Applied System Innovation

Planning and managing the production process are key challenges faced by every manufacturing organization. The main contribution of this article lies in the analysis and design of a planning algorithm that takes into consideration the specifics of this environment. The proposed algorithm encompasses elements of batch production, including a just-in-time approach. The article focuses on scenarios within batch production. Managers of manufacturing and supply companies must ensure smooth fulfillment and uninterrupted production of the agreed-upon quantity of parts. However, this task presents complex challenges. The product portfolio requires meticulous sequencing of production batches, and subsequent parts need to be temporarily stored in their raw state for further processing. Moreover, product variability necessitates frequent adjustments to the production line, resulting in delays. Shortages in manpower additionally place demands on shift organization. The company’s primary objective is to increase production efficiency while simultaneously reducing inventory and minimizing non-standard shift work. The challenge was to reconcile seemingly conflicting company requirements and to concentrate on solutions with swift implementation and minimal costs. Ensuring seamless production operation can be addressed by expanding supporting technologies or by increasing production capacity, such as acquiring an additional production line. However, these options entail costs and do not align with the company’s expectation for immediate impact and cost savings. However, improving production efficiency can also be achieved by altering the approach to production planning, which is the central theme of this article. The key element is ensuring that the customer plan is adhered to while working with a fixed production logic and variable input factors that must account for various non-standard situations. Full article

According to the European Green Deal, excessive carbon emissions are the origin of global warming and must be drastically reduced. Given that the building sector is one of the major sources of carbon emissions, results imperative to limit these emissions, especially in a city context where the density of buildings is commonly higher and rapidly increasing. All stages of the life cycle of a building, including raw material harvesting, manufacturing of products, use phase of the building, end of life, all generate or reduce carbon. The manufacture of construction materials accounts for 11% of all energy and process-related emissions annually. Additionally, recent estimates indicate that over 80% of all product-related environmental impacts of a building are determined during the design phase of the building. These indicators reflect the urgent need to explore a low-carbon measure method for building design. This is here done using a linear regression Reverse Engineering model and percentage calculation. One of the hypotheses formulated relates Global Warming Potential (GWP) of −30.000 CO₂eq or lower (around −165 CO₂eq/m²) in the 25% of a block of houses, to carbon further reductions by 11%. This paper has identified barriers in terms of the databases needed to achieve this task. Full article

Change and innovation are increasingly exerting a significant influence on the daily activities of companies. To ensure optimal control, innovative solutions are employed that are encapsulated in the concept of change management. In the engineering change sector, the proposed approach involves developing solutions and making continuous adjustments to the manufacturing process to enhance productivity and to meet customer needs. Within the automotive industry, companies utilize innovations and process change management to continuously improve and strengthen their position in the market, such as KPI/KPRS and PCI. To achieve this, the present study gathers real digital data from the Romanian branches of two renowned automotive companies. The data regarding change requests include 215 registrations for the first company and 734 registrations for the second company. By employing complex statistical methods such as ANOVA, Student’s t-test, the Mann–Whitney test, and a regression model, the primary objective of this study is to model and to identify the best predictor of change request status. Additionally, this study aims to explore how this change process influences the economic performances of the companies and the performance indicators of change management in manufacturing processes. The findings indicate that, both in the organizations in general and within the automotive industry, when products experience high demand in the market, the number of change requests increases. This highlights the importance of internal optimization of the automation system. Moreover, the study results underscore the crucial role of an effective smart manufacturing and optimal change management system to uphold and to enhance the economic performance of automotive companies. Full article

The modern tourist industry is characterized by an abundance of applied multicriteria decision-making tasks. Several researchers have demonstrated that such tasks can be effectively resolved using aggregation operators based on fuzzy integrals and fuzzy measures. At the same time, the implementation of this mathematical tool is limited by weak intuitive understanding by the practicing specialists of the aggregation process as well as fuzzy measures in general. Some researchers have proposed different aggregation visualization methods, but these methods have several properties that block their wide implementation in decision-making practice. The purpose of this study is to develop a decision-making approach that will allow practitioners to have a clear intuitive vision of the aggregation process and fuzzy measures. This article proposes an approach to decision making in the tourist industry based on the synthesis of the aggregation operator that includes 3D visualization graphics in virtual reality. Firstly, some research devoted to decision-making methods in tourism was assessed along with “smart” tourism, aggregation operators and their visualization. Secondly, a 3D visualization in the form of a balance model was introduced. Thirdly, the method of aggregation-operator synthesis based on the 3D balance model and the 2-order Choquet integral was developed. Finally, an illustrational example of implementing such an approach for resolving the task of assessing and choosing a hotel was described. Full article

A scheme for solving the problem of determining the probability of failure and probabilistic (statistical) characteristics of the failure loading magnitude of a composite material plate is considered. The plate structure is a flat homogeneous matrix with stochastically distributed rigid rod inclusions. The geometric parameters of inclusions are considered independent random variables with given probability distribution laws. The expressions for the failure loading distribution function, the probability of failure, the mean value, and the dispersion of the failure loading were received and presented. Their dependence on the type of stress state, the inclusion number, and matrix Poisson’s ratio were studied graphically. Full article

Mechatronics as a science is a synergic combination of mechanical engineering, electronic control, and software design in product development and manufacturing processes. To understand how the field of knowledge that incorporates mechatronics in innovative products, given that it is not in itself a basic engineering discipline but an integration of fields of knowledge, has advanced, it was developed a bibliometric and qualitative study through systematic review with an analytical framework for the establishment of variables to subsidize the construction of the selected theoretical body. The results and conclusions of the sampled publications show that mechatronics performs one of the principal roles in innovation due to the multidisciplinary integration that the scope of innovation in product engineering is propitiating. The study classified five global scenarios: practical approaches aimed at product development, research that studies curricula and education in engineering, studies involving components of a mechatronic system, use of artificial intelligence, and methodologies for designing mechatronic systems. In addition to underscoring that the use of the term innovation associated with mechatronics in a large proportion of the publications extrapolates the operational level, characterizing an attribution to the term that is always associated with the applications, ramifications, and perspectives that the respective product, design, robot, or system could offer to the market or future research. Similarly, it was found that the results of many publications associate the term innovation with a return on investments or operational costs and emphasize the advantages of using the technology for commercial ends. Full article

The exploitation of solar irradiation is a critical weapon for facing the energy crisis and critical environmental problems. One of the most emerging solar technologies is the use of solar towers (or central receiver systems) coupled with high-performance thermodynamic cycles. In this direction, the present investigation examines a solar tower coupled to a closed-loop Brayton cycle which operates with supercritical CO₂ (sCO₂) as the working medium. The system also includes a storage system with two molten salt tanks for enabling proper thermal storage. The sCO₂ is an efficient fluid that presents significant advancements, mainly reduced compression work when it is compressed close to the critical point region. The novelty of the present work is based on the detailed dynamic investigation of the studied configuration for the year period using adjustable time step and its sizing for achieving a continuous operation, something that makes possible the establishment of this renewable technology as a reliable one. The analysis is conducted with a developed model in the Modelica programming language by also using the Dymola solver. According to the simulation results, the yearly solar thermal efficiency is 50.7%, the yearly thermodynamic cycle efficiency is 42.9% and the yearly total system efficiency is 18.0%. Full article

Voting is a democratic process that allows individuals to choose their leaders and voice their opinions. However, the current situation with physical voting involves long queues, paper-based ballots, and security challenges. Blockchain-based voting models have appeared as a method to address the limitations of traditional voting methods. As blockchain is distributed and decentralized, which uses hash functions for securing transactions, it dramatically improves the existing voting system. These digital platforms eliminate the need for physical presence, reduce paperwork, and ensure the integrity of votes through transparent and tamper-proof blockchain technology. This paper introduces a blockchain-based voting model to enhance accessibility, security, and efficiency in the voting process. The research focuses on developing a robust and user-friendly voting system by leveraging the advantages of decentralized technology. The proposed model employs Ethereum as the underlying blockchain platform through an innovative and iterative approach. The model uses Smart contracts to record and validate votes, while AI-based facial recognition technology is integrated to verify the identity of voters. Rigorous testing and analysis are conducted to validate the effectiveness and reliability of the proposed blockchain-based voting model. The system underwent extensive simulation scenarios and stress tests to evaluate its performance, security, and usability. Full article

The measurement and assessment of indoor air quality in terms of respirable particulate constituents is relevant, especially in light of the COVID-19 pandemic and associated infection events. To analyze indoor infectious potential and to develop customized hygiene concepts, the measurement monitoring of the anthropogenic aerosol spreading is necessary. For indoor aerosol measurements usually standard lab equipment is used. However, these devices are time-consuming, expensive and unwieldy. The idea is to replace this standard laboratory equipment with low-cost sensors widely used for monitoring fine dust (particulate matter—PM). Due to the low acquisition costs, many sensors can be used to determine the aerosol load, even in large rooms. Thus, the aim of this work is to verify the measurement capability of low-cost sensors. For this purpose, two different models of low-cost sensors are compared with established laboratory measuring instruments. The study was performed with artificially prepared NaCl aerosols with a well-defined size and morphology. In addition, the influence of the relative humidity, which can vary significantly indoors, on the measurement capability of the low-cost sensors is investigated. For this purpose, a heating stage was developed and tested. The results show a discrepancy in measurement capability between low-cost sensors and laboratory measuring instruments. This difference can be attributed to the partially different measuring method, as well as the different measuring particle size ranges. The determined measurement accuracy is nevertheless good, considering the compactness and the acquisition price of the low-cost sensors. Full article

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively. Full article

Security remains a top priority for those users in the hotel, even with the advent of innovative technological advances. This is because many tragic incidents, such as theft and crime, have occurred with unrestricted access. This paper proposes an intelligent door access system that would allow hotel guests to authenticate into their rooms without resorting to traditional closeness access methods. Therefore, research was conducted to solidify the understanding and refine the capabilities of the proposed system. This project aims to promote high-security aspects access system technology, which is Near-Field Communication with the use of application that have the function of simulated smart keys for explicit validation access. A Host-Card Emulator opens the opportunities for efficient financial benefit and the launch of a protective mechanism in the post-pandemic period. The suggested method was statistically and analytically accessed on hotel guests and staff from various hotels in Malaysia. The proposed system is a contactless NFC access control system that employs smartphone Host Card Emulation application technology to reduce the need for appropriate physical access, enhance security, and publicize the use of mobile access systems in the hospitality industry. Full article

Owing to the expansion of non-face-to-face activities, security issues in video conferencing systems are becoming more critical. In this paper, we focus on the end-to-end encryption (E2EE) function among the security services of video conferencing systems. First, the E2EE-related protocols of Zoom and Secure Frame (SFrame), which are representative video conferencing systems, are thoroughly investigated, and the two systems are compared and analyzed from the overall viewpoint. Next, the E2EE protocol in a Government Public Key Infrastructure (GPKI)-based video conferencing system, in which the user authentication mechanism is fundamentally different from those used in commercial sector systems such as Zoom and SFrame, is considered. In particular, among E2EE-related protocols, we propose a detailed mechanism in which the post-quantum cryptography (PQC) key encapsulation mechanism (KEM) is applied to the user key exchange process. Since the session key is not disclosed to the central server, even in futuristic quantum computers, the proposed mechanism, which includes the PQC KEM, still satisfies the E2EE security requirements in the quantum environment. Moreover, our GPKI-based mechanism induces the effect of enhancing the security level of the next-generation video conferencing systems up to a quantum-safe level. Full article

Electricity is widely regarded as the most adaptable form of energy and a major secondary energy source. However, electricity is not economically storable; therefore, the power system requires a continuous balance of electricity production and consumption to be stable. The accurate and reliable assessment of electrical energy consumption enables planning prospective power-producing systems to satisfy the expanding demand for electrical energy. Since Saudi Arabia is one of the top electricity consumers worldwide, this paper proposed an electricity consumption prediction model in Saudia Arabia. In this work, the authors obtained a never-before-seen dataset of Saudi Arabia’s electricity consumption for a span of ten years. The dataset was acquired solely by the authors from the Saudi Electrical Company (SEC), and it has further research potential that far exceeds this work. The research closely examined the performance of ensemble models and the K* model as novel models to predict the monthly electricity consumption for eighteen service offices from the Saudi Electrical Company dataset, providing experiments on a new electricity consumption dataset. The global blend parameters for the K* algorithm were tuned to achieve the best performance for predicting electricity consumption. The K* model achieved a high accuracy, and the results of the correlation coefficient (CC), mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), mean absolute error (MAE), and root mean squared error (RMSE) were 0.9373, 0.1569, 0.5636, 0.016, and 0.0488, respectively. The obtained results showed that the bagging ensemble model outperformed the standalone K* model. It used the original full dataset with K* as the base classifier, which produced a 0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE, and 0.0484 RMSE. The outcomes of this work were compared with a previous study on the same dataset using an artificial neural network (ANN), and the comparison showed that the K* model used in this study performed better than the ANN model when compared with the standalone models and the bagging ensemble. Full article

The design and implementation of a measuring device for the determination of pigment content in plant leaves is a topic of essential importance in plant biology, agriculture, and environmental research. The timely and sufficiently accurate determination of the content of these molecules provides valuable insight into the health, photosynthetic activity, and physiological state of plants. This paper presents the key aspects and results of the development and implementation of such a measuring device. It makes it possible to measure a larger number of pigments per type compared with the devices for commercial use that are currently known to us, and the accuracy of measurements depends mostly on the specific type of plant that is being tracked. The developed device presents a measurement accuracy ranging between 72% and 97% compared with a reference method and between 87% and 90% compared with a reference technique. Also, by using the device, a significant reduction in time and required resources can be achieved in measuring the content of pigments and nitrogen in plant leaves. This is a prerequisite for the more effective monitoring of the growth and health of plants, as well as optimizing the process of growing and caring for them. The work will be continued with the focus of the research aimed at generalizing the models for determining pigments and nitrogen in plants. Full article

The emergence of Industry 5.0 took place in the mid-2010s, presenting a novel vision for the future of an industry that places emphasis on human involvement in the production process. Following the outbreak of the COVID-19 pandemic, there has been a substantial surge in the popularity of this concept, gaining traction not only in the business realm but also within academic circles. This increased attention can be attributed to a heightened focus on crucial aspects such as sustainability and resilience. The objective of this study is to present an updated overview of key bibliometric trends in Industry 5.0 research. The findings indicate a remarkable expansion of research activities in the field of Industry 5.0, as evidenced by a substantial increase in the number of publications and citations. Concurrently, the growth of Industry 5.0 research has led to the emergence of diverse perspectives and the exploration of related research themes such as artificial intelligence, big data, and human factors. In summary, this study enhances our understanding of the Industry 5.0 concept by providing an updated overview of the current state of research in this area and suggesting potential avenues for future investigations. Full article

In the current era of technology, the internet of things (IoT) plays a vital role in smart drug delivery systems. It is an emerging field that offers promising solutions for improving the efficacy, safety, and patient compliance of drug therapies. IoT-based drug delivery systems leverage advanced devices, sophisticated sensors, and smart tools to monitor and analyse the health matrices of the patient in real-time, allowing for personalised and targeted drug delivery. This technology is implemented through various types of devices, including wearable and implantable devices such as infusion pumps, smart pens, inhalers, and auto-injectors. However, the development and implementation of IoT-based drug delivery systems pose several challenges, such as ensuring data security and privacy, regulatory compliance, compatibility, and reliability. In this paper, the latest research on smart wearable devices and its analysis are addressed. It also focuses on the challenges of ensuring the safe and efficient use of this technology in healthcare applications. Full article

Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: ‘i.cluster’ was used to generate image classes; ‘i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as ‘i.group’, ‘r.support’, ‘r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa. Full article

Opportunistic networks allow for communication between nearby mobile devices through a radio connection, avoiding the need for cellular data coverage or a Wi-Fi connection. The limited spatial range of this type of communication can be overcome by using nodes in a mesh network. The purpose of this research was to examine a commercial application of electronic mesh communication without a mobile data plan, Wi-Fi, or satellite. A mixed study, with qualitative and quantitative strategies, was designed. An experimental session, in which participants tested opportunistic networks developing different tasks for performance, was carried out to examine the system. Different complementary approaches were adopted: a survey, a focus group, and an analysis of participants’ performance. We found that the main advantage of this type of communication is the lack of a need to use data networks for one-to-one and group communications. Opportunistic networks can be integrated into professional communication workflows. They can be used in situations where traditional telephones and the Internet are compromised, such as at mass events, emergency situations, or in the presence of frequency inhibitors. Full article

The e-Archeo project, commissioned from ALES S.p.A.by the Ministry of Culture (MIC), aims to valorise the multimedia experience of eight Italian archaeological sites. This paper discusses the University of Verona’s contribution to this project, which focuses on the virtual reconstruction of two Roman villas located in Sirmione and Desenzano (Lombardy). This paper outlines the 3D survey methodologies and scientific back-end approach using Extended Matrix. The architectural and decorative reconstruction process for each site is elucidated, providing a comprehensive understanding of the process followed. Furthermore, the University developed a narrative to accompany virtual visits. One of the main project outputs was e-Archeo 3d, a virtual reality web app that allows remote and on-site use. Full article