Appl. Syst. Innov., Volume 6, Issue 1 (February 2023) – 31 articles

The modern oil industry is characterized by a strong trend towards the digitalization of all technological processes. At the same time, during the transition of oil fields to the later stages of development, the issues of optimizing the consumed electricity become relevant. The purpose of this work is to develop a digital automated system for distributed control of production wells using elements of machine learning. The structure of information exchange within the framework of the automated system being created, consisting of three levels of automation, is proposed. Management of the extractive fund is supposed to be based on the work of four modules. The “Complications” module analyzes the operation of oil wells and peripheral equipment and, according to the embedded algorithms, evaluates the cause of the deviation, ways to eliminate it and the effectiveness of each method based on historical data. The “Power Consumption Optimization” module allows integrating algorithms into the well control system to reduce energy consumption by maintaining the most energy-efficient operation of pumping equipment or optimizing its operation time. The module “Ensuring the well flow rate” allows you to analyze and determine the reasons for the decrease in production rate, taking into account the parameters of the operation of adjacent wells. The Equipment Anomaly Prediction module is based on machine learning and helps reduce equipment downtime by predicting and automatically responding to potential deviations. As a result of using the proposed system, many goals of the oil company are achieved: specific energy consumption, oil shortages, and accident rate are reduced, while reducing the labor costs of engineering and technological personnel for processing the operation parameters of all process equipment. Full article

The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging. Full article

To address the challenges of incomplete knowledge representation, independent decision ranges, and insufficient causal decisions in bogie welding decisions, this paper proposes a hybrid decision-making method and develops a corresponding intelligent system. The collaborative case, rule, and knowledge graph approach is used to support structured documents and domain causality decisions. In addition, we created a knowledge model of bogie welding characteristics and proposed a case-matching method based on empirical weights. Several entity categorizations and relationship extraction models were trained under supervised conditions while building the knowledge graph. CRF and CR-CNN obtained high combined F1 scores (0.710 for CRF and 0.802 for CR-CNN) in the entity classification and relationship extraction tasks, respectively. We designed and developed an intelligent decision system based on the proposed method to implement engineering applications. This system was validated with some actual engineering data. The results show that the system obtained a high score on the accuracy test (0.947 for Corrected Accuracy) and can effectively complete structured document and causality decision-making tasks, having large research significance and engineering value. Full article

The aim of the paper was the implementation of low-cost smart sensors for the collection of bathymetric data in shallow water and the development of a 3D modelling methodology for the reconstruction of natural and artificial aquatic scenarios. To achieve the aim, a system called GNSS > Sonar > Phone System (G > S > P Sys) was implemented to synchronise sonar sensors (Deeper Smart Sonars CHIRP+ and Pro+ 2) with an external GNSS receiver (SimpleRTK2B) via smartphone. The bathymetric data collection performances of the G > S > P Sys and the Deeper Smart Sonars were studied through specific tests. Finally, a data-driven method based on a machine learning approach to mapping was developed for the 3D modelling of the bathymetric data produced by the G > S > P Sys. The developed 3D modelling method proved to be flexible, easily implementable and capable of producing models of natural surfaces and submerged artificial structures with centimetre accuracy and precision. Full article

Agriculture has observed significant advancements since smart farming technology has been introduced.The Green Movement played an essential role in the evolution of farming methods. The use of smart farming is accelerating at an unprecedented rate because it benefits both farmers and consumers by enabling more effective crop budgeting. The Smart Agriculture domain uses the Internet of Things, which helps farmers to monitor irrigation management, estimate crop yields, and manage plant diseases. Additionally, farmers can learn about environmental trends and, as a result, which crops to cultivate and how to apply fungicides and insecticides. This research article uses the primary and subsidiary keywords related to smart agriculture to query the Scopus database. The query returned 146 research articles related to the keywords inputted, and an analysis of 146 scientific publications, including journal articles, book chapters, and patents, was conducted. Node XL, Gephi, and VOSviewer are open-source tools for visualizing and exploring bibliometric networks. New facets of the data are revealed, facilitating intuitive exploration. The survey includes a bibliometric analysis as well as a word cloud analysis. This analysis focuses on publication types and publication regions, geographical locations, documents by year, subject area, association, and authorship. The research field of IoT in agricultural plant disease detection articles is found to frequently employ English as the language of publication. Full article

In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to understand the logic of how machine learning models work, and in order to compare the methods, it is necessary to evaluate them. The paper analyzes various approaches to the evaluation of XAI methods, defines the requirements for the evaluation system and suggests metrics to determine the various technical characteristics of the methods. A study was conducted, using these metrics, which determined the degradation in the explanation quality of the SHAP and LIME methods with increasing correlation in the input data. Recommendations are also given for further research in the field of practical implementation of metrics, expanding the scope of their use. Full article

Human eyes generally perform product defect inspection in Indonesian industrial production lines; resulting in low efficiency and a high margin of error due to eye tiredness. Automated quality assessment systems for mass production can utilize deep learning connected to cameras for more efficient defect detection. However, employing deep learning on multiple high frame rate cameras (HFRC) causes the need for much computation and decreases deep learning performance, especially in the real-time inspection of moving objects. This paper proposes optimizing computational resources for real-time product quality assessment on moving cylindrical shell objects using deep learning with multiple HFRC Sensors. Two application frameworks embedded with several deep learning models were compared and tested to produce robust and powerful applications to assess the quality of production results on rotating objects. Based on the experiment results using three HFRC Sensors, a web-based application with tensorflow.js framework outperformed desktop applications in computation. Moreover, MobileNet v1 delivers the highest performance compared to other models. This result reveals an opportunity for a web-based application as a lightweight framework for quality assessment using multiple HFRC and deep learning. Full article

Considering the advantage of the ability of data-mining techniques (DMTs) to detect and classify patterns, this paper explores their applicability for the protection of voltage source converter-based high voltage direct current (VSC-HVDC) transmission systems. In spite of the location of fault occurring points such as external/internal, rectifier-substation/inverter-substation, and positive/negative pole of the DC line, the stated approach is capable of accurate fault detection, classification, and location. Initially, the local voltage and current measurements at one end of the HVDC system are used in this work to extract the feature vector. Once the feature vector is retrieved, the DMTs are trained and tested to identify the fault types (internal DC faults, external AC faults, and external DC faults) and fault location in the particular feeder. In the data-mining framework, several state-of-the-art machine learning (ML) models along with one advanced deep learning (DL) model are used for training and testing. The proposed VSC-HVDC relaying system is comprehensively tested on a symmetric-monopolar-multi-terminal VSC-HVDC system and presents heartening results in diverse operating conditions. The results show that the studied deep belief network (DBN) based DL model performs better compared with other ML models in both fault classification and location. The accuracy of fault classification of the DBN is found to be 98.9% in the noiseless condition and 91.8% in the 20 dB noisy condition. Similarly, the DBN-based DMT is found to be effective in fault locations in the HVDC system with a smaller percentage of errors as MSE: 2.116, RMSE: 1.4531, and MAPE: 2.7047. This approach can be used as an effective low-cost relaying support tool for the VSC-HVDC system, as it does not necessitate a communication channel. Full article

This study explored college students’ learning experiences and outcomes as club committee members. Using a linear regression model, it investigated the relevance of personal background variables and club learning experiences to club learning outcomes. This study selected 15 universities and colleges’ student club committee members in TaiwanA total of 1850 questionnaires were distributed, and 1761 valid questionnaires were recovered, with a recovery rate of over 95%. The study findings are as follows: Regarding learning experiences and learning outcomes, the student club committee members was good. According to this study’s linear regression analysis: The personal background of student club committee members and their club learning experience had significant explanatory power on the learning outcomes, with R² values ranging from 39.6% to 61.1% for each dimension. This indicates that learning from club activities can be an essential pathway to cultivating students’ learning outcomes and a valuable reference for promoting club education in colleges and universities in Taiwan. Higher education practitioners should plan activities or programs for student club leaders with learning outcomes in mind, and design learning programs to meet the needs of student club leaders in each school so that students can achieve higher quality learning outcomes. In addition, this study also found that the assessment indicators of learning outcomes of the CAS of the U.S. can be applied to check the learning outcomes of student clubs in higher education in Taiwan. Full article

Lean and flexible manufacturing is a matter of necessity for the automotive industries today. Rising consumer expectations, higher raw material and processing costs, and dynamic market conditions are driving the auto sector to become smarter and agile. This paper presents a machine learning-based soft sensor approach for identification and prediction of lean manufacturing (LM) levels of auto industries based on their performances over multifarious flexibilities such as volume flexibility, routing flexibility, product flexibility, labour flexibility, machine flexibility, and material handling. This study was based on a database of lean manufacturing and associated flexibilities collected from 46 auto component enterprises located in the Pune region of Maharashtra State, India. As many as 29 different machine learning models belonging to seven architectures were explored to develop lean manufacturing soft sensors. These soft sensors were trained to classify the auto firms into high, medium or low levels of lean manufacturing based on their manufacturing flexibilities. The seven machine learning architectures included Decision Trees, Discriminants, Naive Bayes, Support Vector Machine (SVM), K-nearest neighbour (KNN), Ensembles, and Neural Networks (NN). The performances of all models were compared on the basis of their respective training, validation, testing accuracies, and computation timespans. Primary results indicate that the neural network architectures provided the best lean manufacturing predictions, followed by Trees, SVM, Ensembles, KNN, Naive Bayes, and Discriminants. The trilayered neural network architecture attained the highest testing prediction accuracy of 80%. The fine, medium, and coarse trees attained the testing accuracy of 60%, as did the quadratic and cubic SVMs, the wide and narrow neural networks, and the ensemble RUSBoosted trees. Remaining models obtained inferior testing accuracies. The best performing model was further analysed by scatter plots of predicted LM classes versus flexibilities, validation and testing confusion matrices, receiver operating characteristics (ROC) curves, and the parallel coordinate plot for identifying manufacturing flexibility trends for the predicted LM levels. Thus, machine learning models can be used to create effective soft sensors that can predict the level of lean manufacturing of an enterprise based on the levels of its manufacturing flexibilities. Full article

The most suitable method for assessing bone age is to check the degree of maturation of the ossification centers in the radiograph images of the left wrist. So, a lot of effort has been made to help radiologists and provide reliable automated methods using these images. This study designs and tests Alexnet and GoogLeNet methods and a new architecture to assess bone age. All these methods are implemented fully automatically on the DHA dataset including 1400 wrist images of healthy children aged 0 to 18 years from Asian, Hispanic, Black, and Caucasian races. For this purpose, the images are first segmented, and 4 different regions of the images are then separated. Bone age in each region is assessed by a separate network whose architecture is new and obtained by trial and error. The final assessment of bone age is performed by an ensemble based on the Average algorithm between 4 CNN models. In the section on results and model evaluation, various tests are performed, including pre-trained network tests. The better performance of the designed system compared to other methods is confirmed by the results of all tests. The proposed method achieves an accuracy of 83.4% and an average error rate of 0.1%. Full article

Fighting fraudulent insurance claims is a vital task for insurance companies as it costs them billions of dollars each year. Fraudulent insurance claims happen in all areas of insurance, with auto insurance claims being the most widely reported and prominent type of fraud. Traditional methods for identifying fraudulent claims, such as statistical techniques for predictive modeling, can be both costly and inaccurate. In this research, we propose a new way to detect fraudulent insurance claims using a data-driven approach. We clean and augment the data using analysis-based techniques to deal with an imbalanced dataset. Three pre-trained Convolutional Neural Network (CNN) models, AlexNet, InceptionV3 and Resnet101, are selected and minimized by reducing the redundant blocks of layers. These CNN models are stacked in parallel with a proposed 1D CNN model using Bagged Ensemble Learning, where an SVM classifier is used to extract the results separately for the CNN models, which is later combined using the majority polling technique. The proposed method was tested on a public dataset and produced an accuracy of 98%, with a 2% Brier score loss. The numerical experiments demonstrate that the proposed approach achieves promising results for detecting fake accident claims. Full article

Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and lowcomplexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention of Industrial Wireless Sensor Networks (IWSNs), thanks to its wide diffusion (e.g., on smartphones and tablets) and the lower cost of the devices compared to other wireless industrial communication technologies. However, neither the BLE nor the Bluetooth Mesh specifications can provide real-time messages with bounded delays. To overcome this limitation, this work proposes RESEMBLE, a real-time stack developed on top of BLE that is able to realize low-cost IWSNs over mesh topologies. RESEMBLE offers support to both real-time and non-real-time communications on the same network. Moreover, RESEMBLE provides clock synchronization, thus allowing for Time Division Multiple Access (TDMA) transmissions. The clock synchronization provided by RESEMBLE can be also exploited by the upper layers’ industrial applications to implement timecoordinated actions. Full article

The massive development of mobile computing and mobile networks attracts developers and researchers to emerge a new direction for augmented reality on the Web. Web augmented reality is inherently considered platform independent, no pre-installation is required, easy to apply, easy to access, and easy to develop. The current study aims to introduce a vision of including Web AR in school instructional material to keep the teaching methods in line with the tremendous technological growth and invest students’ knowledge in this field. The main objective of this study is to develop a QR code-based tracking Web AR application to investigate the effect of Web AR on the achievement of 4th-year primary school pupils in English. The procedure of Web AR application includes two basic steps. Firstly, the convincing Web AR development platform is selected according to three evaluation criteria. Secondly, developing the Web AR with 73 English vocabularies included in the last four units of the Iraqi English pupil’s book. The procedures of the study include; First, a random selection of a sample of pupils and assigning them to experimental and control groups. Second, equalize the elected pupils in the factors that may affect their performance. Then, the control and experimental groups have been taught English for 12 weeks, and finally, three achievement posttests are constructed and applied to the involved groups in order to assess their performance. Having a long period of learning on using Web AR is significant, as the pupils’ tendencies and acceptance of the experiment tools require time and effort. Full article

At this time the effectiveness of the COVID-19 vaccines has been proven, and it is crucial to carry out the complete vaccination of the population. Therefore, it is imperative to optimize the vaccine distribution fleets. This paper discusses the optimization of distribution routes for the Pfizer vaccine in Portugal in terms of transportation time, total costs, and CO₂ emissions. To this end, the Vehicle Routing Problem with Time Windows (VRPTW) model with a vehicle capacity restriction was used. The VRPTW model was tested for two scenarios. The first scenario allowed the driver to work overtime (585 min). The second scenario considered that the driver works 8 h (480 min). The results are presented to compare and justify the proposed method with large significance placed in terms of safety concerns, economic savings, environmental protection, and energy consumption. This paper aims to contribute to the healthcare system by optimizing the COVID-19 vaccine distribution routes and minimizing this process’s carbon footprint. Full article

The work presents an extensive simulative assessment of a LoRaWAN network that adopts the Listen Before Talk (LBT) Adaptive Frequency Agility (AFA) channel access technique in compliance with the ETSI regulations. The paper presents the results obtained in several scenarios with a different number of nodes and different configurations of the LoRaWAN Medium Access Control (MAC) parameters. The aim of the paper is to give insights about the performance achievable by changing the configuration parameters. For example, in all the scenarios considered in this work, once the number of nodes is fixed, the impact on the message loss ratio of the considered MAC parameters is always lower than 7%. Conversely, the impact of such parameters on the end-to-end delay is much more significant. The methodology of this assessment is of general validity and can be exploited by the network designer during the network configuration phase to obtain the most suitable combination of the MAC parameters for the network under consideration, based on the number of nodes and the application requirements. Full article

In this paper, the problem of finding an assignment of “n” surgeries to be presented in one of “m” identical operating rooms (ORs) or machines as the surgical case scheduling problem (SCSP) is proposed. Since ORs are among NP-hard optimization problems, mathematical and metaheuristic methods to address OR optimization problems are used. The job or surgical operation ordering in any OR is a permanent part of all sequencing and scheduling problems. The transportation times between ORs are defined based on the type of surgical operations and do not depend on distance, so there is no surgical operation waiting time for transferring. These problems are called no-wait open-shop scheduling problems (NWOSP) with transportation times. The transportation system for the problems is considered a multi-transportation system with no limitation on the number of transportation devices. Accordingly, this study modeled a novel combined no-wait open-shop surgical case scheduling problem (NWOSP-SCSP) with multi-transportation times for the first time to minimize the maximum percentile of makespan for OR as a single objective model. A mixed-integer linear program (MILP) with small-sized instances is solved. In addition to the small-sized model, a novel metaheuristic based on a hybrid simulated annealing (SA) algorithm to solve large-sized problems in an acceptable computational time is suggested, considering the comparison of the SA algorithm and a new recommended heuristic algorithm. Then, the proposed hybrid SA and SA algorithms are compared based on their performance measurement. After reaching the results with a numerical analysis in Nova Scotia health authority hospitals and health centers, the hybrid SA algorithm has generated significantly higher performance than the SA algorithm. Full article

Bioenergy production from waste is one of the emerging and viable routes from renewable resources (in addition to wind and solar energy). Many developing countries can benefit from this as they are trying to solve the large amounts of unattended garbage in landfills. This waste comes in either liquid (wastewater and oil) or solid (food and agricultural residues) form. Waste has negative impacts on the environment and, consequently, any form of life that exists therein. One way of solving this waste issue is through its usage as a resource for producing valuable products, such as biofuels, thus, creating a circular economy, which is in line with the United Nations (UN) Sustainable Development Goals (SDGs) 5, 7, 8, 9, and 13. Biofuel in the form of biogas can be produced from feedstocks, such as industrial wastewater and municipal effluent, as well as organic solid waste in a process called anaerobic digestion. The feedstock can be used as an individual substrate for anaerobic digestion or co-digested with two other substrates. Research advancements have shown that the anaerobic digestion of two or more substrates produces higher biogas yields as compared to their single substrates’ counterparts. The objective of this review was to look at the anaerobic digestion process and to provide information on the potential of biogas production through the co-digestion of sugarcane processing wastewater and municipal solid waste. The study deduced that sugar wastewater and municipal solid waste can be considered good substrates for biogas production in SA due to their enormous availability and the potential to turn their negative impacts into value addition. Biogas production is a feasible alternative, among others, to boost the country from the current energy issues. Full article

Tank level control has some unavoidable factors such as disturbance, non-linearity, and time lag. This paper proposes a simple and robust control scheme with nice energy-saving effects and smooth output to improve the quality of the controller and meet real-world application requirements. A linear controller is first designed using a third-order closed-loop gain-shaping algorithm. We then use an arcsine function to modify the system with non-linear switching to reduce the effect of the non-linear modification on the dynamic performance of the control system. Furthermore, we use the Nyquist stability criterion to demonstrate the stability of the closed-loop system in the presence of time lag. The results of the final simulation experiment show that the controller not only has high control quality but also has the characteristics of energy saving and smooth output under the condition of lag and pump performance constraints. These features are necessary for extending the life of the pump and enhancing the applicability of the tank level controller. Full article

Integrating technology innovation within human behaviour challenged a new paradigm shift during business uncertainty. Virtualized grid server implementation is still in its infancy for the “Medium Size Enterprise” that is sandwiched between financial constraints and growing economies of scale opportunities. Grounded by the “Technology Acceptance Model” with the “Theory of Planned Behaviour”, the proposed “Technology Behaviour Model” is developed. A “convenient sampling” method was applied to gather feedback through mixed methods from various enterprises in Malaysia on several considerations that influence technology and behaviour. The research applies “PLS-SEM” with statistic triangulation, exploratory, and cross-sectional analysis. The results show that the adoption of the BOINC share-product solution to structure the returns, by encouraging monetary savings, utilized composed interworks which were consequently further developed. In ascertaining union legitimacy, the extensive unwavering quality is more noteworthy than 0.9, and the mean Average Variance Extracted is above 0.85. The Hetero-Trait-Mono-Trait relationship proportion above 0.85 is normally used to assess the Dependent Variable, with a time span of 95% arriving at 1. This new model uses “Exostructure as a Service” organic server virtualization to drive digital transformation that relooks into the infrastructure overheads. From the tested hypothesis at the Slightly Critical and Most Critical correlation, further theoretical and management contributions were elaborated. Full article

In this paper, we present the optimized air space of the lightning protection rod (SK-AOR380) with the function of a charge transfer system (CTS). For evaluation of CTS in the laboratory setting, some studies have focused on the modification of the structure and shape of the CTS; the air space is designed (>2 mm) as an empirical design without quantitative data. However, in this paper, we have focused on the air space between the ionizer conductor and current collector to control the inception and occurrence position of corona discharge in air insulation. This is because the performance, such as the initial corona discharge inception of CTS, is determined by the air space. The simulation analysis was performed in a narrow, micro-sized air space as a first step, where the air space was reduced to the extent possible for simulation. To evaluate the performance of SK-AOR380 according to the narrow air space, we considered the numerical analysis method. The fundamental equations consist of Poisson’s equation and the charge continuity equation. Poisson’s equation for electric fields is a fully coupled numerical model based on the charge continuity equations for a positively charged ion, negatively charged ion, and free electron. Fowler–Nordheim electron emission was employed for the boundary condition at the surface of the ionizer conductor. To simulate the corona discharge behavior under standard lightning impulse voltage, we used a source of lightning voltage with 1.2/50 $\mathsf{\mu }\mathrm{s}$ based on a double exponential equation; the corona discharge behaviors (electric field distribution, free electron density, positive and negative ion density) were investigated dependent on each time step (0.5, 1 and 1.2 $\mathsf{\mu }\mathrm{s}$) until 3.5 $\mathsf{\mu }\mathrm{s}$. The results revealed that the characteristics graph of free electron density, positive and negative ion density showed similar trends, with lightning impulse voltage increasing with increasing time steps until 1.2 $\mathsf{\mu }\mathrm{s}$ and each density resulted in a decreasing trend from 1.2 $\mathsf{\mu }\mathrm{s}$ to 3.5 $\mathsf{\mu }\mathrm{s}$. The SK-AOR380 is improved with a decreasing air space in terms of electric field distribution, electron, and ion density. In other words, the 0.0005 mm air space created a non-uniform electric field distribution with a large field enhancement, causing ionization to initiate corona discharge. In addition, in the case of a 0.0005 mm air space, the electric field and electron density are increased by 1.3 and 1.9 times, respectively, than that of 0.001 mm. However, there was no longer a significant difference under 0.0005 mm in the simulation results. To improve the CTS, we suggest the air space between the ionizer conductor and current collector should be less than 2 mm than that of conventional CTS from our research work. Full article

Modern computers’ network interface cards (NICs) are undergoing changes in order to handle greater data rates and assist with scaling problems caused by general-purpose CPU technology. The inclusion of programmable accelerators to the NIC’s data channel is one of the ongoing improvements that is particularly intriguing since it gives the accelerator the chance to take on a portion of the CPU’s network packet processing duties. Accelerators are frequently developed using platforms like field-programmable gate arrays because packet processing operations have severe latency requirements (FPGAs). When implementing packet processing activities, FPGAs’ gain for through put is the number of data packets being successfully sent per second and latency is the actual time those packets take. However, due to their restricted resources, programming may need to be shared throughout a variety of applications. We provide hXDP, a software solution for FPGAs that targets the Linux eXpress Data Path and performs packet processing functions outlined with the eBPF technology. While maintaining performance on par with top-tier CPUs, hXDP only uses a tiny portion from the field programmable gate arrays, which are semiconductor devices that are based around a matrix of configuration logic blocks (CLB) connected over programmable interconnects. However, we demonstrate that when aiming towards a purpose-built FPGA architecture, many extended Berkeley packet filters (eBPF) allow programmers to use Berkeley packet filter byte code that makes use of certain kernel resources and instruction set architecture, to collocate and even eliminate, with considerably productivity and effectiveness. On an FPGA NIC, we implement hXDP and test its effectiveness using authentic eBPF programmes from the real world. Our version consumes 15% of the FPGA resources and operates at 156.25 MHz. This can constantly change and lead to the act of identification, inspection, extraction, and manipulation so that a network may make more intelligent management decisions. Full article

Thanks to continuously evolving data management solutions, data-driven strategies are considered the main success factor in many domains. These strategies consider data as the backbone, allowing advanced data analytics. However, in the agricultural field, and especially in fish farming, data-driven strategies have yet to be widely adopted. This research paper aims to demystify the situation of the fish farming domain in general by shedding light on big data generated in fish farms. The purpose is to propose a dedicated data lake functional architecture and extend it to a technical architecture to initiate a fish farming data-driven strategy. The research opted for an exploratory study to explore the existing big data technologies and to propose an architecture applicable to the fish farming data-driven strategy. The paper provides a review of how big data technologies offer multiple advantages for decision making and enabling prediction use cases. It also highlights different big data technologies and their use. Finally, the paper presents the proposed architecture to initiate a data-driven strategy in the fish farming domain. Full article

In recent years, the Smart Grid (SG) has been conceptualized as a burgeoning technology for improvising power systems. The core of the communication infrastructure in SGs is the Synchrophasor Communication Network (SCN). Using the SCN, synchrophasor data communication is facilitated between the Phasor Measurement Unit (PMU) and Phasor Data Concentrator (PDC). However, the SCN is subjected to many challenges. As a result, the components, such as the links, PMUs, PDCs, nodes, etc., of the SCN are subjected to failure. Such failure affects the operation of the SCN and results in the performance degradation of the SG. The performance degradation of the smart grid is observed either temporarily or permanently due to packet loss. To avoid dire consequences, such as a power blackout, the SCN must be resilient to such failures. This paper presents a novel analytical method for the resiliency analysis of SCNs. A graph-theoretic approach was used to model SCN from the resiliency analysis perspective. Furthermore, we proposed a simulation framework for validating the analytical method using the Network Simulator-3 (ns-3) software. The proposed non-intrusive simulation framework can also be extended to design and analyse the resiliency of generic communication networks. Full article

Local governments are the motors that drive the lives of their citizens. There is no human individual who does not live under a local government, regardless of where they are situated. This is why every local authority’s environment requires a wide range of investments to make it safe and clean. In this research, we assess the expenditure arrangements of Nigerian local governments to guarantee environmental safety. A green and healthy environment is the ultimate goal of all nations throughout the world; thus, local governments are also working to reduce CO₂ pollution in their communities. Nigeria has 774 local governments, and the bulk of these areas have significant pollution densities, owing to CO₂ emissions from crude oil refining for both commercial and domestic use. The Niger Delta regions, where commercial quantities of crude are tapped, are the most affected by this predicament. The two techniques of spending (recurrent and capital) in local government are examined in this paper for the period from 1993 to 2020 using a multiple regression method to determine their influence on CO₂ emissions reduction. The results reveal that the combination of the two methods reduce the effect of CO₂ emissions, but capital spending has a greater positive benefit than recurrent spending. Examination of this link reveals that there is a very weak association between CO₂ emissions and the two types of local government expenditure. The obtained results suggest that local administrations should deploy necessary environmental statutes, fines, and penalties using security officers for enforcement in order to put a halt to illegal crude oil refining and pollution. Full article

The spike in internet users led healthcare companies to confer their agile presence on various digital platforms and engage customers online to increase their viability amid the rising competition. Online customer engagement takes place through branded content, i.e., the content provided by the brand or the company. Healthcare companies can leverage customer engagement by focusing on various crucial enablers. Therefore, this study explores the enablers of customer engagement for branded content in healthcare and devises a model depicting interrelationships among them. The enablers were ascertained using the literature review and validated by experts. Further, the interrelationship among the enablers was analyzed using TISM (Total Interpretive Structural Modeling) approach, and Fuzzy MICMAC (Cross-impact matrix multiplication) classified the enablers into different clusters. Results exhibited that informativeness is the most significant enabler, deriving other enablers. In contrast, shareability and co-creation of content are the most dependent and strategic enablers in the model hierarchy. The outcomes of this research will aid healthcare companies in knowing and prioritizing the enabler’s contribution in engaging customers towards branded content. Full article

Since the 1990s, mobile telecommunication networks have gradually become denser around the world. Nowadays, large parts of their backhaul network consist of commercial microwave links (CMLs). Since CML signals are attenuated by rainfall, the exploitation of records of this attenuation is an innovative and an inexpensive solution for precipitation monitoring purposes. Performance data from mobile operators’ networks are crucial for the implementation of this technology. Therefore, a real-time system for collecting and storing CML power levels from the mobile phone operator “Telecel Faso” in Burkina Faso has been implemented. This new acquisition system, which uses the Simple Network Management Protocol (SNMP), can simultaneously record the transmitted and received power levels from all the CMLs to which it has access, with a time resolution of one minute. Installed at “Laboratoire des Matériaux et Environnement de l’Université Joseph KI-ZERBO (Burkina Faso)”, this acquisition system is dynamic and has gradually grown from eight, in 2019, to more than 1000 radio links of Telecel Faso’s network in 2021. The system covers the capital Ouagadougou and the main cities of Burkina Faso (Bobo Dioulasso, Ouahigouya, Koudougou, and Kaya) as well as the axes connecting Ouagadougou to these cities. Full article

Retailers depend on accurate forecasts of product sales at the Store × SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives. Full article

A slotless self-bearing motor (SSBM) is a new type of electric motor, with its levitating and rotating capability as a drive system. In the design of motor, the iron core of stator was removed, it could have many advantages such as small size, light, no friction loss, low losses, high speed. Besides, disturbance and uncertainty factors are the unexpected values, which impacting strongly to the output of the control system. In this paper, to reject the effects of these factors, an optimal Lyapunov-based (OLB) sliding mode control (SMC) was proposed to control the movements and rotation of SSBM system. First, the mathematical model with uncertainty and disturbance factors of the SSBM system was rewritten to show the detail configuration of the proposed motor. Second, the OLB-SMC controllers were designed for the control of displacements on $x$-, y-axes, rotor speed on $\omega$-axes, respectively. Third, the stability analysis of control algorithm was demonstrated via the Lyapunov stability theory. Finally, the experimental test was implemented to prove the high performance of the OLB-SMC for SSBM system. The practical results show that the effectiveness of OLB-SMC controller for SSBM system. The novelty of the proposed method is that the stability condition was newly proposed based on the transformation from scalar equation to state-space equation, where the gains of controller were found based on the linear matrix inequality. Full article