Non-invasive hydration level estimation in human body using Galvanic Skin Response

Dehydration and overhydration, both have mild to severe medical implications on human health. Tracking Hydration Level (HL) is, therefore, very important particularly in patients, kids, elderly, and athletes. The limited solutions available for the estimation of HL are commonly inefficient, invasive, or require clinical trials. Need for a non-invasive auto-detection solution is imminent to track HL on a regular basis. To the best of authors’ knowledge, it is for the first time a Machine Learning (ML) based auto-estimation solution is proposed that uses Galvanic Skin Response (GSR) as a proxy of HL in the human body. Various body postures, such as sitting and standing, and distinct hydration states, hydrated vs dehydrated, are considered during the data collection and analysis phases. Six different ML algorithms are trained using real GSR data, and their efficacy is compared for different parameters (i.e., window size, feature combinations etc). It is reported that a simple algorithm like K-NN outperforms other algorithms with accuracy upto 87.78% for the correct estimation of the HL

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover

Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH honey badger algorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm (SCA), and GMDH particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90 percent. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.Comment: 65 pages, 10 figures, 5 table

Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Age and household solid waste arising in suburban Malaysia: a statistical approach

An ageing society could be construed as a socioeconomic disaster as it entails many multi-layered complications across many aspects essential to the running of an efficient and sustainable city. This paper aims to examine the relationship between age and the rate of household solid waste (HSW) generation per capita from households in selected residential areas of the suburban Malaysian townships of Bandar Baru Bangi, Putrajaya and Kajang. Primary HSW data arising was collected directly via door-to-door bin sampling method from an initial sample of 423 individual households during pre-determined two-week sampling periods over a span of 13 months. The demography of the sample was obtained from face-to-face questionnaire surveys with the households’ respondents. Subsequent data refinement resulted in a final sample of 219 households consisting of 4623 discrete measurements being used for statistical analysis in the IBM SPSS software package. Results of statistical analyses show that weighted average age has a small and positive but statistically insignificant correlation to average daily per capita HSW arising. It was also determined that older households possessed a slightly higher albeit statistically insignificant rate of per capita HSW generation compared to younger ones. Additionally, households with elderly residents were found to produce more HSW per capita. Households with toddlers discharged HSW at a comparable rate to households without. Likewise, households with toddlers exclusive of elders have similar rates of HSW arising to their counterparts. In conclusion, there is evidence to suggest that a positive linkage between age and the rate of HSW arising exists. However, evidence from this study shows that the relationship between the two is insignificant from a statistical perspective

Strength and Chemical Characterization of Ultra High-Performance Geopolymer Concrete: A Coherent Evaluation

The objective of this review article is to analyze published data encompassing compressive strength, tensile strength, elastic modulus, and flexural strength, as well as the utilization of scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDS), and x-ray diffraction (XRD) for Ultra High-Performance Geopolymer Concrete (UHP-GC), with the focus of establishing the current research trends regarding its mechanical, microstructural, and chemical characteristics. After a critical evaluation of the published data from the literature findings, it became evident that UHP-GC can attain a remarkably high level of engineering performance. In UHP-GC, the optimum percentage of silica fume as a slag partial replacement to achieve high compression, tensile, and elastic modulus were traced to be 25, 30, and 35%, respectively. The optimum ratio of sodium silicate to sodium hydroxide and sodium hydroxide molarity for UHP-GC were identified to be 3.5 and 16, respectively. All in all, the review provides a thorough understanding of the review gap and distinct functions of different raw materials in decreasing porosity and enhancing the formation of geopolymeric gels that not only bond but also strengthen UHP-GC. UHP-GC stands as an energy-saving material in concrete technology, poised to forge a path towards a sustainable future for the building sector. Doi: 10.28991/CEJ-2023-09-12-020 Full Text: PD

Spatio-Temporal Modelling of Noise Pollution

An undesired or hazardous outdoor sound produced by human activities is referred to as environmental noise. For example, the noise emitted through industrial activities and transportation networks such as road, rail and air traffic. In Malaysia, most of the schools located very close to the roadside and near busy places such as cities, shops, and residential areas. This study aims to analyze the environmental noise in terms of spatial and temporal analysis in two primary schools in Terengganu State.  The noise monitoring had conducted in two (2) primary schools with different land use; residential area (Batu Rakit Primary School) and commercial area (Paya Bunga Primary School) on the school and non-school days by using Sound Level Meter (SLM). The spatial mapping had constructed by using SketchUp® 2018 and Surfer® version 11 software. The noise level between both study areas was significantly different based on a p-value of less than 0.05. It also surpassed the Department of Environment (DOE) of Malaysia's permitted limit, with the Equivalent Noise Level (LAeq) in residential areas being greater than in commercial areas due to traffic volume and noise from nearby activities. Lastly, the area near the roadside has higher critical noise pollution compared with the location that furthers from the roadside. In conclusion, this study is useful in creating awareness to the public about the noise pollution effect on primary school students and is also can be used for mitigation measures to have a better place for students to study

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions

Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network

Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196→0.049→0.012, i.e. ANN→ENN→Hybrid), mean absolute errors (MAE) (0.271→0.094→0.069) and Nash–Sutcliffe efficiencies (NSE) (0.7255→0.9321→0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model