IMRT delivery techniques (Sliding Window & Multiple Static Segments) for head & neck cancer cases were evaluated in this study. Planning target volume (PTV) coverage, Conformity Index, Homogeneity ...Index, doses to organ at risks (OARs) and PQI are considered as evaluation parameters. Detail about the parameters used for comparison is given in section 2.9 of materials and methods. While comparing techniques, it was made sure that target coverage and doses to OARs stayed within the constraints given by International Commission on Radiation Units and Measurements guidelines (ICRU).
Dosimetric comparison of Dynamic intensity modulated radiotherapy and Static Intensity modulated radiotherapy using seven and nine fields was done in this study. Twenty patients i.e. 15 patients of NPC and 5 patients of larynx were randomly selected. All treatment plans were created by SIB (Simultaneous Integrated Boost) technique with a prescribed dose of 66.9 Gy within 33 fractions. After CT simulation, delineation of planning target volume (PTV) and organs at risk (OARs) was done by oncologist. Brainstem, spinal cord, and parotid glands were contoured as OARs. Four different plans (7FSW, 7FSS, 9FSW, and 9FSS) were created for each patient using Eclipse treatment planning system.
The quality of IMRT plans has been significantly affected by the number of different techniques used in this study. In NPC patients, the 9F-IMRT techniques (SW and SS) produced an appropriate homogeneous dose of PTVs and resulted in pronounced sparing of nearby OARs from 7F-IMRT (SW and SS). This study shows that the results relating to PTV coverage are identical between SW and SS IMRT but in case of beam angles, 9-fields showed better PTVs coverage than 7-fields. PTV has a major contribution to the conformity index, homogeneity index, PQI, and D95%.
PTV coverage and low doses to OARs were obtained better in Static segment than sliding window. In case of 7 and 9 beam angles, nine fields showed good results than seven fields.
Accurate and reliable air quality index (AQI) forecasting is extremely crucial for ecological environment and public health. A novel optimal-hybrid model, which fuses the advantage of secondary ...decomposition (SD), AI method and optimization algorithm, is developed for AQI forecasting in this paper. In the proposed SD method, wavelet decomposition (WD) is chosen as the primary decomposition technique to generate a high frequency detail sequence WD(D) and a low frequency approximation sequence WD(A). Variational mode decomposition (VMD) improved by sample entropy (SE) is adopted to smooth the WD(D), then long short-term memory (LSTM) neural network with good ability of learning and time series memory is applied to make it easy to be predicted. Least squares support vector machine (LSSVM) with the parameters optimized by the Bat algorithm (BA) considers air pollutant factors including PM2.5, PM10, SO2, CO, NO2 and O3, which is suitable for forecasting WD(A) that retains original information of AQI series. The ultimate forecast result of AQI can be obtained by accumulating the prediction values of each subseries. Notably, the proposed idea not only gives full play to the advantages of conventional SD, but solve the problem that the traditional time series prediction model based on decomposition technology can not consider the influential factors. Additionally, two daily AQI series from December 1, 2016 to December 31, 2018 respectively collected from Beijing and Guilin located in China are utilized as the case studies to verify the proposed model. Comprehensive comparisons with a set of evaluation indices indicate that the proposed optimal-hybrid model comprehensively captures the characteristics of the original AQI series and has high correct rate of forecasting AQI classes.
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•A novel optimal-hybrid model is developed for AQI forecasting.•A new idea of SD method is proposed to decompose AQI series.•the proposed BA-LSSVM model considering air pollutant factors is applied to forecast WD(A).•LSTM neural network is employed to forecast WD(D).•The proposed optimal-hybrid model outperforms other hybrid models.
•At midterm after sleeve gastrectomy, a higher intake of energy from carbohydrates was related to a lower percentage of total weight loss.•At midterm after sleeve gastrectomy, isocaloric substitution ...of carbohydrate or fat with protein was associated with lower odds of nonresponse to the surgery.•At midterm after sleeve gastrectomy, more dietary fat intake was associated with a higher proportion of fat-free mass loss.•At midterm after sleeve gastrectomy, higher fiber intake was associated with lower odds of excessive fat-free mass loss.
This study aimed to investigate the associations of macronutrient quantities and qualities with percentage total weight loss and percentage of fat-free mass loss relative to total weight loss in adults undergoing sleeve gastrectomy.
This cross-sectional study included 146 patients on postoperative time since sleeve gastrectomy of 2 to 4 y. Diet was assessed using a food frequency questionnaire. Macronutrient quality index, carbohydrate quality index, fat quality index, and healthy plate protein quality index were calculated. The associations of dietary variables with percentage total weight loss and percentage of fat-free mass loss relative to total weight loss were determined using linear regression. Logistic regression was used to estimate the odds of non-response (percentage total weight loss < 25%) and excessive fat-free mass loss (percentage of fat-free mass loss relative to total weight loss > 28%) based on dietary intakes.
Forty-six (31.5%) were non-responders, and 49 (33.6%) experienced excessive fat-free mass loss. The fully adjusted model showed a 0.75 decrease in percentage total weight loss per 5% carbohydrate increase (95% CI, –1.45 to –0.05). The odds of non-response were 53% lower per 5% increase in protein (95% CI, 0.23–0.94). Each 5-g higher intake of fat was associated with 0.29 higher percentage of fat-free mass loss relative to total weight loss (95% CI, 0.03–0.55). The odds of excessive fat-free mass loss were reduced by 5% per gram of fiber intake (95% CI, 0.90–0.99). Each 5% increment in energy intake from protein that was isocalorically substituted for either carbohydrate or fat was associated with lower odds of nonresponse. Macronutrient quality indices had no significant associations.
Adherence to a high-protein, high-fiber diet after sleeve gastrectomy may enhance surgical success by improving total weight loss and preventing excessive fat-free mass loss.
•Soil and water losses are negatively correlated to soil quality indices.•Cover crop management systems improve soil quality and reduce water erosion.•Spontaneous vegetation as cover crop showed ...better results in soil quality.•Olive on bare soil can highlighting environmental degradation over the years.•Cover crop management requires care to conciliate with olive plantation growth.
The production of olive trees in shallow soils in hillslopes requires adequate soil and water conservation systems. In this context, soil use and management besides vegetation cover contributes to better soil quality and the sustainability of the olive cultivation system. Thus, the objective was to determine the physical and chemical properties that are indicative of soil quality in areas of Dystrudepts, under different soil cover management systems in olive cultivation in tropical region. Olive tree culture field experiment was established in 2015, where soil was sampled over three years (2016, 2017 and 2021). The effect of five cover management systems were compared for each agricultural year, namely BS: bare soil in 2015/2016, 2016/2017 and 2017/2021; OBS: olive trees on bare soil in 2015/2016, 2016/2017 and 2017/2021; OSV: olive trees intercropped with mowed spontaneous vegetation in 2015/2016, 2016/2017 and 2017/2021; OJB: olive trees intercropped with jack beans (Canavalia ensiformis L.) in 2015/2016 and 2016/2017, and olive trees crowned intercropped with mowed spontaneous vegetation in 2017/2021; OMI: olive trees intercropped with millet (Pennisetum glaucum L.) in 2015/2016, olive trees intercropped with sunn hemp (Crotalaria juncea L.) in 2015/2017 and olive trees with spontaneous vegetations treated with herbicide (Roundup) in 2017/2021. The soil quality indices (SQI) proved to be sensitive to variations in different types of soil management from the second year of assessment, regarding water erosion. OSV and OJB showed the highest SQI, while OBS showed the lowest values. The SQI demonstrated an inverse correlation with soil and water losses starting from the second year of assessment. This highlights its efficacy in assessment the effects of adopted management on soil quality in the context of water erosion. Moreover, it reinforces the importance of vegetation cover in maintaining soil fertility, resulting in increase of soil organic matter and improving the structuring of Dystrudepts. The attributes that most correlated with the SQI were soil organic matter, effective cation exchange capacity, sum of bases, geometric mean diameter of aggregates, pH and unsaturated soil hydraulic conductivity. These attributes collectively provide valuable insights into soil health and water erosion susceptibility within the olive tree production system.
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Water resources and water quality are restrictive factors in the Chinese Loess Plateau (CLP), a unique area with most severe soil erosion, fragile ecology and water shortage. River and well water ...samples were firstly analyzed, and multiple methods and indexes, including principal component analysis (PCA), correlation analysis (CA), sodium adsorption ratio (SAR), water quality index (WQI), hazard quotient (HQ), and hazard index (HI), were used to investigate characteristics, water quality and health risk assessment of trace elements in CLP. The average trace elements concentrations were higher than the world average with a slightly alkaline characteristic. PCA and CA showed that Al, Fe, Li, B, As, and F had natural origins from loess weathering and leaching; Cr, Pb, Cd, Cu, Ag, and Tl were mainly from anthropogenic input; Co, Ni, and Mn were dominated by both anthropogenic and natural sources. The poor river water quality was mainly related with high sodium (alkalinity) and salinity hazard. The poor well water quality samples with high WQI values, especially for As, Cr, and B, were distributed in the northwest and the Fenhe River sub-basin. The pollution level of trace elements in rivers in CLP was in medium level compared with other rivers worldwide. Arsenic pollution was the worst in well water and was the potential pollutant in river water especially for children. Arid climate together with anthropogenic input and special water characteristics (high Na, pH, and low Ca) aggravated As pollution. More work should be done to monitor the secular variation and remove As in the high As areas. The results of this study can provide the basic data for efficient water management and human health protection in CLP.
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•The pollution level of trace elements in Loess Plateau is in medium level.•As, Cr and B were the main pollutants of the natural waters.•Arsenic was the most important pollutant for non-carcinogenic risks.•Poor quality waters are distributed in the northwest and Fenhe River basin.•Arid climate and anthropogenic input are the main reasons for water pollution.
To ensure safe drinking water sources in the future, it is imperative to understand the quality and pollution level of existing groundwater. The prediction of water quality with high accuracy is the ...key to control water pollution and the improvement of water management. In this study, a deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest (RF), eXtreme gradient boosting (XGBoost), and artificial neural network (ANN). A total of 226 groundwater samples are collected from an agriculturally intensive area Arang of Raipur district, Chhattisgarh, India, and various physicochemical parameters are measured to compute entropy weight-based groundwater quality index (EWQI). Prediction performances of models are determined by introducing five error metrics. Results showed that DL model is the best prediction model with the highest accuracy in terms of R2, i.e., R2 = 0996 against the RF (R2 = 0.886), XGBoost (R2 = 0.0.927), and ANN (R2 = 0.917). The uncertainty of the DL model output is cross-verified by running the proposed algorithm with newly randomized dataset for ten times, where minor deviations in the mean value of performance metrics are observed. Moreover, input variable importance computed by prediction models highlights that DL model is the most realistic and accurate approach in the prediction of groundwater quality.
•Groundwater quality is assessed using EWQI method.•Machine learning (ML) algorithms are used for predicting groundwater quality.•Prediction performance of RF, XGBoost, ANN and DL models are compared.•DL based quality prediction model performs much better than other ML models.
Numerous indicator models have been developed and utilized for the assessment of pollution levels in water resources. In the present study, modified water quality index (MWQI), integrated water ...quality index (IWQI), and entropy-weighted water quality index (EWQI) were integrated with statistical analysis for the assessment of drinking water quality in Umunya suburban district, Nigeria. There is no known study that has simultaneously compared their performances in water quality research. Overall, the results of this study showed that the water supplies are threatened by heavy metal pollution. The parametric quality rating analysis observed that Pb contamination has the most significant impact on the water supplies. Hierarchical cluster analysis was proved very efficient in the allotment of the possible sources of pollution in the study area. MWQI results classified the water supplies as “marginal”, signifying that they are frequently threatened. Based on the IWQI, 26.67% of the samples are suitable for drinking, 13.33% are acceptable for domestic uses, and 60% are unfit for drinking purposes. Similarly, the EWQI results showed that 60% of the samples are unfit for human consumption, whereas 40% are suitable. Investigation into the performance and sensitivity of the MWQI, IWQI and EWQI models in water quality assessment was analyzed and the results showed that they are all sensitive, efficient and effective tools. This study has indicated that the integration of the three models gives a better understanding of water quality. The excessive concentration of some potentially toxic heavy metals in the water supplies suggests that the contaminated water supplies should be treated before use.
Introduction: Due to various components, materials, and processes, industrial indoor air quality differs from building indoor air. Air quality and the working environment impact health, performance, ...and comfort. This study developed an Indoor Work Environmental Air Quality Index (IWEAQI) to assess and characterize industrial work environments. Materials and methods: Surat “Textile city” is situated in the western part of India in Gujarat state. The small-scale dyeing and printing industry has been selected as a study area. The industry locations like Jet dyeing machine area, stenter machine area, printing machine area, looping machine area and washing basin area has been selected. Various chemicals, adhesives, solvents, dyes, and varied temperature and humidity conditions are used to transform the raw cloth into the finished product. CO, CO2, SO2, NO2, O3, Total Volatile Organic compounds (TVOC), Formaldehyde, Particulate Matters (PM10, PM2.5), WBGT index, humidity, noise, and light were considered to construct IWEAQI. Continuous observations were recorded at minute intervals with a real-time monitoring system. To account for all contributing aspects, United States Environmental Protection Agency (USEPA) air quality index technique was updated for index formulation. IWEAQI was validated using the Pollution Index approach. Results: The proposed approach calculated IWEAQI from results. Both approaches gave an index value of 46-80. The developed approach and pollution index method were compared using regression analysis. All study locations had regression values between 0.93 and 0.99. Conclusion: The technique classifies IWEAQI as excellent (0-20), good (21-40), moderate (41-60), poor (61-80), and very poor (81-100). From the developed index value, which parameters are influencing the most can be judged.
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