUDY DESIGNRetrospective multicenter. OBJECTIVEThe aim was to investigate the factors involved in, and their relative contributions to, the overall accuracy of robot-assisted pedicle screw ...placement. SUMMARY OF BACKGROUND DATARobot-assisted surgery has reportedly resulted in greater accuracy for placement of pedicle screws than conventional methods. There are many potential factors affecting the accuracy of pedicle screws placed with a robot. No study has investigated these factors in a robust way. MATERIALS AND METHODSRadiographic and clinical data of three centers were pooled. Preoperative and postoperative computerized tomographies were obtained by all three centers to assess the accuracy of the placed screws. The primary outcome measured was accuracy of pedicle screws placed with the robot. The authors performed a multivariate regression analysis to determine the significant patient-related and screw-related variables and their relative contribution to the overall accuracy. In addition, an ordinal regression analysis was conducted to investigate the effects of different variables on accuracy of robot-placed screws graded by Gertzbein-Robbins grading system (GRS). RESULTSThe total contribution of all studied variables to overall accuracy variation as measured by offsets between the placed and planned screws was only 18%. Obesity, long constructs, female gender, surgeon, and vertebral levels were among the factors that had small contributions to the different screw offsets. For GRS grades, significant variables were gender (Log odds: 0.62, 95% CI: 0.38-0.85), age (Log odds: 0.02, 95% CI: 0.01-0.03), length of constructs (Log odds: 0.07, 95% CI: 0.02-0.11), screw diameter (Log odds: 0.55, 95% CI: 0.39-0.71), and length of the screws (Log odds: 0.03, 95% CI: 0.01-0.05). However, these variables too, regardless of their significant association with the accuracy of placed screws, had little contribution to overall variability of accuracy itself (only about 7%). CONCLUSIONThe accuracy of screws placed with robotic assistance, as graded by GRS or measured offsets between planned and placed screw trajectories, is minimally affected by different patient-related or screw-related variables due to the robustness of the robotic navigation system used in this study. LEVEL OF EVIDENCELevel III.
Natural oils are increasingly being used in the food, cosmetic, and agrochemical industries in recent years. However, natural oils with high market values are often counterfeited and adulterated with ...cheap, poor quality oils, with serious economic implications for the food and cosmetic industries, and potential health implications to consumers. This study reports the first combined use of Fourier transform infrared spectroscopy (FTIR) and partial-least-square (PLS) multivariate regression analysis for rapid, accurate, and low cost determination of the % compositions of two natural oils (neem oil (NO) and flaxseed oil (FO)) adulterated either with edible vegetable oil (VO) or extra virgin olive oil (EVOO). The FTIR of the calibration sample sets containing adulterated NO and FO with VO and EVOO at a concentration range of 1–99% w/w were measured and subjected to PLS multivariate regression analyses. The obtained FTIR spectra profile of the adulterated samples are highly dependent on the type of natural oil as well as the type of adulterant oil. The developed PLS models were subsequently used to determine the % compositions of independently prepared validation samples of NO and FO adulterated with VO and EVOO. The figures-of-merit of the PLS regression models were excellent and with good linearity (R2 > 0.998814). The score plots of the PLS regressions revealed interesting and useful information for pattern recognition of adulterated NO and FO samples. The PLS regression models correctly determined % compositions of adulterated NO and FO with VO with low root-mean-square-relative-percent errors (RMS%RE) of determination of 3.02%, and 4.24%, with an overall RMS%RE of 3.63%. The models also correctly determined % compositions of adulterated NO and FO with EVOO with RMS%RE of 7.13% and 2.00%, with an overall RMS%RE of 4.56%. The simplicity and high accuracy of this low-cost study presents an attractive model with potential real-world applications in quality control and quality assurance for consumer products in the food, cosmetics, and agrochemical industries.
•PLS regression of FTIR spectra was used for analysis of adulterated natural oils.•FTIR spectra changes with % compositions of adulterated oil samples.•PLS regression models demonstrated good linearity (R2 > 0.999702).•The models predicted % compositions of adulterated oil samples with low error.
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•A high prevalence of probable PTSD (20.87%) was found about six months after the first local outbreak of COVID-19 among healthcare workers at the Central Hospital of Wuhan.•Healthcare workers with ...negative COVID-19 tests, those with high Social Support Self-Rating Scale (SSRS) scores, and HCWs whose family members tested negative were less likely to have probable PTSD.•High levels of psychiatric and somatic illness and insomnia were associated with probable PTSD.
This study examined the prevalence of posttraumatic stress disorder (PTSD) symptoms and assessed mental illness via an online survey among healthcare workers (HCWs) at the Central Hospital of Wuhan after the peak of the COVID-19 outbreak. PTSD symptoms were measured using the PTSD Checklist Civilian Version (PCL-C), with a cutoff score of 50. Among the 642 HCWs, the prevalence of probable PTSD was 20.87%. Additionally, 88.88%, 82.09%, 100%, and 95.52% of HCWs with probable PTSD reported varying degrees of anxiety, depression, somatic symptoms, and insomnia, respectively. HCWs with probable PTSD scored higher on the Hospital Anxiety and Depression Scale (HADS), Patient Health questionnaire-15 (PHQ-15), and Insomnia Severity Index (ISI) than non-PTSD HCWs (all p < 0.05). Multivariate regression analysis revealed that HCWs with negative COVID-19 tests (OR, 0.35; 95% CI, 0.21–0.58; p < 0.00), those with high Social Support Self-Rating Scale (SSRS) scores (OR, 0.30; 95% CI, 0.17–0.52; p < 0.00), and HCWs whose family members tested negative (OR, 0.64; 95% CI, 0.42–0.96; p = 0.03) were less likely to have probable PTSD. This study found a high prevalence of probable PTSD and severe mental illness among local HCWs. Our finding emphasizes the need to provide mental health support for HCWs.
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To simulate the propagation law of hydraulic fractures in deep Coal Bed Methane (CBM) reservoirs in Qin-shui basin, a 3D numerical model is established using a continuous-discontinuous algorithm, and ...a discrete fracture network (DFN) is included in the model to increase its heterogeneity. The control variable method was employed to study the effects of in situ stress, random fracture number, fracturing fluid flow and viscosity on hydraulic fracture propagation. The results show that the fracture surface of hydraulic fracture can extended rapidly to the upper and lower boundaries of the model, and then extend to the two ends perpendicular to the minimum principal stress. Simultaneously, there was also a short-distance extension along the direction of the minimum principal stress. The fracture width in the horizontal section is oval, that is, the fracture width at the injection point is the largest, and the fracture width decreases gradually in the process of extending forward. The pressure data of the fracturing fluid also decrease gradually along the expansion process. With an increase in the buried depth of the coal seam, the fracture pressure of hydraulic fracture sharply increases, while the width of fracture sharply decreases. The random fracture in the model will be conducive to the expansion of hydraulic fracture only when the angle between its strike angle and the maximum horizontal principal stress is very small and will “hinder” the expansion of hydraulic fracture in most cases. The greater the density of random fractures in the reservoir is, the more obvious the “blocking” effect and the smaller the fracture width of hydraulic fractures. The increase in the fracturing fluid injection flow can significantly affect the propagation speed and width of hydraulic fractures. The increase in the fracturing fluid viscosity will change the shape of hydraulic fractures from “oval” to “round”, which is not conducive to the expansion of hydraulic fractures. Volume heterogeneity is introduced to quantitatively determine the heterogeneity characteristics of the model, and multiple regression analysis is used to obtain the relationship among hydraulic fracture length, maximum fracture width and volume heterogeneity, buried depth, injection flow and injection time when low viscosity water is used as fracturing fluid. It is determined that the volume heterogeneity of coal reservoirs, burial depth (in situ stress), fracturing fluid flow rate and viscosity are the main controlling factors affecting the fracture length and maximum fracture width.
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For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to ...assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs.
•Multivariate statistical techniques were used to analyze wastewater treatment plants.•Wastewater Quality Index was introduced to determine overtime condition of wastewater.•Factor analysis and correlation were applied to identify treatment performance.•5 variables were extracted accounted for 75.25% of wastewater information.•Predictive models were developed to estimate influent and effluent BOD, TP and WWQI.
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