Inventory Categorization Using Multiple Criteria Classification

Inventory management holds paramount importance in modern business landscapes, where expert resource handling is essential for success. This study investigates the correlation between specific Stock Keeping Units (SKUs) and age categories of items, exploring factors influencing the speed of warehouse movement. Categorization based on product life cycle, pricing, and remaining stock is examined alongside savings levels. Employing multi-criteria classification algorithms, including traditional and machine learning techniques, the research illuminates inventory dynamics. This study compares bi-criteria, multi-criteria of traditional, and machine learning inventory classification methods, providing a comprehensive analysis of inventory categorization strategies. Machine Learning method achieved highest accuracy up to 99% by Decision Tree and 68% by Support Vector Machine. The accuracy score followed by the traditional method by using FSN-fuzzy method accuracy score up to 86.7%. The outcomes of the FSN analysis and fuzzy classification experiment will offer stakeholders valuable insights, potentially sparking innovative ideas for their business

Seismic Performance Assessment for Serviceability of Geosynthetic Reinforced Embankments

Embankments in the field of civil engineering were historically not given much attention in terms of their ability to withstand seismic activity, as they were considered to be non-critical structures. However, in recent years, the seismic stability of embankments has gained increasing importance in the field of geotechnical engineering. This increased significance is due to the need to quickly restore functional infrastructure after earthquakes. The occurrence of lateral spreading, resulting from foundation liquefaction, is the primary factor leading to embankment distress in geotechnical engineering during seismic events. To address this issue, this study presents a novel approach that employs geosynthetic basal reinforcement. The research paper presents a technique that uses geosynthetic basal reinforcement to manage the lateral spreading of embankments both pre and post earthquakes, guaranteeing their ongoing effectiveness. The proposed approach utilizes a pseudo-static limit equilibrium method for determining the tensile load produced in the basal reinforcement. The serviceability criteria establishes the maximum strain permitted in the basal reinforcement by imposing a restriction on the horizontal movement of the embankment toe. By taking into account the tensile load of the reinforcement and the maximum allowable strain, it is feasible to establish an appropriate geosynthetic reinforcement that considers various factors including tensile strength, strain, design life, installation, and durability impacts

Monitoring of River Morphological Change Using Remote Sensing and Hec-Ras in Lusi River, Indonesia

Changes in river flow have become a natural cycle of the river. The Lusi River has problems with meanders due to sediment transport, which is difficult to control. A combination of GIS with RS and HEC-RAS simulation was used to monitor river flow. The water monitoring method utilizes four water indices: NDWI, MNDWI, ANDWI, and SAVI, while the MPM-Toffaleti method is used for simulation. By combining the four methods, accuracy values of 0.68 to 0.92 and precision levels of 0.60 to 0.93 were obtained. MNDWI obtained quite high results compared to other indices. The results showed that the Lusi River experienced quite extreme flow changes. Two flow cuts occurred. Considerable erosion occurred from 2003 to 2013 with a total lost area of 0.1943 km2, while from 2013 to 2023, there was erosion of 0.1177 km2. The HEC-RAS simulation of the Lusi River experienced erosion of 60 to 70.4 percent of the length of the stream. Changes in the riverbed in 2013 were found to be -1.1 ± 2.03 m, and then in 2023, the range of changes in the riverbed was -2.69 ± 1.29 m. Based on the results of the index and simulation of the Lusi River experiencing erosion in the flow, the level of erosion has increased every period

Effect of Heating Power on The Synthesis of Titanium Dioxide Particles by Direct Heating Method for Photodegradation of Methylene Blue Solution

Dyes are extensively used in the textile industry. Untreated dye-containing wastewater is hazardous to humans and the environment. Advanced oxidation processes (AOPs) based on heterogeneous TiO2 photocatalyst is a technology that has been proven successful for effluent treatment. Nevertheless, use of TiO2 photocatalyst in particles form could result in loss of TiO2 photocatalyst over time during effluent treatment. Immobilization of TiO2 photocatalyst on substrates could address this problem. In this study, a method called direct heating (DH) was devised to deposit TiO2 particles on kanthal wires to prevent the particles from being washed away during effluent treatment. This method has low equipment cost and short synthesis duration when compared to others. The TiO2 particles synthesized at 50 W in 15 min gave the best photodegradation efficiency, which was 36.3 % under 90 mins of UV light irradiation. It contained a mixture of anatase (22.5 %) and brookite (77.5 %), with an average particle size of 139.34 ± 15.89 nm. The TiO2 particles had high surface coverage on kanthal wire with little agglomeration. The current work presents a captivating and low-cost setup, providing easy control over synthesis conditions and scaling potential for immobilizing TiO2 particles on the kanthal wires

Integrating Numerical Simulation and Experimental Validation to Analyze Temperature Distribution in Friction Stir Welding of Dissimilar Aluminum Alloy

In this paper, a 3D finite element model is developed by COMSOL Multiphysics software to study the temperature distribution during the friction stir welding process of dissimilar aluminum alloys AA6351-T6 and AA2024-T351. Dissimilarity comes from the alloying elements with different melting point and latent heat requirements. The input process parameters included different tool rotational speeds (800, 1000 and 1200 rev/min), different welding speeds (0.35, 0.7 and 1.2 mm/s) while keeping the axial load of 3kN constant. High speed steel (HSS) is considered as the tool material and AA6351-T6 and AA2024-T351 are considered to be the workpiece materials. The increase of maximum temperature observed with the increase of the tool rotational speed. Numerical simulations are found to be useful to gain process understanding and conduct process planning as well as tool design

IoT-Based Automatic Transfer Switch System Design on Solar Home System

The challenge hybrid system power sources face is switching from one source to another. This transfer is required to anticipate the depletion of energy sources in the battery owing to unfavorable weather, such that the solar panels do not receive sufficient sunlight. An automatic switching process with minimal time lag is required to maintain the continuity of the electrical energy flow. In addition, there is a growing need to analyze the energy consumption in certain areas and periods. This project designed and built an Internet of Things (IoT) based Automatic Transfer Switch (ATS) system. The ATS prototype uses the Arduino MEGA 2560 microcontroller to switch the power source and the ESP32 DevKit V1 microcontroller to send the data logger to the IoThingsHub cloud platform for monitoring systems that are useful for sustainable ecosystems. The sensor exhibited accuracies of 99.8% for voltage and 96.5% for current readings. The ATS prototype could switch between power sources with an average time lag of 47 ms. The results of the field trials show that the ATS prototype design utilized solar photovoltaic for approximately 26% of the usage, with a 100 Ah 12V battery system and three 100 Wp solar panels in sunny/partly cloudy conditions for 50 W lamp loads

Model of Training Learning Based on Video Tutorial for Fashion Major of Vocational Education on Embroidery Course

Students\u27 difficulties in understanding innovative design concepts in fashion major of vocational education require a learning model that has a balance between theory and practice to facilitate understanding and improve students\u27 practical skills.  This study aims to develop a valid, practical, and effective training learning model based on video tutorials on Minangkabau embroidery learning material. The research is a Design and Development Research (DDR) using the ADDIE framework. The research subjects involved experts namely media experts, learning material experts, design experts, and learning experts to assess the validity of the learning model, and the experimental subjects involved Fashion Major of vocational education on undergraduate students. Data collection in the study was carried out using questionnaires, learning achievement tests, and observation sheets. The results indicate a significant contribution to enhancing the cognitive, affective, skill, and attitude abilities of Fashion Education undergraduate students. These results also indicate that this model meets the criteria of valid, practical, and effective. The implications of these findings underscore the potential positive impact of the developed tutorial model on the overall learning experience and outcomes in Fashion Education

Vocational Education Practices for Empowerment of Vulnerable Students in Lithuania and Latvia: The Pedagogical and Institutional Factors

This article aims to disclose the institutional and pedagogical factors of VET provider practices targeted to empowerment of vulnerable students for learning and employment in Lithuania and Latvia.  The research methodology and design are based on comparative study involving desk research, case studies by applying focus group interviews with representatives of the VET institutions’ teaching, support and administrative staff. Research disclosed the central role of VET teachers and other teaching staff members in empowering at-risk VET students, as well as the issue of their capacities and capacity building needs in empowering increasing volume of at-risk students in VET schools. VET schools seek to empower students by abandoning standardization and narrow specialization of skills and increased use of the individual approach in pedagogical practices. Preference is given to expanding the scope and individualization of the applied pedagogical practices for at-risk students. Increased attention is being paid to the living conditions of at-risk students outside school, addressing students\u27 social problems, development of key skills and attitudes, coaching and methods of individual co-operation

Analysis of Moderating Effect of Turnover Intention on the Relationship of Intrinsic Motivation and Employee Performance

This paper presents an analysis of the moderating effect of turnover intention on the relationship between intrinsic motivation and the employee performance model. The moderation effect analysis was conducted using SmartPLS software version 3.0. The model was constructed within the software, and the analysis was conducted in two stages. The first stage involved a bootstrapping process to generate and determine the significance level and strength of the path. The second stage employed the PLS Algorithm process, generating a simple slope graph. Results from the bootstrapping process revealed a noticeable moderating effect attributed to turnover intention, supported by a t-statistics value of 2.514 and a statistically significant p-value of 0.006. Investigating the specifics of the path coefficient, the moderating impact on the relationship was quantified at -0.097, indicating a negative moderating influence. In practical terms, this suggests that turnover intention exerts a dampening effect on the connection between motivation and employee job performance. Regarding the simple slope analysis, it was found that the moderation effect of turnover intention on the relationship between intrinsic motivation and employee performance is particularly robust when turnover intention is lower. However, this effect diminishes to some extent when turnover intention is higher. These findings provide valuable insights into how the dynamics of these variables interact under varying levels of turnover intention

Classifying Phishing Websites Using Multilayer Perceptron

The prevalence of phishing as a cybercrime continues to escalate, posing significant threats to individuals\u27 sensitive information. This paper addresses the urgent need for effective phishing detection methods, considering the limitations of existing approaches. The study employs Artificial Neural Networks, specifically Multilayer Perceptrons (MLP), trained using the backpropogation algorithm. The study also highlights MLP’s advantages in handling complex and noisy data. Through a comprehensive review of related works, the paper identifies gaps in current research and establishes the groundwork for an innovative phishing website classification framework. The proposed solution utilizes MLPs, offering a detailed explanation of the methodology, dataset, model architecture, and training processes. The research concludes by summarizing key findings, emphasizing the solution\u27s contributions to cybersecurity, and outlining potential avenues for future research