Edher Duvan Fonseca , Clara Isabel López Gualdrón , Juan Dayal Castro Bermudez
{"title":"Characterization of the mechanical properties of a laminated composite material, from PLA and Carbon fiber-acrylic resin, obtained by hybrid manufacturing for the development of a new product","authors":"Edher Duvan Fonseca , Clara Isabel López Gualdrón , Juan Dayal Castro Bermudez","doi":"10.1016/j.procs.2025.01.075","DOIUrl":"10.1016/j.procs.2025.01.075","url":null,"abstract":"<div><div>The exponential growth in the demand for additive manufacturing technologies such as 3D printing has been repre- sented a significant advance in the transformation of the industry in the development of new products, generating a demand not only for prototypes but for functional products made with these techniques. In this paper, the impact on the variation of the printing parameters on the mechanical properties of the PLA to adhere with a polymeric matrix was evaluated; this process was made adding layers of material in order to modify its mechanical behavior. By means of mechanical characterization tests, destructive testing was carried out with ASTM D638 and ASTM D7078 standards as guidelines, to establish the mechanical properties of a layered composite with polylactic acid (PLA), carbon fiber (CF) Resin and textile, describing its elastic behavior. Due the composite characterization the following mechanical properties where obtained: The elasticity modulus, the stress and strain in the yield limit, for 4 treatments, in addition, shear modulus, shear stress and angular strain where determined. The mechanical properties of the laminated composite are not considerably affected for the PLA print direction. Varying the printed PLA infill the mechanical behavior of the composite is affected. The composite with 30% infill is a transversely isotropic material The composite material was used in the manufacture of lower limb prostheses for patients who were victims of anti- personnel mines.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 114-123"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Green Purchasing as a Catalyst for Suppliers’ Compliance to Environmental Standards: E-grocery Perspective","authors":"Marcia Mkansi, Phumlani Masilela","doi":"10.1016/j.procs.2025.01.064","DOIUrl":"10.1016/j.procs.2025.01.064","url":null,"abstract":"<div><div>This paper uses the elements of logistics to frame a sustainable model that serves as a lens through which the green purchasing power of global clicks and bricks grocery retailers can be used as a catalyst for suppliers’ compliance to environmental standards. An exploratory systematic literature review was used to purposefully select ten (10) different product lines, based on their significant financial flows, high frequency of purchase, significant environmental impact, source of production, and potential for data access. It was discovered that the product lines across the logistic elements are associated with varying degrees of environmental emissions.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring the drivers of customer loyalty for energy-efficient home appliances","authors":"Bao Ngoc Le , Thi Mai Le , Thi Minh Ngoc Luu","doi":"10.1016/j.procs.2025.01.116","DOIUrl":"10.1016/j.procs.2025.01.116","url":null,"abstract":"<div><div>Sustainable development is a chief concern in Vietnam, and energy conservation is pivotal. The country risks energy scarcity due to the growing discrepancy between domestic energy supply and demand, as well as facing serious environmental issues. Purchasing energy-efficient appliances can reduce energy consumption and mitigate climate change. While much research in the past decade has explored the factors that influence the procurement of such appliances, the post-purchase phase remains largely understudied. This research seeks to fill this void by constructing a model based on consumer perceived value and relationship marketing to investigate the elements affecting customer loyalty towards energy-efficient appliances via customer trust. The study, conducted with 458 consumers in Hanoi who have previously purchased energy-efficient appliances, reveals that functional value, price value, and environmental value all positively impact customer trust, which leads to customer loyalty. These significant findings fill a gap in the literature and provide valuable insights for authorities, manufacturers, and retailers to heighten the sustained use of green appliances.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 541-550"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Energy Efficiency Analysis in Industry 4.0 set up leveraging Industry 5.0 methods for Sustainable Manufacturing: Case Study","authors":"Shantall Cisneros Saldana , Arshit Kapoor , Sampat Acharya , Heike Markus","doi":"10.1016/j.procs.2025.01.121","DOIUrl":"10.1016/j.procs.2025.01.121","url":null,"abstract":"<div><div>This study explores the integration of Industry 5.0 principles into existing Industry 4.0 configurations to address excessive heat dissipation, energy efficiency and promote carbon neutral manufacturing. Industry 5.0 represents a paradigm shift, emphasizing human-centered approaches, energy efficiency and environmental sustainability alongside traditional productivity goals. The case study analyzes energy consumption in an Industry 4.0 setup, quantifies heat waste and applies innovative solutions to optimize energy use. Considering digital manufacturing through the Industrial Internet of Things (IIoT), big data analytics, smart automation technologies in cyber-physical production systems and sustainable practices, the study aims to mitigate and minimize energy waste and carbon emissions. Through quantitative analysis, it identified energy inputs, mapped energy transformations, and assessed energy waste hotspots. Despite all the benefits of Industry 4.0, it showed that responsible practices and new initiatives are required to reuse and capitalize on that wasted energy or heat, underscoring the importance of integrating Industry 5.0 principles for sustainable manufacturing.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 594-602"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predicting Institute Graduation Rate using Evolutionary Computing and Machine Learning","authors":"Mala H. Mehta , N.C. Chauhan , Anu Gokhale","doi":"10.1016/j.procs.2025.01.036","DOIUrl":"10.1016/j.procs.2025.01.036","url":null,"abstract":"<div><div>There are diverse parameters available for measuring performance of an academic institute. Graduation rate of an institute is an important indicator of institute’s success. It is essential to understand which factors lead to better graduation rates. Hence, a prediction system which helps institutes well in advance to avoid poor graduation rate is required. In this study, a novel adaptive dimensionality reduction model is proposed using evolutionary computing and machine learning to better predict institute graduation rate. This work has explored the feature optimization capacity of evolutionary algorithm with weight assignment approach to each dimension. A high dimensional dataset is considered for analyzing attributes that affect institute graduation rates. Proposed model uses adaptive approach of incrementing weights of contributing features which lead to minimum error. Experimental results show that proposed model yields optimum dimensions, low execution time and minimum error. Predictive analysis presented could lead to useful future directions for education domain stakeholders.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 758-767"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches","authors":"Milind Shah , Kajal Parmar , Priyanka Padhiyar , Naina Parmar , Monali Parikh , Dhruvansh Patni","doi":"10.1016/j.procs.2024.12.006","DOIUrl":"10.1016/j.procs.2024.12.006","url":null,"abstract":"<div><div>Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 53-62"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of Solar Panel Power Investigation using Fixed Axis, Single Axis and Dual Axis Solar Tracker","authors":"Md. Humayun Kabir , Md. Himel Abu Jihad , Suman Chowdhury","doi":"10.1016/j.procs.2025.01.031","DOIUrl":"10.1016/j.procs.2025.01.031","url":null,"abstract":"<div><div>This study investigates the performance of PV panels using three configurations: a fixed, a single-axis, and a dual-axis tracker, all controlled by an Arduino UNO system. The primary aim is to find whether a stationary PV panel or a solar energy tracker yields better power output. The study is partitioned into 2 key stages: practical and software implementation. In the experimental stage, four LDRs are utilized to track sunlight. Based on the solar detection, 2 servo motors adjust the coordination of the solar panel. For the software component, the Arduino IDE is utilized to write a program that interprets signals from the LDRs and sends commands to the motors, ensuring optimal positioning of the panel. The solar tracking system’s performance was contrasted with that of a stationary panel. Results presented that the solar energy tracker significantly outperforms the fixed set up, delivering higher power output. As a result, it has been demonstrated that the solar tracker is more successful at maximizing solar radiation, improving overall energy production. The maximum average solar tracking efficiency obtained from the dual axis set up is 35.5% where it is 33.23% for the single axis rotation set up.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 708-714"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Advancing Bionic Solution through Artificial Intelligence in Healthcare IoT Environment","authors":"Girish Wali , Chetan Bulla Dr.","doi":"10.1016/j.procs.2025.01.032","DOIUrl":"10.1016/j.procs.2025.01.032","url":null,"abstract":"<div><div>The convergence of human and artificial intelligence (AI) holds great potential for revolutionary changes in healthcare. This study investigates the possibility of a mutually beneficial relationship between artificial intelligence algorithms and human knowledge in creating bionic healthcare solutions. This paper emphasizes the revolutionary effect of this multidisciplinary strategy on healthcare delivery, patient outcomes, and quality of services by reviewing extensively recent developments and case examples. The Deep learning model is designed to predict diabetics using a standard dataset. The Convolution LSTM model is used to predict diabetics to improve accura-cy and reduce the latency. The proposed model is simulated in the Google Colab framework with Python programming language. The simulation results show that the proposed model is more accurate and lesser communication delay as compared to existing works.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 715-727"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Integrated Approach Based on Fuzzy Logic and Machine Learning Techniques for Reliable Wine Quality Prediction","authors":"Narinder Kaur , Gaganjot Kaur , Prasanth Aruchamy , Neelu Chaudhary","doi":"10.1016/j.procs.2025.01.021","DOIUrl":"10.1016/j.procs.2025.01.021","url":null,"abstract":"<div><div>In recent decades, wine quality has been one of the predominant problems in many wine industries. However, the analysis of wine quality is inherently complex owing to its multivariate characteristics and the instigation of several sensory features. Most of the existing prediction methods lagged in providing higher detection accuracy for multi-dimensional datasets. To overcome this, a novel Adaptive Wine Quality Prediction (AWQP) approach has been proposed to assess the quality of the wine in an accurate manner. The proposed AWQP methodology entails the development of a Hybrid detection model that encompasses the fuzzy logic principles with the predictive influence of machine learning techniques. Primarily, the sensory features like aroma, taste, and color are delineated by exemplifying the linguistic variables. The fuzzy rules are then determined to collare the qualitative relationships among these different variables. Subsequently, the finest machine learning algorithm can be carried out to train and test the prediction model. The proposed AWQP methodology ameliorates the comprehensibility of the decision-making process through the hybridization of fuzzy logic and the finest machine learning. This proper hybridization facilitates the proposed method to achieve a superior detection accuracy of 98.75% when compared to the existing prediction methods.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 613-622"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing","authors":"Sharadadevi Kaganurmath , Nagaraj Cholli","doi":"10.1016/j.procs.2025.01.023","DOIUrl":"10.1016/j.procs.2025.01.023","url":null,"abstract":"<div><div>This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm’s adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism’s efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 633-642"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}