Appl. Syst. Innov., Volume 5, Issue 5 (October 2022) – 18 articles

This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F₉.₅ = 19.10 and p = 0.0023) can be fairly accurately predicted with an R² of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272. Full article

The Internet is becoming increasingly important in our daily lives, allowing people to exchange and receive a wide variety of data. It can be utilized in a variety of ways for maximum benefit. For example, the concept of the Internet of Things (IoT) suggests that objects can be linked to the Internet. Based on this concept, in this paper, we describe the creation of modern smart-wheelchair accessories. These make the wheelchair simple to use, suitable for the elderly, and foldable. A health monitoring accessory is one of the critical functions. The Internet of Things is central to the concept of an electric-powered smart wheelchair. Residential communication networks connect electrical appliances and services, enable monitoring, and provide access from which to control various devices. The controls of a smart wheelchair comprise three essential components: a smart device that connects to the wheelchair, an Internet network, and a microcontroller. The results of our tests enable remote operation of the electric-powered wheelchair; command and control are excellent. Most significantly, our method provides consumers with an extra stage of security. Full article

Diabetic retinopathy (DR) is an increasingly common eye disorder that gradually damages the retina. Identification at the early stage can significantly reduce the severity of vision loss. Deep learning techniques provide detection for retinal images based on data size and quality, as the error rate increases with low-quality images and unbalanced data classes. This paper proposes a hybrid intelligent framework of a conventional neural network and a fuzzy inference system to measure the stages of DR automatically, Diabetic Retinopathy Stage Measurement using Conventional Neural Network and Fuzzy Inference System (DRSM-CNNFIS). The fuzzy inference used human experts’ rules to overcome data dependency problems. At first, the Conventional Neural Network (CNN) model was used for feature extraction, and then fuzzy rules were used to measure diabetic retinopathy stage percentage. The framework is trained using images from Kaggle datasets (Diabetic Retinopathy Detection, 2022). The efficacy of this framework outperformed the other models with regard to accuracy, macro average precision, macro average recall, and macro average F1 score: 0.9281, 0.7142, 0.7753, and 0.7301, respectively. The evaluation results indicate that the proposed framework, without any segmentation process, has a similar performance for all the classes, while the other classification models (Dense-Net-201, Inception-ResNet ResNet-50, Xception, and Ensemble methods) have different levels of performance for each class classification. Full article

Wireless sensor networks (WSNs) are a comparatively new revolutionary technology that has the potential to revolutionize how we live together with the present system. To enhance data archiving, WSNs are frequently used in scientific studies. Many applications have proved the value of wired sensors; however, they are prone to wire cutting or damage. While preventing wire tangles and damage, wireless sensor networks provide autonomous monitoring. The WS network suffers from a number of fundamental restrictions, including insufficient processing power, storage space, available bandwidth, and information exchange. Consequently, energy-efficient strategies are necessary for maximizing the performance and lifespan of WSNs. As a result, the special cluster head relay node and energy balancing techniques will be applied to deal with WSN energy consumptions. This extends the life of the network. In wireless sensor networks, clustering is a smart approach to reduce energy consumption. Energy scarcity and consumption are serious issues that must be addressed with effective and dependable solutions. The proposed MGSA considers the distance between each node and its corresponding CHs, as well as the residual energy and delay, as important factors in the relay node selection. The proposed approach outperforms the current methods, such as low-energy adaptive clustering hierarchy, LEACH (in terms of data delivery rate), energy efficiency, and network longevity. The next level, which will boost the efficiency of wireless sensor networks, with two fitness functions, is proposed. The cluster head (CH) is in charge of collecting and transmitting data from all other cluster nodes. The flow of the consistency of the cluster head selection process will beat the improved data delivery rate, energy efficiency, recommended fuzzy clustering performance experiments, and assessments. As a result, energy-efficient operations are necessary to maximize the WSN performance and lifespan. Full article

Bioluminescence imaging has been used to visualize the biological effects of human beings and is a promising technique in a recent modality. In this study, the digital image technique is used to improve quality and recover images. The optical fluence that emerges from the source is generated using a camera, and a low resgolution is observed. In this paper, the diurnal change of ultra-weak photon emission was successfully imaged with an improved, highly sensitive imaging system using a charge-coupled device (CCD) camera. The changes in energy metabolism might be linked with diurnal changes in photon emission, and when observed, the body emits extremely weak light spontaneously without external photoexcitation. Therefore, to obtain accurate information, a combined Barn Door Star Tracker approach has been proposed to improve the accuracy of the method and has been implemented to test on celestial bodies. The ability to temporally assess the location of star movement can be monitored accurately with bioluminescence imaging. Full article

Renewable energy exploitation in the building sector can lead to significant energy savings and carbon dioxide emission avoidance. The objective of this study is the detailed investigation of a biomass-driven absorption heat pump for heating and cooling. The heat pump is practically a single-effect absorption chiller operating with the Lithium-bromide/water solution and it has been properly modified for heating production during the winter. This system is a novel one and its combination with a biomass boiler was examined for the first time, especially for covering both heating and cooling needs. For the present study, a typical building in Athens, Greece, with a 400 m² floor area is selected to be coupled with the suggested heating/cooling configuration. The analysis was conducted by using TRNSYS software for the estimation of the building’s thermal loads and with the Engineering Equation Solver for determining the heat pump behavior. According to the results, the yearly biomass consumption is found to be 3.76 tons covering a heating demand of 9136 kWh and cooling demand of 8168 kWh. The seasonal energy cooling performance was found to be 0.751, while the seasonal energy heating performance was at 1.307. Moreover, the proposed configuration was found to have economic and environmental benefits compared to conventional units with an oil boiler and heat pump for cooling. Specifically, the present system leads to 10.8% lower operational costs and 4.8% lower primary energy demand, while there are significant amounts of CO₂ avoidance. Full article

This article presents the development of a core ontology for describing knowledge about the technological and technical parts of a production plant, in particular, theoretical knowledge for monitoring, diagnosing and forecasting of production equipment, taking into account the concept of Industry 4.0. This study is related to the definition of terms and their relationships for the processing industry in the core ontology. The core ontology is the basis for the development of domain and application ontologies, which create conditions for the system solution for the complex problems of operating industrial equipment. It consists of an ontological classification of core concepts according to the fundamental basic formal ontology. The essences of BFO were specified and revealed by methods of decomposition and generalization according to generally accepted structures of industrial enterprises. The proposed ontology contains 33 classes, 7 object properties and 34 individuals. The ontology is conceptually transparent and semantically clear, so it is suitable for theoretical knowledge transfer, sharing and retrieval. The ontology is implemented in the OWL language and validated. This article provides examples of requests for work with ontology, which prove the effectiveness of its use in industrial enterprises. Full article

Multi-plate clutches play safety-critical roles in many applications. For this reason, correct functioning and safe operation are essential. Spontaneous damages are particularly critical because the failure of the clutch can lead to a failure of the system. Such damage mainly occurs due to very high loads and ultimately very high temperatures. Finite Element Analysis (FEA) enables simulation and prediction of these temperatures, but it is very time-consuming and costly. In order to reduce this computational effort, surrogate models can be created using machine learning (ML) methods, which reproduce the input and output behavior. In this study, various ML methods (polynomial regression, decision tree, support vector regressor, Gaussian process and neural networks) are evaluated with respect to their ability to predict the maximum clutch temperature based on the loads of a slip cycle. The models are examined based on two use cases. In the first use case, the axial force and the speed are varied. In the second use case, the lining thickness is additionally modified. We show that ML approaches fundamentally achieve good results for both use cases. Furthermore, we show that Gaussian process and backpropagation neural network provide the best results in both cases and that the requirement to generate predictions during operation is fulfilled. Full article

In large scale, complex and low volume manufacturing systems, robotics are now considered unavoidable for automating the factory operations. The aerospace industry focuses on a high variety and quality but extremely low volume. The precision it requires for numerous tasks is unique and distinct from any other manufacturing industry. This can comprise accurate position, module assembly, inspection, fastening, etc. The scale of the robot invites different types of errors during operation, which can be either because of the kinematics of the robot or because of the environment (noise, temperature, load, etc.). There are packages available from robot manufacturers for the correction and compensation of errors on the robot to achieve accuracy. There are two associated problems: 1. cost and 2. static nature. They are very costly and they do not provide correction in realtime fashion (dynamic); the robot stops, waits for the correction, and then moves to the next position. The external tool to monitor the accuracy also requires attaching with the robot permanently. These are dedicated resources. These tools for accurate measurement are expensive and attached permanently to a robot, which means wastage of resources. These measuring tools are called metrology devices and attaching these devices and the robot to the network means that other robots/machines can also use these expensive tools for measurement. Our aim was to address two problems in this project: 1. calibration (error correction and compensation of robot) and 2. dynamic and realtime processing. It helped to perform the dynamic error correction and the compensation of an industrial robot. The results showed the error correction was achieved in the region of 0.02 mm. Full article

The aim of this concept article is to articulate multiple contributions from socio-technical fields into an approach for sustaining human-centred lifecycle management of industrial systems. Widespread digitalization and advanced robotics have fostered interest on innovative human-machine integration and sophisticated organizational transformation that is conducive to meeting the challenges of sustainability. Complementing technology-driven and data-driven approaches to industrial systems development, the human factors approach offers a systems perspective that is at once human-centred while striving for overall system performance, by considering technological and organizational perspectives alike. The paper presents a set of recent human factors developments, selected based on their potential to advance sustainability in industrial systems, including an activity-centred design perspective of industrial systems, and a unified and entangled view on organizational goals yielding a dynamic change approach to socio-technical systems management. Moreover, developments in organizational resilience are coupled with recent breakthrough empirical understanding of conditions conducive to attaining resilience in operations. The cross-pollination of the human factors developments is further pursued, resulting in a proposal of combined key organizational vectors that can mutually leverage and sustain human-centred design and management of industrial systems (production and logistics systems alike) for resilience. Systems thinking encompassing human, organizational and technological perspectives supports integration of insights across entangled domains; this can leverage both system enhancements that promote the satisfaction of dynamic situation-dependent goals, as well as the fulfilment of objectives derived from long-term values of an organization. Full article

This study aims to determine the effect of the treatment of banana stem fibers (BSF) with grade three liquid smoke on changes in the micromechanical properties of the BSF, single fiber tensile strength, morphology, crystal properties, and functional groups. This study used four variations of the specimen model, namely, fiber without treatment and immersion in liquid smoke for 1, 2, and 3 h. BSF with treatment was dried in an oven at 40 °C for 30 min. Several tests were carried out, including the tensile test for single fiber capacity of 50N standard ASTM 3379-02, Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD), and Fourier Transform Infra-Red (FTIR) observation. The results showed that the highest increase in fiber strength occurred in P2J, which was 43.78%, with crystal intensity of 34.97%, compared to TP fiber. Treatment of fiber with liquid smoke can form a strong C-C elemental bond caused by the H₂O degradation process in BSF so that the carbon atom (C) becomes solid; under conditions of excessive H₂O degradation, the fiber strength will become brittle, however, liquid smoke can increase the fiber tensile strength. The morphology of the fiber changed where the untreated fiber was covered in lignin, while the treated fiber had a rectangular pattern of elongated lines, was porous, and the lignin was eroded. The fiber crystallization index increased due to changes in fiber structure, where the highest peak of TP BSF occurred at point two, while the highest peaks in BSF P1J, P2J, and P3J occurred respectively at points two and three. These results prove that the innovation of BSF treatment with liquid smoke can change the morphology, crystalline, and functional aspects of BSF, so that it becomes the choice of composite reinforcement material in the future, an option that is lightweight and environmentally friendly. Full article

A system with multiple controllers and a multiple-model architecture is one of the most effective solutions for the real-time control of nonlinear systems. The employment of these structures necessitates the resolution of certain difficulties, such as selecting the optimal algorithm or switching control algorithms. Based on the concepts of auto-transfer, the paper provides a way for switching the numerous controller structures’ algorithms. This paper presents a real-time dynamic model and platform of a magnetic levitation system (Maglev). The method’s applicability was demonstrated by utilizing a real-time architecture with an RST controller mechanism and real. In conclusion, the software was implemented and demonstrated by using the LabVIEW platform in real-time, and the results reveal that this solution can stabilize the ball’s location and has strong disturbance rejection because of the multi-zone effect. Full article

This paper describes the design and development of an AI-driven, interactive and user-aware virtual assistant aimed at helping users to focus their attention on reading or attending to other long-lasting visual tasks. The proposed approach uses computer vision and artificial intelligence to analyze the orientation of the head and the gaze of the user’s eyes to estimate the level of attention during the task, as well as administer effective and balanced stimuli to correct significant deviations. The stimuli are provided by a graphical character (i.e., the virtual assistant), which is able to emulate face expressions, generate spoken messages and produce deictic visual cues to better involve the user and establish an effective, natural and enjoyable experience. The described virtual assistant is based on a modular architecture that can be scaled to support a wide range of applications, from virtual and blended collaborative spaces to mobile devices. In particular, this paper focuses on an application designed to integrate seamlessly into tablets and e-book readers to provide its services in mobility and exactly when and where needed. Full article

Flexible electronic devices have received great attention in the fields of foldable electronic devices, wearable electronic devices, displays, actuators, synaptic bionics and so on. Among them, high-performance flexible memory for information storage and processing is an important part. Due to its simple structure and non-volatile characteristics, flexible resistive random access memory (RRAM) is the most likely flexible memory to achieve full commercialization. At present, the minimum bending radius of flexible RRAM can reach 2 mm and the maximum ON/OFF ratio (storage window) can reach 10⁸. However, there are some defects in reliability and durability. In the bending process, the cracks are the main cause of device failure. The charge trap sites provided by appropriate doping or the use of amorphous nanostructures can make the conductive filaments of flexible RRAM steadier. Flexible electrodes with high conductivity and flexible dielectric with stable storage properties are the main development directions of flexible RRAM materials in the future. Full article

Health-care-sector-related activities are more accessible and faster as a result of technological development. Technology such as the Internet of Things (IoT) can work with blood bank services to manage and provide healthy blood in emergencies. However, there are many problems in blood bank management and inventory monitoring, especially in developing countries as compared to developed ones. The lack of an adequate and safe blood supply is a major limitation to health care in the developing world. The instability of the electric power in developing countries may lead to a temperature departure from the recommended for keeping blood inventory, and the use of manual systems, which are characterized by time and resource exhaustion and human mistakes, augments the management problems. This study aims to introduce a reliable, practical application to manage and organize the blood bank, manage donor information, monitor inventory, and obtain matching blood types as quickly as possible. The proposed system was designed and implemented in two parts: using Web technology for enhanced data management and using an IoT sensor for blood inventory temperature monitoring in real time. The test stage helped us to measure the Web application’s functionality with sensors, and the results were encouraging. Obtaining and monitoring blood bank data were made easier in real time by using the black box method for functionalities testing. The evaluation step was performed using a questionnaire instrument based on three parameters: Satisfaction, Effectiveness, and Efficiency. The questionnaire was answered by 22 participants working in the blood bank management field. The results indicated that end users generally responded positively to the system which improved blood bank administration and services. This indicated efficiency of the application and the desire to adopt it. Integrating the two technologies can enhance usability and applicability in the health care sector. Full article

Cooling towers remove economically or technically unusable heat using considerable amounts of electricity and, in many cases, water. Several approaches, which vary in methodology, scope, and level of detail, are used for environmental evaluations of these cooling systems. Although the chosen approach has a significant impact on decisions made at the plant level, no methodology has yet been standardized for selecting the approach that best serves the objectives of the evaluation. Thus, this paper provides comparison criteria for the systematic selection of suitable evaluation methods for cooling towers and classifies how the methods score in this respect. These criteria, such as ‘life cycle thinking’, ‘inventoried physical quantities’, ‘temporal resolution’, ‘formalization’, and ‘data availability’, are grouped by overall evaluation objectives such as ‘thoroughness’, ‘scientific soundness’, and ‘usability’. Subsequently, these criteria were used to compare material flow analysis, energy analysis, environmental network analysis, life cycle inventory, life cycle assessment, environmental footprint methods, emergy analysis, exergy analysis, and the physical optimum method. In conclusion, material flow analysis is best suited for the analysis of cooling towers when impact assessment is not required; otherwise, life cycle assessment meets most of the defined criteria. Moreover, only exergy-based methods allow for the inclusion of volatile ambient conditions. Full article

This paper addresses the design of a system to facilitate individual environmental risk assessment and long-term risk management in the Industry 4.0 work context. The solution is based on IoT to provide data related to workers’ exposure to hazardous agents (dust, noise, ultraviolet radiation, poor lighting, and inappropriate temperature and humidity) through a simple interface for employees and employers. The system includes a monitoring device and a server and performs employee registration, receives secure messages from the monitoring devices, shows information about the workers’ exposure, and triggers alarms when the measures reach or exceed the limits established by applicable legislation and/or standards. The system was tested in a controlled environment, and the results are presented in this work. Our solution is still under development, and in future stages, it will include a smartphone app for employees to check their exposures and the use of artificial intelligence on the server, which is expected to enable long-term planning by companies based on the analysis of employees’ health history and their exposures to hazardous agents. Full article

Pathologists use histopathology to examine tissues or cells under a microscope to compare healthy and abnormal tissue structures. Differentiating benign from malignant tumors is the most critical aspect of cancer histopathology. Pathologists use a range of magnification factors, including 40x, 100x, 200x, and 400x, to identify abnormal tissue structures. It is a painful process because specialists must spend much time sitting and gazing into the microscope lenses. Hence, pathologists are more likely to make errors due to being overworked or fatigued. Automating cancer detection in histopathology is the best way to mitigate humans’ erroneous diagnostics. Multiple approaches in the literature suggest methods to automate the detection of breast cancer based on the use of histopathological images. This work performs a comprehensive analysis to identify which magnification factors, 40x, 100x, 200x, and 400x, induce higher prediction accuracy. This study found that training Convolutional Neural Networks (CNNs) on 200x and 400x magnification factors increased the prediction accuracy compared to training on 40x and 100x. More specifically, this study finds that the CNN model performs better when trained on 200x than on 400x. Full article