This article reviews the latest technological innovations in the use of Asbuton, natural asphalt originating from Buton Island, for road rehabilitation. Asbuton is known to have a number of advantages, such as its high bitumen content, resistance to extreme temperatures, and the potential to provide economic benefits to local communities. Various treatment methods, including conventional methods, Hot Water Process, and Cold Paving Hot Mix Asbuton (CPHMA), have been studied to improve the quality and efficiency of their use. In addition, innovations such as polymer modifications, cold recycling techniques, and the implementation of modern quality control systems are also discussed as factors that can improve Asbuton's performance and sustainability. Despite having significant potential, challenges in terms of production facilities, standardization, skilled human resource development, and regulations still need to be considered. The recommendations proposed in this review include accelerating the standardization process, training for the workforce, investment in infrastructure, and collaboration with research institutions to support the optimal and sustainable use of Asbuton in road infrastructure development.

This study investigates the impact of varying soot aerosol concentrations and relative humidity (RH) on the polarizability and phase function of non-spherical urban atmospheric aerosols. Using the Optical Properties of Aerosols and Clouds (OPAC 4.0) software, urban aerosol optical properties at visible wavelengths (0.4 - 0.8 μm) were simulated across eight relative humidities (0%, 50%, 70%, 80%, 90%, 95%, 98%, 99%). The Stokes parameters are employed to derive phase functions, while the Claussi-Massoti formulation, combined with Maxwell and Lorentz-Lorentz relations, were used for computation of effective polarizabilities. The results provide insights into RH and soot aerosol concentration effects on urban aerosol optical properties, including effective polarizabilities, polarization, and phase function data. The extraction of these data using OPAC 4.0 and determination of aerosol size growth curves' mean exponent through effective hygroscopic growth. Effective polarizability exhibits a decrease with increasing wavelength and soot aerosols concentration, whereas it increases with increasing relative humidity.

The utilization of Buton asphalt (Asbuton) as an alternative material in road rehabilitation represents a strategic step in supporting sustainable infrastructure development in Indonesia. Asbuton, a local resource with high carbon content and superior technical characteristics, offers significant potential to replace petroleum-based bitumen, which has a substantial environmental impact. This article examines the sustainability potential of Asbuton, its associated environmental risks, and the necessary mitigation strategies for its implementation. The findings indicate that Asbuton can reduce carbon emissions, extend pavement service life, and decrease reliance on imported materials. However, several challenges remain, including pollution from chemicals used in the emulsification process, the management of solid waste such as Asphalt Solid Waste (ASW), and social impacts arising from mining activities. Therefore, a comprehensive mitigation strategy is required—one that includes regulatory reinforcement, innovations in waste treatment technology, and an environmental justice approach that prioritizes local communities. Cross-sectoral synergy is essential to ensure that the use of Asbuton contributes meaningfully to the sustainability of Indonesia's road sector.

This paper investigates the effects of variation of Water soluble concentrations (WASO), relative humidity, phase functions and scattering angles with wavelength, effective polarizability of desert aerosols. OPAC 4.0 (Optical Properties of Aerosols and Clouds) software package using FORTRAN program to model the effect of Water soluble concentrations at the visible spectral range of (0.4-0.80µm) and at eight relative humidities (0%,50%,70%,80%,90%,95%,98% & 99%) was used. Water soluble concentrations (WASO) were varied while that of Mineral accumulation, Mineral nucleation and Mineral coarse was kept constant. From the results obtained it was observed that the phase functions decreases as the scattering angle increased. Comparing phase functions at 0%RH to 99%RH, at each model there is more scattering at shorter wavelength due to smaller particles it signifies more absorption at larger scattering angle. The results have shown the clear increase in concentration and decrease in volume mix ratio as the relative humidity (RH) increases. The concentrations were varied to obtain five different models. Stokes parameter was use to obtain the phase functions and Lorentz- Lorentz relation for refractive index to determine the polarizability for single and effective polarizability aerosol, present in the desert atmosphere.

The rapid growth of IoT-based smart environments has created a multitude of considerable security vulnerabilities due to heterogeneous devices and constantly changing network conditions. Traditional intrusion detection systems (IDS) often lack the sophistication necessary to effectively detect new attacks, requiring innovative, dynamic methods. In this paper we propose a unique intrusion detection framework that utilizes a Hierarchical Bi-directional Long Short Term Memory (Bi-LSTM) model optimized with a Multi-Objective Bat Algorithm (MOBA). The Bi-LSTM model learns from future, and past data sequences to improve intrusion detection performance by enhancing accuracy when learning temporal patterns. The MOBA model optimizes the Bi-LSTM model by learning to select the best features for a supervised classifier, optimizing the model concurrently with optimizing the parameter tuning. Experimental verification of the proposed framework utilizing benchmark datasets confirms the proposed IDS framework is generally superior to the other methodologies for detection accuracy, total cost of false alarms, and total time to execute the IDS framework. The proposed IDS also shows a distinctive balance of balance feature selection and optimizing parameters, and great opportunity of performance without sacrificing execution times, potentially enabling the deployment of the IDS in resource-limited IOT environments and the ability to create an intelligent and scalable response to changing cyber-threats.

The purpose of this article is to review on few parameters of water quality assessment methods like pH, alkalinity, salinity, hardness, conductance etc. commonly employed. Various methods have been discussed which are employed to analyze cations and anions in natural sources of water, which are key indicators of water quality. The major cation such as Calcium (Ca²⁺), Magnesium (Mg²⁺), Sodium (Na⁺), and Potassium (K⁺) and anions such as carbonate (CO₃²⁻), bicarbonate (HCO3-), Chloride (Cl⁻), Fluoride (F-), Arsenate (AsO₄³⁻), Sulfate (SO₄²⁻), nitrate (NO₃⁻), and phosphate (PO₄³⁻) significantly influence the chemical balance of water system. In this review, we have discussed some analytical methods for few cations and anions present in water sample. The permissible limits of these ions present in water sample are also discussed according to Indian Council of Medical Research (ICMR) and World Health Organization (WHO) standards for drinking purposes. The review is mainly on the different parameters for the quality measurement of water for drinking purposes, respectively. The review highlights the strengths and limitations of each method, and discusses the growing need for integrated approaches that combine chemical, physical, and biological assessments to achieve comprehensive water quality evaluation. Understanding the concentration and distribution of cations and anions is essential for managing water resources, mitigating pollution, and ensuring public health safety.

Application of machine learning techniques in power systems has a wide scope in research work due to their benefits in classification and regression characteristics in load balancing, fault tolerance, task processing time, and statistical nature of renewable energy resources. We have analyzed the characteristics of networked microgrids in context of their connectivity with main grid. In this paper, machine learning techniques are discussed which are applicable to distribution control, energy trading, optimization and power restoration in case of networked microgrid schemes. Paper focuses on recent progress of machine learning techniques in several aspects of networked microgrids such as control and optimization process. We have used machine learning techniques for networked microgrids applications and their review. The main problem and challenge are to achieve renewable energy resources benefits and space for high number of microgrids penetration to design a comprehensive networked microgrids management system. This comprehensive networked microgrids management system is the robust decision center by using intelligent machine learning techniques. Machine learning techniques mapped the human learning process of improving accuracy of solution over time with data and algorithms. Machine learning algorithms used the data sets, training the computers for output values within the required limits. With the support of machine learning techniques, the existing information and experience lead to take right decision for controlling and management of networked microgrids. In addition, this review paper demonstrates the development of machine learning techniques based on distributed and centralized control framework for networked microgrids.

Hydrocarbon pollution of the environment due to oil spilled has been a major environmental problem in Nigeria in the recent past. The need to continuously seek to find consortium of organisms that will be used to remediate the oil contaminated environment is on the increase. The study assayed oil-degrading potential of fungi isolated from cow dung, goat dung and poultry dung contaminated soils. The samples were collected aseptically from nine different locations with no history of crude oil pollution in Lokoja metropolis. These samples were analyzed for fungal loads and oil-degrading fungi using potato dextrose agar (PDA) and mineral salt agar (MSA) respectively. The biodegradation of engine oil was observed for a period of fourteen days on PDA and MSA respectively. The fungi were identified based on the microscopic and macroscopic features of the hyphal mass, nature of the fruiting bodies and the morphology of cells and spores using standard charts. The fungi identified from the contaminated soil include Aspergillus fumigatus, Rhizopus stolonifer. and Mucor ramosissimus. All fungi showed degradation of the crude oil, with Aspergillus fumigates demonstrating the best degradation ability. The result obtained revealed that oil-degrading fungi can be isolated from cow, goat and poultry dung soils and they are competent mycoflora for the biodegradation of crude oil polluted soils. They can thus be used as a better approach to restoring oil contaminated environments through bioremediation process.