Journal of Applied Research and Technology

Simultaneous Optimization of Investment in Technology Implementation and Regulatory Compliance: A Supply Chain Decision Model

2025-07-04T11:22:48-06:00

A mathematical optimization model is proposed for strategic decision-making in supply chain management (SCM). The proposed model simultaneously optimizes investments to comply with government regulations and investments in technology to improve efficiency across three performance dimensions: ordering, just-in-time (JIT), and operating efficiency. Real company data is used to test the model. This data comes from a German company. The behavior of the proposed model is analyzed by solving four scenarios under different investment strategies. Results reveal counterintuitive findings, for example, JIT efficiency does not necessarily increase when technology investment increases; in comparison compliance with government regulations can improve companies’ operational efficiencies. These results demonstrate the sensitivity of companies’ operations to the allocation of technology investment and highlight the importance of simultaneously optimizing investments in government regulations compliance, and in the implementation of new technology. The optimization model informs the decision-making process that companies follow when investing in new technology while ensuring compliance with government regulations. Therefore, the model offers practical insights and utility for both private companies and government policymakers.

Estimation of the elastic modulus of Au nanofilms by subtracting the polymeric substrate influence

2025-03-12T16:41:23-06:00

The elastic modulus of Au nanofilms deposited onto a polymeric substrate was estimated by subtracting the substrate mechanical influence from the Au/polymer system under axial tensile test. To select the most appropriate substrate between polyethylene- terephthalate (PET) and Kapton (polyimide), the crystallinity and structural isotropic conditions were previously examined by X-ray diffraction. Kapton shows a lower % of crystallinity and enhanced isotropic conditions than PET. The elastic modulus of 60 nm-thick Au films deposited onto Kapton was measured, resulting in an average value of 37 GPa, which is lower than the Au bulk value. Crystalline structure and elastic properties of polymeric substrates are found to be of great relevance to be considered for the determination of the mechanical properties of metallic films.

Current Trends in Ergonomic Innovations for Continuous Improvement in Different Work Fields

2024-12-06T15:10:11-06:00

Due to the constant technological evolution and social changes in industry, it is indispensable for ergonomics to adapt and effectively integrate new technologies. This review article aims to provide a detailed analysis of the latest developments in ergonomics, examining how the field is responding to the evolving needs of various industry sectors. For instance, in the medical field, 3D scanning and modeling technologies have been used to create prostheses; in design, pictograms have been developed to help agricultural workers in Africa work more efficiently; and in the workplace, artificial vision has been employed to study and reduce musculoskeletal disorders
and fatigue among construction company employees. Additionally, areas of opportunity are identified where ergonomics could have a significant impact, such as its application in studies of e-sports players. A total of 50 articles were reviewed and categorized in terms of ergonomics evolution, innovations in design, technology, work, health, and areas of opportunity. This literature review will serve as a guide for researchers in planning future projects.

Machine learning predictive model for an intelligent tourism recommendation system

2025-01-07T13:07:02-06:00

Recommendation systems, powered by machine learning, are essential in offering personalized recommendations to consumers based on their preferences in different areas, including literature, educational programs, and lodging. In the field of recommendation systems, there are numerous techniques and strategies. Given that tourism plays a crucial role in driving the economies of regions and countries, there is an increasing desire to enhance the tourist experience by improving the way information is provided. Nevertheless, current research often fails to address the need for comprehensive manuals on activities and attractions while traveling.
This project aims to fill this gap by developing a machine learning predictive model for an intelligent tourist recommendation system. The system is designed to help travelers choose the best routes for their travels. This study uses machine learning algorithms such as Naive Bayes, Decision Trees, and Linear Regression to analyze the “Tourism rating” dataset obtained from Kaggle. The dataset consists of 12 significant features. The results indicate that Linear Regression surpasses other methods, exhibiting greater predictive accuracy and decreased error rates. The importance of this study lies in its ability to offer customized suggestions and a wide range of choices to travelers, thereby improving their travel experiences by directing them towards the most suitable destinations and activities.

Toward multi relay selection aided two-way multi-users cooperative GSPIM-DCSK communication system

2024-11-12T13:33:24-06:00

To improve the error quality of a single-relay two-way half-duplex cooperative communication system based on a Joint Grouping Subcarrier and Permutation Index Modulations DCSK (GSPIM-DCSK), a novel multi-relay two-way multi-user half-duplex cooperative communication system is offered in this work. The GSPIM-DCSK scheme serves as the foundation for this system's architecture. In this system, many relays are established, but during the relaying phase, only one relay is used to transmit network-coded data to the consumers. Every relay node in this network is assumed to be using the protocol method called Decode and Forward (DF). To determine which relay should be chosen, the max-sum ( ), max-min ( ), and max-product ( ) relay-selection methodologies are employed. Particularly, these requirements are used at the relay's reception to produce two judgment numbers, which are subsequently subjected to further processing via procedures such as sum, product, or min. The parameters of all relays are then compared to see which one is more reliable for relaying after this process. The performance of the novel system is evaluated by acquiring and applying the simulation findings at multipath Rayleigh fading channels. Furthermore, a comparative analysis is conducted between the cooperative system-based DCSK and GSPIM-DCSK that utilizes a single relay and a novel cooperative-based GSPIM-DCSK system that utilizes multiple relays. The average bit error rate (BER) performance is included in this. Moreover, an examination shows how the suggested cooperative system raises the effectiveness of traditional cooperative systems.

An adaptive neuro-fuzzy inference system applied for the design of a firefighting robot using a photovoltaic panel

2024-11-29T13:43:57-06:00

Firefighting robots are one of the most effective tools for extinguishing early fires that cause loss of life and property, as well as permanent disability to the affected victims. These robots are used to save the lives of engineers in industrial sites with hazardous conditions. This paper aims to design and implement a mobile robot that detects and extinguishes fires using artificial intelligence and solar energy techniques. A mobile robot capable of moving is designed using a rotary motor, a flame sensor, a pump and a solar panel to supply power to the electronic components, all controlled by an Arduino Uno microcontroller and programmed using Adaptive
Neuro-Fuzzy Inference System (ANFIS) using MATLAB, where the inputs for training the network were the front, right and left flame sensors and the output is a pump (pump off, pump on). In this study, the performance of the solar panels is first tested using MATLAB and then experimentally under various weather conditions. The performance of the pump is also experimentally tested.
The robot is also tested to detect and extinguish fires. The results showed the effect of temperature change on the solar panel, as when it increases, the panel’s ability to produce decreases, as well as the effect of the reduction in solar radiation resulting from clouds and others, and the extent of its impact on the efficiency of solar panel performance, and monitoring the pump performance in terms of flow rate and height. From this, it can be observed that the designed robot can effectively detect fire sources and extinguish them with minimal errors. Thus, it can be applied in industrial settings to prevent fire damage and extinguish it when it occurs.

Enhanced beam attachment recognition for massive MIMO systems in dense distributed renewable energy networks

2024-11-01T00:13:31-06:00

Incorporating large-scale multiple-input multiple-output (MIMO) systems in densely deployed renewable energy systems (RES) represents a significant challenge in developing next-generation wireless networks. This field combines cutting-edge communication technologies with sustainable energy systems to enhance network communication and energy management in smart grid applications. Furthermore, varying energy availability in RES-based environments and dynamic load profiles make it difficult to achieve optimal beam attachment in mmWave massive MIMO systems. Conventional beam attachment techniques perform poorly in such dynamic conditions, resulting in poor network performance and high latency. This has created the need for better and more versatile approaches to beam attachment that can address this inherent variability of RES while at the same time providing highly accurate and low-complexity solutions. This paper presents an improved beam attachment recognition system explicitly designed to operate in RES conditions. Thus, the innovative strategy presented in this work is based on ensemble learning, which includes Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers, making the prediction more accurate and the system more stable. The proposed method integrates RES-specific signal strength, interference, traffic load, and renewable energy availability into the choice of the preferred beam. Cohesive simulations support our approach in this case. The Random Forest (RF) classifier test accuracy was 97.56%, and the XGBoost classifier was 97.84% – both of which are higher than conventional methods. Analyzing the feature importance of the problem, it was found that distance, angle, and signal strength were the most significant factors in beam assignment. The performance of the system was also very impressive in terms of scalability, with accuracy rates barely flinching even as the number of samples reached 50,000. Also, the energy efficiency analysis showed that the proposed beam attachment approach could lead to more energy-efficient network operations.

A novel miniaturized frequency reconfigurable microstrip patch antenna for modern wireless applications

2024-11-09T05:34:26-06:00

In this paper a novel miniaturized frequency reconfigurable microstrip patch antenna for wireless communication is presented. The antenna consists of a square patch and for achieving miniaturization the ground plane of the antenna is modified. The antenna incorporates two switches for achieving frequency reconfigurability. By controlling the states of the switches, the antenna exhibits three distinct operating modes, two single band (i.e. 2.42 GHz and 5.55 GHz) and a dual band (i.e. 3.77 GHz, 6.21 GHz). For the proposed antenna the reflection coefficient (S11) is well below -10 dB. The proposed antenna having geometric configuration of 16 × 17 × 0.8 mm3 is designed and simulated using CST Microwave studio. A prototype of the antenna is fabricated using low cost FR-4 glass epoxy substrate and the reflection coefficient is measured using Vector Network Analyzer. The simulated reflection coefficient for all the modes are presented and compared with the experimental data validating its usefulness for modern wireless system applications.

Online Learning in Saudi HEIs during COVID-19: Embracing Challenges and Artificial Intelligence-Enhanced Opportunities

2024-11-19T14:22:16-06:00

Introduction: Artificial Intelligence (AI) holds significant potential in addressing various challenges within online instruction. This includes personalizing instruction, enhancing content delivery, and managing large-scale virtual classes. This study aims to bridge this gap and illuminate the intricate dynamics. This research paper explores how Saudi university teachers have navigated the transformation and faced challenges during the COVID-19 pandemic, with the added complexity of the integration of AI-driven tools.
Research Aim: This paper aims to fill the gap in understanding the complex dynamics of online learning and the application of AI in Saudi HEIs during the pandemic. Methodology: A qualitative research design was employed, conducting interviews with 13 teachers,
selected through purposive sampling based on their gender, years of teaching, and academic discipline. Thematic analysis was used to interpret the data, and a structured framework was developed to obtain meaningful insights. Findings: The results showed that AI in education supports teachers, focusing on automated AI technologies, including automated assessment, intelligent tutoring, learning analytics, and data mining. AI accelerated online teaching during COVID-19 when the need for online learning became essential.
Conclusion: Regardless of the advantages, there were apprehensions regarding the negative impact of AI on student learning, particularly on creativity and critical thinking, as it may hinder problem-solving skills. The shift towards online learning, as necessitated by the COVID-19 pandemic, brought about an increased emphasis on the student-centered approach to teaching.

Continuous improvement of concrete properties using recycled cardboard ash: A sustainable alternative to cement

2024-10-14T20:22:48-06:00

The use of alternative materials in construction is a growing trend aimed at reducing the environmental impact of cement production. This study evaluated the effect of cardboard ash as a partial cement substitute on the properties of concrete. An experimental methodology was used with 2.5%, 5% and 7.5% additions of cardboard ash. The results indicated that concrete with 5% cardboard ash reduced the absorption speed to 0.229 mm at 831.38 s^½ compared to the reference concrete. In addition, an improvement in mechanical strength was observed, with increases of 15.10%, 16.99% and 18.41% in compressive strength, and 13.76%, 19.63% and 27.33% in flexural strength, highlighting the potential of cardboard ash to optimize concrete properties. These results were evaluated according to ASTM standards. In conclusion, the use of cardboard ash in concrete production contributes significantly to sustainability by reducing CO(2) emissions, making this material a greener and more environmentally efficient option.