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Sustainability in pavement construction is very much required in the present scenario. Utilization of industrial waste as pavement material during construction is one of the methods for achieving sustainability. For the present study, Steel Slag and ceramic fibers were used as partial replacement of natural aggregate and as modifiers for binder respectively. SSA at 0, 20 40, and 60% total mix and ceramic additives at 0,3,5, and 7% were used. The rheological properties of the modified binder were studied using a Dynamic Shear Rheometer. Marshall stability test and moisture liability test were carried out to examine performance evaluation. Results show that the mix with 5% ceramic fiber with 60% SSA showed better performance compared to other mixes. [ABSTRACT FROM AUTHOR]

In Indonesia, there are 2,273 species of fungi that are classified into various genera. With these numerous varieties, fungi bring a lot of impacts on human life and the environment. Fungi can be employed to make foods, create drugs, and make fertile soil. In reverse, fungi also bring many negative impacts, such as causing diseases in humans, plants, and animals. To get the benefit and avoid the harm of the fungi, it needs to be classified in the right group. One way to easily identify and classify fungi is by observing their morphological traits in their image. To accelerate the image-based fungi genera classification process, a multistage convolutional neural network is proposed in this research. There are two schemes in two stages of training, with adjustments in each stage. The result indicates that the scheme with cross-validation in the first step gives better classification result and can handle overfitting issues. Scheme with cross-validation gives a higher average f-1 score of 59 % compared to the scheme without cross-validation with an average f-1 score of 28%. [ABSTRACT FROM AUTHOR]

Tidal and lowland swamps have the potential to be used as agricultural land. However, to manage their sustainably, it needs technological improvements to increase productivity such as the diversity of indigenous microbes. This research was conducted to explore the information on culturable bacterial diversity and their potency in the tidal swamp soils of South Kalimantan and lowland swamp soils of South Sumatra. Our current research was conducted in laboratory at the Indonesian Center for Agricultural Biotechnology and Genetic Resources Research and Development. Two samples were isolated from tidal swamp soils at Jerapat Baru Village, Tamban, Barito Kuala District, South Kalimantan Province and lowland swamp soils at Tapus Village, Pampangan, Ogan Komering Ilir District, South Sumatra Province. Serial dilution and molecular identification were used to assess the diversity of bacterial species. Production of IAA, siderophores, and ACC deaminase were evaluated. A total of 11 species comprising Lysinibacillus xylanilyticus 26.1, Bacillus cereus 26.2, B. cereus 26.3, B. subtilis 27.1, B. siamensis 27.2, Brevibacillus halotolerans 27.3, Peanibacillus alvei 27.4, Lysinibacillus xylanilyticus 27.5, B. nitratireducens 27.6, B. cereus 27.7, and B. cereus 27.8 was chacterized from two samples collected from these areas. The most abundant group was Firmicutes. The dominant species in both tidal and lowland swamp soils were B. cereus and L. xylanilyticus, while common species was B. cereus. Potential analysis showed that all bacterial isolates were able to produce IAA, siderophores, and ACC deaminase. The bacterial diversity was higher in South Sumatra than that of South Kalimantan. The differences were observed in percentage occurrence of bacterial species between two type of swamp lands. The reason may be that the soil pH condition, quality of litters, different micro-environments and other characteristics in those areas. Soil pH in South Sumatera is higher than South Kalimantan, hence, it may have provided more resources for bacterial growth. [ABSTRACT FROM AUTHOR]

This paper explores the utilization of Artificial Intelligence and Machine Learning algorithms to empower visually impaired individuals, enabling satisfactory movement in their daily lives. The research focuses on enhancing the lives of the visually impaired by addressing challenges they face in unexpected situations. In today's highly technological society, the need for independent living is crucial, especially for visually impaired individuals who often experience social exclusion detection of objects, facial based recognitions, emotion-based recognitions, currency-based recognitions, web type automations, screen readings & emergency callings, we enhance the quality of life for the visually impaired. Implemented through ML, DL, speech to text & vice-versa, Selenium based web type automation frameworks, these technologies empower individuals to effectively adapt and respond to their environment. [ABSTRACT FROM AUTHOR]

The continuous of purchasing online products, For the buy of any product, we must see the reviews of that product after that we purchase the products. Review places the main role in online purchasing the product. Mainly positive reviews make customers buy a product online, similarly, the negative reviews make confusion about whether the customer buys a product or not. The product seller wrote a fake review to satisfy the customer. Thus, identifying fake reviews is a faithful and ongoing research area. Identifying a fake review depends on key features of the review but also it depends on behave of the reviews. This paper proposes a Machine learning accession to the identification of fake reviews. In addition to the features eradication process of the reviews. This paper appeal to some features engineering techniques to essence various behave of the reviews. This paper equates the performance of some trail runs on a real dataset of online shopping reviews with and without features estimations of users' behavior. In both cases, we compare the performance of some classifiers: K-Nearest Neighbor (KNN), Navy Bayes, Logistic Regression, and Random Forest algorithm. [ABSTRACT FROM AUTHOR]