Important Indicator Research Articles

Estimation of Water Footprint and Its Most Important Indicators for Some Egyptian Export Crops

Estimation of Water Footprint and Its Most Important Indicators for Some Egyptian Export Crops

Low Body Fat Percentage and Menstrual Cycle Disorders in Female Elite Adolescent Dancers.

Introduction: The menstrual cycle is an important indicator of health in female athletes. Female elite adolescent dancers are expected to control their weight while also training intensely, which can lead to menstrual disorders. This study aimed to investigate the relationship between body composition and menstrual status in female elite adolescent dancers. Methods: In total, 131 female elite adolescent dancers (age: 15.9 ± 1.5 years) were enrolled in this study. We measured the height, weight, and body fat percentage (BFP) for each participant and calculated their body mass index (BMI). We gathered information on individual menstruation patterns and the participants' menstrual cycles over the previous year using recall methods. We then compared the differences between dancers with menstrual cycle disorders and those without. Primary amenorrhea was defined as menarche occurring after the age of 15, while secondary amenorrhe was defined as experiencing fewer than 5 or no menstrual periods for at least 3 of the previous 12 months. We conducted a reliability test using the same questionnaire 2 weeks later. Statistical significance was defined as P < .05, and we calculated the effect sizes (d) and 95% confidence intervals (95% CI). Results: The average BMI and BFP were 22.6 ± 3.0% and 19.4 ± 2.2 kg/m2, respectively. Low BFP and low BMI were observed in 51 (38.6%) and 47 (35.6%) participants, respectively. Primary amenorrhea in 3 participants (2.3%) and 29 (22.1%) reported experiencing secondary amenorrhea; they had lower BFP than the dancers who did not experience amenorrhea (P = .041, 95% CI, -2.51 to -0.05). Conclusion: Female elite adolescent dancers in China may have lower BFP and menstrual problems. Given that lower BFP may contribute to the occurrence of menstruation disorders, it is essential to pay an attention to both BFP and the menstruation status in female elite adolescent dancers.

Predicting mixed venous oxygen saturation (SvO2) impairment in COPD patients using clinical-CT radiomics data: A preliminary study.

Chronic obstructive pulmonary disease (COPD) is one of the most common chronic airway diseases in the world. To predict the degree of mixed venous oxygen saturation (SvO2) impairment in patients with COPD by modeling using clinical-CT radiomics data and to provide reference for clinical decision-making. A total of 236 patients with COPD diagnosed by CT and clinical data at Xiangyang No. 1 People's Hospital (n= 157) and Xiangyang Central Hospital (n= 79) from June 2018 to September 2021 were retrospectively analyzed. The patients were divided into group A (SvO⩾2 62%, N= 107) and group B (SvO<2 62%, N= 129). We set up training set and test set at a ratio of 7/3 and time cutoff spot; In training set, Logistic regression was conducted to analyze the differences in general data (e.g. height, weight, systolic blood pressure), laboratory indicators (e.g. arterial oxygen saturation and pulmonary artery systolic pressure), and CT radiomics (radscore generated using chest CT texture parameters from 3D slicer software and LASSO regression) between these two groups. Further the risk factors screened by the above method were used to establish models for predicting the degree of hypoxia in COPD, conduct verification in test set and create a nomogram. Univariate analysis demonstrated that age, smoking history, drinking history, systemic systolic pressure, digestive symptoms, right ventricular diameter (RV), mean systolic pulmonary artery pressure (sPAP), cardiac index (CI), pulmonary vascular resistance (PVR), 6-min walking distance (6MWD), WHO functional classification of pulmonary hypertension (WHOPHFC), the ratio of forced expiratory volume in the first second to the forced vital capacity (FEV1%), and radscore in group B were all significantly different from those in group A (P< 0.05). Multivariate regression demonstrated that age, smoking history, digestive symptoms, 6MWD, and radscore were independent risk factors for SvO2 impairment. The combined model established based on the abovementioned indicators exhibited a good prediction effect [AUC: 0.903; 95%CI (0.858-0.937)], higher than the general clinical model [AUC: 0.760; 95%CI (0.701-0.813), P< 0.05] and laboratory examination-radiomics model [AUC: 0.868; 95%CI (0.818-0.908), P= 0.012]. The newly created nomogram may be helpful for clinical decision-making and benefit COPD patients. SvO2 is an important indicator of hypoxia in COPD, and it is highly related to age, 6MWD, and radscore. The combined model is helpful for early identification of SvO2 impairment and adjustment of COPD treatment strategies.

IoT-based Pregnant Mother Contraction Monitoring System Design

Pregnancy is an important stage in a woman's life that requires special monitoring and care to ensure the well-being of both mother and fetus. The presence of uterine contractions is an im-portant indicator of imminent labour, and prompt monitoring is essential to spot difficulties and ensure a safe delivery. This study aims to track contractions in pregnant women to improve pre-natal and labour care. Real-time monitoring, recording and analysis of uterine contractions. This research is a type of RD research with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation. The result of this tool is that it can precisely track contraction activity and send information to the IoT network. Through an easy-to-use mobile app, this in-formation can be accessed by medical staff caring for pregnant women and pregnant women themselves. To provide continuous monitoring, provide early notification of alarming changes, and facilitate rapid medical response, this monitoring system utilizes IoT technology. This can ease the burden on medical institutions, lower the possibility of difficulties during labour, and provide a sense of security for pregnant women. As an innovative and effective tool, the Inter-net of Things-based Maternal Contraction Monitoring System is anticipated to improve prenatal and delivery healthcare, reduce maternal and infant mortality, and monitor contractions in preg-nant women. The healthcare of pregnant women around the world could be significantly im-proved with the successful implementation of this approach.

ПРОИЗВОДСТВО ОВОЩЕЙ: СОСТОЯНИЕ ОТРАСЛИ И ПЕРСПЕКТИВЫ РАЗВИТИЯ

The scientific article conducted a theoretical study on the issues of ensuring the efficiency of vegetable production, the development of the vegetable growing industry, and providing the population with vegetable growing products. The dynamics of sown areas over the past 25 years is presented. It was revealed that the largest share in the structure of sown areas is occupied by potato plantings, while in dynamics over 25 years, plantings have decreased by almost half. The smallest share in the structure of sown areas is occupied by the cultivation of industrial crops. Their share in the structure of sown areas has almost doubled over the past 25 years. From 10 to 20 percent of the sown area is used for planting vegetables; in the period under study, their area almost doubled. An analysis of the dynamics of the yield of open-ground vegetables showed that over the past 25 years it has almost doubled (from 143 c/ha to 242 c/ha). It is shown that the gross harvest of open-ground vegetables has been trending upward over the past 25 years, with an increase of about 15 percent. It was revealed that in the period from 2007 to 2019, there was an increase in vegetable production per capita by 21.5 percent. Consumption of vegetables exceeds its production within the country; during the study period it increased by 17.4 percent. It was noted that vegetable import indicators are ahead of export indicators. In 2019, vegetable exports were almost 15 times higher than in 1996.

High-density lipoprotein cholesterol to apolipoprotein A-1 ratio is an important indicator predicting in-hospital death in patients with acute coronary syndrome.

Dyslipidemia plays a pivotal role in the pathogenesis of acute coronary syndrome (ACS). This study aims to investigate the value of two indices associated with lipid metabolism, low-density lipoprotein cholesterol to apolipoprotein B ratio (LBR) and high-density lipoprotein cholesterol to apolipoprotein A-1 ratio (HAR), to predict in-hospital death in patients with ACS. This single-center, retrospective, observational study included 3,366 consecutive ACS patients in Zhongda Hospital, Southeast University from July 2013 to January 2018. The clinical and laboratory data were extracted, and the in-hospital death and hospitalization days were also recorded. All patients were equally divided into four groups according to quartiles of HAR: Q1 (HAR < 1.0283), Q2 (1.0283 ≤ HAR < 1.0860), Q3 (1.0860 ≤ HAR < 1.1798), and Q4 (HAR ≥ 1.1798). Overall, HAR was positively associated with the counts of neutrophils and monocytes, whereas negatively correlated to lymphocyte counts. HAR was negatively correlated to left ventricular ejection fraction (LVEF). Compared to other three groups, in-hospital mortality (vs. Q1, Q2, and Q3, p < 0.001) and hospitalization length (vs. Q1, Q2, and Q3, p < 0.001) were significantly higher in the Q4 group. When grouped by LBR, however, there was no significant difference in LVEF, in-hospital mortality, and hospitalization length among groups. After adjusting potential impact from age, systolic blood pressure, creatine, lactate dehydrogenase, albumin, glucose, and uric acid, multivariate analysis indicated that HAR was an independent factor predicting in-hospital death among ACS patients. HAR had good predictive value for patients' in-hospital death after the occurrence of acute coronary events, but LBR was not related to in-hospital adverse events.

Interval Type-2 Fuzzy Neural Network Based on Active Semi-Supervised Learning for Non-Stationary Industrial Processes

Accurate models ensure the efficient and safe operations of industrial processes. However, modeling of industrial processes with non-stationary characteristic is challenging. In this study, an interval type-2 fuzzy neural network (IT2FNN) based on active semi-supervised learning (ASSL-IT2FNN) is proposed for such industrial processes. First, an IT2FNN with adaptive fuzzy membership function (FMF) and hierarchical learning method is utilized to achieve considerable modeling accuracy and efficiency. Second, to better tackle with the non-stationary characteristic, an unsupervised distribution detection method is proposed to identify the occurrence moments of concept drift actively from the perspective of probability and spatial projection. Then, by an active semi-supervised learning method, the concept drift samples with partial labels are used to build a subnetwork, guaranteeing the performance of the constructed IT2FNN models with incomplete data in non-stationary environments. Finally, the proposed ASSL-IT2FNN is verified by benchmark simulations and real industrial data, and the experimental results demonstrate its outperformance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Concept drift is an important indicator of a non-stationary environment. It manifests as changes in the distribution of samples over time, which may reduce the accuracy of models constructed based on historical samples. This study aims to alleviate the impact of concept drift on model performance in non-stationary industrial processes while reduce the cost of sample labeling. By using the adaptive FMF and hierarchical learning method, the designed IT2FNN can effectively deal with the inherent nonlinearity of complicated industrial processes. The samples representing concept drift are actively identified by the Kullback-Leibler (KL) divergence and maximum mean difference (MMD), enabling timely detections of concept drift moments in unsupervised situations. The active semi-supervised learning is used to adapt the constructed model to handle the concept drift, wherein a subnetwork is designed based on concept drift samples and similar historical samples.

Transarterial chemoembolization plus stent placement for hepatocellular carcinoma with main portal vein tumor thrombosis: A meta-analysis.

Portal vein tumor thrombus is an important indicator of poor prognosis in patients with hepatocellular carcinoma. Transarterial chemoembolization is recommended as the standard first-line therapy for unresectable hepatocellular carcinoma. Portal vein stent placement is a safe and effective therapy for promptly restoring flow and relieving portal hypertension caused by tumor thrombus. To assess the clinical significance of transarterial chemoembolization plus stent placement for the treatment of hepatocellular carcinoma with main portal vein tumor thrombosis. We searched English and Chinese databases, assessed the quality of the included studies, analyzed the characteristic data, tested heterogeneity, explored heterogeneity, and tested publication bias. In total, eight clinical controlled trials were included. The results showed that the pressure in the main portal vein after stent placement was significantly lower than that with no stent placement. The cumulative stent patency and survival rates at 6 and 12 months were lower in the transarterial chemoembolization + stent placement group than in the transarterial chemoembolization + stent placement + brachytherapy/radiotherapy group. The survival rates of patients treated with transarterial chemoembolization + stent placement for 6 and 12 months were higher than those of patients treated with transarterial chemoembolization alone. For Chinese patients with hepatocellular carcinoma with main portal vein tumor thrombosis, transarterial chemoembolization plus stenting is effective. Transarterial chemoembolization + stent placement is more effective than transarterial chemoembolization alone. Transarterial chemoembolization + stent placement + brachytherapy/radiotherapy is more effective than transarterial chemoembolization + stenting.

Seed bank and aboveground vegetation of Atlantic Forest re‐growing on mining tailings in Mariana: Highlighting diversity patterns of functional groups

AbstractNatural regeneration after mining activities depends largely on the soil seed bank (SSB) and aboveground vegetation (AGV) richness and composition. We evaluated the species diversity pattern of the SSB and AGV on soil and technosol across second‐growth Atlantic Forests (SGFs) after mining tailings deposition in Mariana, southeastern Brazil. Two second‐growth Atlantic seasonal semideciduous forests of different stand ages were selected: a 3‐year‐old stand of SGF re‐growing on tailings (SGF tailings) and a 30‐year‐old stand of native reference forest not affected by tailings (SGF nontailings). Overall, the results showed significant differences in forest attributes (species richness and composition) in the SSB and AGV between the two forest patches evaluated (SGF tailings and SGF nontailings). Moreover, both nonzoochoric and zoochoric dispersed species of AGV showed significant differences between second‐growth forests, with higher variability of community‐weighted mean in SGF tailings. The result shows smaller differences in species richness between forest types for both SBB (ranging 85 and 90 species) and AGV (~55 species). Despite the time difference between the secondary forest and the absence of zoochoric species in the tailings seed bank, the AGV that are re‐growing on mining tailings presented a high proportion of zoochoric species. This pattern of species richness and community composition variability observed in the technosol seed bank, and AGV that are re‐growing after the deposition of mining tailings are important recovery indicators for plant communities affected.

Influence of acidic metabolic environment on differentiation of stem cell-derived cardiomyocytes.

Stem cell-based myocardial regeneration is a frontier topic in the treatment of myocardial infarction. Manipulating the metabolic microenvironment of stem cells can influence their differentiation into cardiomyocytes, which have promising clinical applications. pH is an important indicator of the metabolic environment during cardiomyocyte development. And lactate, as one of the main acidic metabolites, is a major regulator of the acidic metabolic environment during early cardiomyocyte development. Here, we summarize the progress of research into the influence of pH value and lactate on cardiomyocyte survival and differentiation, as well as related mechanisms.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

Correlation analysis between combined deflection angle and osteonecrosis of femoral head after femoral neck fracture

To evaluate the correlation between pelvic incidence (PI) angle, hip deflection angle (HDA), combined deflection angle (CDA) and osteonecrosis of the femoral head (ONFH) after femoral neck fracture, in order to explore early predictive indicators for ONFH occurrence after femoral neck fracture. A study was conducted on patients with femoral neck fractures who underwent cannulated screw internal fixation between December 2018 and December 2020. Among them, 208 patients met the selection criteria and were included in the study. According to the occurrence of ONFH, the patients were allocated into ONFH group and non-NOFH group. PI, HDA, and CDA were measured based on the anteroposterior X-ray films of pelvis and axial X-ray films of the affected hip joint before operation, and the differences between the two groups were compared. The receiver operating characteristic curve (ROC) was used to evaluate the value of the above imaging indicators in predicting the occurrence of ONFH. Among the 208 patients included in the study, 84 patients experienced ONFH during follow-up (ONFH group) and 124 patients did not experience ONFH (non-ONFH group). In the non-ONFH group, there were 59 males and 65 females, the age was 18-86 years (mean, 53.9 years), and the follow-up time was 18-50 months (mean, 33.2 months). In the ONFH group, there were 37 males and 47 females, the age was 18-76 years (mean, 51.6 years), and the follow-up time was 8-45 months (mean, 22.1 months). The PI, HDA, and CDA were significantly larger in the ONFH group than in the non-ONFH group ( P<0.05). ROC curve analysis showed that the critical value of PI was 19.82° (sensitivity of 40.5%, specificity of 86.3%, P<0.05); the critical value of HDA was 20.94° (sensitivity of 77.4%, specificity of 75.8%, P<0.05); and the critical value of CDA was 39.16° (sensitivity of 89.3%, specificity of 83.1%, P<0.05). There is a correlation between PI, HDA, CDA and the occurrence of ONFH after femoral neck fracture, in which CDA can be used as an important reference indicator. Patients with CDA≥39.16° have a higher risk of ONFH after femoral neck fracture.

Operando Entropy Profiling of Sodium-Ion Batteries via Optical Fiber Sensing for Thermal Management and Ageing Monitoring

Batteries are essential for the electrification of transport and the replacement of internal combustion engines. Thermodynamics was largely established with the development of the engines, and this knowledge has been applied to batteries for years. In particular, entropy profiles are sensitive to structural changes and are useful for diagnosing and understanding battery ageing. However, entropy profiling of aged batteries is mainly limited to the potentiometric method, which makes the technique in situ and time-consuming. Herein, we rely on optical fiber calorimetry to perform operando entropy profiling of commercial sodium-ion cells. Firstly, we directly compare the entropy profile of sodium-ion Na3V2(PO4)2F3/hard carbon (NVPF/HC) chemistry against those of commercialized lithium-ion chemistries, highlighting the uniqueness of NVPF/HC chemistry in battery thermal management. Operando entropy profiling of NVPF/HC chemistry further elucidates the structural degradations that take place during cycling and provide features that can be important indicators of the battery’s state of health. This work reintroduces thermodynamic analyses as a valuable tool for batteries and spotlights the new horizons offered by the convergence of battery sensing, thermodynamics, and other disciplines.

Impact assessment of the fuel and energy complex on the ecological state of the Gulf of Ob waters

Ensuring environmental safety in the Arctic, where most large hydrocarbon deposits are concentrated, is one of the main problems of the 21st century. At the same time, the Gulf of Ob is a strategically important area where grandiose energy resource and transport projects are being implemented. An important indicator of environmental safety is the research of atmospheric impurities on the surface of the large bay of the Kara Sea, which has not been sufficiently studied to date. The purpose of the research is to assess the impact of the activities of the fuel and energy complex on the ecological state of the waters of the Gulf of Ob for 2016-2020 using geographic information systems. The Earth program was used in the research; statistical processing of the results was carried out using IBM SPSS Statistics 21 software. It has been established that in the winter period of the year, in contrast to the summer, on the surface of the water area of the Gulf of Ob there is a significant increase in the concentration of gaseous impurities and a decrease in suspended particles, especially over the water area of the Gulf of Ob at the northern latitude of 68.80º an area of intensive gas production. Moreover, in the summer, the content of suspended particles (PM1, PM2.5, PM10), as well as the CO concentration at this latitude were higher compared to the more northern latitude. Thus, the intensive development of the fuel and energy complex in this region is associated with an environmental component (carbon footprint) on the surface of the Gulf of Ob, which requires a more in-depth research within the carbon polygon.

Classification of Patient Satisfaction Level on Health Services Using the C4.5 Algorithm

Quality health services are related to patient satisfaction. Patient satisfaction can be used as a benchmark for improving the quality of health services. Problems often occur when implementing health techniques, such as service problems at the Restu Clinic. Patients and their families indicated that although the Restu Clinic was established with adequate facilities, it had not yet reached the maximum level of service. These indications include long waiting times for examinations, a lack of thoroughness by medical personnel, and services that are not timely. Service quality cannot be separated from the dimensions that are the core of quality services, which are expected to meet patient needs. Patient satisfaction is considered an important indicator of good quality. This research will only discuss four aspects of service quality, which are reliability, responsiveness, assurance, and empathy, from health workers at the Restu Clinic. The C4.5 algorithm is known to be superior in producing decision trees that efficiently solve discrete and numerical variables and provide satisfactory accuracy. Therefore, the author conducted a study to assess service quality using the C4.5 algorithm. This research aims to determine the factors that influence the quality of health services and to know patient satisfaction with health services at the Restu Clinic. Knowing the intensity of patient satisfaction with services at the Restu Clinic can improve the quality of optimal services and gain patients' trust in government agencies.

Aviation subsidy policy and regional wellbeing: Important indicators from relevant stakeholders’ perspectives

A failure to adequately reconcile stakeholder interests and opinions can increase the probability of a failed aviation subsidy request or a loss of regional opportunity. This study, instead of arguing the importance and offering critiques of aviation subsidies, conducts a survey of stakeholders in New Zealand and Taiwan and uses fuzzy analytic hierarchy process to evaluate and prioritise key air transport activities and regional wellbeing indicators regarding the design and implementation of aviation subsidy policies in the early stage of the COVID-19 pandemic. The findings show that destination served, flight frequency, local business activities, medical treatment, and rapid hazard response were considered the key factors of aviation subsidies. Integrating economic and social wellbeing into subsidy policy design and implementation is highly necessary. The results provide useful insights for the development of aviation subsidy policies aimed at improving regional wellbeing in New Zealand and Taiwan during the post-COVID-19 era.

Molecular docking, pharmacokinetic profiling and toxicity studies of 2-substituted benzimidazole derivatives against breast cancer

The most prevalent malignancy among women is breast cancer, which had almost 1.3 million new cases in 2020. It is the second most common cancer in the world, followed by lung cancer. The survival rate would be 99% if the cancerous tumour was limited to the breast. If the cancer migrated to neighbouring lymph nodes, the survival percentage would be 85% and it would drop to 27% if it spread to distant regions. In fact, the most prevalent breast cancer subtype is that caused by excessive estrogen levels. The enhancement of pertinent treatment techniques depends on the estrogen receptors (ER) in both healthy and pathological conditions. There are two primary types of ER, ERα and ERβ, which are each encoded by a different gene. ER status is the most important indicator of breast cancer prediction. To develop novel therapeutics for breast cancer, 30 newly designed benzimidazole compounds targeting the ER were docked. Among them, a compound with a glide score of −9.293 was discovered to be the leading compound. ADME investigations provided additional validation of the docking results. The pyrazole fused benzimidazole nucleus is therefore suggested as a potential pharmacophore for the development of innovative anticancer treatment for breast cancer.

PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model’s ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.

Optimized kernel extreme learning machine using Sine Cosine Algorithm for prediction of unconfined compression strength of MICP cemented soil.

Microbially induced calcite precipitation (MICP) is an eco-friendly bio-remediation technology. The unconfined compressive strength (UCS) of MICP cemented soil is an important indicator of repair effectiveness. This study proposes a machine learning technique utilizing the Sine Cosine Algorithm (SCA) to optimize the regularization coefficient C and kernel width γ of the kernel extreme learning machine (KELM) to predict the UCS of MICP cemented soil. To evaluate the performance of the proposed models, a dataset containing 180 groups of the UCS of MICP cemented soil was obtained. The results obtained by SCA-KELM were compared with those obtained by the Random Forest algorithm (RF), Support Vector Machine (SVM), and KELM. The performance of these models was evaluated by the scores of MAE, RMSE, and R2. The results indicate that the SCA-KELM algorithm exhibits optimal prediction performance (Total score: 21). After optimizing KELM with SCA, the total score improved by 110%, suggesting that SCA significantly enhances the KELM performance. After model development, the optimal population size for SCA-KELM was determined to be 50. Based on the mutual information test, an innovative method was developed for categorizing factor sensitivity by employing importance scores as the partitioning criterion. This method categorizes the influencing factors into three tiers: high (importance score: 8.03-11.14%), medium (importance score: 5.93-7.25%), and low (importance score: 3.23-5.18%). These results suggest that the proposed SCA-KELM algorithm can be regarded as a powerful tool for predicting the UCS of MICP cemented soil.