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Seasonal variations of limnological parameters and phytoplankton dynamics in high-altitude oligotrophic lakes form a critical area of interest in assessing the implications and impact of climate change on fragile ecosystems and formulating effective conservation strategies. Thus, a study was conducted by sampling water from five High-Altitude Lakes (HALs) of Tawang district, Arunachal Pradesh between December 2019 and December 2021. The seasonal variations of seven physico-chemical parameters and phytoplankton's composition, distribution, and seasonal abundance were assessed. Further, the diversity, dominance, and evenness of phytoplankton were estimated and the relationship between limnological parameters and the phytoplankton species was established using Canonical Correspondence Analysis (CCA). The limnological parameters such as pH, dissolved oxygen, free carbon dioxide (FCO2), alkalinity, hardness, conductivity, and total dissolved solids (TDS) show significant seasonal variations among the HALs. A total of 50 phytoplankton species belonging to 8 Class and 33 Family were recorded. The order of phytoplankton dominance was Bacillariophyceae > Cyanophyceae > Zygnematophyceae > Chlorophyceae > Chrysophyceae = Dinophyceae, Euglenophyceae = Klebsormidiophyceae. The most abundant species were Cymbella sp., Gomphonema sp., Fragilaria sp., Navicula sp., Nitzchia sp. (Bacillariophyceae), Volvox sp., Pediastrum sp., Microspora sp. (Chlorophyceae), Oscillatoria sp., Lyngbya sp., Calothrix sp., Chroococcus sp. (Cyanophyceae), Spirogyra sp., Mougeotia scalaris, Cosmarium sp., Closterium sp. (Zygnematophyceae). The abundance of phytoplankton species varied seasonally with most copious in winter. The distribution, abundance, and diversity of phytoplankton were largely influenced by pH, hardness, FCO2, conductivity, and TDS levels. So, the findings of the study could be helpful in exploring the potentialities of introducing cold-water fisheries in HALs to ensure food security under climate change scenarios. [ABSTRACT FROM AUTHOR]

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels' lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38% F 1 -score rates for pH, 96.98% F 1 -score rates for temp, 97.88% F 1 -score rates for DO, and 98.11% F 1 -score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative. [ABSTRACT FROM AUTHOR]

The present study was carried out to assess the water quality of Lakhota Lake. Objective water samples from five different sites were collected and analyzed from January 2023 to December 2024. Furthermore, the data obtained were processed to calculate the water quality index (WQI). The values obtained were compared with the guidelines for drinking purposes suggested by the World Health Organization and Bureau of Indian Standard. The results revealed that all the studied parameters were within the permissible limits except turbidity, TDS and EC, which exceeded the permissible limits. Among all the sites, site 5 was more heavily polluted than all the other sites. Among the heavy metals, iron was found above the permissible limits at Site 3, Site 4 and Site 5. Eight water quality parameters were used in the WQI approach to estimate the integrated groundwater quality. The WQI values ranged from 63.8 to 81.9, indicating that the Lakhota Lake water is not suitable for drinking water, including water from both humans and animals. At sites 1-4, the WQI falls in the poor category, while at site 5, it falls under the very poor category. There is a need for proper wastewater management in and around Lakhota Lake to protect the water quality and aesthetic properties of the lake. It is finally suggested that vegetation should also be planted at the boundaries of the lake, which will work as a natural purifier for the water of the lake. [ABSTRACT FROM AUTHOR]

Imphal Valley, situated in Manipur, India, stands as an intermontane valley of great ecological significance, notably hosting Loktak Lake. This research delves into the Land Use Land Cover Change (LULCC) within the Imphal Valley from 2016 to 2021 and assesses their impact on air quality across distinct land cover types. We used Sentinel‐1 & 2 data, ALOS PALSAR Digital Elevation Model, and applied Random Forest (RF), a machine learning algorithm for effective land use and land cover (LULC) mapping. Additionally, Sentinel‐5P data was utilized to monitor air quality parameters (CO, HCHO, NO2, SO2, Aerosol Index) spanning 2019 to 2021. The overall accuracies for the LULC maps employing a k (k = 3) fold approach for accuracy assessment varied between 88% and 92%, with corresponding Kappa coefficient ranging from 0.85 to 0.90. Noteworthy trends emerged from our analysis, revealing an increase in settlements and horticulture farms and a decline in forested areas and phumdis (floating biomass). Our findings highlight a mean concentration of CO ranging between 0.045 mol/m2 and 0.055 mol/m2 in different land cover types during February and March (2019–2021). Furthermore, we observed maximum mean HCHO, NO2, SO2, and aerosol index concentrations in March. Pollution levels surged during forest fires and shifting agriculture seasons, while aerosol levels declined during the lockdown period. This integrated approach emphasized on the comprehensive analysis of the dynamic interplay between LULCC and air quality in the Imphal Valley. This intricate relationship between LULC changes and air quality dynamics in the Imphal Valley, contribute to our understanding of the environmental dynamics in this ecologically vital region. Plain Language Summary: This research article titled "ML based Hybrid Synthetic Aperture Radar (SAR) and Optical LULC Mapping and change analysis with variations in air quality of the Imphal Valley, North‐East India" investigates LULC changes in the Imphal Valley from 2016 to 2021 and their impact on air quality (2019–2021). It employs SAR images, Sentinel 2 satellite images, and topographic features to map LULC using a RF Machine Learning Classifier. The study reveals changes in land cover types and their influence on pollutant concentrations, utilizing Sentinel 5P data for air quality monitoring. The integration of Sentinel data and topographic features enhances the accuracy of the analysis. Key Points: We mapped land use and land cover from 2016 to 2021 using Sentinel 1 and Sentinel 2 data sets with the Random Forest ML Algorithm of Imphal Valley located in Manipur, India and analyzed air quality parametersThere was a significant increase in settlement and a substantial decrease in Phumdis, Vegetation, and PaddyThe air quality was found to be adversely affected from January to April (2019, 2020, and 2021) owing to forest fires caused possibly due to shifting cultivation practices [ABSTRACT FROM AUTHOR]

River water quality is of utmost importance because the river is not only one of the key water resources but also a natural habitat serving its surrounding environment. In a bid to address whether it has a qualified quality, various analytics are required to be considered, but it is challenging to measure all of them frequently along a river reach. Therefore, estimating water quality index (WQI) incorporating several weighted analytics is a useful approach to assess water quality in rivers. This study explored applications of ten machine learning (ML) models to estimate WQI for the Southern Bug River, which is the second-longest river in Ukraine. The ML methods considered in this study include artificial neural networks (ANNs), Support Vector Regressor (SVR), Extreme Learning Machine, Decision Tree Regressor, random forest, AdaBoost (AB), Gradient Boosting Regressor, XGBoost Regressor (XGBR), Gaussian process (GP), and K-nearest neighbors (KNN). Each data measurement consists of nine analytics (NH4, BOD5, suspended solids, DO, NO3, NO2, SO4, PO4, Cl), while the quantity of data is more than 2700 data points. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. However, GP outperformed the other models, followed by XGBR, SVR, and KNN. Furthermore, ANN and AB demonstrated relatively weaker performance. Moreover, a reliability assessment conducted on both training and testing datasets also confirmed the results of the comparative analysis. Overall, the results enhance the assertion that ML models can sufficiently predict WQI, thereby enhancing water quality management. [ABSTRACT FROM AUTHOR]

The water supply system plays a major role in the community. The water source is carefully selected based on quality, quantity, and reliability. The quality of water at its sources is continuously deteriorating due to various anthropogenic activities and is a major concern to public health as well. The Kalu River is one of the major water resources in Sri Lanka that supplies potable water to the Kalutara district (a highly populated area) and Rathnapura district. But, there has been no significant research or investigation to examine anthropogenic activities in the river. Due to this, it is difficult to find any proper study related to the overall water quality in the Kalu River. Therefore, this study covers a crucial part related to the water quality of the Kalu River. The spatiotemporal variation of river water quality is highly important not only to processing any treatment activities but also to implementing policy decisions. In this context, water quality management is a global concern as countries strive to meet the United Nations Sustainable Development Goal 6, which aims to ensure the availability and sustainable management of water and sanitation for all. Poor water quality can have severe consequences on human health, ecosystems, and economies. Contaminated water sources pose risks of waterborne diseases, reduced agricultural productivity, and ecological imbalances. Hence, assessing and improving water quality is crucial for achieving sustainable development worldwide. Therefore, this paper presents a comprehensive analysis of spatiotemporal analysis of the water quality of the Kalu River using the water quality data of eight locations for 6 years from 2017 to 2023. Nine water quality parameters, including the pH, electrical conductivity, temperature, chemical oxygen demand, biological oxygen demand, total nitrate, total phosphate, total sulfate, total chlorine, and hardness, were used to develop a simple equation to investigate the water quality index (WQI) of the river. Higher WQI values were not recorded near the famous Kalutara Bridge throughout the years, even though the area is highly urbanized and toured due to religious importance. Overall, the water quality of the river can be considered acceptable based on the results of the WQI. The country lockdowns due to COVID-19 might have impacted the results in 2020; this can be clearly seen with the variation of the annual WQI average, as it clearly indicates decreased levels of the WQI in the years 2020 and 2021, and again, the rise of the WQI level in 2022, as this time period corresponds to the lockdown season and relaxation of the lockdown season in the country. Somehow, for most cases in the Kalu River, the WQI level is well below 25, which can be considered acceptable and suitable for human purposes. But, it may need some attention towards the areas to find possible reasons that are not in the range. Nevertheless, the results suggest the importance of continuous water quality monitoring in the Kalu River. [ABSTRACT FROM AUTHOR]

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Rivers are valuable sources of water supply for various purposes. These sources are seriously exposed to quality degradation due to the entry of various contaminants. Therefore, proper monitoring and quality management of these resources could be very important. In the present study, the quality of the Balikhlou river in Ardabil province, northwest of Iran, was investigated by surveying the trend of changes in river quality parameters, as well as water quality indices. In this study, after collecting the available data, by determining the trend of changes in quality parameters, the suitability of the studied river water quality for drinking and irrigation purposes was determined. The results indicated that there is an inverse relationship between changes in river discharge rate and the concentration of qualitative parameters. Therefore, it was found that salinity-containing pollution sources enter the river upstream of the desired station. Besides, the results of water quality indices showed that the water quality of the studied river is very poor for drinking (WQI>75). In addition, the results of water quality assessment for irrigation use based on SAR, KI, and SSP indices were generally considered inappropriate (SAR>26, KI>1, SSP>50), EC was often permissible (750

This work helps to identify the source of pollution in water and characterize the water quality which is essential to water management for sustainable development. Therefore, the main objective of this work is to evaluate the spatial distribution of the water quality of Ratuwa river and its tributaries. The water samples were collected from six discrete sampling locations and fifteen parameters were tested using respective well-calibrated equipment and standard APHA methods. The physicochemical analysis, water quality index, and correlation matrix method were employed to evaluate the spatial variation of the water quality of Ratuwa river. Turbidity was the most polluting factor in river water. The results showed the spatial variation of the water quality index (WQI) from 39.3 to 70.5, which fell in the range of "good" to "poor" water quality status. None of the water samples was either "excellent" or "unsuitable for drinking." The water quality was "Poor" upstream and downstream of Ratuwa river due to the high value of turbidity. Chaju river was found to have unpolluted whereas Dipeni river was slightly polluted due to domestic and municipal wastes. Hence, the deterioration of water quality can be attributed to natural and anthropogenic sources. [ABSTRACT FROM AUTHOR]

Pumqu is the biggest river in the Qomolangma National Natural Reserve in China. It is one of the headwaters of the river Ganges and is an important international river in southern Tibet Autonomous Region (T.A.R). However, there are a lack of systematic studies on Pumqu's geochemical properties. In this study, water samples were collected systematically from all the river catchments in different seasons in 2021, the spatiotemporal variations of the geochemical characteristics of the catchments and their controlling factors were studied, and the overall water quality of the river was also evaluated. Overall, the results showed that the water from the Pumqu catchments was slightly alkaline, with an average pH of 8.22. The number of total dissolved solids was comparable to the other rivers on the Tibetan Plateau and showed a similar variation over the year. Affected by the natural geothermal spring water discharge, the values of oxidation reduction potential at some sampling sites were negative in the wet season. Generally, Ca2+ and HCO3− were the dominant ions. Carbonate weathering was the main factor affecting the geochemical features of the studied catchments. The results of correlation analysis showed the influence of rock weathering, glacial meltwater, geothermal water discharge and riverine physicochemical processes on the trace elements. The natural geothermal water discharge was particularly responsible for the elevated Li, which appeared in some of the sampling sites and resulted in relatively high WQI values in the sites. The overall water quality of the Pumqu catchments was excellent. This study provides first-hand information on the overall spatial and temporal span of the physicochemical characteristics and water quality of all the catchments of the river Pumqu, one of the major water systems in the central Himalayas. [ABSTRACT FROM AUTHOR]

The Flexural After Impact (FAI) behaviour of epoxy and vinyl-ester based carbon fiber reinforced composite laminates was investigated at elevated temperatures. Carbon/Epoxy (CE) and Carbon/Vinyl-ester (CV) laminates with cross-ply configuration (0/90/90/0)3S were manufactured via a compression moulding technique and subjected to Low-Velocity Impact (LVI) s at ∼1.5 and ∼2.5 m/s under temperatures 30, 60 and 90°C. The flexural behaviour of composite laminates was investigated via three-point bending tests. The non-impacted and impacted CE and CV samples' failure profile during the flexural tests was examined using the real-time Acoustic Emission (AE) monitoring technique via peak frequency analysis of AE events. Flexural after impact strength of CE samples at both the velocities were higher than that of the CV ones. For CE and CV samples, the flexural after impact strength increases at 60°C, and decreases when approaching 90°C. At 90°C, flexural strength degradation was considerably higher in the CE ones because the Co-efficient of Thermal Expansion (CTE) of the epoxy matrix occurs at a much higher rate than the vinyl-ester, which generates higher residual stress at the carbon fiber-epoxy interfaces. Acoustic emission (AE) monitoring allowed to capture the interface among the fiber and matrix due to the exposure temperature and impact velocity. [ABSTRACT FROM AUTHOR]

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (RNARNET = 96.17 % and RLSTM = 94.21 %). This kind of promising research can contribute significantly to water management. [ABSTRACT FROM AUTHOR]

Fifty-four groundwater samples were collected from the highly industrialized area of north Chennai. These groundwater samples were tested for Fe, Mn, Cu, Ni, Pb, Zn and Cr in pre-monsoon and post-monsoon periods of 2015–2016. Most of the samples in the area were found to have high concentration of heavy metals. Geographical information system was used to develop contour maps for the analysis of heavy metals, and it has been found that most of the Ambattur area was affected by the heavy metals in both the seasons. ANOVA tests were carried out on the hydro-chemical data for both the monsoon periods, and it was found that there was a common source of origin for most of the heavy metals, which was also confirmed by the correlation and principal component analysis. T-test indicates that there was a common source of origin of heavy metals in the study area, viz. industrial and domestic pollutants, that were found to be the main source of heavy metals in both the monsoon periods. Principal component analysis gave three important factors (principal components) for both the seasons. Pre-monsoon groundwater samples showed a common cause of origin of heavy metals than the post-monsoon samples. Heavy metal pollution index indicates that almost all the samples were not fit for drinking purpose in both the monsoon periods and metal index also indicates the non-usability of the water for drinking purpose. [ABSTRACT FROM AUTHOR]

The capability of faecal indicator bacteria (FIB) to be environmental in origin and the deficient specificity of markers can mean uncertain outcomes when just a single marker is distinguished. This can give a bogus thought regarding water quality. A water quality analysis and the correlation between FIB, multiple physiochemical parameters and a viral marker in recreational waters were studied. For water concentration, carefully validated virus filtration techniques were used coupled with PCR-based assays for virus quantification. Around 32.0% of analysed surface water samples were positive for human adenovirus (HAdV, qPCR), 16.0% for bovine adenovirus (BAdV, nested PCR) and 11.0% for porcine adenovirus (PAdV, qPCR). For all the types of the adenoviruses, detection frequencies were not significantly different except for HAdV that showed higher frequency in water samples from tehsil Ludhiana. Only a sporadic significant correlation was observed with the few components; there was no constant, stable correlation between the microbiological and physiochemical parameters. Although both canals and river showed a stable and significant positive correlation between bacteriological parameters (coliform and enterococci) and between biological oxygen demand (BOD) and chemical oxygen demand (COD), regression analysis revealed no significant correlations between concentration of enterococci count to a concentration of AdVs, unlike coliform that showed sporadic but significant correlation with PAdV and no correlation with HAdVs/BAdVs. On principal component analysis, the first factor had strong positive loadings on BOD, COD, total dissolved solids (TDS), turbidity and PAdV and is largely a pollution loading factor which is also supported by their mutual cluster in cluster analysis. Factor 2 had strong loading on enterococci/coliform count due to the natural quality of water and domestic waste and factor 3 had loading on HAdV. This factor shows the impact of human activities overwhelmed in the catchment area of the canals and river. Hence, a significant level of certainty on the contamination source can be acquired if more than one marker is distinguished. Our study thus gives a case of how quantifying both physiochemical, FIB and adenoviral markers in surface water can improve water quality evaluation and help tailor CPCB -India programs for impaired water bodies in the future. [ABSTRACT FROM AUTHOR]

This work involves an experimental investigation of the static and fatigue behavior of patch repaired laminates under flexural loading. At first, a static flexural study is conducted for two types of parent laminates comprising 4‐plies ([0°]4 & [45°]4) and 8‐plies ([0°]8 & [45°]8) of woven carbon fiber. The repair is conducted by two types of patches with stacking sequence [0°]2 and [45°]2. Three different repair configurations involving patch bonded on the compressive side, tensile side, and both the sides of the parent specimens, have been tested. Digital image correlation has been employed to capture the deflection of the specimens. In order to understand the vulnerability of patch repaired laminates to cyclic loading, the load controlled constant amplitude fatigue tests have been conducted on 8‐ply repaired laminates. Different failure modes, distinctive to each type of repair configurations have been observed. The results of this work provide more insight to the influence of various parameters viz. number of plies, fiber orientation, patch placement, and so forth, on the flexural behavior of repaired laminates. [ABSTRACT FROM AUTHOR]

High power fiber laser was proved to be feasible to cut carbon fiber reinforced plastic (CFRP) laminate with high efficiency, while the machined quality on mechanical performance was not fully understood. This paper studied the effect of high temperature up to 220°C on tensile strength degradation and strain distribution of unidirectional CFRP laminate with open hole machined with fiber laser. Differential scanning calorimetry (DSC) was firstly employed to determine the glass transition temperature (Tg) of polymer‐matrix composite. Digital image correlation technique was applied to evaluate whole‐field strain distribution and crack propagation/evolution of CFRP laminate featured with open hole under tensile loading. Results showed that the tensile strength of laser machined CFRP laminate degraded significantly during working in high temperature above 100°C. The value of strains distributed around center hole was highly related to the elevated temperature and applied load levels. Splitting was the prevalent failure mode of CFRP laminate under elevated temperature up to 180°C. Finally, semi‐empirical model was developed for evaluating tensile performance of laser machined CFRP laminate at elevated temperature based on regression analysis. [ABSTRACT FROM AUTHOR]

The increasing rate of water pollution and consequent increase of waterborne diseases are compelling evidence of danger to public health and all living organisms. Preservation of flora and fauna by controlling various unexpected pollution activities has become a great challenge. This paper presents an artificial neural network (ANN)-based method for calculating the water quality index (WQI) to estimate water pollution. The WQI is a single indicator representing an overall summary of various water test results. However, selection of the weight values of the water quality parameters for WQI calculation is a tedious task. Therefore, the ANN approach is found to be useful in this study for calculating the weight values and the WQI in an efficient manner. This work is novel because we propose a methodology that uses a mathematical function to calculate the weight values of the parameters regardless of missing values, which were randomly decided in previous work. The results of the proposed model show increased accuracy over traditional methods. The accuracy of the calculated WQI also increased to 98.3%. Additionally, we also designed a web interface and mobile app to supply contamination status alerts to the concerned authorities. [ABSTRACT FROM AUTHOR]

In this research work, the low‐velocity impact behavior and post impact performance of carbon/epoxy laminates modified with milled glass fibers was investigated. Unidirectional (UD) and Cross‐ply (CP) carbon/epoxy laminates were incorporated with milled glass fibers by 5 wt% of epoxy. Low‐velocity impact test was performed at impact velocity of 3, 3.5, 4, 4.5, and 5 m/s, respectively and the results were correlated with the baseline samples without filler. The filler loaded samples exhibited higher peak force, lesser deformation, and lower damage degree than the baseline samples. The milled glass fibers in the interlaminar zone restricts the delamination crack propagation resulting in reduced impact damage area and also promotes additional energy dissipation mechanism including filler debonding/pullout. Furthermore, the CP filler‐loaded samples show higher residual load‐bearing capacity for impact velocities 3, 3.5, and 4 m/s, respectively. Although the unidirectional filler‐loaded samples showed better load bearing capacity for impact velocities beyond 4 m/s. POLYM. COMPOS., 40:2441–2451, 2019. © 2018 Society of Plastics Engineers [ABSTRACT FROM AUTHOR]

This article investigates the effect of multiphase fiber system based on plain weave woven fabrics and chopped short fibers in glass/epoxy composite laminates on low‐velocity drop weight impact and quasi‐static tensile after impact (TAI) behaviors. In this regard, glass/epoxy composites with two stacking sequences (intercalated and sandwich‐like lay‐ups) were tested and compared with homogeneous standard composites fabricated with either only woven or only chopped fibers. In all the samples with multiphase fiber system, the amount of woven and chopped glass fibers were equal (4 layers and 3 layers, respectively), while stacking sequences were different. This enabled us to investigate the associated influences of fiber architecture and stacking sequence on low‐velocity drop weight impact and TAI behavior of the glass/epoxy composite laminates. All the glass/epoxy samples were impacted at four various impact energy levels, particularly 2, 4, 6, and 8 J. Results depicted that fiber architecture and stacking sequence played a vital role in low‐velocity drop weight impact and TAI behavior (i.e., damage degree, absorbed energy, peak contact force, residual deformation, residual tensile strength, displacement to ultimate failure, and failure profile) of the glass/epoxy samples. The results depicted that the sandwich‐like lay‐up configuration presented superior impact properties and damage tolerance capabilities at low impact energies (up to 4 J). On the contrary, intercalated lay‐up arrangement suited better to withstand widespread damage generated by higher impact energy levels (6 and 8 J), because they enabled a more efficient redistribution of low‐velocity impact damage in the composite. POLYM. COMPOS., 40:1450–1462, 2019. © 2018 Society of Plastics Engineers [ABSTRACT FROM AUTHOR]

This article investigates the compressive response of damaged glass/epoxy composite laminates repaired using hybrid external patches. Hybrid external patches based on glass and Kevlar woven fabrics bonded on both faces of the damaged parent laminate were considered. Five different kinds of plain weave woven fabrics with a different ratio between glass and Kevlar fibers (100/0, 75/25, 50/50, 25/75, and 0/100) were used as the external patches. The intention of using these hybrid patches is to build up a patch of optimum stiffness that facilitate to restrict the critical adhesive or cohesive failure on the joints unlike high stiff glass fibers or extensive fiber micro‐buckling of the external patches unlike low stiff Kevlar fibers under compression loading and consequently to produce repaired laminates with a strain to failure and residual strength nearly close or similar as that of virgin or normal components. The virgin and damaged specimens were taken as the reference specimens for comparison of residual mechanical properties and damage mechanisms. Damage evolution and the failure progression of the repaired glass/epoxy specimens were monitored using real‐time Acoustic Emission (AE) monitoring technique. Specimens repaired with the equal volume fraction of glass and Kevlar fibers in the external patches presented the most favorable residual compressive response by effectively releasing the stress concentration in the damaged area. POLYM. COMPOS., 39:1922–1935, 2018. © 2016 Society of Plastics Engineers [ABSTRACT FROM AUTHOR]

Generally, the aircraft structural parts are economically high in cost so the materials need to be inspected for defects or damages using various non-destructive testing (NDT) methods like ultrasonic, thermography and acoustic emission. The aim of this project is to characterize the defects in composite laminates before and after the flexural loading using infra-red thermography NDT method. GFRP and hybrid (GFRP+CFRP) composite laminates are fabricated with different orientation such as uni-directional, cross ply, anti-symmetric and angle ply and then tested under flexural loading according to ASTM D790 standard. The volume fraction of the fibre and matrix needs to be found out to know the void content and the mixing ratio of reinforcement and binder. [ABSTRACT FROM AUTHOR]

This paper demonstrates the results of an experimental study on cross ply carbon/epoxy composite laminates fabricated from high temperature hardener HT972 subjected to impact loading at different velocities and temperatures. The carbon fiber reinforced plastic (CFRP) samples were impacted at velocities 1.5 m/s and 2.5 m/s, each at a temperature level of 30°C, 60°C, 90°C, and 120°C. The impact response of the material towards various velocities and temperatures was determined using impact parameters like peak force, absorbed energy, maximum deflection, and rebound velocity. Result reveals that the velocity and temperature play a significant role in the impact response of the material. The variation in the trend of Flexural After Impact (FAI) strength of composite laminates at different velocities and temperatures was determined using FAI test and these results were further correlated with impact results. The dominating failure modes affecting the residual strength of the samples were found using acoustic emission (AE) monitoring. [ABSTRACT FROM AUTHOR]

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. [ABSTRACT FROM AUTHOR]

Heavy metal pollution in groundwater is a substantial environmental risk for Bangladesh. The Meghna Ghat industrial area in Bangladesh becomes a promising site for installing various industries for few decades. It was necessary to assess the heavy metal level in the groundwater of this area, and current study took the initiative. We collected 20 groundwater samples and tested pH, DO, TDS, EC, turbidity, COD, and DOC as well as four heavy metals (Cr, Cd, Pb, and Ni) to calculate four water quality indices, i.e., water quality index (WQI), degree of contamination (DC), heavy metal evaluation index (HEI), and heavy metal pollution index (HPI). Ni was too low to detect by the instrument, whereas the mean concentrations of Cr, Cd, and Pb were 0.07, 0.007, and 0.18 mg/L which exceeded the drinking water standards set by Bangladesh. According to the water quality indices, only 10% samples were good according to WQI; 30% and 15% samples were subjected to low level of pollution considering DC and HEI, respectively. Although according to HPI 35% samples were unsuitable for drinking, rest of the values were very close to characterize as unsuitable. Finally, we proposed two best-fitted models that can represent relationships between the metals and water quality indices. Water quality was comparatively better near the open spaces of the study area. The area needed to be under continuous monitoring for checking further pollution distribution. [ABSTRACT FROM AUTHOR]

Wetlands are essential for protein production, water sanctification, groundwater recharge, climate purification, nutrient cycling, decreasing floods and biodiversity preservation. The Mursidabad district in West Bengal (India) is situated in the floodplain of the Ganga–Padma and Bhagirathi rivers. The region is characterized by diverse types of wetlands; however, the wetlands are getting depredated day-by-day due to hydro-ecological changes, uncontrolled human activities and rapid urbanization. This study attempted to explore the health status of the wetland ecosystem in 2013 and 2020 at the block level in the Mursidabad district, using the pressure–state–response model. Based on wetland ecosystem health values, we categorized the health conditions and identified the blocks where the health conditions are poor. A total of seven Landsat ETM+ spaceborne satellite images in 2001, 2013 and 2020 were selected as the data sources. The statistical data included the population density and urbanization increase rate, for all administrative units, and were collected from the census data of India for 2001 and 2011. We picked nine ecosystem indicators for the incorporated assessment of wetland ecosystem health. The indicators were selected considering every block in the Mursidabad district and for the computation of the wetland ecosystem health index by using the analytical hierarchy processes method. This study determined that 26.92% of the blocks fell under the sick category in 2013, but increased to 30.77% in 2020, while the percentage of blocks in the very healthy category has decreased markedly from 11.54% to 3.85%. These blocks were affected by higher human pressure, such as population density, urbanization growth rate and road density, which resulted in the degradation of wetland health. The scientific protection and restoration techniques of these wetlands should be emphasized in these areas. [ABSTRACT FROM AUTHOR]