The effects of exercise on daytime sleepiness remain unclear, with conflicting findings in the literature. We reviewed the existing literature on the relationship between exercise and daytime ...sleepiness in healthy individuals. We conducted a systematic search of PubMed and Google Scholar (1991 to present) for interventional studies that used the Epworth Sleepiness Scale (ESS) to measure change in self-reported degree of sleepiness before and after an exercise regimen. Seven studies were included in the review. Exercise significantly improved self-reported sleepiness after the intervention, as measured by ESS, in 4 of the 7 studies; the other studies indicated no significant difference. Additionally, exercise interventions enhanced sleep quality, evident in lower Pittsburgh Sleep Quality Index scores in 4 of 5 studies, thus indirectly alleviating daytime sleepiness. Results were variable and influenced by exercise type, intensity, and timing, as well as participant adherence. Factors that may contribute to the effect of exercise on daytime sleepiness include improved sleep quality, regulation of circadian rhythms, neurotransmitter release, stress reduction, increased energy levels, and weight reduction. This review suggests benefits of exercise for reducing daytime sleepiness and improving sleep quality. Future research is essential for assessing the mechanisms of these effects.
•Daytime sleepiness decreases productivity, cognition, and overall well-being.•Regular exercise improves nighttime sleep quality, reducing daytime sleepiness.•Type, intensity, and timing of exercise affect daytime sleepiness.
Coastal water quality assessment is an essential task to keep “good water quality” status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess ...water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R2, and PREI metrics were used in this study. The tree-based DT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = 0.0) and the ExT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = 0.0) and ensemble tree-based XGB (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = +0.16 to −0.17) and RF (RMSE = 2.0, MSE = 3.80, MAE = 1.10, R2 = 0.98, PERI = +3.52 to −25.38) models outperformed other models. The results of model performance and PREI indicate that the DT, ExT, and GXB models could be effective, robust and significantly reduce model uncertainty in predicting WQIs. The findings of this study are also useful for reducing model uncertainty and optimizing the WQM-WQI model architecture for predicting WQI values.
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•A newly developed weighted quadratic mean (WQM) WQI model is used for assessing coastal water quality.•Predicting coastal WQM-WQIs using eight widely used ML algorithms.•The lowest prediction errors were found for the DT, ExT, XGB and RF models, respectively.•A higher percent of relative index prediction errors was found for the SVM models (+22.7 to −75).•Relatively, higher prediction uncertainty was found at the upper monitoring sites in Cork Harbour.
•Water Quality Index gives a comprehensive picture of water quality.•Use of least required number of parameters ensures economic viability of the index.•Parameters are selected on bases of data ...availability.•Principal Component Analysis provides statistical base for parameter reduction.
Water Quality Index (WQI) is one of the most widely used concepts for representation of the quality of a water resource. This concept has wide acceptance among policy makers and other stakeholders as this gives a clear and comprehensive picture of the status of the pollution of a water body. The standard step of development of a WQI are – parameter selection, assignment of weights, development of sub-index functions and final aggregation of weighted sub- index values. Out of these, the current study focusses on the first step, i.e. parameter selection. The results of this study shall play a crucial role in the development of Ganga Water Quality Index in the future. For the current study, the initially available data has been subjected to Principal Component Analysis (PCA) and this led to reduction of number of parameters from 28 to 9. This has been done to make the process more feasible and economic as this would drastically reduce the time, effort and cost required to monitor samples for a large number of parameters. The finally shortlisted 9 parameters were- Dissolved Oxygen (DO), pH, Conductivity, Biological Oxygen Demand (BOD), Total Coliform (TC), Chlorides, Magnesium, Sulphate, Total Dissolved Solids (TDS). PCA utilizes the variance in the entire data set and projects it in new dimensions, thereby reducing the number of parameters but retaining maximum variance. The use of statistical techniques in WQI development makes it less biased and more objective in nature and forms the basis of development of a Ganga Water Quality Index (GWQI) in future.
In view of higher pollution strength of Indian rivers, prevalent water quality indices of the western countries like the National sanitation foundation water quality index (NSFWQI) and indigenous ...Vedprakash water quality index (VWQI) cannot truly represent the water quality status of Indian rivers. To overcome this limitation, fuzzy modeling has been used in this study for the prediction of water quality of Indian rivers. The fuzzy models have been developed using triangular and trapezoidal membership functions with centroid, bisector and mean of maxima (MOM) methods for defuzzification. It is observed that the fuzzy model with triangular membership function utilizing the bisector method of defuzzification performs better, compared to triangular and trapezoidal membership function utilizing the centroid and MOM method of defuzzification. The values of water quality index based on fuzzy logic have been compared with the NSFWQI and VWQI. It is observed that the values of fuzzy based water quality index are more representative to actual river water quality status of Indian rivers as compared to NSFWQI and VWQI. This is due to the fact that the adopted fuzzy logic approach is equally sensitive to all parameters and can truly represent the minor change in the value of any parameter, especially in case of river stretches having higher pollution.
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•In view of high pollution strength of Indian rivers, NSFWQI cannot truly represent the water quality status of Indian rivers.•The use of NSFWQI becomes deficient in indian scenario, since its Q-value curves have limitations for BOD5 and total solids.•To overcome the limitation, fuzzy modelling has been used in this study for the prediction of water quality of Indian rivers.•It is observed that the values of FWQI are more representative to actual river water quality status of Indian rivers.•FWQI-Triangular model using the Bisector defuzzification method outperforms other considered FWQI models of the study.
Understanding the influences of land use conversions on soil quality (SQ) and function are essential to adopt proper agricultural management practices for a specific region. The primary objective of ...this study was to develop soil quality indices (SQIs) to assess the short-term influences of different land uses on SQ in semiarid alkaline grassland in northeastern China. Land use treatments were corn cropland (Corn), alfalfa perennial forage (Alfalfa), monoculture Lyemus chinensis grassland (MG) and successional regrowth grassland (SRG), which were applied for five years. Twenty-two soil indicators were determined at 0–20cm depth as the potential SQ indicators. Of these, thirteen indicators exhibited treatment differences and were identified as the total data set (TDS) for subsequent analysis. Principal component analysis was used with the TDS to select the minimum data set (MDS), and four SQIs were calculated using linear/non-linear scoring functions and additive/weighted additive methods. Invertase, N:P ratio, water-extractable organic carbon and labile carbon were identified as the MDS. The four SQIs performed well, with significant positive correlations (P<0.001, n=16) among them. However, the SQI calculated using the non-linear weighted additive integration (SQI-NLWA) had the best discrimination under different land-use treatments due to the higher F values and larger coefficient of variance as compared to the other SQIs. The SQI value under the MG treatment was the highest, followed by that under the SRG and Alfalfa treatments, and all of these were significantly higher than that of Corn treatment. These results indicated that conversion of cropland to perennial forage or grassland can significantly improve the SQ in the Songnen grassland. In addition, SQI-NLWA can provide a better practical, quantitative tool for assessing SQ and is recommended for soil quality evaluation under different land uses in semiarid agroecosystems.
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•Invertase, N:P ratio, water-extractable organic C and labile C were identified as the MDS.•SQI was an effective tool to assess the impacts of agricultural management practices on SQ.•SQI-NLWA showed the best discrimination by different land-use treatments.•Conversion of cropland to forage or grassland significantly improves SQ in NE China.
•This paper discussed differences between the quality requirements of water used for drinking purposes compared to irrigation.•Considering sensitivities about quality of drinking water, the paper ...focused on the assessment methods of drinking/irrigation water quality.•Drinking/Irrigation Water Quality Indices (DWQI & IWQI) were employed to analyze quality of drinking and agriculture water.•Using the above methods as the single assessment techniques without any modification gave rise to conflicting results of water quality ranking.•Using Multi-criteria Decision Making methods were found successful to remove probable conflicts and presented reasonable results of ranking.
Groundwater resources play a crucial role in most arid/semi-arid regions such as Karaj plain, Iran. Excavation of wells and exploiting water resources of aquifers have long been known as ordinary solutions to supply water demands for drinking, agricultural and industrial purposes. In many agricultural areas such as the above-mentioned region, extraction wells have been utilized for both drinking and agricultural consumptions, while measures taken for water quality monitoring and protecting public health are seriously limited. On the other hand, most of the shared extraction wells in the region used for drinking purpose have been located near the agricultural lands and they are highly under the risk of getting polluted by Agricultural pesticides. The current paper firstly intends to demonstrate the results obtained from Drinking water Quality Index (DWQI) as well as Irrigation Water Quality Index (IWQI) and secondly determines probable conflicts that may be aroused in ranking of water wells using these two methods Subsequently, Multi Criteria Decision Making (MCDM) techniques such as Ordered Weighted Averaging (OWA), Compromise Programing (CP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were employed to decrease effects of the conflicts. It was clarified that MCDMs, to some extent, alleviated contradictions in wells’ ranks −determined by DWQI/IWQI- and authenticated this procedure as an appropriate method for water quality ranking in agricultural societies.
•A novel method developed for the assessment of coastal water quality.•WQI model was improved using systematic mathematical approaches.•Advanced machine learning approach was utilized to reduce model ...uncertainty.•Three novel linear interpolation sub-index functions were designed.•For the aggregation method, the weighted quadratic mean was found to be effective.
Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components – selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation – with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD5, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer; R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer; R2 = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.
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Assessment of soil quality index (SQI) using only the surface soil properties provides an incomplete information as the crop productivity is influenced by both surface and subsurface properties, with ...the latter being inherently linked to pedogenic processes. Two different SQIs were estimated for soil surface (0–15cm) and control section (0–100cm) using soil profile data of six identified soil series in part of semi-arid tropical (SAT) Deccan plateau and correlated with crop yield. Principal component analysis (PCA) and expert opinion (EO) methods were used for selecting minimum soil data set (MDS). Additive and weighted index methods were compared for SQI estimation. SQI obtained showed variation as PCA and EO methods produced different results. In general, weighted index SQIs were better correlated with crop yield than the additive index SQIs for both PCA and EO methods. EO derived weighted index SQI were comparable for both surface and control section except for few cases and consistent in their correlation with the crop yield, indicating its better performance as compared to PCA. Reason is that the PCA is a data dimension reduction technique whereas EO method is primarily conceived by the experts on cause-effect relationship of soil properties (such as hydraulic conductivity, CaCO3 and exchangeable sodium percentage) that are influenced by regressive pedogenic processes in SAT environments. Results showed that consideration of both surface and control section soil properties helps in establishing a good relationship between soil functions and management goal. In addition, it also satisfies the need to integrate both surface and subsurface soil information for soil quality assessment.
•Soil quality indexes improved when considering topsoil and subsoil properties.•Expert opinion method performed better than principal component analysis.•Regressive pedogenesis limits crop productivity.•CaCO3, sHC and ESP were identified as soil degradation indicators.
A total of 211 water samples were collected from 53 key sampling points from 5–10th July 2013 at four different depths (0m, 2m, 4m, 8m) and at different sites in the Huaihe River, Anhui, China. These ...points monitored for 18 parameters (water temperature, pH, TN, TP, TOC, Cu, Pb, Zn, Ni, Co, Cr, Cd, Mn, B, Fe, Al, Mg, and Ba). The spatial variability, contamination sources and health risk of trace elements as well as the river water quality were investigated. Our results were compared with national (CSEPA) and international (WHO, USEPA) drinking water guidelines, revealing that Zn, Cd and Pb were the dominant pollutants in the water body. Application of different multivariate statistical approaches, including correlation matrix and factor/principal component analysis (FA/PCA), to assess the origins of the elements in the Huaihe River, identified three source types that accounted for 79.31% of the total variance. Anthropogenic activities were considered to contribute much of the Zn, Cd, Pb, Ni, Co, and Mn via industrial waste, coal combustion, and vehicle exhaust; Ba, B, Cr and Cu were controlled by mixed anthropogenic and natural sources, and Mg, Fe and Al had natural origins from weathered rocks and crustal materials. Cluster analysis (CA) was used to classify the 53 sample points into three groups of water pollution, high pollution, moderate pollution, and low pollution, reflecting influences from tributaries, power plants and vehicle exhaust, and agricultural activities, respectively. The results of the water quality index (WQI) indicate that water in the Huaihe River is heavily polluted by trace elements, so approximately 96% of the water in the Huaihe River is unsuitable for drinking. A health risk assessment using the hazard quotient and index (HQ/HI) recommended by the USEPA suggests that Co, Cd and Pb in the river could cause non-carcinogenic harm to human health.
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•Zn, Cd and Pb were identified as the dominant pollutants in the water body.•Approximately 96% of the waters in the Huaihe River were unsuitable for drinking.•Co, Cd and Pb in the river could pose potential non-carcinogenic effects on human health.
