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This study was aimed to investigate and evaluates the degree of environmental sensitivity of desertification and to provide solutions, treatments by identifying the elements and indicators that are directly linked to desertification in southern Iraq.  GIS technique was used, and mapping of environmentally sensitive areas for desertification. Five important indices including (soil quality index SQI, climate quality index CQI, vegetation quality index VQI, management quality index MQI, and groundwater quality index GWQI) were used. Results showed that the SQI index was varied between high, moderate, and low quality, the VQI index was varied between moderate to low quality, as for the indices of (CQI, MQI, and GWQI), all classifications were within the lower class. This was reflected negatively on the values of environmental sensitivity to desertification (ESAI) and it became in the critical category within the study area. Consequently, the environmental sensitivity to desertification was calculated by excluding the indices of MQI, and GWQI. Four classes were detected (None affected, Potential, Fragile, and Critical) divided into sub-classes (N, P, F3, C1, C3).

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.

The objective of this study was to identify the locations sensitivity to land desertification based on the Mediterranean Desertification and Land Use (MEDALUS) approach by the Geographic Information Systems (GIS) in the south of Maysan governorate at Iraq for mapping environmentally sensitive areas to desertification. Three indicators, which included climate, vegetation, and soil, were employed to estimate the ESAI and then to classify the land in critical, fragile potentially, and non-influenced sensitive areas. The results of the soil quality index (SQI) indicated that 25% of the studied area was classified as moderate quality and 21% was low quality while 54% was very low quality. Vegetation qualities were classified into moderate and low quality 19% and 81%, respectively, and climate quality was classified as moderate.

Objective The present study was designed to investigate the relationship between food quality indices, including Macronutrient Quality Index (MQI), and Carbohydrate Food Quality Score (CFQS), and the risk of bacterial vaginosis (BV) in Iranian women. Results In the adjusted model, after adjusting for potential confounders, significantly lower odds of BV were observed in the last highest tertile of the CFQS (odds ratio (OR): 0.317, confidence interval (CI) 95%: 0.155–0.646, p -value˂0.001), MQI (OR: 0.325, CI 95%: 0.176–0.599, p -value˂0.001), carbohydrate quality index (CQI) (OR: 0.288, CI 95%: 0.143–0.580, p -value˂0.001) and protein quality index (PQI) (OR: 0.498, CI 95%: 0.268–0.924, p -value = 0.029) compared to the lowest tertile of these indices. Clinical trial number Not applicable.

•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.

The air quality index has a severe effect on the determination of health conditions of a city. The prediction of air quality index can aid in determining the optimum route in case of traffic and it can also aid in determining the pollutants which have severe impact on human health conditions. The paper presents an air quality prediction system using various machine learning based models. The air quality index is determined by measuring the different gases present in the atmosphere. In this paper we have considered seven such parameters as concentration levels of Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), Carbon Mono oxide (CO), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulphur Dioxide (SO2) and Ozone (O3) levels for the duration between the year January 2019 to October 2023 for a crowded area of Varanasi city. The various pre processing techniques have been used in the dataset for the implementation of machine learning models. The performance of the models have been compared for the prediction of the air quality. The results show that the Random Forest and Decision Tree based model achieves the maximum accuracy of approximately 100% as compared to 98%, 95% and 93% and 79% for the SVM, Multi layer Perceptron network, KNN classification and Linear Regression.

The present paper deals with performance evaluation of application of three machine learning algorithms such as Deep neural network (DNN), Gradient boosting machine (GBM) and Extreme gradient boosting (XGBoost) to evaluate the ground water indices over a study area of Haryana state (India). To investigate the applicability of these models, two water quality indices, namely Entropy Water Quality Index (EWQI) and Water Quality Index (WQI) are employed in the present study. Analysis of results demonstrated that DNN has exhibited comparatively lower error values and it performed better in the prediction of both indices, i.e. EWQI and WQI. The values of Correlation Coefficient (CC = 0.989), Root Mean Square Error (RMSE = 0.037), Nash–Sutcliffe efficiency (NSE = 0.995), Index of agreement (d = 0.999) for EWQI and CC = 0.975, RMSE = 0.055, NSE = 0.991, d = 0.998 for WQI have been obtained. From variable importance of input parameters, the Electrical conductivity (EC) was observed to be most significant and ‘pH’ was least significant parameter in predictions of EWQI and WQI using these three models. It is envisaged that the results of study can be used to righteously predict EWQI and WQI of groundwater to decide its potability.

The continuous deterioration of drinking water quality supplies by several anthropogenic activities is a serious global challenge in recent times. In this current study, the drinking water quality of Ikem rural agricultural area (southeastern Nigeria) was assessed using chemometrics and multiple indexical methods. Twenty-five groundwater samples were collected from hand-dug wells and analyzed for physicochemical parameters such as pH, major ions, and heavy metals. The pH of the samples (which ranged between 5.2 and 6.7) indicated that waters were slightly acidic. Cations and anions (except for phosphate) were within their respective standard limits. Except for Mn, heavy metals were also found to be below their maximum allowable limits. Factor analysis identified both geogenic processes and anthropogenic inputs as possible origins of the analyzed physicochemical parameters. Modified heavy metal index, geoaccumulation index, and overall index of pollution revealed that all the hand-dug wells were in excellent condition, and hence safe for drinking purposes. However, pollution load index, water quality index (WQI), and entropy-weighted water quality index (EWQI) revealed that some wells (about 8–12%) were slightly contaminated, and hence are placed in good water category. A hierarchical cluster analysis (HCA) was performed based on the integration of the WQI and EWQI results. The HCA revealed two major quality categories of the samples. While the first cluster comprises of samples classified as excellent drinking water by both WQI and EWQI models, the second cluster comprises of about 12% samples which were identified as good water by either the WQI or EWQI.

In many parts of the world, groundwater is considered to be a key source of fresh water for both the domestic and non-domestic sectors. Where groundwater extraction is implemented, systems to monitor water quality must ensure a safe and sustainable supply. Over the years, Iraq has suffered from surface water quality and supply problems, necessitating groundwater extraction in many regions. This study investigates groundwater quality in a region of central Iraq around Babylon city, covering an area of 5119 km 2 . The data gathered for this study included maps, well locations and water quality data and was sourced from the relevant governmental departments. A base map of the focussed region was initially prepared following data collection. The analysed water quality parameters were used as an attribute database to produce thematic maps using a geographical information system (GIS) environment. In this paper, the water quality index (WQI) and the irrigation water quality index (IWQI) were calculated for different groundwater samples using various parameters including the Electrical Conductivity (EC), Cl − , HCO3 − , Na + and pH. Moreover, the groundwater suitability for irrigation purposes has been assessed using indices such as Kelly’s ratio (KR), sodium absorption ratio (SAR), residual sodium carbonate (RSC), soluble sodium percentage (SSP) and permeability index (PI). Water quality index maps have been developed using the GIS environment. The obtained results reveal that the groundwater in the study location requires specific treatments to be usable.

Keeping in view the severe lack of research in the field of quantification of pavement quality, an analysis of suitable methods for quantifying the quality of pavement construction and rehabilitation was carried out in this study. The main and sub-factors that significantly impact the quality of pavement construction and rehabilitation from managerial and constructional perspectives were identified using a questionnaire survey. A pavement construction and rehabilitation quality index equation has been developed that yields a quantified level of quality achieved in a project in terms of percentage. Ranking analysis of the top six influencing main factors revealed that constructional factors, such as paving and compaction practices and subsurface drainage, are more important than managerial factors, such as the client’s and quality consultant’s capability, payment and finances, and contractor’s capability. Sensitivity analysis revealed that each sub-factor has its individual significance on the overall quality index, irrespective of the rank of its main factor, and the quality index equation developed in this study duly encompasses the impact of each sub-factor and main factor, collectively, on the overall quality index. The practical application of the developed index was also validated through a case study. This study is one of the pioneer studies that specifically explored the quantification of pavement rehabilitation and construction quality in terms of a quality index, duly encompassing both managerial and constructional aspects. The results of this study and recommendations for future research can be effectively used as a benchmark checklist to improve particular main and sub-factors in order to enhance the overall quality of pavement rehabilitation and construction in future projects.