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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.

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.

In the shipbuilding industry, each production process has a respective lead time, that is, the duration between start and finish times. Lead time is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it only references past records. This paper proposes a new self-organizing hierarchical particle swarm algorithm (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC)-support vector machine (SVM) regression model to increase the accuracy of lead-time prediction by combining the advanced PSO and SVM models. Moreover, this paper compares the prediction results of each SVM-based model with those of other conventional machine-learning algorithms. The results demonstrate that the proposed NHPSO-JTVAC-SVM model can achieve further meaningful enhancements in terms of prediction accuracy. The prediction performance of the NHPSO-JTVAC-SVM model is also better than that of the other SVM-based models or other machine learning algorithms. Overall, the NHPSO–JTVAC-SVM model is feasible for predicting the lead time in shipbuilding.

In order to effectively reduce the impact of Global Navigation Satellite System (GNSS) attacks while providing mobile terminals with credible navigation and positioning results, this paper proposes a credible navigation algorithm for GNSS attack detection using an auxiliary sensor system. Based on a credible Kalman filter and measurement information provided by the auxiliary sensor system on mobile terminals, the proposed algorithm can verify the credibility of the GNSS positioning result and determine whether it has suffered from a GNSS attack using the credible verification window and the credible verification threshold. According to the verification results, the algorithm can adaptively select an updated model for measurement correction and achieve a credible navigation result. The algorithm proposed in this paper has been verified on a self-developed mobile terminal, and the experimental results show that the algorithm can provide credible navigation and positioning services for mobile terminals in the context of GNSS attacks.

Solar air collectors installed on buildings can significantly reduce conventional energy consumption in winter and summer. However, some problems arise in the utilization process, such as overheating, inconvenient operation control and low energy efficiency, etc. This work is a parametric analysis focusing on the automatic control and thermal efficiency improvement of the solar wall. An improved color-changing solar wall integrated with automatic control components, such as a photoelectric fan and temperature-controlled damper, was proposed in this paper. Based on the experimental data, the average daily heat output of the color-changing solar wall is 1.08 MJ per unit floor area on clear days in winter and the average thermal efficiency is 56.8%. Meanwhile, a quantitative analysis was carried out based on monitoring experiments for evaluating the thermal characteristic of automatic control components. Furthermore, in order to improve the thermal performance of the solar wall, parametric analysis was performed by numerical simulation. Results from this paper can provide a theoretical basis for the application of solar air collectors in modern buildings.

In this paper, we propose a new type of visually induced reduction method for virtual reality (VR) sickness. This method induces a gaze based on a visual guide (VG). Although VGs are used in commercial VR game contents as crosshairs, no studies have been conducted related to VR sickness. VGs can have various properties determined by position, size, shape, color, etc., and it was necessary to investigate which properties affect VR sickness. In particular, size and position of VG are properties that directly affect the user’s gaze movement. Therefore, in this paper, five VR sickness reduction methods with different position and size of VG are proposed. Then, an experiment was conducted to investigate the effectiveness of the proposed VR sickness reduction method. To this end, a SSQ (including nausea, oculomotor discomfort, disorientation, and total score) and a questionnaire on fatigue and immersion were used. From the experimental results, a VG with a size 30% that of the aspect ratio, and a position synchronized to the user’s head movement direction, was most effective in terms of VR sickness reduction and immersion.

The usage of imbalanced databases is a recurrent problem in real-world data such as medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class imbalance problems, the classifiers are commonly biased by the class with more objects (majority class) and ignore the class with fewer objects (minority class). There are different ways to solve the class imbalance problem, and there has been a trend towards the usage of patterns and fuzzy approaches due to the favorable results. In this paper, we provide an in-depth review of popular methods for imbalanced databases related to patterns and fuzzy approaches. The reviewed papers include classifiers, data preprocessing, and evaluation metrics. We identify different application domains and describe how the methods are used. Finally, we suggest further research directions according to the analysis of the reviewed papers and the trend of the state of the art.

Summaries of scientific publications enable readers to gain an overview of a large number of studies, but users’ preferences have not yet been explored. In this paper, we conduct two user studies (i.e., short- and long-term studies) where Japanese university students read summaries of English research articles that were either manually written or automatically generated using text summarization and/or machine translation. In the short-term experiment, subjects compared and evaluated the two types of summaries of the same article. We analyze the characteristics in the generated summaries that readers regard as important, such as content richness and simplicity. The experimental results show that subjects are mainly judged based on four criteria, including content richness, simplicity, fluency, and format. In the long-term experiment, subjects read 50 summaries and answered whether they would like to read the original papers after reading the summaries. We discuss the characteristics in the summaries that readers tend to use to determine whether to read the papers, such as topic, methods, and results. The comments from subjects indicate that specific components of scientific publications, including research topics and methods, are important to judge whether to read or not. Our study provides insights to enhance the effectiveness of automatic summarization of scientific publications.

Social media rumor precise governance is conducive to better coping with the difficulties of rumor monitoring within massive information and improving rumor governance effectiveness. This paper proposes a conceptual framework of social media rumor precise governance system based on literature mining. Accordingly, insightful directions for achieving social media rumor precise governance are introduced, which includes (1) rational understanding of social media rumors, especially large-scale spreading false rumors and recurring false rumors, (2) clear classification of rumor spreaders/believers/refuters/unbelievers, (3) scientific evaluation of rumor governance effectiveness and capabilities. For the above three directions, advanced analysis technologies applications are then summarized. This paper is beneficial to clarify and promote the promising thought of social media rumor precise governance and create impacts on the technologies’ applications in this area.

In new and refurbished buildings, different energy sources are usually exploited to reach the Near Zero Energy Building target. Heat pumps and renewables are the most common adopted technologies. The coupling of the different components with a control logic conceived to exploit all energy contributions causes an implied design complexity. In this paper, two case studies were reported regarding the use of multisource heat pump systems: as main novelties, the correct design of the solar field (thermal or photovoltaic/thermal) in relation with the other sources (ground heat exchangers, ventilation heat recovery) for a given building and climate was reported in order to balance the energy drawn and injected into the ground around the year, and to attempt to reach the independency from the electric grid. Moreover, the relatively complex (compared to conventional heating or cooling) system controlling for multisource heat pump plants was simulated. The paper reported on the design of the plant, of the control logic, and the energy performance of two original multisource heat pump systems by means of dynamic simulation. In one case, real measured data were available as well. Very high primary energy ratios were obtained due to suitable control logics of the multisource plants, around 1.4 (based on measured data) and 4.7 (based on simulated data) for the first and second case, respectively. As a consequence, non-renewable primary energy consumptions of 37 and 3.9 kWh m−2 y−1 were determined, respectively.

Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.

Altres ajuts: ICREA, and by FEDER funds, Secreteraria d'Universitats i Recerca (Generalitat de Catalunya) and European Social Fund for the FI grant This paper presents a parametric analysis relative to the effects of the dielectric constant of the substrate, substrate thickness and slot width on the edge capacitance of a slot-based resonator. The interest is to find the conditions (ranges of the previously cited parameters) compatible with the presence of a quasi-magnetic wall in the plane of the slot (or plane of the metallization). If such magnetic wall is present (or roughly present), the electric field in the plane of the slot is tangential (or quasi-tangential) to it and the edge capacitance can be considered to be the parallel combination of the capacitances at both sides of the slot. Moreover, variations in one of such capacitances, e.g., caused by a change in the material on top of the slot, or by a modification of the dielectric constant of the substrate do not affect the opposite capacitance. Under the magnetic wall approximation, the capacitance of certain electrically small slot-based resonators can be easily linked to the dielectric constant of the material present on top of it. The consequence is that such resonators can be used as sensing elements in a permittivity sensor and the dielectric constant of the so-called material under test (MUT) can be determined from the measured resonance frequency and a simple analytical expression. In this paper, the results of this parametric analysis are validated by considering several sensing structures based on dumbbell defect ground structure (DB-DGS) resonators of different dimensions.

Global efforts to decarbonize heavy industry remain insufficiently aligned. While relatively new forms of international collaboration between and among states and companies are emerging, there is still considerable room to embark on more structured knowledge-sharing activities and coherent action among nations. In order to assess the concrete needs of an industry transition at scale, this paper analyzes 29 industry transition roadmaps across 13 countries, spanning the value chain of extractive, processing, and end-use heavy industry sectors. We compare and contrast these roadmaps according to the degree of ambition in decarbonization targets, the financial costs of implementing the roadmaps, and the key mitigation measures to achieve decarbonization targets. Importantly, this paper synthesizes and categorizes key policy, finance, and technology requirements called for to enable roadmap implementation. We demonstrate that the implementation of roadmaps across different industries and countries encounters common and comparable barriers and challenges, highlighting the need for international cooperation to facilitate global industry transitions.

This paper investigated the effect of air leaking into the working fluid on the performance of a steam ejector. A simulation of the mixing of air into the primary and secondary fluids was performed using CFD. The effects of air with a 0, 0.1, 0.3 and 0.5 mass fraction on the entrainment ratio and internal flow structure of the steam ejector were studied, and the coefficient distortion rates for the entrainment ratios under these air mass fractions were calculated. The results demonstrated that the air modified the physical parameters of the working fluid, which is the main reason for changes in the entrainment ratio and internal flow structure. The calculation of the coefficient distortion rate of the entrainment ratio illustrated that the air in the primary fluid has a more significant impact on the change in the entrainment ratio than that in the secondary fluid under the same air mass fraction. Therefore, the air mass fraction in the working fluid must be minimized to acquire a precise entrainment ratio. Furthermore, this paper provided a method of inspecting air leakage in the experimental steam ejector refrigeration system.

Water and energy are two of the most important inputs for a community to thrive. While water is dominant on earth, only 2.5% of the water is fresh water and over 98% of that water is either ground water or locked up in glaciers and ice caps. Therefore, only about 1.2% of all the freshwater is surface water which is able to meet human needs. About 2 billion people currently do not have sufficient access to fresh water. One of the solutions deployed in the last decades for island and coastal areas has been desalination. Desalination of seawater and brackish groundwater is commercially available and still a fast-advancing technology. The decreasing cost of renewable energy coupled with strategies based on renewables for powering populations without access to electricity and policies for complete decarbonization of the economy such as the European Green Deal make the combination of renewables and desalination a really interesting approach. This paper investigates combinations of small-scale RO desalination systems which are able to produce up to a few thousand m3 of desalinated water per day coupled with photovoltaic (PV) and wind energy systems, both in grid-connected, as well as in autonomous scenarios. The results show that RO desalination coupled with renewables can address cost-effectively the current issues in terms of water scarcity, while minimizing the environmental footprint of the process. In this paper, it has been showcased that desalination powered by renewables can be deployed in practically any location on earth having access to sea or a brackish water source. The results show that even for grid-connected systems it is more cost-effective and profitable to include a renewable energy system to power the plant, apart from the corresponding environmental benefits.

New technologies, such as cloud computing, the Internet of Things, wireless communications, etc., have already become part of our daily lives. This paper provides an insight into one of the new technologies, i.e., augmented reality (AR), as part of the manufacturing paradigm Industry 4.0 (I4.0). The aim of this paper is to contribute to the current state in the field of AR by assessing the main areas of the application of AR, the used devices and the tracking methods in support of the digitalization of the industry. Searches via Science Direct, Google Scholar and the Internet in general have resulted in the collection of a large number of papers. The examined works are classified according to several criteria and the most important data resulting from them are presented here. A comprehensive analysis of the literature has indicated the main areas of application of AR in I4.0 and, among these, those that stand out are maintenance, assembly and human robot collaboration. Finally, a roadmap for the application of AR in companies is proposed and the most promising future areas of research are listed.

This paper presents a new content-based image retrieval (CBIR) method based on image feature fusion. The deep features are extracted from object-centric and place-centric deep networks. The discrete cosine transform (DCT) solves the strong correlation of deep features and reduces dimensions. The shallow features are extracted from a Quantized Uniform Local Binary Pattern (ULBP), hue-saturation-value (HSV) histogram, and dual-tree complex wavelet transform (DTCWT). Singular value decomposition (SVD) is applied to reduce the dimensions of ULBP and DTCWT features. The experimental results tested on Corel datasets and the Oxford building dataset show that the proposed method based on shallow features fusion can significantly improve performance compared to using a single type of shallow feature. The proposed method based on deep features fusion can slightly improve performance compared to using a single type of deep feature. This paper also tests variable factors that affect image retrieval performance, such as using principal component analysis (PCA) instead of DCT. The DCT can be used for dimensional feature reduction without losing too much performance.

There are promising prospects on the way to widespread use of AI, as well as problems that need to be overcome to adapt AI&amp, ML technologies in industries. The paper systematizes the AI sections and calculates the dynamics of changes in the number of scientific articles in machine learning sections according to Google Scholar. The method of data acquisition and calculation of dynamic indicators of changes in publication activity is described: growth rate (D1) and acceleration of growth (D2) of scientific publications. Analysis of publication activity, in particular, showed a high interest in modern transformer models, the development of datasets for some industries, and a sharp increase in interest in methods of explainable machine learning. Relatively small research domains are receiving increasing attention, as evidenced by the negative correlation between the number of articles and D1 and D2 scores. The results show that, despite the limitations of the method, it is possible to (1) identify fast-growing areas of research regardless of the number of articles, and (2) predict publication activity in the short term with satisfactory accuracy for practice (the average prediction error for the year ahead is 6%, with a standard deviation of 7%). This paper presents results for more than 400 search queries related to classified research areas and the application of machine learning models to industries. The proposed method evaluates the dynamics of growth and the decline of scientific domains associated with certain key terms. It does not require access to large bibliometric archives and allows to relatively quickly obtain quantitative estimates of dynamic indicators.

This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions.

With the increase in renewable energy connected to the grid, new challenges arise due to its variable supply of power. Therefore, it is crucial to develop new methods of storing energy. Hydrogen can fulfil the role of energy storage and even act as an energy carrier, since it has a much higher energetic density than batteries and can be easily stored. Considering that the offshore wind sector is facing significant growth and technical advances, hydrogen has the potential to be combined with offshore wind energy to aid in overcoming disadvantages such as the high installation cost of electrical transmission systems and transmission losses. This paper aims to outline and discuss the main features of the integration of hydrogen solutions in offshore wind power and to offer a literature review of the current state of hydrogen production from offshore wind. The paper provides a summary of the technologies involved in hydrogen production along with an analysis of two possible hydrogen producing systems from offshore wind energy. The analysis covers the system components, including hydrogen storage, the system configuration (i.e., offshore vs. onshore electrolyzer), and the potential uses of hydrogen, e.g., Power to Mobility, Power to Power, and Power to Gas.

Most object detection methods use rectangular bounding boxes to represent the object, while the representative points network (RepPoints) employs a point set to describe the object. The RepPoints can provide more fine-grained localization and facilitates classification. However, it ignores the difference between localization and classification tasks. Therefore, a lightweight RepPoints with decoupling of the sampling point set (LRP-DS) is proposed in this paper. Firstly, the lightweight MobileNet-V2 and Feature Pyramid Networks (FPN) is employed as the backbone network to realize the lightweight network, rather than the Resnet. Secondly, considering the difference between classification and localization tasks, the sampling points of classification and localization are decoupled, by introducing classification free sampling method. Finally, due to the introduction of the classification free sampling method, the problem of the mismatch between the localization accuracy and the classification confidence is highlighted, so the localization score is employed to describe the localization accuracy independently. The final network structure of this paper achieves 73.3% mean average precision (mAP) on the VOC07 test dataset, which is 1.9% higher than original RepPoints with the same backbone network MobileNetV2 and FPN. Our LRP-DS has a detection speed of 20FPS for the input image of (1000, 600), on RTX2060 GPU, which is nearly twice as fast as the backbone network of ResNet50 and FPN. Experimental results show the effectiveness of our method.

The paper deals with problems in the post-implementation phase of management Enterprise Resource Planning (ERP) systems. Proper management of the system maintenance stage is a basis for efficient system development in terms of business needs. Based on the research and analysis of collected materials, it turns out that making a decision to upgrade the system is equally crucial. We present revealed mechanisms determining the post-implementation approach to upgrade or reimplement the ERP system. The main aim is to determine the methodology and difference understanding to achieve success in the post-implementation stage. The paper shows that the systemic approach to the maintenance stage of the ERP system affects its further decisions: upgrade or reimplement. It has a direct impact on future maintenance costs and the scope of new business demands. This research is an outcome of industry–academia collaboration and based on several developed implementation systems, achieved upgrade and reimplementation projects. Based on case study analysis, we show that reimplementation means an evolution of the current ERP processes rather than another attempt to “reimplement” an unsuccessful system implementation. On the other hand, upgrades are not only a tool or system actualization but the easiest way to bolster company sustainability and to have the information system up to date. The issues discussed in the article will be used to develop changes in the implementation methodology of ERP systems.

Low-carbon economy advances the sustainable development of the transportation of hazardous chemicals. This paper focuses on the multi-trip heterogeneous vehicle routing problem that includes the prioritization of customers and transportation of incompatible cargoes (MTHVRP-PCIC) in which some customers are prioritized for delivery by heterogeneous vehicles and more than one type of cargo is transported. This is an issue because some cargoes are incompatible with each other and therefore cannot be loaded into the same vehicle. MFHVRP-PCIC aims to find a set of routes resulting in minimal costs including fixed cost, travel cost and carbon emission cost. This problem occurs in real-life applications in the hazardous chemicals road transportation industry. This paper contributes to addressing the MTHVRP-PCIC from a problem definition, model, and methodological point of view. We establish a mathematical formulation for this problem. A two-stage hybrid metaheuristic approach (TSHM) is also devised to solve this problem. First, an improved greedy randomized adaptive search procedure is designed to generate initial feasible solutions. Then, a hybrid genetic algorithm including local search strategies, split-feasibility procedure, and simulated annealing is designed to solve this problem. Finally, the proposed approach is applied to solve a real case of hazardous chemical delivery and a benchmark dataset, and the resulting solutions indicate the advantage of our algorithm compared with those solutions obtained from managerial experience and classical algorithms.

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel’s size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN’s problem: that it is difficult to fully extract the sEMG’s temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%.

The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method.

These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face manner between instructors and students. Thus, while students are taking classes on a web-based basis, the problem arises that concentration on the lectures is significantly reduced. Therefore, in order to solve these problems, in this paper, we propose a level-wise learning algorithm based on the difficulty level of the test problem, and we present the simulation results. In order to improve this problem, in this paper, we propose an automatic music recommendation algorithm based on fuzzy reasoning that can improve the level of learning and lecture concentration, and we show our results on developing a web-based, smart e-learning software. As a result of computer simulation, it was proved that the learning test method, considering by level the difficulty of the test and the incorrect answer rate, was more effective than the existing test method, judged the student’s grades fairly, and improved the risk of unfairly failing the test by 30%.

Producción Científica, This paper shows an experimental application case to convert a part of the grid formed by renewable generation sources, storage systems, and loads into a smart microgrid. This transformation will achieve greater efficiency and autonomy in its management. If we add to this the analysis of all the data that has been recorded and the correct management of the energy produced and stored, we can achieve a reduction in the electricity consumption of the distribution grid and, with this, a reduction in the associated bill. To achieve this transformation in the grid, we must provide it with intelligence. To achieve this, a four steps procedure are proposed: identification and description of the elements, integration of the elements in the same data network, establishing communication between the elements and the control system, creating an interface that allows control of the entire network. The microgrid of CEDER-CIEMAT (Renewable Energy Centre in Soria, Spain) is presented as a real case study. This centre is made up of various sources of generation, storage, and consumption. All the elements that make up the microgrid are incorporated into free software, Home Assistant, allowing real-time control and monitoring of all of them thanks to the intelligence that has been provided to the grid. The novelty of this paper is that it describes a procedure that is not reported in the current literature and that, being developed with Home Assistant, is free and allows the control and management of a microgrid from any device (mobile, PC) and from any place, even though not on the same data network as the microgrid.

This paper proposes a method to detect the defects in the region of interest (ROI) based on a convolutional neural network (CNN) after alignment (position and rotation calibration) of a manufacturer’s headlights to determine whether the vehicle headlights are defective. The results were compared with an existing method for distinguishing defects among the previously proposed methods. One hundred original headlight images were acquired for each of the two vehicle types for the purpose of this experiment, and 20,000 high quality images and 20,000 defective images were obtained by applying the position and rotation transformation to the original images. It was found that the method proposed in this paper demonstrated a performance improvement of more than 0.1569 (15.69% on average) as compared to the existing method.

With the increasing growth of different types of data, search engines have become an essential tool on the Internet. Every day, billions of queries are run through few search engines with several privacy violations and monopoly problems. The blockchain, as a trending technology applied in various fields, including banking, IoT, education, etc., can be a beneficial alternative. Blockchain-based search engines, unlike monopolistic ones, do not have centralized controls. With a blockchain-based search system, no company can lay claims to user’s data or access search history and other related information. All these data will be encrypted and stored on a blockchain. Valuing users’ searches and paying them in return is another advantage of a blockchain-based search engine. Additionally, in smart environments, as a trending research field, blockchain-based search engines can provide context-aware and privacy-preserved search results. According to our research, few efforts have been made to develop blockchain use, which include studies generally in the early stages and few white papers. To the best of our knowledge, no research article has been published in this regard thus far. In this paper, a survey on blockchain-based search engines is provided. Additionally, we state that the blockchain is an essential paradigm for the search ecosystem by describing the advantages.

Protecting sensitive information transmitted via public channels is a significant issue faced by governments, militaries, organizations, and individuals. Steganography protects the secret information by concealing it in a transferred object such as video, audio, image, text, network, or DNA. As text uses low bandwidth, it is commonly used by Internet users in their daily activities, resulting a vast amount of text messages sent daily as social media posts and documents. Accordingly, text is the ideal object to be used in steganography, since hiding a secret message in a text makes it difficult for the attacker to detect the hidden message among the massive text content on the Internet. Language’s characteristics are utilized in text steganography. Despite the richness of the Arabic language in linguistic characteristics, only a few studies have been conducted in Arabic text steganography. To draw further attention to Arabic text steganography prospects, this paper reviews the classifications of these methods from its inception. For analysis, this paper presents a comprehensive study based on the key evaluation criteria (i.e., capacity, invisibility, robustness, and security). It opens new areas for further research based on the trends in this field.

As an alternative to conventional artificial heads, a virtual artificial head (VAH), i.e., a microphone array-based filter-and-sum beamformer, can be used to create binaural renderings of spatial sound fields. In contrast to conventional artificial heads, a VAH enables one to individualize the binaural renderings and to incorporate head tracking. This can be achieved by applying complex-valued spectral weights—calculated using individual head related transfer functions (HRTFs) for each listener and for different head orientations—to the microphone signals of the VAH. In this study, these spectral weights were applied to measured room impulse responses in an anechoic room to synthesize individual binaural room impulse responses (BRIRs). In the first part of the paper, the results of localizing virtual sources generated with individually synthesized BRIRs and measured BRIRs using a conventional artificial head, for different head orientations, were assessed in comparison with real sources. Convincing localization performances could be achieved for virtual sources generated with both individually synthesized and measured non-individual BRIRs with respect to azimuth and externalization. In the second part of the paper, the results of localizing virtual sources were compared in two listening tests, with and without head tracking. The positive effect of head tracking on the virtual source localization performance confirmed a major advantage of the VAH over conventional artificial heads.

Accounting information systems (AISs), the core module of any enterprise resource planning (ERP) system, are usually designed as centralised systems. Nowadays, the continuous development and applications of blockchain, or more broadly—distributed ledger technology (DLT), can change the architecture, overcome and improve some limitations of centralised systems, most notably security and privacy. An increasing number of authors are suggesting the application of blockchain technologies in management, accounting and ERPs. This paper aims to examine the emerging literature on this field, and an immediate result is that blockchain applications can have significant benefits. The paper’s innovative contribution and considerable objective are to examine if blockchain can be successfully integrated with AIS and ERPs. We find that blockchain can facilitate integration at multiple levels and better serve various purposes as auditing compliance. To demonstrate that, we analyse e-procurement systems and operations using case study research methodology. The findings suggest that DLT, decentralised finance (DeFI), and financial technology (FinTech) applications can facilitate integrating AISs and ERP systems and yield significant benefits for efficiency, productivity and security.

In the robotic engineering field, the main target, especially in industry, manufacturing, and surgical operations, is reaching the optimal performance of manipulators. The purpose of this paper is to quantify the contour tracking performance of collaborative universal manipulator robot (UR5) by setting the gain of position domain controller. In order to improve and enhance the track of manipulator in experimental applications we utilize differential evolution (DE) optimization, using MATLAB toolbox with an applied robot operating system (ROS). The adopted current approach does not only optimize the gain of position domain controller but also prevent collisions by detecting a “border crossing” without turning off the manipulator and allowing the automation agent to be on the scene, coexisting in harmonic mode and avoiding collisions. This requires the implementation of an algorithm that detects an obstacle to avoid anticipated collisions. For this purpose, the adopted algorithm uses the DE algorithm to modify the artificial potential field (APF). The results of this paper present that on one hand, meta-heuristic optimization algorithm features give the best performance indices for linear and non-linear contours, and on the other hand, DE algorithm features give good modification to APF to generate collision free contour path planning.

Hard-and-software integrated systems such as command and control systems (C4ISR systems) are typical systems that are comprised of both software and hardware, the failures of such devices result from complicated common cause failures and common (or shared) signals that make classical reliability analysis methods will be not applicable. To this end, this paper applies the Goal-Oriented (GO) methodology to detailed analyze the reliability of a C4ISR system. The reliability as well as the failure probability of the C4ISR system, are reached based on the GO model constructed. At the component level, the reliability of units of the C4ISR system is computed. Importance analysis of failures of such a system is completed by the qualitative analysis capability of the GO model, by which critical failures of hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are ascertained. This method of this paper contributes to the reliability analysis of all hard-and-software integrated systems.

The depletion of natural resources and the intermittence of renewable energy resources have pressed the need for a hybrid microgrid, combining the benefits of both AC and DC microgrids, minimizing the overall deficiency shortcomings and increasing the reliability of the system. The hybrid microgrid also supports the decentralized grid control structure, aligning with the current scattered and concentrated load scenarios. Hence, there is an increasing need to explore and reveal the integration, optimization, and control strategies regarding the hybrid microgrid. A comprehensive study of hybrid microgrid’s performance parameters, efficiency, reliability, security, design flexibility, and cost-effectiveness is required. This paper discusses major issues regarding the hybrid microgrids, the integration of AC and DC microgrids, their security and reliability, the optimization of power generation and load management in different scenarios, the efficient management regarding uncertainty for renewable energy resources, the optimal placement of feeders, and the cost-effective control methodologies for the hybrid microgrid. The major research areas are briefly explained, aiming to find the research gap that can further improve the performance of the grid. In light of the recent trends in research, novel strategies are proposed that are found most effective and cost-friendly regarding the hybrid microgrid. This paper will serve as a baseline for future research, comparative analysis, and further development of novel techniques regarding hybrid microgrids.

The balise transmission system (BTS) is an automatic identification system for vehicle–ground communication based on radio frequency identification (RFID) technology. The electromagnetic coupling characteristics of BTS have a very important effect on the transmission quality of the uplink telegram signals. However, signal transmission problems of BTS often occur due to unreasonable installation mode or parameter setting. In order to solve these problems, it is necessary to fully discuss the electromagnetic coupling characteristics of the BTS. In this paper, the transmission process of energy and data between the onboard antenna unit and the balise was analyzed using digital twin technology. A high-precision dynamic electromagnetic coupling model of the BTS was established from four aspects of three-dimensional structure, physical properties, behavior patterns, and rule restrictions. Then the accuracy of the model was verified by experiments. Finally, the influence of typical parameters on the uplink signal is calculated and analyzed quantitatively. The results showed that compared with other factors discussed in this paper, the vertical distance and the installation mode had greater effects on the uplink signal. These results can be used to guide the engineering installation and related optimization of the BTS.

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

When the shearer cuts coal or rock with different hardness, it will produce corresponding cutting state information. This paper develops a simulation cutting experiment system for the drum shearer based on similarity theory. It took the spiral cutting drum of a shearer as the research target and derived the principal similarity coefficients through the dimensional analysis method. Meanwhile, this paper designed the structure of the cutting power system and hydraulic system. Then, it chose a certain amount of coal powder as an aggregate, cement 325# as cementing material, sand, and water as auxiliary materials to prepare simulated coal samples. The paper adopted the orthogonal experiment method and used a proportion of cement, sand, and water as the influencing factors in designing a simulated coal sample preparation plan. In addition, it utilized the range analysis method to research the influence of various factors on the density and compressive strength of simulated coal samples. Finally, it conducted simulated coal sample cutting tests. The results show that the density of the simulated coal samples is between 1192.59 Kg/m3–1483.51 Kg/m3, and the compressive strength range reaches 0.16 MPa–3.94 MPa. The density of the simulated coal sample is related to the mass proportion of cement and sand. When the ratio gradually increases, the influence of sand increases. Furthermore, the compressive strength is linearly proportional to the proportion of cement. The self-designed simulation cutting experiment system could effectively carry out the relevant experiments and obtain the corresponding cutting condition signals through the sensors. There are differences in vibration signals generated by cutting different strength materials. Extracting the kurtosis value as the characteristic value can distinguish various cutting modes, which can provide a reliable experimental solution for the research of coal-rock identification.

DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.

An efficient water supply management system can be one of the applications of the Internet of Things (IoT). Water is a basic physiological need, and smart management of water plays a significant role in a smart city. This paper focuses on a mathematical model and IoT framework that aid in developing a smart city. A framework is developed for water supply management. The efficiency of the water supply can be measured by monitoring leakage conditions, overflow of water, automatic meter reading and online bill payments, and water consumption status of households, community, state, and eventually the whole country as well as the automatic water supply line cut-off. The system where the IoT is being deployed consists of embedded hardware in which sensors and microcontrollers provide messages and gain feedback from each other with the help of the internet, and this process can not only be monitored but also can be controlled from a remote location. The developed framework addresses all these aspects and mathematical equations are used and formulated while developing the IoT application. The mathematical equations are concentrated on consumption level (CL), leakage reporting (LR), and bill amount (BA) based on consumption. These become the point of contact for deploying IoT and eventually a framework is developed. This framework can be useful not only in water supply management but also in the management of road traffic, pollution, garbage, home automation and so on. In a nutshell, this paper illustrates the usage of the IoT framework in water supply management which contributes to developing the smart city.

Air conditioning energy consumption accounts for most building energy consumption, indoor dehumidification is the main cause of air conditioning energy consumption. Optimize the dehumidification methods of air conditioning systems have great significance to the development of green buildings and people’s pursuit of comfort. Improvement of fins on air conditioning heat exchangers is a hot topic of current research and has achieved considerable results in terms of indoor dehumidification and energy saving compared to traditional air conditioners. This paper reviews two kinds of heat exchangers modified by coating, including desiccant-coated heat exchangers and hydrophobic/hydrophilic coated heat exchangers. For desiccant-coated heat exchangers, the preparation methods of advanced desiccant materials and the possibilities of using this material to achieve excellent energy efficiencies were presented, and the operating parameters that affect thermal performance and dehumidification are determined, including airflow temperature, air velocity, inlet air relative humidity, and regeneration temperature. For hydrophobic/hydrophilic coated heat exchangers, different kinds of hybrid hydrophobic-hydrophilic surfaces are highlighted for they are a high water droplet nucleation rate and surface heat transfer efficiency. In addition, the challenges and future works are explained at last. This paper will provide a valuable reference for the follow-up research, which will be helpful for indoor humidity control and reducing the energy consumption of air conditioning.

Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects, the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

Modern consumer installations can be equipped with renewable power sources (RESs) and stationary energy storage systems (ESSs). In addition, electric vehicles (EVs) are expected to become part of such installations in the not-too-distant future. The paper presents the control strategy that allows for efficient energy management and the option of EV “fast-home” charging. The novelty of this approach includes the use of the “time-dependent energy storage” (EV battery) together with ESS and PV sources with the focus on prosumer benefits. All goals can be achieved without the need for extensive expenses in the home electric infrastructure. To enable the synergy effect, it was necessary to develop a controller algorithm that uses the operating status of the prosumer infrastructure (current power generation and consumption), the state of charge of both the stationary storage and the EV battery, and the possibility to control the EV drive inverter during the parking state. The paper presents a developed simulator built in the PSCAD environment and the simulation results.

In the European Union, almost 40% of all energy consumption comes from buildings, while another 20–25% comes from transport. In the European Union, including Hungary, only buildings with almost-zero energy demand could be built after 2020, and the use of renewable energies must be strengthened. The Renewable Directive stipulated that by 2020, the share of renewable energy in buildings must be 25%, and in transport it must be 10%, the use of electric vehicles is vital. There are about four million dwellings in Hungary, of which approximately three million need to be renovated, and only some of these (a few hundred) meet the cost-optimized level of the 2020 directive. The use of insulation materials is very important in the transport sector, too. Insulation materials are also used by aircraft and electric vehicles. To reduce the energy loss from buildings, different insulation materials can be used, investigations of insulation materials are very important. This paper presents a comprehensive research report on insulation materials which could be used for building elements, HVAC (heating, ventilation, and air conditioning appliances) equipment, and vehicles. In this paper, laboratory investigations will be presented along with calculations to better understand the properties and behavior of these materials. For this, firstly structural analysis with scanning electron microscope will be presented. Moreover, the paper will present thermal conductivity and combustion heat measurement results. The sorption and hydrophobic behavior of the materials will be also revealed. Finally, the article will also display differential scanning calorimetry measurements and Raman spectroscopy results of the samples. The research was conducted on four different types of colorized microfiber lightweight wool insulation.

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.

Autonomous mobile vehicles need advanced systems to determine their exact position in a certain coordinate system. For this purpose, the GPS and the vision system are the most often used. These systems have some disadvantages, for example, the GPS signal is unavailable in rooms and may be inaccurate, while the vision system is strongly dependent on the intensity of the recorded light. This paper assumes that the primary system for determining the position of the vehicle is wheel odometry joined with an IMU (Internal Measurement Unit) sensor, which task is to calculate all changes in the robot orientations, such as yaw rate. However, using only the results coming from the wheels system provides additive measurement error, which is most often the result of the wheels slippage and the IMU sensor drift. In the presented work, this error is reduced by using a vision system that constantly measures vehicle distances to markers located in its space. Additionally, the paper describes the fusion of signals from the vision system and the wheels odometry. Studies related to the positioning accuracy of the vehicle with both the vision system turned on and off are presented. The laboratory averaged positioning accuracy result was reduced from 0.32 m to 0.13 m, with ensuring that the vehicle wheels did not experience slippage. The paper also describes the performance of the system during a real track driven, where the assumption was not to use the GPS geolocation system. In this case, the vision system assisted in the vehicle positioning and an accuracy of 0.2 m was achieved at the control points.

With the increasing application of CHP and an industry transition to distributed energy, it is necessary to make a comprehensive economic analysis and comparison of the entire lifetime of CHP from the net present value (NPV), payback period, and cost-saving ratio (CSR). Five systems, including micro-CHP, gas boiler (GB), air-source heat pump (ASHP), domestic gas-fired heater and domestic electric hot water-heater, are simulated. First, this paper takes annual heat use efficiency (AHUE) into account to compare the economy of each domestic hot water (DHW) system. The results show that a domestic gas-fired heater system is the most economical option in the AHUE of 31.28%. The economic influence of CHP and gas-fired heater under different AHUE are then analyzed. The results show that the DHW system based on CHP is the best when the AHUE is more than 55.35%. Finally, three different operation strategies of CHP are considered in this paper. From the perspective of annual energy cost and payback, the internal combustion-based CHP with thermal energy system (TES) is superior to the other two strategies being studied. Considering the optimal economic benefits, the CSR of the three different operation strategies is 41.3%, 69.69% and 69.77%, respectively.