Supported nano zero-valent iron is receiving great attention nowadays due to its effectiveness in treating heavy metal pollutants. Therefore, this study aimed to investigate the effectiveness of granitic residual soil-supported nano zero-valent iron (Gr-nZVI) for the removal of the heavy metals Pb²⁺, Cu²⁺, Co²⁺, Cd²⁺ Ni²⁺ and Zn²⁺ in mixture solutions under different experimental conditions of batch equilibrium tests. In this study, Gr-nZVI was successfully synthesized by using the chemical reduction of Ferric Chloride Hexahydrate (FeCl₃.6H₂O) and Sodium Borohydride (NaBH₄)_. The physical and chemical properties, morphology and mineralogy of all adsorbents were characterized by the Braeuer–Emmett–Teller (BET) method, cation exchange capacity (CEC), X-ray fluorescence (XRF), scanning electron microscopy (SEM), field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Isotherm, kinetic and diffusion model analyses were conducted to fit the experimental data. The results show rapid adsorption within 5 min in the initial adsorption stage for Pb²⁺ on nZVI (q_e.Pb = 17.89 mg/g) and Gr-nZVI (q_e.Pb = 15.29 mg/g). nZVI and Gr-nZVI also showed no significant effects on pH and temperature, serving as a good example of an energy-efficient process. The isotherm data fitted better to the Langmuir model and the pseudo-second-order kinetic model for the adsorption of all of the heavy metals. The diffusion models revealed that adsorption was not the only rate-limiting step. In this study, nZVI compared to Gr-nZVI and Gr demonstrated superior adsorption capacity for the heavy metal adsorption selectivity. Hence, these materials can be utilized as alternative energy-efficient adsorbents for the adsorption of metal ions from wastewater.

Dyes used in textiles, foods, cosmetics, and chemicals have become a major environmental pollution issue around the world. To address this issue, a number of technologies have been created to remove these pollutants from the environment. Due to their superior properties at nanoscale, numerous nanomaterials have been applied to remove dyes from polluted waters. This research presents the findings of the development of bentonite nano zero-valent iron (B-nZVI) for the treatment of synthetic cationic dyes. This study has three objectives: (i) to produce bentonite nano zero-valence iron (B-nZVI), (ii) to characterize its adsorbents (B-nZVI), (iii) to characterize its adsorption capacity. Four main tests were used for this purpose: (i) a physical test (Brunauer–Emmett–Teller (BET) surface area), (ii) a chemical test (cation exchange capacity (CEC) and X-ray fluorescence (XRF)), (iii) morphology (field emission scanning electron microscopy (FESEM) and (iv) mineralogy (Fourier transform infrared spectroscopy (FTIR). The five factors for the batch equilibrium test are adsorbent dose, concentration, kinetic, pH, and temperature. The batch test showed that the optimum dose for all adsorbents is 0.5 g. For the concentration factor, B-nZVI exhibits larger adsorption capacity (K_L = 30,314.0536 L/g; R² = 1) compared to bentonite (K_d = 0.0219 L/g; R² = 0.8892). The kinetic factor showed that the adsorption capacity by pseudo-second-order model was the best for both adsorbents (q_e = 1.2038 mg/g, R² = 0.9993 for bentonite and q_e = 6.9979 mg/g, R² = 1 for B-nZVI). For B-nZVI, the interparticle diffusion model (K_f = 0.8645 m² g⁻¹ min L⁻¹; R² = 0.9) and intraparticle diffusion model (K_d = 2.3829 m² g⁻¹ min L⁻¹; R² = 0.9189) showed a good correlation with the adsorption data, while bentonite showed a lower correlation with the interparticle diffusion model (K_f = 0.0002 m² g⁻¹ min L⁻¹; R² = 0.6253) and intraparticle diffusion model (K_d = 0.2886 m² g⁻¹ min L⁻¹; R² = 0.6026), respectively. The pH factor showed that the adsorption capacity of bentonite (q_e = 0.5674 mg/g) and B-nZVI (q_e = 5.3284 mg/g) was highest in acidic conditions (pH 2). As for the temperature factor, there was no significant effect on bentonite and B-nZVI. Therefore, tests can be conducted at room temperature, saving energy. It was also concluded that B-nZVI is the best material for removing MB compared to bentonite and can be considered for the treatment materials of contaminated water.

Over the years, machine learning, which is a well-known method in artificial intelligent (AI) field has become a new trend and extensively applied in various applications to solve a realworld problem. This includes slope failure prediction. Slope failure is among the major geo-hazard phenomenon which gives the significant impact to the environment or human beings. The estimation of slope failure in slope stability analysis is a complex geotechnical engineering problem that involves many factors such as geology, topography, atmosphere, and land occupancy. Generally, slope failure can be estimated based on traditional methods such as limit equilibrium method (LEM) or finite equilibrium method (FEM). However, beside the methods are quite tedious and time consuming, LEM and FEM have their own limitations and do not guarantee the effectiveness when dealing against problem with various geometry or assumptions. Hence, the introduction of machine learning approaches provides the alternative tools for the prediction of slope failure. Current study applies two mostly used supervised machine learning approaches, support vector machine (SVM) and decision tree (DT) to predict the slope failure based on classification problem using historical cases. 148 of slope cases with six input parameters namely “unit weight, cohesion, internal friction angle, slope angle, slope height and pore pressure ratio and factor of safety (FOS) as an output parameter”, was collected from multinational dataset that has been extracted from the literature. For development of the prediction model, the slope data was divided into 80% training data and 20% testing data. The prediction result from testing data was validated based on statistical analysis. The result shows that SVM model has outperformed DT model by giving the prediction accuracy of 97%. ith the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based on historical circular slope cases. Using the feed-forward backpropagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. However, the prediction result of ANN can be improved by integrating the statistical analysis method, namely grey relational analysis (GRA), to the ANN model. GRA is capable of identifying the influencing factors of the input data based on the correlation level of the reference sequence and comparability sequence of the dataset. This statistical machine learning model can analyze the slope data and eliminate the unnecessary data samples to improve the prediction performance. Grey relational analysis-artificial neural network (GRANN) prediction model was developed based on six slope factors: unit weight, friction angle, cohesion, pore pressure ratio, slope height, and slope angle, with the factor of safety (FOS) as the output factor. The prediction results were analyzed based on accuracy percentage and receiver operating characteristic (ROC) values. It shows that the GRANN model has outperformed the ANN model by giving 99% accuracy and 0.999 ROC value, compared with 91% and 0.929.

Crowdsourced Software Engineering (CSE) is an application of online problem-solving model that provides a dynamic way of harnessing crowd intelligence in obtaining creative solutions. It consists of four elements: crowdsourcer, crowd, platform, and tasks. Although CSE offers various advantages to software engineering practices, recent research highlighted that there is still a lack of thorough understanding of Intellectual Property (IP) ownership rights of CSE elements. Therefore, management and control of CSE for obtaining adequate IP from the crowd is inevitable to reduce the risks of using that IP. This paper reviews existing CSE platforms and analyses associated IP challenges encountered by software engineering activities. 51 platforms were preselected and amongst these, analyses were performed on 37 CSE platforms. Exclusion of 11 platforms is on the basis of unavailability and 3 of which are linked to the same webpage. It also shows that 59% of the platforms acquire IP ownership of deliverables submitted by the crowd while 19% do not have a statement which provides clarity on IP ownership in their legal documents. This paper is significant in providing better understanding of IP risks of these CSE platforms and in assisting both crowdsourcers and the crowd in choosing crowdsourcing platforms based on the default IP ownership specification, which is often specified in legal documents of these platforms.

Due to the world’s population growth, excessive solid waste generation is a serious environmental issue. The landfill leachate infiltrates the soils, pollutes the groundwater, and puts all living things at risk. This study investigates the geotechnical properties of the soils (marine clay and granitic residual soil) and the migration of cadmium (Cd) using a high-speed centrifuge column test. All soil samples were subjected to physicochemical, morphology and mineralogy properties analyses, including the determination of their particle size distribution, Atterberg limits, specific gravity, compaction, permeability, pH, organic content, cation exchange capacity (CEC) and specific surface area (SSA). They were also subjected to analyses by X-ray diffraction (XRD) and scanning electron microscope (SEM). This research utilizes two types of adsorption tests: batch tests and column infiltration tests. For the Batch test, the elimination percentage of Cd in marine clay was up to 86% (SBMC2) to 98% (SBMC1) at an initial value of 75 mg/L. While the granitic residual soil showed the maximum removal percentages of Cd were 39% (KGR) to 47% (BGR). For the column infiltration test, the soils were subjected to different g-force, (i.e., 10× g and 20× g) and two different soil weights (i.e., 10 and 20 g of soils). The study revealed that marine clay (partition coefficient, K_d = 10–23 L/Kg) has better adsorption on Cd compared to granitic residual soils (K_d = 0.6 to 0.9 L/Kg). The study also concludes that marine clay (SBMC) is one of the natural clay-based energy materials which can effectively use as an engineered clay liner.

Effective management of landslide events requires robust mapping and visualization tools to ensure prompt responses and a thorough understanding of the situation. This study undertakes a comparative analysis of web-based tools specifically tailored for landslide reporting, mapping, and viewing, to evaluate their functionalities, usability, and suitability across diverse stakeholder groups and operational contexts. This article offers valuable considerations for users seeking specific functionalities and geographical coverage, contributing to ongoing efforts to leverage the capabilities of web-based technologies in the realm of landslide management in addressing this critical geological hazard. The research encompasses a comprehensive literature review and hands-on evaluations of selected platforms designed to meet the needs of landslide management. Key dimensions under scrutiny include data input mechanisms, spatial analysis features, visualization capabilities, user interfaces, accessibility, and customization options. Four publicly available web-based tools underwent examination in this study: the NASA Landslide Viewer, the Global Landslide Detector, the Western North Carolina (WNC) Landslide Hazard Data Viewer, and the Landslide Susceptibility Map Viewer. Each platform enables users to visualize landslide occurrences, access pertinent datasets, and scrutinize specific details about each landslide event. Notably, the Landslide Viewer, to monitor global landslides. In contrast, the WNC Landslide Hazard Data Viewer focuses specifically on North Carolina, United States, while the Landslide Susceptibility Map Viewer prioritizes comprehensive mapping within Ireland. Of all the platforms, only the Landslide Susceptibility Map Viewer actively encourages users to contribute by submitting landslide reports, thereby providing real-time observations that enhance the dataset.