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To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Applied Sciences has achieved a major milestone this year: it is the 10th anniversary of the journal [...]

The use of waste from the regional production of waste wood, waste paper, and cardboard in the form of briquettes may be causing an increase in local emissions, both of major elements and trace elements. When burning paper and cardboard briquettes, more than 70% of Mn, Zn, As, and Pb is released into the air from the total content of trace elements in the fuel. The largest amounts of major and trace elements are released when burning paper briquettes (56 g/kg of fuel), half of these amounts are released from burning briquettes from waste wood and coal (23 g/kg of fuel). The pursuit of alternative uses for those cardboard components that are not suitable for recycling cannot be directed to the production of briquettes for residential combustion in the framework of the application of the principles of the circular economy. In particular, the high concentrations of undesirable elements in the emissions released in the gas phase into the atmosphere are an obstacle existing even when the parameters of the PM10 emissions of a boiler are met. This is related to the high ash content of the cardboard (13.5%). Waste paper or cardboard could be added to waste wood at a maximum of 10% to make the pollution produced comparable to the burning of coal briquettes.

[EN] Emerging Industry 4.0 applications require ever-increasing amounts of data and new sources of information to more accurately characterize the different processes of a production line. Industrial Internet of Things (IIoT) technologies, and in particular Wireless Sensor Networks (WSNs), allow a large amount of data to be digitized at a low energy cost, thanks to their easy scalability and the creation of meshed networks to cover larger areas. In industry, data acquisition systems must meet certain reliability and robustness requirements, since other systems such as predictive maintenance or the digital twin, which represents a virtual mapping of the system with which to interact without the need to alter the actual installation, may depend on it. Thanks to the IEEE 802.15.4e standard and the use of Time-Slotted Channel Hopping (TSCH) as the medium access mechanism and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as the routing protocol, it is possible to deploy WSNs with high reliability, autonomy, and minimal need for re-configuration. One of the drawbacks of this communication architecture is the low efficiency of its deployment process, during which it may take a long time to synchronize and connect all the devices in a network. This paper proposes an analytical model to characterize the process for the creation of downstream routes in RPL, whose transmission of multi-hop messages can present complications in scenarios with a multitude of interfering nodes and resource allocation based on minimal IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH). This type of multi-hop message exchange has a different behaviour than the multicast control messages exchanged during the synchronization phase and the formation of upstream routes, since the number of interfering nodes changes in each retransmission., The research leading to these results is part of the i4Q project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 958205.

Plant fibers are sustainable sources of materials for many industries, and can be obtained from a variety of plants. Cellulose is the main constituent of plant-based fibers, and its properties give the characteristics of the fibers obtained. Detailed characterization of cellulosic fibers is often performed after lengthy extraction procedures, while fast screening might bring the benefit of quick qualitative assessment of unprocessed stems. The aim of this research was to define some marker spectral regions that could serve for fast, preliminary qualitative characterization of unprocessed stems from some textile plants through a practical and minimally invasive method without lengthy extraction procedures. This could serve as a screening method for sorting raw materials by providing an accurate overall fingerprint of chemical composition. For this purpose, we conducted comparative Fourier Transform Infrared Spectroscopy (FT-IR) prospecting for quality markers in stems of flax (Linum usitatissimum L.), velvet leaf (Abutilon theophrasti Medik.), hemp (Cannabis sativa L.) and jute (Corchorus olitorius L.). Analysis confirmed the presence of major components in the stems of the studied plants. Fingerprint regions for cellulose signals were attributed to bands at 1420–1428 cm−1 assigned to the crystalline region and 896–898 cm−1 assigned to the amorphous region of cellulose. The optimization of characterization methods for raw materials is important and can find immediate practical applications.

International audience; Pharmaceutical products have become a necessary part of life. Several studies have demonstrated that indirect exposure of humans to pharmaceuticals through the water could cause negative effects. Raw sewage and wastewater effluents are the major sources of pharmaceuticals found in surface waters and drinking water. Therefore, it is important to consider and characterize the efficiency of pharmaceutical removal during wastewater and drinking-water treatment processes. Various treatment options have been investigated for the removal/reduction of drugs (e.g., antibiotics, NSAIDs, analgesics) using conventional or biological treatments, such as activated sludge processes or bio-filtration, respectively. The efficiency of these processes ranges from 20-90%. Comparatively, advanced wastewater treatment processes, such as reverse osmosis, ozonation and advanced oxidation technologies, can achieve higher removal rates for drugs. Pharmaceuticals and their metabolites undergo natural attenuation by adsorption and solar oxidation. Therefore, pharmaceuticals in water sources even at trace concentrations would have undergone removal through biological processes and, if applicable, combined adsorption and photocatalytic degradation wastewater treatment processes. This review provides an overview of the conventional and advanced technologies for the removal of pharmaceutical compounds from water sources. It also sheds light on the key points behind adsorption and photocatalysis.

With increasing coffee production and consumption, the amount of coffee by-product is also increasing. Therefore, there is growing worldwide interest in using these by-products as a renewable energy source. In this study, hydrothermal carbonization was conducted with subcritical water to improve the fuel characteristics of spent coffee grounds. The water content was varied, with the mass ratio between the dry sample and water set to 1:1.5 and 1:4. The reaction temperature was increased by 10 °C from 180 to 250 °C. The fuel and thermal characteristics of the reaction products were investigated through mass and energy yields, elemental, proximate, and heating value analysis. In analysis results, as the reaction temperature increased, carbon and fixed carbon content increased, and oxygen and volatile matter content decreased, resulting in an increase in calorific value. Thermogravimetric analysis, derivative thermogravimetry, and Fourier transform infrared spectroscopy were also conducted on the reaction products. To investigate their storage characteristics, chemical oxygen demand analysis was conducted. The results showed that with increasing reaction temperature, the fixed carbon content and heating value increased, also, the fuel characteristics became similar to those of coal. In addition, the reaction products became more hydrophobic as the reaction temperature increased.

International audience; The advent of telerobotic systems has revolutionized various aspects of the industry and human life. This technology is designed to augment human sensorimotor capabilities to extend them beyond natural competence. Classic examples are space and underwater applications when distance and access are the two major physical barriers to be combated with this technology. In modern examples, telerobotic systems have been used in several clinical applications, including teleoperated surgery and telerehabilitation. In this regard, there has been a significant amount of research and development due to the major benefits in terms of medical outcomes. Recently telerobotic systems are combined with advanced artificial intelligence modules to better share the agency with the operator and open new doors of medical automation. In this review paper, we have provided a comprehensive analysis of the literature considering various topologies of telerobotic systems in the medical domain while shedding light on different levels of autonomy for this technology, starting from direct control, going up to command-tracking autonomous telerobots. Existing challenges, including instrumentation, transparency, autonomy, stochastic communication delays, and stability, in addition to the current direction of research related to benefit in telemedicine and medical automation, and future vision of this technology, are discussed in this review paper

In this paper, we provide a holistic survey of multimedia content protection applications in which blockchain technology is being used. A taxonomy is developed to classify these applications with reference to the technical aspects of blockchain technology, content protection techniques, namely, encryption, digital rights management, digital watermarking and fingerprinting (or transaction tracking), and performance criteria. The study of the literature reveals that there is currently no complete and systematic taxonomy dedicated to blockchain-based copyright protection applications. Moreover, the number of successfully developed blockchain-based content protection systems is very low. This points towards a research gap. To fill this gap, we propose a taxonomy that integrates technical aspects and application knowledge and can guide the researchers towards the development of blockchain-based multimedia copyright protection systems. Furthermore, the paper discusses some technical challenges and outlines future research directions.

Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 &times, 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement.

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: Authors thanks to the Molde University College-Specialized Univ. in Logistics, Norway for the support of Open access fund. Scopus 2-s2.0-85120897733

Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.

This paper seeks to underline the driving factors of farmers&rsquo, engagement in Participatory Research Projects (PRPs). This is a critical issue for formulating efficient and effective technology transfer channels, essential for improving the operational status of agricultural holdings. A survey was conducted on a sample of 326 Greek arable crops farmers. An explanatory framework consisting of three major factor categories and 11 variables was developed. A logistic regression analysis empirically tests the effect of the variables on the participation of farmers in PRP. Furthermore, the relative importance of variables and factors is extracted with the Shapley&ndash, Owen decomposition analysis. The results show that Farmers&rsquo, Willingness and Social Influences are the factors that mostly affect their decision to engage in a PRP. The farmers&rsquo, ability consisting of socioeconomic and demographic variables has a small effect on their decision-making process. The estimated effects can help decision-makers to shape and prioritize more targeted policies for farmers&rsquo, engagement in research. Additionally, this paper sets the basis for shifting research from simple estimations of the effect of variables on farmers&rsquo, decision-making, to a more comprehensive estimation that also accounts for the strength of these relationships. The paper fills a gap in the literature of studies on farmers&rsquo, decisions for participating in PRPs, by developing and testing an explanatory framework which also accounts for the relative importance of each factor/variable.

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

With the world population set to approach an estimated 9 billion by 2050, against a background of finite natural resources, we need renewable biological resources for securing healthy food and animal feed to meet the nutritional requirements of the growing population. In this paper, key findings from ReValue Eranet project financed by Norway, India, and Spain are presented. The project aims to contribute to the UN Sustainable Development Goals (SDG) target on food losses reduction, by developing innovative technologies for the surimi industry, namely reducing losses by improved cold chain management and efficient conversion of rest raw materials (RRM) and wash water into value-added protein and oil ingredients for food and feed applications. A multidisciplinary research approach was applied with expertise from supply chain management, life cycle assessment, biotechnology, energy, and process engineering to propose several solutions for improving the overall resource efficiency of the surimi supply chains in India. This paper presents a synthesis of proposed solutions from ReValue project and potential contribution towards SDGs as well as market exploitation strategies.

Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for learning process. This paper tries to find a way for continual learning which does not require prior high-cost training data construction by imitating a biological memory model. We employ SDR (sparse distributed representation) for information processing and semantic memory model, which is known as a representation model of firing patterns on neurons in neocortex area. This paper proposes a novel memory model to reflect remembrance of morphological semantics of visual input stimuli. The proposed memory model considers both memory process and recall process separately. First, memory process converts input visual stimuli to sparse distributed representation, and in this process, morphological semantic of input visual stimuli can be preserved. Next, recall process can be considered by comparing sparse distributed representation of new input visual stimulus and remembered sparse distributed representations. Superposition of sparse distributed representation is used to measure similarities. Experimental results using 10,000 images in MNIST (Modified National Institute of Standards and Technology) and Fashion-MNIST data sets show that the sparse distributed representation of the proposed model efficiently keeps morphological semantic of the input visual stimuli.

In a Fizeau interferometer, off-axis illumination will lead to fringe optimization. Primarily due to the unique structure of our interferometer, we first analyze the influence of the optical properties of the parallel plate as a part of the interferometer on the optimal incident angle. Generally, the incident angle determination is mainly based on the graphing method proposed by Langenbeck and the estimation formula proposed by Kajava. However, Langenbeck’s method is cumbersome, and the error of Kajava’s estimation formula is large. Based on the predecessors, this paper proposes a modified method of determining the optimal angle of incidence and further derives more accurate optimal angle expressions than Kajava’s. By simply substituting the wedge angle of the wedge cavity and the reflectivity of the cavity, the optimum incidence angle can be obtained immediately. Thus, it eliminates the tedious and complex process of finding the optimum incident angle by graphing method and makes the formula method the simplest method to find the optimum incident angle. Finally, the comparison of the interference intensity at the optimum incidence angle calculated by the improved method and normal incidence is given. It is found that the beam has a good suppression effect on the sub-peak when it is incident at the optimum incident angle calculated by the method in this paper.

IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time.

In this paper, we summarize the research on photodarkening in optical fibers. The causes of photodarkening in fiber, the influence of photodarkening on fiber laser, the experimental device of photodarkening, and the mathematical model used to study the phenomenon of photodarkening are described in detail. At the end of the paper, we summarize the means and methods to suppress photodarkening.

A space station is a typical phased-mission system, and assessing its reliability during its configuration is an important engineering action. Traditional methods usually require extensive data to carry out a layered reliability assessment from components to the system. These methods suffer from lack of sufficient test data, and the assessment process becomes very difficult, especially in the early stage of the configuration. This paper proposes a reliability assessment method for the space station configuration mission, using multi-layer and multi-type risks. Firstly, the risk layer and the risk type for the space station configuration are defined and identified. Then, the key configuration risks are identified comprehensively, considering their occurrence likelihood and consequence severity. High load risks are identified through risk propagation feature analysis. Finally, the configuration reliability model is built and the state probabilities are computed, based on the probabilistic risk propagation assessment (PRPA) method using the assessment probability data. Two issues are addressed in this paper: (1) how to build the configuration reliability model with three layers and four types of risks in the early stage of the configuration, (2) how to quantitatively assess the configuration mission reliability using data from the existing operational database and data describing the propagation features. The proposed method could be a useful tool for the complex aerospace system reliability assessment in the early stage.

This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.

The chloride ion (Cl−) is a type of anion which is commonly found in the environment and has important physiological functions and industrial uses. However, a high content of Cl− in water will do harm to the ecological environment, human health and industrial production. It is of great significance to strictly monitor the Cl− content in water. Following the recent development of society and industry, large amounts of domestic sewage and industrial sewage are discharged into the environment, which results in the water becoming seriously polluted by Cl−. The detection of Cl− has gradually become a research focus. This paper introduces the harm of Cl− pollution in the environment and summarizes various Cl− detection methods, including the volumetric method, spectrophotometry method, electrochemical method, ion chromatography, paper-based microfluidic technology, fluorescent molecular probe, and flow injection. The principle and application of each technology are described; their advantages, disadvantages, and applicability are discussed. To goal of this research is to find a more simple, rapid, environmental protection and strong anti-interference detection technology of Cl−.

There is an obvious trade-off between information obtained from passenger surveys and cost and time investment. This paper offers a new approach for this problem and its detailed step-by-step procedure description. Parsimonious Analytic Hierarchy Process (PAHP) is a recently created methodology that combines the simplicity of direct evaluations with the consistency and reliability of the Analytic Hierarchy Process (AHP). In the paper, the first large sample survey of passenger satisfaction by a new, PAHP-based model and procedure is presented as a case study. Moreover, a comparison with an AHP survey on the same public transport system and the same pattern are demonstrated. Since the comparative analysis produced a strong correlation between AHP and PAHP outcomes, it can be stated that the new procedure is less time consuming and costly than the AHP, while possessing the same benefits, and thus, it is more trustworthy than satisfaction measured by direct evaluations. Consequently, our proposed model can be applied both in theoretical and practical cases. Theoretically, it solves the problem of avoiding the use of large pairwise comparison matrices, and practically, it is a useful support to public satisfaction surveys, especially in the transportation sector.

The fourth industrial revolution is promoting the Operator 4.0 paradigm, originating from a renovated attention towards human factors, growingly involved in the design of modern, human-centered processes. New technologies, such as augmented reality or collaborative robotics are thus increasingly studied and progressively applied to solve the modern operators’ needs. Human-centered design approaches can help to identify user’s needs and functional requirements, solving usability issues, or reducing cognitive or physical stress. The paper reviews the recent literature on augmented reality-supported collaborative robotics from a human-centered perspective. To this end, the study analyzed 21 papers selected after a quality assessment procedure and remarks the poor adoption of user-centered approaches and methodologies to drive the development of human-centered augmented reality applications to promote an efficient collaboration between humans and robots. To remedy this deficiency, the paper ultimately proposes a structured framework driven by User eXperience approaches to design augmented reality interfaces by encompassing previous research works. Future developments are discussed, stimulating fruitful reflections and a decisive standardization process.

Pneumoconiosis has become one of the biggest threats to the occupational health and life safety of mining workers in China. The number of pneumoconiosis cases has continued to rise in recent years. The main task of occupational health development is to study the pathogenesis of pneumoconiosis and to develop mine dust prevention and control technology. Therefore, this paper summarizes the research progress of coal worker pneumoconiosis and dust prevention and control in mines. Firstly, the research progress of coal worker pneumoconiosis is analyzed from the aspects of pathogenesis, animal model and pathological changes of coal worker pneumoconiosis. Then, the existing basic theory and technology of dust prevention are described, including ventilation and dust removal, spray and dust suppression, and chemical dust suppression methods. Finally, based on the dust removal theory of wet shotcrete, the progress of shotcrete dust control technology and equipment used for shotcrete is summarized from the aspects of shotcrete technology process and shotcrete materials. At the same time, in view of the shortcomings of the existing research, the next research prospect is given in the pathogenesis of pneumoconiosis, intelligent dust prevention, jet spraying dust removal and so on. This paper provides theoretical support for realizing the separate source and efficient treatment of mine dust control and helps to improve the clean production level of mine, control and prevent pneumoconiosis.

The present paper aims to be useful for public transport operators and municipalities, as it should enable them to make decisions and to optimize public transport schedules during peak hours. In this study, we outline the data and the means necessary for the creation and use of a specific database for a dynamic spatial analysis of the public transportation network. This will facilitate the analysis of public transport vehicle operating programs and the simulation of new transport programs using open-source software. This paper delivers the first digital map of the public transport in Bucharest. Using the QGIS software and the PostgresSQL database, (i) we analyzed the accessibility of public transport stops for residential areas (5-min isochrones, corresponding to walking distances of 400 m), and (ii) we determined the correlation of transport vehicle programs with the existing transport network to optimize the headway of vehicles. These two elements were considered for the analysis of public transport performance. The research study was based on the tram network in Bucharest, but it can be easily upscaled for the entire public transport network and may be replicated in other large cities.

This paper aims to numerically validate the aerodynamic performance and benefits of variable camber rate morphing wings, by comparing them to conventional ones with plain flaps, when deflection angles vary, assessing their D reduction or L/D improvement. Many morphing-related research works mainly focus on the design of morphing mechanisms using smart materials, and innovative mechanism designs through materials and structure advancements. However, the foundational work that establishes the motivation of morphing technology development has been overlooked in most research works. All things considered, this paper starts with the verification of the numerical model used for the aerodynamic performance analysis and then conducts the aerodynamic performance analysis of (1) variable camber rate in morphing wings and (2) variable deflection angles in conventional wings. Finally, we find matching pairs for a direct comparison to validate the effectiveness of morphing wings. As a result, we validate that variable camber morphing wings, equivalent to conventional wings with varying flap deflection angles, are improved by at least 1.7% in their L/D ratio, and up to 18.7% in their angle of attack, with α = 8° at a 3% camber morphing rate. Overall, in the entire range of α, which conceptualizes aircrafts mission planning for operation, camber morphing wings are superior in D, L/D, and their improvement rate over conventional ones. By providing the improvement rates in L/D, this paper numerically evaluates and validates the efficiency of camber morphing aircraft, the most important aspect of aircraft operation, as well as the agility and manoeuvrability, compared to conventional wing aircraft.

When vehicles interact with a bridge, a vehicle–bridge interaction (VBI) system is created. The frequency and modal shape of VBI systems have been widely studied, but the damping of VBI systems has not been adequately investigated. In recent years, several incidents of abnormal bridge vibration due to changes in bridge damping have occurred and aroused widespread concern in society. Damping is an important evaluation index of structural dynamic performance. Knowing the damping ratio of a VBI system is useful for analyzing the damping changes while a bridge is in service. This paper presents a method to extract bridge damping values from a VBI system, which can serve as a guide for bridge damping evaluation. First, a double-beam theoretical model was used to simplify the VBI system for cases involving uniform traffic flow. The damping ratio equation for the simplified VBI system was obtained using the extended dynamic stiffness method (EDSM). A double-beam finite element model and a VBI finite element model were established. The damping ratios of the two models were separately calculated and then compared with the simplified VBI model results. The results verified the accuracy of the simplified method. This paper then explains that bridge damping values can be extracted by estimating the equivalent traffic flow parameters and using the damping formula for the simplified VBI system. The bridge damping ratios extracted using this method in an engineering case ranged from 0.75% to 0.78%, which is smaller than the range that was directly identified using monitoring data (0.83–1.19%). The results show that the method can effectively extract bridge damping ratios and improve damping ratio identification.

Grid-connected inverters (GCI) are commonly used in PV system applications to generate a regulated AC current to feed into the grid. Transformerless inverters are the most advanced inverters that are used in industry, which provide efficiency with smaller size and lower cost. This paper proposes a grid-connected single-phase transformerless inverter with the technology of common ground and the virtual DC bus concept. In this topology, the grid neutral is connected directly to the PV ground, which generates a constant common mode voltage (CMV), thus leading to the elimination of the leakage current caused by the PV array’s parasitic capacitance. The proposed inverter has a buck–boost circuit with a flying capacitor to generate the DC bus for a negative power cycle, four switches, and two diodes. A unipolar sinusoidal pulse width modulation (SPWM) technique is used which reduces the output filter requirements. In addition, only one switch carries the load current during the active states of both the negative and positive power cycle, thus minimizing the conduction losses. One more advantage presented in the proposed inverter is its ability to charge the flying capacitor during all operation states due to the existence of the buck–boost circuit. Design and theoretical calculations were conducted in this paper to optimize the losses. Moreover, the PSIM simulation was used to validate the proposed topology inverter, verify the performance by showing leakage current elimination, and achieve unipolar voltage in the output bus. The simulation results show a peak efficiency of 98.57% for a 2 kW inverter, which agrees with the theoretical calculations.

Case studies available in the literature clearly point to the numerous benefits of BIM (Building Information Modeling), in addition to the barriers that participants of such projects may face. This paper is a case study of Employer’s Information Requirements (EIR) for preparing and managing BIM models in the design and construction of selected large public construction projects: the Cogiteon Lesser Poland Science Center (LPSC Cogiteon), the Krakow Music Center (KMC) and the Copernican Revolution Studio (CRS). The paper presents the main aspects included in EIRs. It discusses the technical (e.g., requirements for the Common Data Environment platform—CDE), management (including the scopes of responsibility of staff in charge of BIM) and strategic sections (primary expectations concerning Data Drops) of the EIR. Projects executed using BIM by public institutions allow for the application of insight gained as a result of their completion and the creation of a knowledge base or checklist for future projects. The projects discussed here, carried out using advanced BIM solutions, could potentially be developed further by the proposed content extension concerning levels of detail (this paper cites potential guidelines that can be applied), component elements concerning price and qualifications, so as to easily generate bills of costs, and information used in facility management which can also encourage facility manager cooperation. Examples of EIR provisions are presented based on an analysis of three completed construction projects carried out using advanced BIM solutions. BIM is still a new form of management and this paper expands the range of available EIR standards, in addition to presenting guidelines for their practical application in the construction industry.

In this paper, a keyword detection scheme is proposed based on deep convolutional neural networks for personal information protection in document images. The proposed scheme is composed of key character detection and lexicon analysis. The first part is the key character detection developed based on RetinaNet and transfer learning. To find the key characters, RetinaNet, which is composed of convolutional layers featuring a pyramid network and two subnets, is exploited to detect key characters within the region of interest in a document image. After the key character detection, the second part is a lexicon analysis, which analyzes and combines several key characters to find the keywords. To train the model of RetinaNet, synthetic image generation and data augmentation are exploited to yield a large image dataset. To evaluate the proposed scheme, many document images are selected for testing, and two performance measurements, IoU (Intersection Over Union) and mAP (Mean Average Precision), are used in this paper. Experimental results show that the mAP rates of the proposed scheme are 85.1% and 85.84% for key character detection and keyword detection, respectively. Furthermore, the proposed scheme is superior to Tesseract OCR (Optical Character Recognition) software for detecting the key characters in document images. The experimental results demonstrate that the proposed method can effectively localize and recognize these keywords within noisy document images with Mandarin Chinese words.

This paper presents a design of a dual-band integrated space telescope system for visible light and long-wave infrared. The system can simultaneously image the visible light band of 450–900 nm and the long-wave infrared band of 7700–10,500 nm. The dual-band integrated imaging system can freely switch the observation band to adapt to different scenes and environmental changes. The camera can also further expand its capabilities in the fields of multi-spectral observation and low-light observation by collocation with different detectors. This design is based on a coaxial reflection system, the two bands share the camera’s primary and secondary mirrors, and the separation of the two bands is achieved through a separate field of view design. After simulation, the average Modulation Transfer Function (MTF) value of the visible light band of the system at 50 lp/mm (line pairs per millimeter) reaches 0.45, and the average MTF value of the long-wave infrared band at 50 lp/mm reaches 0.36. In addition, tolerance analysis, ambient temperature analysis and transmittance analysis of the integrated system are carried out in this paper to further improve the integrated system scheme, and the feasibility of the system is further verified.

The article is focused on the airflow in a ventilation system in a building. The work examines the methods which enhance the chimney effect. In this paper, three cases with different chimneys were analyzed for the full-scale experiment. These cases were characterized by different geometrical and material parameters, leading to differences in the intensity of the ventilation airflow. The common denominator of the cases was the room with the air inlet and outlet to the ventilation system. The differences between the experimental cases concerned the chimney canal itself, and more precisely its part protruding above the roof slope. The first experimental case concerned a ventilation canal made in a traditional way, from solid ceramic brick. The second experimental case concerned the part that led out above the roof slope with a transparent barrier, called a solar chimney. In the third experimental case, a rotary type of chimney cap was installed on the chimney to improve the efficiency of stack ventilation. All these cases were used to determine the performance of natural ventilation—Air Change per Hour (CH). Additionally, the paper presents a technical and economic comparison of the solutions used.

Digital cash is a form of money that is stored digitally. Its main advantage when compared to traditional credit or debit cards is the possibility of carrying out anonymous transactions. Diverse digital cash paradigms have been proposed during the last decades, providing different approaches to avoid the double-spending fraud, or features like divisibility or transferability. This paper presents a new digital cash paradigm that includes the so-called no-valued e-coins, which are e-coins that can be generated free of charge by customers. A vendor receiving a payment cannot distinguish whether the received e-coin is valued or not, but the customer will receive the requested digital item only in the former case. A straightforward application of bogus transactions involving no-valued e-coins is the masking of consumption patterns. This new paradigm has also proven its validity in the scope of privacy-preserving pay-by-phone parking systems, and we believe it can become a very versatile building block in the design of privacy-preserving protocols in other areas of research. This paper provides a formal description of the new paradigm, including the features required for each of its components together with a formal analysis of its security. This research was funded by the Spanish Ministry of Science, Innovation and Universities grant number MTM2017-83271-R.

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model.

In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%.

Circular and rectangular tunnel shapes are usually chosen when excavating at shallow depths in urban areas. However, special-shaped tunnels such as sub-rectangular tunnels have recently been used to overcome some drawbacks of circular and rectangular tunnels in terms of low space utilization efficiency and stress concentration, respectively. In the literature, experimental studies as well as analytical and numerical models have been developed for the seismic analysis and vulnerability assessment of circular and rectangular tunnels since the early 1990s. However, knowledge gaps regarding the behavior of sub-rectangular tunnels under seismic loading remain and still need to be bridged. The present paper focuses on introducing a numerical analysis of sub-rectangular tunnels under seismic loading. The numerical model of sub-rectangular tunnels is developed based on the numerical analyses of circular tunnels validated by comparing well-known, analytical solutions. This paper aims to highlight the differences between the behavior of sub-rectangular tunnels compared with circular tunnels when subjected to seismic loadings. Special attention is paid to the soil–lining interface conditions. The influence of parameters, such as soil deformations, maximum horizontal acceleration, and lining thickness, on sub-rectangular tunnel behavior under seismic loading is also investigated. The results indicate a significant behavior difference between sub-rectangular and circular tunnels. The absolute extreme incremental bending moments for a circular tunnel (no-slip condition) are smaller than that for the corresponding full-slip condition. The absolute extreme incremental bending moments of sub-rectangular tunnels (no-slip condition) are, however, greater than the corresponding full-slip conditions.

This paper presents the results from monitoring the organization of catering for children in general education institutions. Monitoring was conducted in August–September 2020. We collected relevant databases for 85 Russian subjects: the total number of indicators—113, the total number of sub-indicators—347, and the total number of data—76,414. This paper investigates the problems surrounding resource provision in regard to organizing nutrition for schoolchildren in the Russian Federation. We analyzed the structure of the informatization in regard to the provision of hot meals for schoolchildren in Russian regions. Moreover, we present cases and best practices surrounding informatization management and personalization of children’s nutrition. The research results reveal that Russia possesses a unified regional information system for accounting/monitoring the organization of children’s nutrition, as an independent specialized information resource (in regard to catering). This paper identifies the levels of information technology used in school nutritional programs for each federal district. We present the results of the analysis, the availability of information resources, and the possibility of automated non-cash payments for meals in general education institutions. Moreover, we analyzed information resources and generated reporting documents on the organization of nutrition for schoolchildren in Russia. Finally, we analyzed the automated assessment of satisfaction, in regard to catering and food quality among children and their parents in Russian regions.

A high damping rubber bearing (HDRB) is widely utilized in base-isolation structures due to its good energy dissipation capacity and environmentally friendly properties, however, it is incapable of isolating the vertical vibration caused by earthquakes and subways effectively. Thick rubber bearings with a low shape factor have become one of the important vertical isolation forms. This paper provides an experimental comparative study on high damping rubber bearings with low shape factor (HDRB-LSF), thick lead–rubber bearings (TLRB), and lead–rubber bearings (LRB). The abilities of the bearing and energy dissipation of the above bearings are analyzed contrastively considering the influence of vertical pressure, loading frequency, shear strain, and pre-pressure. Firstly, the HDRB-LSF, TLRB, and LRB are designed according to the Chinese Code for seismic design of buildings. Secondly, cyclic vertical compression tests and horizontal shear tests, as well as their correlation tests, are conducted, respectively. The vibrational characteristics and hysteresis feature of these three bearings are critically compared. Thirdly, a corrected calculation of vertical stiffness for the thick rubber bearings is proposed based on the experimental data to provide a more accurate and realistic tool measuring the vertical mechanical properties of rubber bearings. The test results proved that the HDRB-LSF has the most advanced performance of the three bearings. For the fatigue property, the hysteresis curves of the HDRB-LSF along with TLRB are plump both horizontally and vertically, thus providing a good energy dissipation effect. Regarding vertical stiffness, results from different loading cases show that the designed HDRB-LSF possesses a better vertical isolation effect and preferable environmental protection than LRB, a larger bearing capacity, and, similarly, a more environmentally friendly property than TLRB. Hence, it can avoid the unfavorable resonance effect caused by vertical periodic coupling within the structure. All the experimental data find that the proposed corrected equation can calculate the vertical stiffness of bearings with a higher accuracy. This paper presents the results of an analytical, parametric study that aimed to further explore the low shape factor concepts of rubber bearings applied in three-dimensional isolation for building structures.

Data hiding technology has achieved many technological developments through continuous research over the past 20 years along with the development of Internet technology and is one of the research fields that are still receiving attention. In the beginning, there were an intensive amount of studies on digital copyright issues, and since then, interest in the field of secret communications has been increasing. In addition, research on various security issues using this technology is being actively conducted. Research on data hiding is mainly based on images and videos, and there are many studies using JPEG and BMP in particular. This may be due to the use of redundant bits that are characteristic of data hiding techniques. On the other hand, block truncation coding-based images are relatively lacking in redundant bits useful for data hiding. For this reason, researchers began to pay more attention to data hiding based on block-cutting coding. As a result, many related papers have been published in recent years. Therefore, in this paper, the existing research on data hiding technology of images compressed by block-cut coding among compressed images is summarized to introduce the contents of research so far in this field. We simulate a representative methodology among existing studies to find out which methods are effective through experiments and present opinions on future research directions. In the future, it is expected that various data hiding techniques and practical applications based on modified forms of absolute moment block truncation coding will continue to develop.

This review examines current discussions from the cross-section study between urban heritage conservation and urban facility management fields in the academic literature from 2011–2020. The purpose is to identify the gaps within the examined papers to reveal the challenges and opportunities in the combined fields using the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s recommendation of the historic urban landscape (HUL) approach. The scoping review procedure was followed. The six critical steps and four supporting tools of the HUL approach were used to analyze the examined papers. Most aspects of urban heritage management within the body of literature were directly related to urban-scale facility management. The potential usage of building information modelling became one of the most discussed technological aspects. The expansion of the public–private partnership model into the public–private–people partnership is considered as a new potential business model. At the same time, the adaptive reuse approach is deemed to be the most sustainable method of managing heritage areas. This scoping review identified the financial tools as the most under-researched urban heritage facility management component. Therefore, it needs to be endorsed among the scientific communities to improve the knowledge and provide operable guidelines for the authorities and practitioners in the urban heritage field. Keywords: facility management (FM); urban FM; urban heritage; conservation; the HUL approach

The hanger often needs to be replaced many times during the operation period of hanger arch bridges. To ensure the safety of the hanger replacement in the construction process of pocket hanging, the structural response in the whole construction process needs to be precisely controlled. In this paper, aiming at half-through arches with a suspended deck by cable hangers, the precise displacement controlling method for hanger replacement of an arch bridge based on the pocket hanging method has been proposed. Firstly, the equivalent model of an arch bridge in the hanger replacement process is established, and the boundary conditions of the equivalent model are calculated precisely. Secondly, in the hanger replacement process, including old hanger demolition and new hanger installation, the precise displacement expressions of the suspended deck are derived on the basis of the equivalent model. Finally, the correctness and feasibility of the proposed precise displacement controlling method are verified by the hanger replacement engineering of an arch bridge. Through this research on the hanger replacement of an arch bridge, the equivalent model adopted in this paper has been proven accurate, and only partial boundary conditions need to be considered in practical engineering applications to get accurate results. Meanwhile, the calculation results are accurate enough through the practical engineering verification, and the precise displacement controlling method is feasible in the hanger replacement process of an arch bridge based on the pocket hanging method. It is also found that satisfactory results can be achieved using hanger demolition and installation by equal step length.

In line with the qualitative and quantitative growth of academic papers, it is critical to understand the factors driving citations in scholarly articles. This study discovered the up-to-date academic structure in the tourism and hospitality literature and tested the comprehensive sets of factors driving citation counts using articles published in first-tier hospitality and tourism journals found on the Web of Science. To further test the effects of research topic structure on citation counts, unsupervised topic modeling was conducted with 9910 tourism and hospitality papers published in 12 journals over 10 years. Articles specific to online media and the sharing economy have received numerous citations and that recently published papers with particular research topics (e.g., rural tourism and eco-tourism) were frequently cited. This study makes a major contribution to hospitality and tourism literature by testing the effects of topic structure and topic originality discovered by text mining on citation counts.

One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It would help in knowledge mining, idea generation, and information classification from scientific texts. It would also help to compare scientific papers and automatically generate review articles in a given field. However, computational research on problem–solution patterns has been scarce. The linguistic analysis, instructional-design research, theory, and empirical methods have not paid enough attention to the study of problem–solution patterns. This paper tries to solve this issue by applying the computational techniques of machine learning classifiers and neural networks to a set of features to intelligently classify a problem phrase from a non-problem phrase and a solution phrase from a non-solution phrase. Our analysis shows that deep learning networks outperform machine learning classifiers. Our best model was able to classify a problem phrase from a non-problem phrase with an accuracy of 90.0% and a solution phrase from a non-solution phrase with an accuracy of 86.0%.

The need to compete for users’ attention and provide them with the best user experience has increased the use of persuasion strategies in modern systems. Currently, Persuasive Systems (PSs) promote healthy behavior for well-being, energy consumption, and learning. Although the educational domain has limited investigation compared to other areas, PSs in education have been shown to be effective in motivating students. This paper summarizes the existing evidence on Persuasive Educational Systems (PESs), emphasizing research on the current design methods, evaluation methods, and characteristics. We follow Kitchenham’s method to perform a systematic literature review about PESs published between 2014 and 2020, with 19 relevant studies selected. We highlight some results from the analysis of selected papers such as persuasion strategies, use of a personalized persuasion technique, study of students’ susceptibility to strategies, integration of gamification mechanisms, and proposed tools to design PES. Moreover, we discuss interesting facts such as the common practice of using more than one tool to design PES, aspects of interaction, persuasion, learning, and the challenges in evaluating persuasive impact. Finally, as the main contribution of the paper we identify the seven necessary characteristics to build a persuasive educational system.

The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation.

When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%.

This paper focuses on generating distributed flocking strategies via imitation learning. The primary motivation is to improve the swarm robustness and achieve better consistency while respecting the communication constraints. This paper first proposes a quantitative metric of swarm robustness based on entropy evaluation. Then, the graph importance consistency is also proposed, which is one of the critical goals of the flocking task. Moreover, the importance-correlated directed graph convolutional networks (IDGCNs) are constructed for multidimensional feature extraction and structure-related aggregation of graph data. Next, by employing IDGCNs-based imitation learning, a distributed and scalable flocking strategy is obtained, and its performance is very close to the centralized strategy template while considering communication constraints. To speed up and simplify the training process, we train the flocking strategy with a small number of agents and set restrictions on communication. Finally, various simulation experiments are executed to verify the advantages of the obtained strategy in terms of realizing the swarm consistency and improving the swarm robustness. The results also show that the performance is well maintained while the scale of agents expands (tested with 20, 30, 40 robots).

The identification of a person is a natural way to gain access to information or places. A face image is an essential element of visual validation. In this paper, we present the Card3DFace application, which captures a single-shot image of a person’s face. After reconstructing the 3D model of the head, the application generates several images from different perspectives, which, when printed on a card with a layer of lenticular lenses, produce a 3D visualization effect of the face. The image acquisition is achieved with a regular consumer 3D camera, either using plenoptic, stereo or time-of-flight technologies. This procedure aims to assist and improve the human visual recognition of ID cards and travel documents through an affordable and fast process while simultaneously increasing their security level. The whole system pipeline is analyzed and detailed in this paper. The results of the experiments performed with polycarbonate ID cards show that this end-to-end system is able to produce cards with realistic 3D visualization effects for humans.

This paper presents a theoretical study of the dynamic behaviour of a wind turbine consisting of a wind rotor, a speed increaser with fixed axes, and a counter-rotating electric generator, operating in variable wind conditions. In the first part, the dynamic analytical model of the wind turbine mechanical system is elaborated based on the dynamic equations associated with the component rigid bodies and the linear mechanical characteristics associated with the direct current (DC) generator and wind rotor. The paper proposes a method for identifying the coefficients of the wind rotor mechanical characteristics depending on the wind speed. The numerical simulations performed in Simulink-MATLAB by MathWorks on a case study of a 10 kW wind turbine highlight the variation with the time of the kinematic parameters (angular speeds and accelerations), torques and powers for wind system shafts, as well as the mechanical efficiency, both in transient and steady-state regimes, considering variable wind speed. The analytical and numerical results are helpful for researchers, designers, developers, and practitioners of wind turbines aiming to optimise their construction and functionality through virtual prototyping.