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The development of diagnostic tools for measuring a wide spectrum of target analytes, from biomarkers to other biochemical parameters in biological fluids, has experienced a significant growth in the last decades, with a good number of such tools entering the market. Recently, a clear focus has been put on miniaturized wearable devices, which offer powerful capabilities for real-time and continuous analysis of biofluids, mainly sweat, and can be used in athletics, consumer wellness, military, and healthcare applications. Sweat is an attractive biofluid in which different biomarkers could be noninvasively measured to provide rapid information about the physical state of an individual. Wearable devices reported so far often provide discrete (single) measurements of the target analytes, most of them in the form of a yes/no qualitative response. However, quantitative biomarker analysis over certain periods of time is highly demanded for many applications such as the practice of sports or the precise control of the patient status in hospital settings. For this, a feasible combination of fluidic elements and sensor architectures has been sought. In this regard, this paper shows a concise overview of analytical tools based on the use of capillary-driven fluidics taking place on paper or fabric devices integrated with solid-state sensors fabricated by thick film technologies. The main advantages and limitations of the current technologies are pointed out together with the progress towards the development of functional devices. Those approaches reported in the last decade are examined in detail.

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community? This research received no external funding

A flexible pressure sensor with a rudimentary, ultra-low cost, and solvent-free fabrication process is presented in this paper. The sensor has a graphite-on-paper stacked paper structure, which deforms and restores its shape when pressure is applied and released, showing an exceptionally fast response and relaxation time of &asymp, 0.4 ms with a sensitivity of &minus, 5%/Pa. Repeatability of the sensor over 1000 cycles indicates an excellent long-term stability. The sensor demonstrated fast and reliable human touch interface, and successfully integrated into a robot gripper to detect grasping forces, showing high promise for use in robotics, human interface, and touch devices.

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching&ndash, learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.

Food safety and access to systematic approaches for ensuring detection of food hazards is an important issue in most developing countries. With the arrival of paper-based analytical devices (&micro, PADs) as a promising, rapid, easy-to-use, and low-cost analytical tool, we demonstrated a simple microfluidic-based titration study for the analysis of packaged fruit juices. Similar, to the titration experiments using traditional glassware in chemistry laboratories, in this study the titration experiments were developed using paper microfluidics for the analysis of several analytes such as pH, vitamin C, sugars, and preservatives present in the packaged fruit juices. The allergen found commonly in dairy based mixtures and the non-pathogenic biochemical component responsible for food spoilage in cider based fruit juices were also determined. The results obtained using paper microfluidics were compared with those obtained using a conventional spectrophotometric technique. Finally, a paper microfluidics based multiplexed sensor was developed for the analysis of common nutritional ingredients, an allergen, and a non-pathogenic byproduct present in packaged fruit juices on a single platform. Overall, the results presented in this study reveal that the proposed paper microfluidic assisted colorimetric multiplexed sensor offers a quick and reliable tool for on-spot routine analysis for food safety applications.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

Tetracycline is widely used as medicine for disease treatments and additives in animal feeding. Unfortunately, the abuse of tetracycline inevitably causes tetracycline residue in animal-origin foods. Though classical methods can detect tetracycline in high sensitivity and precision, they often rely on huge and expensive setups as well as complicated and time-consuming operations, limiting their applications in rapid and on-site detection. Here, we propose a handheld inkjet printing paper chip based smart tetracycline detector: tetracycline can be determined by inkjet printing prepared paper chip based enzyme-linked immunosorbent assay (ELISA) with the advantages of high sensitivity, excellent specificity and low cost, moreover, a smartphone based paper chip reader and application is designed for automatically determining tetracycline with simple operations, high precision and fast speed. The smart tetracycline detector with a compact size of 154 mm × 80 mm × 50 mm and self-supplied internal power can reach a rather low detection limit of ~0.05 ng/mL, as proved by practical measurements. It is believed the proposed handheld inkjet printing paper chip based smart tetracycline detector is a potential tool in antibiotic sensing for routine uses at home and on-site detection in the field.

Microfluidic paper-based analytical devices (microPADs) have emerged as a promising platform for point-of-care diagnostic devices. While the inherent wicking properties of microPADs allow for fluid flow without supporting equipment, this also presents a major challenge in achieving robust fluid control, which becomes especially important when performing complex multi-step assays. Herein, we describe an ideal method of fluid control mediated by wax-printed fluidic time delays. This method relies on a simple fabrication technique, does not utilize chemicals/reagents that could affect downstream assays, is readily scalable, and has a wide temporal range of tunable fluid control. The delays are wax printed on both the top and bottom of pre-fabricated microPAD channels, without subsequent heating, to create hemi-/fully-enclosed channels. With these wax printed delays, we were able to tune the time it took aqueous solutions to wick across a 25 mm-long channel between 3.6 min and 13.4 min. We then employed these fluid delays in the sequential delivery of four dyes to a test zone. Additionally, we demonstrated the automation of two simple enzymatic assays with this fluid control modality. This method of fluid control may allow future researchers to automate more complex assays, thereby further advancing microPADs toward real-world applications.

Untethered small-scale soft robots have been widely researched because they can be employed to perform wireless procedures via natural orifices in the human body, or other minimally invasive operations. Nevertheless, achieving untethered robotic motion remains challenging owing to the lack of an effective wireless actuation mechanism. To overcome this limitation, we propose a magnetically actuated walking soft robot based on paper and a chained magnetic-microparticle-embedded polymer actuator. The magnetic polymer actuator was prepared by combining Fe3O4 magnetic particles (MPs, diameter of ~50 nm) and silicon that are affected by a magnetic field, thereafter, the magnetic properties were quantified to achieve proper force and optimized according to the mass ratio, viscosity, and rotational speed of a spin coater. The fabricated polymer was utilized as a soft robot actuator that can be controlled using an external magnetic field, and paper was employed to construct the robot body with legs to achieve walking motion. To confirm the feasibility of the designed robot, the operating capability of the robot was analyzed through finite element simulation, and a walking experiment was conducted using electromagnetic actuation. The soft robot could be moved by varying the magnetic flux density and on&ndash, off state, and it demonstrated a maximum moving speed of 0.77 mm/s. Further studies on the proposed soft walking robot may advance the development of small-scale robots with diagnostic and therapeutic functionalities for application in biomedical fields.

Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the process nominal states and therefore are not enough for classification problems intended to determine abnormal process conditions. This work proposes the use of techniques to augment the original real data standards, dismissing the need for experiments that could jeopardize process safety. It uses the Monte Carlo technique to artificially increase the number of model inputs coupled to the nearest neighbor search (NNS) by geometric distances to consistently classify the generated patterns in normal or faulty statuses. Finally, a radial basis function neural network is trained with the augmented data. The methodology was validated by a study case in which 3381 pulp and paper industrial data points were expanded to monitor the formation of particles in a recovery boiler. Only 5.8% of the original process data were examples of faulty conditions, but the new expanded and balanced data collection leveraged the classification performance of the neural network, allowing its future use for monitoring purpose.

Today, there are many recent developments that focus on improving the electric vehicles and their components, particularly regarding advances in batteries, energy management systems, autonomous features and charging infrastructure. This plays an important role in developing next electric vehicle generations, and encourages more efficient and sustainable eco-system. This paper not only provides insights in the latest knowledge and developments of electric vehicles (EVs), but also the new promising and novel EV technologies based on scientific facts and figures—which could be from a technological point of view feasible by 2030. In this paper, potential design and modelling tools, such as digital twin with connected Internet-of-Things (IoT), are addressed. Furthermore, the potential technological challenges and research gaps in all EV aspects from hard-core battery material sciences, power electronics and powertrain engineering up to environmental assessments and market considerations are addressed. The paper is based on the knowledge of the 140+ FTE counting multidisciplinary research centre MOBI-VUB, that has a 40-year track record in the field of electric vehicles and e-mobility.

This paper reflects the technology-induced novelty of translation, which is perceived as a bridge between languages and cultures. We debate the extent to which the translation process maintains its specificity in the light of the new technology-enhanced working methods ensured by a large variety of Computer-Assisted Translation (CAT) and Machine Translation (MT) tools that aim to enhance the process, which includes the translation itself, the translator, the translation project manager, the linguist, the terminologist, the reviewer, and the client. This paper also hints at the topic from the perspective of the translation teacher, who needs to provide students with transversal competencies that are suitable for the digital area, supported by the ability to tackle Cloud-based translation tools, in view of Industry 4.0 requirements.

Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...]

Providing adapted e-health services, applications, and platforms responds to a growing need for medical institutions like hospitals or even homes [...]

Sources such as solar and wind energy are intermittent, and this is seen as a barrier to their wide utilization. The increasing grid integration of intermittent renewable energy sources generation significantly changes the scenario of distribution grid operations. Such operational challenges are minimized by the incorporation of the energy storage system, which plays an important role in improving the stability and the reliability of the grid. This study provides the review of the state-of-the-art in the literature on the economic analysis of battery energy storage systems. The paper makes evident the growing interest of batteries as energy storage systems to improve techno-economic viability of renewable energy systems; provides a comprehensive overview of key methodological possibilities for researchers interested in economic analysis of battery energy storage systems; indicates the need to use adequate economic indicators for investment decisions; and identifies key research topics of the analyzed literature: (i) photovoltaic systems with battery energy storage systems for residential areas, (ii) comparison between energy storage technologies, (iii) power quality improvement. The last key contribution is the proposed research agenda.

A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this local knowledge via sketch maps in the context of flooding. The Sketch Map Tool enables not only the visualization of this local knowledge and analyses of OpenStreetMap data quality but also the communication of the results of these analyses in an understandable way. Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues. ispartof: SENSORS vol:21 issue:15 ispartof: location:Switzerland status: published

In this position paper, we present the results of an ongoing theoretical investigation into the phenomenon of interactive architecture. Interaction in architecture deals with the meaningful exchange of information and physical acts between building and person. This goes beyond responsive systems like automated doors, shading systems, and so on. Most examples of interactive architecture are technological explorations that probe possibilities and the potential for interaction. In this paper we claim that this is not enough. The notion of interactive architecture is explored through social aspects, user experience, situatedness, and agent-based theory. From this we argue that interactive buildings need comprehensive and consistent styles of interaction rather than a series of isolated and unrelated interaction events. Different people in various contexts require different sets of behavior from an interactive building. These sets are conceptualized as interaction narratives, following the work of Maria Lehman. We argue that such narratives can provide a better fit of the interactive building with the user, and lead to a more profound understanding of such systems.

In this paper, we present WaterSpy, a project developing an innovative, compact, cost-effective photonic device for pervasive water quality sensing, operating in the mid-IR spectral range. The approach combines the use of advanced Quantum Cascade Lasers (QCLs) employing the Vernier effect, used as light source, with novel, fibre-coupled, fast and sensitive Higher Operation Temperature (HOT) photodetectors, used as sensors. These will be complemented by optimised laser driving and detector electronics, laser modulation and signal conditioning technologies. The paper presents the WaterSpy concept, the requirements elicited, the preliminary architecture design of the device, the use cases in which it will be validated, while highlighting the innovative technologies that contribute to the advancement of the current state of the art.

Extracting effective features from ship-radiated noise is an important way to improve the detection and recognition performance of passive sonar. Complexity features of ship-radiated noise have attracted increasing amounts of attention. However, the traditional definition of complexity based on entropy (information stored in the system) is not accurate. To this end, a new statistical complexity measure is proposed in this paper based on spectrum entropy and disequilibrium. Since the spectrum features are unique to the class of the ship, our method can distinguish different ships according to their location in the two-dimensional plane composed of complexity and spectrum entropy (CSEP). To weaken the influence of ocean ambient noise, the intrinsic time-scale decomposition (ITD) is applied to preprocess the data in this study. The effectiveness of the proposed method is validated through a classification experiment of four types of marine vessels. The recognition rate of the ITD-CSEP methodology achieved 94%, which is much higher than that of traditional feature extraction methods. Moreover, the ITD-CSEP is fast and parameter free. Hence, the method can be applied in the real time processing practical applications.

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

In recent years, reversible data hiding (RDH) has become a research hotspot in the field of multimedia security that has aroused more and more researchers&rsquo, attention. Most of the existing RDH algorithms are aiming at continuous-tone images. For RDH in encrypted halftone images (RDH-EH), the original cover image cannot be recovered losslessly after the watermark is extracted. For some application scenarios such as medical or military images sharing, reversibility is critical. In this paper, a reversible data hiding scheme in encrypted color halftone images (RDH-ECH) is proposed. In the watermark embedding procedure, the cover image is copied into two identical images to increase redundancy. We use wet paper code to embed the watermark into the image blocks. Thus, the receiver only needs to process the image blocks by the check matrices in order to extract the watermarks. To increase embedding capacity, we embed three layers in the embedding procedure and combine the resulting images into one image for convenience of transmission. From the experimental results, it can be concluded that the original image can be restored entirely after the watermarks are extracted. Besides, for marked color halftone images, our algorithm can implement high embedding capacity and moderate visual quality.

The existing LTE mobile system uses the vertical model to handle the session-based security management. However, the goal of this paper is to propose a packet key-based security management scheme on the blockchain control plane to enhance the existing session key-based security scheme and overcome the limitation that the existing vertical model, as well as the Software-Defined Networking (SDN) based horizontal model, confronts within solving end-to-end security management. The proposed blockchain-based security management (BSM) scheme enables each peer to easily obtain the necessary parameters required to manage the packet key-based security system. The important features of the BSM scheme include the renewal process, which enables the different packet data streams to use completely different security parameters for the security management. In addition, because even blind values cannot be exposed to the possible attackers, our BSM scheme guarantees very secure end-to-end data transfer against active attacks such as falsification of data and transactions. Finally, this paper compares the BSM scheme with the existing vertical model to prove the advantageous effects on latency.

Over the past four decades, the prophecy from computer pundits and prognosticators pointed to the looming arrival of the paperless office era. However, forty years later, physical paper documents are still playing a significant role due to the ease of use, superior readability, and availability. The drawbacks of paper sheets are that they are hard to modify and retrieve, have limited space, and are environmentally unfriendly. Augmenting paper documents with digital information from mobile devices extends the two-dimensional space of physical paper documents. Various camera-based recognition and detection devices have been proposed to augment paper documents with digital information. However, there are still some limitations that exist in these systems. This paper presents a novel, low cost, spatially aware, mobile system called Ultrasonic PhoneLens. The Ultrasonic PhoneLens adopts two-dimensional dynamic image presentation and ultrasonic sound positioning techniques. It consists of two ultrasonic sound sensors, one Arduino mini-controller board, and one android mobile device. Based on the location of the mobile device over the physical paper, Ultrasonic PhoneLens can retrieve pre-saved digital information from a mobile database for the object (such as a text, a paragraph, or an image) in a paper document. An empirical study was conducted to evaluate the system performance. The results indicate that our system has a better performance in tasks such as browsing multivalent documents and sharing digital information than the Wiimote PhoneLens system.

With the development of the Internet, network security has aroused people’s attention. It can be said that a secure network environment is a basis for the rapid and sound development of the Internet. Phishing is an essential class of cybercriminals which is a malicious act of tricking users into clicking on phishing links, stealing user information, and ultimately using user data to fake logging in with related accounts to steal funds. Network security is an iterative issue of attack and defense. The methods of phishing and the technology of phishing detection are constantly being updated. Traditional methods for identifying phishing links rely on blacklists and whitelists, but this cannot identify new phishing links. Therefore, we need to solve how to predict whether a newly emerging link is a phishing website and improve the accuracy of the prediction. With the maturity of machine learning technology, prediction has become a vital ability. This paper offers a state-of-the-art survey on methods for phishing website detection. It starts with the life cycle of phishing, introduces common anti-phishing methods, mainly focuses on the method of identifying phishing links, and has an in-depth understanding of machine learning-based solutions, including data collection, feature extraction, modeling, and evaluation performance. This paper provides a detailed comparison of various solutions for phishing website detection.

Maintaining hand hygiene has been an essential preventive measure for reducing disease transmission in public facilities, particularly during the COVID-19 pandemic. The large number of sanitizer stations deployed within public facilities, such as on university campuses, brings challenges for effective facility management. This paper proposes an IoT sensor network for tracking sanitizer usage in public facilities and supporting facility management using a data-driven approach. Specifically, the system integrates low-cost wireless sensors, LoRaWAN, and cloud-based computing techniques to realize data capture, communication, and analysis. The proposed approach was validated through field experiments in a large building on a university campus to assess the network signal coverage and effectiveness of sensor operation for facility monitoring. The results show that a LoRaWAN created from a single gateway can successfully connect to sensors distributed throughout the entire building, with the sensor nodes recording and transmitting events across the network for further analysis. Overall, this paper demonstrates the potential of leveraging the IoT-based Sanitizer Station Network to track public health mitigation methods in a large facility, which ultimately contributes to reducing the burden of maintaining public health during and post-pandemic.

The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems. Reinforcement Learning (RL) is an approach to simulate the human’s natural learning process, whose key is to let the agent learn by interacting with the stochastic environment. The fact that the agent has limited access to the information of the environment enables AI to be applied efficiently in most fields that require self-learning. Although efficient algorithms are being widely used, it seems essential to have an organized investigation—we can make good comparisons and choose the best structures or algorithms when applying DRL in various applications. In this overview, we introduce Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language processing. A follow-up paper will cover applications in transportation, communications and networking, and industries.

This paper attempts to display, analyze and discuss the literature affiliated to the previous research data on road surfacing in pavement engineering reinforcement. In this paper, a review of the background and present status of road surfacing is also provided for supportive explanation of the significance of fiber-reinforced asphalt pavement HMA and its role in providing effective and durable surfacing for heavy-trafficked roads. The paper attempts to clarify some of the terms and notions related to the discussions to give the readers the needed background, to be able to actively understand the experiments and discussions. Results from many studies confirm that fiber specifically enhances the optimum bitumen content in the design of the mixture and halts the bitumen leakage due to its asphalt absorbing susceptibility. Fiber modifies the visco-elastic response, susceptibility against moisture, increase resistance to rutting, as well as lowers the pavement fatigue cracking.

During earthquakes, buildings are subjected to loads well beyond their usual demands, resulting in high stresses in the structural components and additional inertial forces coming from the non-structural elements. When post-installed anchors are used to form the connection between non-structural or structural members and the primary reinforced concrete structure, these anchors are also subjected to high seismic demands. To determine whether a post-installed anchor is suitable for such applications, it is assessed for its performance under seismic demands. In this review paper, the current European approach for testing and qualification of post-installed anchors under seismic actions is reviewed and discussed in the context of structural applications where anchors are used to form the connection between structural members that participate in the load-transfer mechanism against seismic loads. The first part of this paper provides a description of the testing procedures and the criteria against which the anchor performance is assessed. The procedures and assessment criteria are discussed regarding the suitability in the case of the above-described structural applications. In the second part, the qualification of anchors under seismic actions is discussed in the light of an upcoming performance-based design approach for anchors. In such an approach, information on the displacement and hysteretic behavior of an anchor in a broader range of the load–displacement curve is of vital importance. Therefore, additional testing approaches might be required in order to supplement the information on anchor performance provided in the current testing procedures. One such testing approach for pulsating tension load is reported.

Microgrids have become increasingly popular in recent years due to technological improvements, growing recognition of their benefits, and diminishing costs. By clustering distributed energy resources, microgrids can effectively integrate renewable energy resources in distribution networks and satisfy end-user demands, thus playing a critical role in transforming the existing power grid to a future smart grid. There are many existing research and review works on microgrids. However, the thermal energy modelling in optimal microgrid management is seldom discussed in the current literature. To address this research gap, this paper presents a detailed review on the thermal energy modelling application on the optimal energy management for microgrids. This review firstly presents microgrid characteristics. Afterwards, the existing thermal energy modeling utilized in microgrids will be discussed, including the application of a combined cooling, heating and power (CCHP) and thermal comfort model to form virtual energy storage systems. Current trial programs of thermal energy modelling for microgrid energy management are analyzed and some challenges and future research directions are discussed at the end. This paper serves as a comprehensive review to the most up-to-date thermal energy modelling applications on microgrid energy management. Department of Finance and Education of Guangdong Province 2016 (202) Key Discipline Construction Program, China; the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group ; Brunel University London BRIEF Funding.

Q9 is widely used in industries handling flammable fluids and is central to explosion risk assessment (ERA). Q9 transforms complex flammable clouds from pressurised releases to simple cuboids with uniform stoichiometric concentration, drastically reducing the time and resources needed by ERAs. Q9 is commonly believed in the industry to be conservative but two studies on Q9 gave conflicting conclusions. This efficacy issue is important as impacts of Q9 have real life consequences, such as inadequate engineering design and risk management, risk underestimation, etc. This paper reviews published data and described additional assessment on Q9 using the large-scale experimental dataset from Blast and Fire for Topside Structure joint industry (BFTSS) Phase 3B project which was designed to address this type of scenario. The results in this paper showed that Q9 systematically underpredicts this dataset. Following recognised model evaluation protocol would have avoided confusion and misinterpretation in previous studies. It is recommended that the modelling concept of Equivalent Stoichiometric Cloud behind Q9 should be put on a sound scientific footing. Meanwhile, Q9 should be used with caution, users should take full account of its bias and variance.

This paper presents a building construction approach that is based on forty years of experience and a focus on multi-disciplinary synergies. After 1980, the migration science-based design was accelerated by the “Integrated Design Process (IDP)”. As a result, building science became a significant force in reducing the effects of climate change. The component associated with heating, cooling, and ventilation that is labeled “Environmental Quality Management” (EQM) or EQM-retro for interior applications will be discussed. The critical aspects of EQM-retro are: (1) A two-stage process for new and retro construction that modifies financing patterns. In stage one, the object is to develop the best possible performance within an investment limit. In stage two, the cost is minimized; (2) Building Automatic Control Systems (BACS) are important for control thermal mass contributions of while achieving adaptable indoor climate as well as an integration of the HVAC system with the building structure; (3) This is achieved with use of a monitoring application and performance evaluation (MAPE); (4) Introduction of BACS and MAPE during design process improves the integration of building subsystems and energy optimization. Examples showing increaseased occupant-controlled comfort, energy efficiency and flexibility of energy demand are presented in the paper.

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.

A 2017 survey of the state of practice on how agencies are developing their risk-based asset management plan shows that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data. Hence, there is a lack of integration of risk management in driving strategic cross-asset programming and decision-making. This paper proposes and implements a risk management database framework as the missing piece in the full implementation of a risk-based transportation asset management program. This risk management database framework utilizes Geographic Information Systems (GIS) and Application Programming Interface (API) to implement a risk management database of all the relevant variables an agency needs for risk modeling to improve risk monitoring, risk register updates, and decision-making. This approach allows the use of existing enterprise as well as legacy data collection systems, which eliminates the need for any capital-intensive implementation cost. Furthermore, it provides transportation agencies with the ability to track risk in quantitative terms, a framework for prioritizing risk, and the development of an actionable plan for risk mitigation. In this paper, the implementation of the fully integrated GIS-enabled risk management database employs the Iowa department of transportation (DOT) data and risk register.

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

In recent years, the use of deep learning in language models has gained much attention. Some research projects claim that they can generate text that can be interpreted as human writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the machine learning community has been researching this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the deep learning-enabled language models approach, we found a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code. This paper compares different neural network architectures like Average Stochastic Gradient Descent (ASGD) Weight-Dropped LSTMs (AWD-LSTMs), AWD-Quasi-Recurrent Neural Networks (QRNNs), and Transformer while using transfer learning and different forms of tokenization to see how they behave in building language models using a Python dataset for code generation and filling mask tasks. Considering the results, we discuss each approach&rsquo, s different strengths and weaknesses and what gaps we found to evaluate the language models or to apply them in a real programming context.

Modern IT systems collect detailed data on each activity, transaction, forum entry, conversation and many other areas. The availability of large data volumes in the business, industry and research fields opens up new opportunities for the empirical verification of various economic theories and laws. The analysis of big datasets in turn allows us to look at many issues from a new point of view and see the dependencies that are otherwise difficult to derive. In this paper, we propose a new measure for dependencies between goods in market basket data. The introduced measure was inspired by the well-known microeconomic concept of complementarity. Due to its similar properties to those of complementarity, the new measure was called basket complementarity (b-complementarity). B-complementarity not only measures the strength of dependencies between goods but also measures the direction of these dependencies. The values of the proposed measure can be relatively easily calculated using market basket data. This paper also presents a simple example illustrating this new concept, areas of possible application (e.g., in e-commerce) and preliminary results of searching for goods that meet the criteria of basket complementarity in real market basket data.

The paper is devoted to the measurement and to the processing of load spectra of forces and moments acting at the wheel hub of a motorcycle. Smart wheels (SWs) have been specifically developed for the scope. Throughout the paper, the extreme case of a race motorcycle is considered. Accurate load spectra were measured in two race circuits. Standardized load spectra are derived by processing measured data. A way to easily generalize the measured load spectra is proposed for the first time for motorcycles. Several loading conditions, related to the motorcycle straight line motion, cornering, curb hit and gear shift, are identified and extracted from the experimental measures. For each loading condition, by means of simple semi-analytical models (SAMs), a relationship is found between the vertical force on the wheel, the tilt angle of the motorcycle and the remaining forces and moments acting at the wheel hub. Such relationships are nothing else than the standardized load spectra. Additionally, a simple and efficient method based on smart wheels for real-time structural monitoring is proposed. Standardized load spectra prove to provide consistent results even when compared to real-time structural monitoring data. By means of the presented smart wheels, advanced lightweight motorcycle construction is enabled by derivation of standardized load spectra or real time estimation of the damage of structural components.

The starting point of this paper is the fact that multicriteria decision-making systems have not had the greatest impact on cultural studies, and few researchers have addressed this problem. The paper focuses on the analysis of the main object of an ongoing Doctoral thesis. More specifically, it examines the proposal of a new multicriteria evaluation based on a decision-making method. The paper intends to relate culture with multicriteria decision-making methods. This systematic review provides the direct analysis and assessment of the existing bibliographic references, and addresses a gap of knowledge in the intangible research field, identifying trends in the broader cultural heritage sector. The two basic principles of the paper are the recording and the evaluation of the cultural significance of the intangible and tangible heritage assets of a place. More specifically, the paper seeks to address a new scientific tool that initially records and&mdash, in the process&mdash, evaluates, using quantitative and qualitative criteria, specific cultural assets. Moreover, the paper outlines a new approach to the calibration&mdash, from optimal to worse&mdash, of the heritage of a place according to its significance. The purpose of the record and the evaluation is to create a ranking list of the most culturally significant tangible and intangible asset of places, in order to manage them. The design of the multicriteria method is based on a system that can be constantly updated diachronically, and can be fulfilled with new cultural assets and then re-evaluated. The explanation of the system is given through a step-by-step guide to the data analysis process, ensuring that the system has elements that are easy to access, to understand, and to use by each state actor (institution, individual, company, etc.).

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario&mdash, an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.

Current terrestrial mobile communications networks can’t provide worldwide coverage. Satellite communications are expensive, and terminals are large and heavy. Worldwide mobile coverage requires the use of satellites providing an appropriate QoS, including polar regions. The analysis of the potential satellite constellations demonstrates that LEO one is the best solution. A new generation of low cost, small size, lightweight and global mobile coverage LEO satellites is emerging. The main limitation of the terminals is the antenna size factor, and innovative antennas must be developed to meet this goal. This paper investigates the technologies and techniques for designing and developing antennas aimed at LEO satellite communications in Smart Cities and beyond, which are especially beneficial for mobile communications in areas without 4G/5G coverage. The paper focuses on the terrestrial segment and future mobile devices, remarking the design constraints. In this scenario, the paper reviews the most relevant technologies and techniques used to design suitable antennas. The investigation analyses the state-of-the-art and most recent advances in the design of antennas operating in the Ku-band. The main contribution of the authors is a novel antenna design approach based on SIW technology. The antenna features are compared with other approaches, highlighting the benefits, advantages and drawbacks. As a conclusion, the proposed antenna demonstrates to be a good solution to meet the design constraints for such an application: light, low cost, small size factor.

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.

Over the last few years, we have witnessed an impressive growth of data traffic and a progressive Digital Transformation of Industry and Society: the deployment of the ultra-broadband and low latency network infrastructures (e.g., 5G) are leading to a global digitalization of several domains. These techno-economic trends are expected to continue and even accelerate in the next decade, at end of which, 6G and smart networks and services will be exploited. Innovation will continue to drive the global economy into the next decade. This paper draws some technology trends and applications scenarios for this horizon, where Quantum Optical Communications are likely to disrupt Information and Communications Technology (ICT) and Telecommunications. Among the enabling technologies and solutions moving in this direction, this paper briefly addresses: quantum optical switching and computing, THz-to-optical conversions and advanced metamaterials for smart radio-optical programmable environments and Artificial Intelligence. The paper concludes with the description of a future application scenario, called Quantum Optical Twin, where the above Quantum Optical Communications technologies are exploited to provide services such as: ultra-massive scale communications for connected spaces and ambient intelligence, holographic telepresence, tactile Internet, new paradigms of brain computer interactions, innovative forms of communications.

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

The processing of high-precision aerospace parts requires not only ultra-precision machine tools, but also high-efficiency processing. However, in order to realize high-efficiency processing, besides optimizing the system and process parameters, some subversive research can also be done on the machine tool structure. In this paper, the lightweight research is mainly carried out on the structure of machine tool worktable. The traditional workbench is very &ldquo, heavy&rdquo, and &ldquo, slowness&rdquo, If the traditional workbench is subverted and reformed to reduce the weight, the processing efficiency will be improved qualitatively. Therefore, this paper studies the lightweight worktable of CFRP (carbon fiber reinforced polymer) in combination with the biological &ldquo, honeycomb&rdquo, shape. At first, the tensile, bending, compressive and laminar shear analysis of CFRP were carried out, and the comprehensive parameters were obtained. Simultaneously, the theoretical research and the honeycomb structure simulation and verification of CFRP worktable are carried out. The results show that the HACT (honeycomb arrangement of circular tubes) is 18.51% better than the SACT (straight arrangement of circular tubes) and 45.05% better than the OW (original worktable) by comparing and analyzing the weight of the three modes (HACT, SACT and OW). The actual weight of bionic honeycomb lightweight worktable is 1100 kg, while the simulation result is 1080.25 kg, with an error of 1.8%. Meanwhile, it is analyzed that the original workbench weight of the five-axis machining center is 2023 kg, while the simulation result is 1998.6 kg, with an error of 1.2%. The lightweight degree is reduced by 45.05%. However, the actual lightweight degree has been reduced by 45.63%. The error between simulation and actual is less than 1.3%. This kind of structural transformation has brought forward cutting-edge innovations to the machine tool processing industry. It provides a reference scheme for related enterprises in the future equipment renovation.

In this paper, we proposed a scheme that Injects artificial noise from the tag end (IANT) to enhance the physical layer security of the ambient backscatter communication (ABC) system. The difference between the ABC system and the traditional radio frequency identification system is whether it uses the radio frequency (RF) signals in the environment to supply energy and modulation information for passive tags. In the IANT scheme, we select the best tag to communicate with the reader according to the channel quality between tags and reader, and at the same time select another tag to generate artificial noise that affects the receiving effect of the eavesdropper. This paper uses the method of generating noise copies in the reader to reduce the interference of artificial noise on the signal received by the reader. The simulation results show that with the increase in channel quality between tags and reader and the increase in the number of tags, the proposed IANT scheme is significantly superior to the contrast scheme in terms of system achievable secrecy rate, effectively enhancing the physical layer security of the ABC system.

Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.

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.

To realize the adaptive grasping of objects with diverse shapes and to capture the joint angles of the finger, a multi degree of freedom (DOF) adaptive finger for prosthetic hand is proposed in this paper. The fingers are designed with three joints. The maximum rotation angle of the finger joints is 90&deg, The angle at which the finger joints bend can be captured. Firstly, the prototype design, forward kinematics and force analysis of phalanges are described in detail. In order to achieve an adaptive motion pattern similar to that of the human hand, this paper investigates the optimization of the torsion spring stiffness coefficient so that the metacarpophalangeal (MCP) joints, proximal interphalangeal (PIP) joints, and distal interphalangeal (DIP) joints of the bionic finger meet a motion ratio of approximately 3:3:1. Then, in order to realize the joint angle measurement in the process of grasping an object, the mechanical-sensor integrated finger joint is designed, and the composition, angle measurement principle and measurement circuit are introduced in detail. Finally, joint angle measurement, movement law evaluation and object grasping experiments are performed to verify the validity of the designed finger. The experimental results show that the root-mean-square (RMS) of the DIP, PIP and MCP angle measurement errors are 0.36&deg, 0.59&deg, and 0.32&deg, respectively. The designed finger is able to grasp objects with different shapes stably.