Coastal Vulnerability Index sensitivity to shoreline position and coastal elevation parameters in the Niger Delta region, Nigeria.

It is imperative to assess coastal vulnerability to safeguard coastal areas against extreme events and sea-level rise. In the Niger Delta region, coastal vulnerability index assessment in the past focused on open-access parameters without comparing the open-access parameters, especially coastal elevation and shoreline change. This sensitivity to the shoreline method and open-access coastal elevation limits the information for the planning of coastal adaptation. The area under investigation is the Niger Delta, which is distinguished by its low-lying coastal plains and substantial ecological and economic significance. In light of the selected parameters, Sentinel-1 GRD images from 2015 to 2022 during high tidal conditions were used to delineate the shoreline position and change rate. Also, different open-access DEMs were used to derive the coastal elevation using the Geographic Information System (GIS) approach. The study employs 5 parameters, such as shorelines obtained from Sentinel-1 SAR images and several Digital Elevation Models (DEMs), geomorphology, mean sea level rise, significant wave height, and mean tide range, in conjunction with the initial Coastal Vulnerability Index (CVI) approach. The study reveals that the type of DEM used significantly influences the coastal elevation ranking and, subsequently, the CVI. Differences in shoreline change rate estimation methods (EPR and LRR) also impact the vulnerability rankings but to a lesser extent. The findings highlight that 40.1% to 58.9% of the Niger Delta coastline is highly or very highly vulnerable to sea-level rise, depending on the shoreline change rate or DEM used. The study underscores the potential of using CVI methods with open-access data in data-poor countries for identifying vulnerable coastal areas that may need protection or adaptation. Lastly, it points out the need for higher resolution DEMs.

Flame Retardant Coatings: Additives, Binders, and Fillers.

This review provides an intensive overview of flame retardant coating systems. The occurrence of flame due to thermal degradation of the polymer substrate as a result of overheating is one of the major concerns. Hence, coating is the best solution to this problem as it prevents the substrate from igniting the flame. In this review, the descriptions of several classifications of coating and their relation to thermal degradation and flammability were discussed. The details of flame retardants and flame retardant coatings in terms of principles, types, mechanisms, and properties were explained as well. This overview imparted the importance of intumescent flame retardant coatings in preventing the spread of flame via the formation of a multicellular charred layer. Thus, the intended intumescence can reduce the risk of flame from inherently flammable materials used to maintain a high standard of living.

Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms.

Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. In this research, compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement is predicted using boosting machine learning (BML) algorithms, namely, Light Gradient Boosting Machine, CatBoost Regressor, Gradient Boosting Regressor (GBR), Adaboost Regressor, and Extreme Gradient Boosting. In these studies, the BML model's performance is evaluated based on prediction accuracy and prediction error rates, i.e., R2, MSE, RMSE, MAE, RMSLE, and MAPE. Additionally, the BML models were further optimised with Random Search algorithms and compared to BML models with default hyperparameters. Comparing all 5 BML models, the GBR model shows the highest prediction accuracy with R2 of 0.96 and lowest model error with MAE and RMSE of 2.73 and 3.40, respectively for test dataset. In conclusion, the GBR model are the best performing BML for predicting the compressive strength of concrete with the highest prediction accuracy, and lowest modelling error.

Performance Analysis of Full Assembly Glass Fiber-Reinforced Polymer Composite Cross-Arm in Transmission Tower.

The usage of glass fiber reinforced polymer (GFRP) composite cross-arms in transmission towers is relatively new compared to wood timber cross-arms. In this case, many research works conducted experiments on composite cross-arms, either in coupon or full-scale size. However, none performed finite element (FE) analyses on full-scale composite cross-arms under actual working load and broken wire conditions. Thus, this work evaluates the performance of glass fiber reinforced polymer (GFRP) composite cross-arm tubes in 275 kV transmission towers using FE analysis. In this study, the performance analysis was run mimicking actual normal and broken wire conditions with five and three times more than working loads (WL). The full-scale assembly load test experiment outcomes were used to validate the FE analysis. Furthermore, the mechanical properties values of the GFRP composite were incorporated in simulation analysis based on the previous experimental work on coupons samples of GFRP tubes. Additionally, parametric studies were performed to determine the ultimate applied load and factor of safety for both normal and broken wire loading conditions. This research discovered that the GFRP composite cross-arm could withstand the applied load of five times and three times working load (WL) for normal and broken wire conditions, respectively. In addition, the factor of safety of tubes was 1.08 and 1.1 for normal and broken wire conditions, respectively, which can be considered safe to use. Hence, the composite cross-arms can sustain load two times more than the design requirement, which is two times the working load for normal conditions. In future studies, it is recommended to analyze the fatigue properties of the composite due to wind loading, which may induce failure in long-term service.

The occurrence of non-steroidal anti-inflammatory drugs (NSAIDs) in Malaysian urban domestic wastewater.

The water stream has been reported to contain non-steroidal anti-inflammatory drugs (NSAIDs), released from households and premises through discharge from Sewage Treatment Plant (STP). This research identifies commonly consumed NSAIDs namely ibuprofen (IBU), diclofenac (DIC), ketoprofen (KET) and naproxen (NAP) in the influent wastewater from two urban catchments (i.e. 2 STPs). We expand our focus to assess the efficiency of monomer (C18) and dimer (HLB) types of sorbents in the solid phase extraction method followed by gas chromatography mass spectrometry (GCMS) analysis and optimize model prediction of NSAIDs in the influent wastewater using I-Optimal design. The ecological risk assessment of the NSAIDs was evaluated. The HLB produced reliable analysis for all NSAIDs under study (STP1: 6.7 × 10-3 mg L-1 to 2.21 × 10-1 mg L-1, STP2: 1.40 × 10-4 mg L-1 to 9.72 × 10-2 mg L-1). The C18 however, selective to NAP. Based on the Pearson proximity matrices, the DICHLB can be a good indicator for IBUHLB (0.565), NAPC18 (0.721), NAPHLB (0.566), and KETHLB (0.747). The optimized model prediction for KET and NAP based on DIC are successfully validated. The risk quotients (RQ) values of NSAIDs were classified as high (RQ > 1), medium (RQ, 0.1-1) and low (RQ, 0.01-0.1) risks. The optimized models are beneficial for major NSAIDs (KET and NAP) monitoring in the influent wastewater of urban domestic area. An upgrade on the existing wastewater treatment infrastructure is recommended to counteract current water security situation.

Strength enhancement of concrete using incinerated agricultural waste as supplementary cement materials.

The potassium (K) and sodium (Na) elements in banana are needed for hydration reaction that can enhance the strength properties of concrete. This research aims (a) to determine the material engineering properties of banana skin ash (BSA) and concrete containing BSA, (b) to measure the strength enhancement of concrete due to BSA, and (c) to identify optimal application of BSA as supplementary cement materials (SCM) in concrete. The BSA characterization were assessed through X-ray fluorescence (XRF) and Blaine's air permeability. The workability, compressive strength, and microstructures of concrete containing BSA were analysed using slump test, universal testing machine (UTM) and scanning electron microscope (SEM). A total of 15 oxides and 19 non-oxides elements were identified in BSA with K (43.1%) the highest and Na was not detected. At 20 g of mass, the BSA had a higher bulk density (198.43 ± 0.00 cm3) than ordinary Portland cement (OPC) (36.32 ± 0.00 cm3) indicating availability of large surface area for water absorption. The concrete workability was reduced with the presence of BSA (0% BSA: > 100 mm, 1% BSA: 19 ± 1.0 mm, 2%: 15 ± 0.0 mm, 3% BSA: 10 ± 0.0 mm). The compressive strength increased with the number of curing days. The concrete microstructures were improved; interfacial transition zones (ITZ) decreased with an increase of BSA. The optimal percentage of BSA obtained was at 1.25%. The established model showed significant model terms (Sum of Squares = 260.60, F value = 69.84) with probability of 0.01% for the F-value to occur due to noise. The established model is useful for application in construction industries.

Dataset of computed N-value and factual N-value traced for Soil Subsurface Profiling.

Soil requires load bearing impact assessment for stability. Therefore, this study aims to utilize the multi-channel analysis surface wave (MASW) for soil subsurface investigation and profiling around Peninsular Malaysia. The standard penetration test (SPT) was conducted for comparison between factual N-value and computed N-value from shear wave velocity (Vs ) obtained from MASW using the Imai and Tonouchi equation. The correlation coefficient (R) and coefficient of determination, (R2 ), showed strong relationship between factual N-value and computed N-value. The model of Vs and factual N-value data distribution is non-normal but the analyzed relationship shows a significant level of p-value < 0.05. The R2 for each location of Vs -N-value relationship are ranging from 0.5 to 0.9.

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN).

This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pHZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher-Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater.

Dataset on leaching properties of coal ashes from Malaysian coal power plant.

Coal combustion by-products (CCPs) (i.e. fly (FA) and bottom (BA) ashes) generated by power plants contain heavy metals. This research presents leaching properties of coal ashes (FA and BA) collected from Jimah coal-fired power station, Port Dickson, Negeri Sembilan using USEPA standard methods namely toxicity characteristic leaching procedure (TCLP), and synthetic precipitation leaching procedure (SPLP). Heavy metals like lead (Pb), zinc (Zn), copper (Cu) and arsenic (As) were quantified using atomic absorption spectrometer (AAS). The leached of heavy metals fluxes were Cu < Zn < Pb < As. As leached the most whilst indicating of possible contamination from As. Overall, the ranges of leached concentration were adhered to permissible limits of hazardous waste criteria for metal (Pb and As) and industrial effluent (Zn and Cu). The presented data has potential reuse as reference for the coal ash concrete mixed design application in construction industries.

Dataset on specific UV absorbances (SUVA254) at stretch components of Perak River basin.

Perak River basin is in Perak state of Peninsular Malaysia. In this research, the river stretch serves as water intake for domestic, agricultural and industrial purposes in Perak Tengah, Hilir Perak and Manjung regions. It is located in mixed use area whilst exposing the river to anthropogenic elements. The sampling locations were conducted at selected points of Perak River namely Tanjung Belanja Bridge (TBB), Water Treatment Plant Parit (WTPP), Parit Town discharge (PTD), Water Treatment Plant Senin (WTPS) and Water Treatment Plant Kepayang (WTPK). The existence of aromatic hydrocarbons in freshwater samples was pre-assessed via qualification analysis; specific ultraviolet absorbance (SUVA254) method at 254 nm of wavelength. The SUVA dataset were 48.38 L/mg-m (TBB), 50.54 L/mg-m (WTPP), 8.05 L/mg-m (PTD), 85.75 L/mg-m (WTPS) and 217.39 L/mg-m (WTPK). The SUVA254 values of fresh water at the river basin have exceeded the water quality standards value equivalent to 2.0 L/mg-m permitted by the Environmental Protection Agency of United States. The exceeding values were an indication of a large portion of aromatic compounds in the water. Qualification analyses evident the existence of water pollutants at treacherous concentrations for public health in freshwater samples of Perak River basin. Thus, this research has presented important findings towards further research and countermeasure for a better alternative of water treatment in Malaysia.

Step by step procedures: Degradation of polycyclic aromatic hydrocarbons in potable water using photo-Fenton oxidation process.

Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic compounds, composed of two or more fused benzene rings and abundantly found in mixed-use areas. Mixed-use areas consist of dense population, urbanization, industrial and agricultural activities. River pollution are common in mixed-use areas and 98% of Malaysia's fresh water supply originates from surface water. The biological degradation, adsorption and advanced oxidation process were documented as the available PAHs treatment for water. To date, the application of the photo-Fenton oxidation process has been reported for the treatment of PAHs from contaminated soil (review paper), landfill leachate, municipal solid waste leachate, sanitary landfill leachate, aniline wastewater, ammunition wastewater and saline aqueous solutions. As for potable water, the application of Fenton reagent was aided with photo treatment or electrolysis not focusing on PAHs removal. •The presented MethodsX was conducted for PAHs degradation analysis in potable water samples using photo-Fenton oxidation process.•The designed reactor for batch experiment is presented.•The batch experiment consists of parameters like concentration of 17 USEPA-PAHs in the prepared aqueous solution (fixed variable), reaction time, pH and molarity ratio of hydrogen peroxide (H2O2): ferrous sulfate (FeSO4).

Application of response surface methodology for the optimization of polycyclic aromatic hydrocarbons degradation from potable water using photo-Fenton oxidation process.

Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic compounds, composed of benzene rings. The objective of this research was to identify the optimum condition for the degradation of PAHs contaminated water using photo-Fenton oxidation process via response surface methodology (RSM). Aqueous solution was prepared and potable water samples were collected from water treatment plants in Perak Tengah, Perak, Malaysia in September 2016. The reaction time, pH, molarity of H2O2 and FeSO4 were analyzed followed by RSM using aqueous solution. A five level central composite design with quadratic model was used to evaluate the effects and interactions of these parameters. The response variable was the percentage of total organic carbon (TOC) removal. PAHs quantification was done using gas chromatography mass spectrometry analysis. The regression line fitted well with the data with R2 value of 0.9757. The lack of fit test gives the highest value of Sum of Squares (15,666.64) with probability F value 0.0001 showing significant quadratic model. The optimum conditions were established corresponding to the percentage of TOC removal. The PAHs removal efficiency for potable water samples ranged from 76.4% to 91% following the first order of kinetic rates with R2 values of >0.95. Conventional water treatment techniques are not effective for PAHs removal. Thus, advanced oxidation processes may be considered as an alternative to conventional water treatment techniques in Malaysia and other developing countries.

Plagiarism in Publications Using the Unpublished Raw Data of Archived Research.

It is obligatory to educate student researchers before they start their work by teaching them about the various types of plagiarism and how to avoid them. It is also vital that research supervisors take into account the sources of data that are explored in their students' manuscripts. This article tries to draw the reader's attention to the importance of avoiding all types of plagiarism in their research.