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Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus., Graphical Abstract ga1

Background: Operating schools safely under pandemic conditions is a widespread policy goal. We analyse the effectiveness of classroom cohorting, i.e., the decomposition of classrooms into smaller isolated units, in inhibiting the spread of SARS-CoV-2 in European secondary schools and compare different cohorting strategies. Methods: Using real-world network data on 12,291 adolescents collected in classrooms in England, Germany, the Netherlands, and Sweden in 2010/2011, we apply agent-based simulations to compare the effect of forming cohorts randomly to network-based cohorting. Network-based cohorting attempts to allocate out-of-school contacts to the same cohort to prevent cross-cohort infection more effectively. We consider explicitly minimizing out-of-school cross-cohort contacts, approximating this information-heavy optimization strategy by chained nominations of contacts, and dividing classrooms by gender. We also compare the effect of instructing cohorts in-person every second week to daily but separate in-person instruction of both cohorts. Findings: We find that cohorting reduces the spread of SARS-CoV-2 in classrooms. Relative to random cohorting, network-based strategies further reduce infections and quarantines when transmission dynamics are strong. In particular, network-based cohorting inhibits superspreading in classrooms. Cohorting that explicitly minimizes cross-cohort contacts is most effective, but approximation based on chained nominations and classroom division by gender also outperform random cohorting. Every-second-week instruction in-person contains outbreaks more effectively than daily in-person instruction of both cohorts. Interpretation: Cohorting of school classes can curb SARS-CoV-2 outbreaks in the school context. Factoring in out-of-school contacts can achieve a more effective separation of cohorts. Network-based cohorting reduces the risk of outbreaks in schools and can prevent superspreading events. Funding: None.

Background Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. Methods This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. Funding This study was supported by the National Key Research and Development Program (2017YFC1001800).

To manage the risk of insurance companies, a reinsurance transaction is among the myriad risk management mechanisms the top ranked choice. In this paper, we study the design of optimal reinsurance contracts within a risk measure minimization framework and subject to the Vajda condition. The Vajda condition requires the reinsurer to take an increasing proportion of the loss when it increases and therefore imposes constraints on the indemnity function. The distortion-risk-measure-based objective function is very generic, and allows for, for example, an objective to minimize the risk-adjusted value of the insurer’s liability, and for heterogeneous beliefs regarding the loss distribution by the insurer and reinsurer. Under a mild condition, we propose a backward-forward optimization method that is based on a marginal indemnity function formulation. To show the applicability and simplicity of our strategy, we provide three concrete examples with the Value-at-Risk: one with the risk-adjusted value of the insurer’s liability, one with an objective function that follows from imposing Pareto optimality, and one with heterogeneous beliefs. We conclude this paper with an empirical application with Danish fire insurance losses and the Value-at-Risk and the Tail Value-at-Risk.

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In order to build a smart campus teaching system with good teaching efficiency, this paper improves the traditional ZigBee algorithm and builds a smart campus teaching system based on ZigBee wireless sensors. Moreover, this paper improves the estimation algorithm of students' specific location and direction on campus, and analyzes the network structure of the smart teaching system based on the actual needs of smart teaching, and builds a three-tier teaching structure system. In addition, on the basis of demand analysis, this paper sets the system function modules, analyzes the data processing effect of the network nodes of this smart campus through experimental research, and evaluates the system teaching effect. Finally, this paper uses simulation technology to analyze 100 sets of data to evaluate the data processing effect of ZigBee network. Through the experimental research results, we can see that the smart campus teaching system constructed in this paper has good results.

The DC transformer based on the Modular Multilevel Converter (MMC) structure has the advantages of robust scalability, low output voltage harmonic content, easy realization of back-to-back connection, and high power transmission efficiency. It is widely used in DC distribution networks. However, traditional control methods hardly provide inertia for the DC distribution network, thereby reducing the stability of the distribution network when new energy output fluctuations, DC distribution network failures, or other disturbances occur. Aiming at the problem that the DC bus voltage is easily affected by power fluctuations, the paper proposes a virtual inertia control method applied to the DC transformer with MMC structure. Similar to the introduction of virtual synchronous generators in the AC distribution network to enhance the system inertia, the paper designs a control strategy for dynamically adjusting the virtual inertia with a virtual capacitor added, which is based on the traditional two-phase shift control. The paper also presents the calculation method of the virtual capacitance. When the power in the DC microgrid fluctuates significantly, the strategy can effectively enhance the inertia of the DC distribution network and suppress the voltage fluctuation of the DC bus. The paper finally establishes a DC distribution network model with distributed power sources in the PLECS simulation software, and verifies the effectiveness of the control strategy compared with the traditional strategy.

This paper presents the PLC-Based system for controlling the SCR power module which is AC–DC converter proposes to linearity output voltage with trigger signal. The technique converted the trigger signal by arccosine function and analog module in the PLC. Converted signal from PLC system is fed to SCR power module instant the traditional signal. Proposed of this paper describes the PLC hardware configuration, compute/math and trigonometric functions with Studio 5000 software. In addition, the interfacing, parameters configuration, and monitoring are considered for testing. The technique with PLC-based, devices configuration and experimental results have been presented will be useful for electric power supply in the industry with more and more demand to apply the PLC system to develop the traditional industry to semi or automatic control in the industry.

With the continuous deepening of power market reforms, studying the interaction of multi-stakeholder in multi-microgrid (MMG) energy trading is of great significance to improve the economic performance of the distribution network. In order to analyze the economic optimization method of multiple stakeholders in the distributed energy system, this paper proposes a multi-stakeholder benefit optimization method based on Stackelberg game theory (SGT), and establishes a local market energy transaction model. The model is composed of external distribution network, leader-intermediary agent (IA) and follower-microgrids (MGs), and uses the master–slave game method to solve the optimization strategy. Under this mode, the profit of each stakeholder in MMG increased by 2.64%, 4.24%, 1.38% respectively. The case results show that based on the method in this paper, multi-stakeholder in MMG can improve economic performance and can increase the level of energy autonomy of MMG.

With the development of science and technology, the power source of the automobile industry has gradually changed from non-renewable energy sources such as petroleum to electric energy, a clean and renewable energy source. By the end of 2020, scientists have found that the global demand for batteries has greatly increased. The demand for power batteries has reached 50% of all battery demand, occupying half of the market. In addition, scientists also boldly predict that by 2025, China's consumption of batteries in the automobile industry will reach more than 100,000 tons per year. Faced with so much battery consumption, how to dispose of and recycle LiFePO4 used batteries has become the key to restricting the development of the industry. Failure to dispose of used batteries will not only hinder the development of the industry, but also cause serious pollution to the environment. The application of sensors today has been extended to various fields, and the emergence of satellite navigation and positioning technology is one of the driving forces to promote its development. However, the sensor cannot be directly applied to various environments. When in a complex environment, the obstruction of various objects in the environment will seriously affect the stability of the sensor. In order to ensure the normal operation of the sensor, positioning technology began to be applied to the sensor. The industrial production process of solid-phase regeneration and electrochemical performance of waste lithium iron phosphate materials discussed in this article is based on this perspective, through the use of computer vision positioning to reduce the impact of environmental factors on the accuracy of the sensor, thereby improving the quality of the battery. In order to match the image point of the waste lithium iron phosphate material with the space point, this paper creatively uses the image point and the point in the corresponding space field. In addition, the author also studied the camera's rear rendezvous method and proposed a new rear rendezvous method. In addition, in order to strengthen the detection of waste lithium iron phosphate materials, this paper strengthened the construction of the sensor hardware system and software system, and completed the upgrade of the waste lithium iron phosphate material detection system.

In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series classification problem is converted into the visual pattern recognition problem. This transformation allows applying a wide range of algorithms for standard pattern recognition – in this domain, there are more options to choose from than in the domain of time series classification. In this paper, we demonstrated the high effectiveness of the new method in a series of experiments on publicly available time series. We compare our results with several state-of-the-art approaches dedicated to time series classification. The new method is robust and stable. It works for time series of differing lengths and is easy to extend and alter. Even with a baseline variant presented in an empirical study in this paper, it achieves a satisfying classification accuracy. Furthermore, the proposed conversion of raw time series into images that are subjected to feature extraction opens the possibility to apply standard clustering algorithms.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

A comprehensive review about machine learning application in power system especially in smart grid, renewable energy sector etc. is summarized in this paper. In the power sector, the power consumption is increased day by day very tremendously. So, it is very important that we have to generate the more power without disturbing the environment and whatever the generated power must be utilize effectively with minimum losses and higher efficiency. This will be possible with effective way of using the modern technology like machine learning (ML), artificial intelligence etc. This paper also describes the different types of machine learning techniques with diagram which will be very useful for many researchers who want to understand the basic fundamentals of machine learnings.

Smart Grid 2.0 is envisaged to automate the operations of the intelligent electricity grid. Blockchain and smart contracts are integrated to facilitate the transformation from DSO-centric operations to consumer-oriented, distributed electricity grid management. The envisaged smart grids, integrated with blockchain would provoke challenges, which would hinder the maximum utilization of Distribute Energy Resources (DERs). This comprehensive review aims at analyzing the applicability of blockchain technology in Smart Grid 2.0, which would facilitate a seamless decentralization process. Further, the paper elaborates the blockchain-based applications of future smart grid operations and the role of blockchain in each scenario. The paper further provides a concise analysis on the blockchain integration challenges, thereby ensure secure and scalable, decentralized operations of future, autonomous electricity networks.

Due to globalization and industrial development, the need of power is increasing day by day. In addition, due to the high demand for clean energy sources, the use of renewable systems has expanded in recent years. Wind systems can take advantage of the copious and free energy provided by the wind, which is a significant benefit. However, changing incident energy from renewables generate harmonics, which may result in poor system performance. The power quality has therefore become an important issue for both the power suppliers and consumers. Power electronic devices and nonlinear loads have been used frequently in industries hence introducing harmonic distortions in the industrial power distribution network. Harmonics have adverse effects on the power quality like degradation of power generation efficiency, higher operational costs, overheating of equipment, saturation of transformer, electromagnetic interference etc. In order to solve these problems, the implementation of suitable filtration techniques and the evaluation of efficiency with harmonic reduction are crucial. Since, utilities are always aiming to improve the power quality with minimum filtering units, this paper compares the performance of two available filter implementation plan, namely the shunt active power filter (SAPF) and the single tuned filter (STF) in a renewable penetrated industrial distribution network. Based on the outcome of the comparison, a genetic algorithm (GA) based approach is proposed in this paper to determine the optimum size and position of the minimum required filtering units that improves the network’s existing harmonic scenarios. Simulation results to validate the proposed methodology are presented. According to the results, the proposed method of incorporating filtering units significantly outperformed in lowering total harmonic distortion (THD) in an industrial power distribution network with a higher renewable penetration.

Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy. (C) 2021 The Authors. Published by Elsevier Ltd.

To increase the energy utilization efficiency, it becomes fairly promising to convert the surplus electricity from renewable generation to other forms of energy for multi-dimensional consumption. In this paper, we propose a hybrid energy scheduling model for a multi-energy microgrid with the integration of the hydrogen energy storage system (HESS) and the heat storage system (HSS). In our study, the operational uncertainties induced by renewables and loads (including electrical, hydrogen, and heat demand) are comprehensively considered. We investigate such an operating regime that HESS stores the surplus electricity in case of abundant renewable generation and generates electricity through hydrogen fuel cells otherwise. Further, heat units including HESS, combined heat and power (CHP), and external heat suppliers are modeled in this paper. We split the decision-makings of energy scheduling for both the day-ahead stage and real-time stage to tackle the power balancing issues. To effectively solve the aforementioned optimization model, a flexible weighted Model Predictive Control (weighted-MPC) strategy is proposed, in which the receding horizon can be suitably adjusted according to the forecasting accuracy of system uncertainties. The effectiveness of the proposed hybrid model for microgrid energy scheduling is comprehensively verified through extensive case studies.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

In the virtual world, the authentication process is used to prove the identity for both of the server and the clients. Therefore, the authentication is an urgent issue for sharing data between users in the data warehouse systems. This paper presents the enhancement of Data Warehouse Signature (DWS) framework through proposing a novel authentication and group-key distribution model, based on the modular symmetric polynomials, to distribute a secure group-key between the manager and the chosen executors. To the best of our knowledge, we are the first to propose a group-key based on the symmetric polynomial and the modular arithmetic. This leads to increase the security level of the proposed model from t, where t is the symmetric polynomial degree, to ∑ i = 1 c c i × t , where c is the number of executors in the DWS system. In this paper, enhancing the DWS achieves the high performance using a parallel computing in our middleware. The proposed model has the lowest cost of computation, communication and complexity overheads among the existing security models. Additionally, security analysis shows that the proposed model achieves the key security, prevents insider and outsider attacks, and provides forward and backward secrecy.

Transfer learning using Convolution Neural Networks (CNNs) has improved the state of the art results in many research studies. Rolling element bearing fault diagnosis is a domain that has been researched extensively using different data mining and machine learning techniques. In this paper, we prove that deep CNNs, when trained on Bispectrum images of fault signals using transfer learning, provide highly accurate and reliable results for fault diagnosis that are at par with the state of the art results. These transfer learning based models are able to quickly learn patterns from visual features of a vibration signal’s bispectrum, eliminating the need for manual feature extraction. In this paper, four pretrained networks – Alexnet, VGG-19, GoogLeNet, ResNet-50 – have been fine tuned on bispectrum images prepared from vibration signals of machine ball bearing elements. Each network has been trained with 3 optimizers – Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam) and Adamax. These models are able to obtain high classification accuracy within a few epochs. We also visualise and analyze the feature maps associated with intermediate convolution layers for one of these models.

الملخص: عادة ما تكون المقالات ما قبل الطباعة بمثابة مقدمة أولية لأوراق تمت مراجعتها من قِبل النظراء ويتم وضعها على الفور تقريبا، باستثناء بعض الفحص السطحي، في مستودع وصول مفتوح للجمهور للسماح للمعلومات بالوصول إلى القراء بسرعة، والتغلب على الخمول المرتبط عادة بالمعالجة بطريقة المجلات التي تعتمد على مراجعة الأقران. بالنسبة للباحثين في بداية حياتهم المهنية الذين قد يكونون متحمسين للحصول على بعض التقدير لجهودهم، أو الرغبة في الحصول على مدخلات مفتوحة وعلنية حول عملهم، فإن النشر قبل الطباعة هو بالتأكيد خيار نشر مفيد. ومع ذلك، إذا لم يتم فحص البيانات والمعلومات المتعلقة بالصحة بعناية، فقد تشكل خطرا، وقد تعمل أيضا كمصدر لمعلومات مضللة عن الصحة العامة. تشير زيادة النمو والمنافسة بين خوادم ما قبل الطباعة، إلى جانب حجم هائل من المطبوعات المسبقة المتعلقة بـ كوفيد-١٩، وخاصة على خادم ما قبل الطباعة لعلم الأحياء، وخادم ما قبل الطباعة للعلوم الطبية، بالإضافة إلى تحديد الفهرسة التي يتم اختبارها الآن على موقع بوب ميد، إلى أن المطبوعات المسبقة يتم استخدامها بشكل متزايد في العلوم الطبية الحيوية. وهناك حاجة إلى سياسات أخلاقية أقوى وأكثر قوة لفحص المطبوعات المسبقة قبل نشرها للجمهور، وحتى إذا كان هذا يعني تأخيرا طفيفا في النشر، فقد يؤدي ذلك إلى زيادة ثقة الأكاديميين في هذا الشكل من المعلومات العلمية والتواصل. يجب تقديم سياسات أخلاقية واضحة وصارمة بشكل عاجل من قبل مجموعات الأخلاقيات مثل لجنة أخلاقيات النشر، واللجنة الدولية لمحرري المجلات الطبية للمجلات الأعضاء التي تسمح بنشر مقالات ما قبل الطباعة قبل مراجعة النظراء التقليدية. ستفيد الإرشادات الأخلاقية الصارمة التي تتعامل مع سوء السلوك على قدم المساواة في مقالات ما قبل الطباعة والأبحاث التي راجعها النظراء، ستفيد نزاهة النشر الأكاديمي. Abstract: Preprints are typically crude precursors of peer-reviewed papers that are placed almost immediately, save for some superficial screening, on an open-access repository to allow the information to reach readers quickly, circumventing the long-drawn process typically associated with processing in peer-reviewed journals. For early-career researchers who might be enthusiastic about obtaining some recognition for their efforts, or wanting open and public input about their work, preprints are certainly a useful publication choice. However, if health-related data and information have not been carefully scrutinised, they may pose a risk and may even serve as a source of public health misinformation. Surging growth and competition among preprint servers, coupled with a massive volume of COVID-19-related preprints, mainly on bioRxiv and medRxiv, as well as select indexing now being tested on PubMed, suggests that preprints are being increasingly used in the biomedical sciences. Stronger and more robust ethical policies are needed to screen preprints before they are released to the public, and even if this implies a slight delay in publication, it may increase academics' trust in this form of scientific information and communication. Clear and stringent ethical policies need to be urgently introduced by ethics groups such as COPE and the ICMJE, whose many member journals allow preprints to be posted before traditional peer review. Stringent ethical guidelines that treat misconduct equally in preprints and peer-reviewed papers will boost the integrity of academic publishing.

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

This paper presents the optimal placement and sizing (OPAS) of active power filter (APF) considering photovoltaic distributed generation (PVDG) and nonlinear load to control the harmonics as per IEEE-519 standard limits. For the varying nature of PVDG output, the injection of harmonics into the distribution system is thoroughly investigated by incorporating hourly solar irradiance data. In this paper, a novel extended nonlinear load position based APF current injection (ENLPCI) technique is proposed to find the number of APFs and optimal buses for placement of APFs. The optimal placement for APF is calculated here for three distinct cases: a) only one state for minimum number of APFs, b) more than one state with different total harmonic distortion in voltage (THDv) with the same number of APFs and c) more than one state with the same THDv with the same number of APFs. The size and cost of APF are calculated and compared using grey wolf optimization (GWO) and its adaptive version (AGWO) algorithms for each hour. Further, the ENLPCI is also validated by GWO and AGWO algorithms for the optimal results. The proposed algorithm is tested on the IEEE-69 bus test system. The results show that the variation of solar irradiance remarkably affects the OPAS of APF. The AGWO performed better compared to GWO to find the optimal cost of APF.

In order to mitigate the damage caused by accidents in nuclear power plants (NPPs), evacuation strategies are usually managed on the basis of off-site effects such as the diffusion of radioactive materials and evacuee traffic simulations. However, the interactive behavior between evacuees and the accident environment has a significant effect on the consequential gap. Agent-based modeling (ABM) is a method that can control and observe such interactions by establishing agents (i.e., the evacuees) and patches (i.e., the accident environments). In this paper, a radiological emergency evacuation model is constructed to realistically check the effectiveness of an evacuation strategy using NetLogo, an ABM toolbox. Geographic layers such as radiation sources, roads, buildings, and shelters were downloaded from an official geographic information system (GIS) of Korea, and were modified into respective patches. The dispersion model adopted from the puff equation was also modified to fit the patches on the geographic layer. The evacuees were defined as vehicle agents and a traffic model was implemented by combining the shortest path search (determined by an A ∗ algorithm) and a traffic flow model incorporated in the Nagel-Schreckenberg cellular automata model. To evaluate the radiological harm to the evacuees due to the spread of radioactive materials, a simple exposure model was established to calculate the overlap fraction between the agents and the dispersion patches. This paper aims to demonstrate that the potential of ABM can handle disaster evacuation strategies more realistically than previous approaches.

SARISTU was a big cooperation project granted by the European Commission, 7th Framework Programme, carried out between 2011 and 2015. It dealt with smart aeronautic structures, both morphing and sensored; its main target was to demonstrate the feasibility of designing, manufacturing and operating in representative environment, instrumented structures. Till now, it represents the major effort carried out within the European Union on the development of adaptive architectures for air systems. Inside that big activity, the realization of an Adaptive Trailing Edge Device (ATED) for wing camber adaptations aimed at compensating the weight reduction following the fuel consumption during cruise was addressed. It made the core of investigations target variable geometry aircraft components together with two other analyses concerning the development of shape-changing winglet and droop nose. ATED activities were conducted by the Italian Aerospace Research Centre (CIRA) in tight cooperation with the University of Napoli, “Federico II”, who coordinated a group of 12 different partners from 8 different nations (France, Germany, Greece, the Netherlands, Israel, Spain, Turkey, and Italy). In this paper, an integral synthesis of that work is reported, with a focus on the definition and realization of the components of the presented device. The publication is in fact meant as the first part of a series that is aimed at overviewing the whole adaptive trailing edge development, till wind tunnel tests execution. Such a concise report is a critical and harmonized review of what have been performed by many colleagues spread all over Europe, all of which are duly recalled in the reported bibliography where the reader may access more detailed information and descriptions. In detail, the paper starts with a general introduction of the concept and its aims, to move to the specs definition immediately after. Then, it deals with a short but comprehensive description of the main ATED components: structural skeleton, skin, actuation and sensing systems. It is worth remarking that the paragraph dedicated to the body frame includes some discussion about aeroelastic assessment and manufacture, seen as complementation for a complete assessment of the design constraints.

The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness.

Facial Expression Recognition (FER) is an attractive and demanding problem in human computer interaction. Facial expressions are one of the most dominant and usual means for a human to communicate their feelings and intentions. This paper proposes the Enhanced Modified Decision Based Unsymmetric Trimmed Median Filter (EMDBUTMF) method which removes the noisy pixels from the face image. Also, this paper proposes the Local Directional Number (LDN) pattern, Dominant Gradient Local Ternary Pattern (DGLTP) descriptor for feature extraction and Support Vector Machine (SVM) classifier for classification. The histogram features are extracted from face images using the LDN and DGLTP descriptors. The experimental result shows that the proposed method gives an improved performance of 88% accuracy on two publicly available facial expression datasets JAFFE and CK.

Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) are two of the essential tools to design and develop new products. CAD systems help to design a new product efficiently, whereas CAE systems are used to predict and assess the future functionalities (durability, structural strength, etc.) of a product. Usually, these two systems work independently; however, their mutual dependency is a topic of interest for the researchers. This paper discusses the contemporary research efforts in the domain of CAD to CAE integration and later find the research gap. Based on the research gap this paper develop a methodology to integrate a CAD to CAE systems where scientific data format HDF (Hierarchical Data Format) acts as a bridge to link the gap between a CAD and CAE system. The virtual prototype of a product is getting accurate with the advancement of CAD systems; hence, the transferability of those models to a CAE system has become more sophisticated using traditional (e.g., .step, .iges files) methodologies. Since HDF is often used to handle massive and complex datasets for various research domains; hence, in this research effort HDF acts as a data repository neutral system to interact with the CAD and CAE systems. Based on the comprehensive literature review, this research effort aims to develop an uninterrupted integrated platform to handle complex design and analysis models using a big open-source data handling platform HDF (Hierarchical Data Format). MATLAB is used to bridge from the CAD system to HDF primarily and later from the HDF to CAE. A simple case study of analyzing a classic fluid mixing elbow demonstrates the preliminary implementation effort towards the proposed HDF-based integrated platform.

The enormous expansion of information, which is assembled from various design tools, has caused challenges in data exchange and compelled companies to find various solutions to improve collaboration. Data exchange within Building Information Modelling (BIM) context has been mainly focused on individual disciplines. Even though several attempts have been made to develop data exchange requirements for BIM models, there is still a lack of homogeneity since no method for classifying and sharing those requirements is clearly outlined. A clearly defined “single truth of information” is still not acknowledged yet. Software tools require unambiguous clarity of the semantics, which can help various stakeholders to proceed with their design tasks. However, there is still a lack of multi-dimensional knowledgebase for holistic decision-making within a BIM workflow. Therefore, this paper presents a common data analysis (CDA) referencing various concepts such as the standardised Information Delivery Manual (IDM) method, model view definition (MVD) and the concept of semantic intersection to conclude “single truth of information” and “partial truth of information” data sets that form the basis for a theoretical holistic decision-making framework to support collaborative design. The information defined in this research was validated based on existing resources and literature. Following the concluded data sets, a model can be transformed automatically at the minimum commonality level, creating a starting point for other professions. Following the analysis, a theoretical holistic decision-making framework was proposed. The innovation of the proposed framework lies in providing a holistic decision-making system that combines both data extraction using the concluded data sets and semantic web technology to eliminate inefficiencies in data sharing and improve the decision-making process in the early design stage by providing the stakeholders with rational solutions with less effort and time. This paper provides the essential requirement for a holistic decision framework from a data processing perspective.

X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to review computerised X-ray security imaging algorithms by taxonomising the field into conventional machine learning and contemporary deep learning applications. The first part briefly discusses the classical machine learning approaches utilised within X-ray security imaging, while the latter part thoroughly investigates the use of modern deep learning algorithms. The proposed taxonomy sub-categorises the use of deep learning approaches into supervised, semi-supervised and unsupervised learning, with a particular focus on object classification, detection, segmentation and anomaly detection tasks. The paper further explores wellestablished X-ray datasets and provides a performance benchmark. Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery.

It is important to manage operational disruptions to ensure the success of supply chain operations. To achieve this aim, researchers have developed techniques that determine the occurrence of operational risk events which assists supply chain operational risk managers develop plans to manage them by detection/monitoring, mitigation/management, or optimization techniques. Various artificial intelligence (AI) approaches have been used to develop such techniques in the broad activities of operational risk management. However, all of these techniques are black box in their working nature. This means that the chosen technique cannot explain why it has given that output and whether it is correct and free from bias. To address this, researchers argue the need for supply chain management professionals to move towards using explainable AI methods for operational risk management. In this paper, we conduct a systematic literature review on the techniques used to determine operational risks and analyse whether they satisfy the requirement of them being explainable. The findings highlight the shortcomings and inspires directions for future research. From a managerial perspective, the paper encourages risk managers to choose techniques for supply chain operational risk management that can be auditable as this will ensure that the risk managers know why they should take a particular risk management action rather than just what they should do to manage the operational risks.

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: • A Bayesian model assessment which compares two sets of values using nested sampling. This approach is new in NF grouping. • A Arsinh-Ordered Weighted Average model (Arsinh-OWA) which applies the arsinh transformation to the distance between two NFs, and then rescales the result to the arsinh of a biologically relevant threshold before grouping using OWA based distance. This approach is new in NF grouping. • An x-fold comparison as used in the ECETOC NanoApp. • Euclidean distance, which is a highly established distance metric. The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation., 28 pages, 8 figures, journal paper accepted by Applied Energy

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

This chapter explores the concept of a circular bioeconomy. The concept of circular economies suggests that optimal flow of goods and services can be represented as a loop; within a circular bioeconomy, the inputs to the loop are primarily biomass and outputs range from solid bioproducts, to paper and paperboard, and bioenergy. The circular bioeconomy concept also has a significant link to regional geographies, where resources may flow from point to point for processing and use, and where the final leg of the process brings materials back to the starting point. The global carbon cycle, which transfers carbon between the atmosphere and the terrestrial and aquatic ecosystems of the planet, is an example of circularity, which is emulated by circular bioeconomies. Feedstocks from forests, agriculture, or marine ecosystems may be utilized in a cascade of applications where products at every stage are designed for eventual recovery and reuse. Examples of existing circular bioeconomy applications include paper recycling—wood fiber may be reused in multiple paper applications before finally failing and being disposed of. The circular bioeconomy concept can be expanded to primary forest products (such as lumber), which might be used in one or more structural applications before being recycled into nonstructural applications (such as panels), chemicals (such as resins or coatings), or for energy production. Given proper design, a product cascade can be developed that would see biomass being utilized in sequential applications, and where each product in the chain is designed to ultimately supply the next product category. This vision would allow a single biomass input to deliver multiple bio-based products over an extended timeframe, including materials, chemicals, and energy. In this fashion, the feedstock supply is effectively multiplied.

Smart Grid technology has been considered an attractive research issue due to its efficiency in solving energy demand, storage, and power transmission. The integration of IoT technology in the Smart Grids is a critical way to accelerate the digitization of the power grid and is useful for the efficient performance of the energy grid infrastructure. For efficient real-time data analysis and decision-making, the Internet of Things will incorporate various communication systems seamlessly. To achieve efficient communication between all Internet of Things, devices are expected to use multiple means, including smart sensors, cable and wireless communication. Improved Internet of Things sensor technologies and connectivity could theoretically prevent or minimize the potential to natural disaster transmission lines, improve transmission power capacity and reduce economic losses. A smart grid is a variety of sensors, devices, and data sets that continuously capture high-resolution data equal to individual IoT conditions. A vast amount of data is one of the biggest challenges on the Internet of Things. Edge Computing is trying to process data close to linked sensors to address this problem, where the data is gathered and processed. This paper aims to investigate the edge computing solutions for the smart grid. A comprehensive review of both emerging issues and edge computing in the Smart Grid environment is discussed and explained. There are two primary components to the energy sharing process among Prosumers: information/digital technologies and Artificial Intelligence Scheduling Techniques. Each of them is mentioned in detail to discuss the Prosumer smart Grid. Furthermore, Edge Computing and classifications (cloudlet, Fog computing and Multi-Access) are among the suitable network methods mentioned in this paper. Some techniques and methodologies have been extensively covered to improve reader awareness of the Prosumer smart grid system.

During the last decade, the Integrated Circuit industry has paid special attention to the security of products. Hardware-based vulnerabilities, in particular Hardware Trojans, are becoming a serious threat, pushing the research community to provide highly sophisticated techniques to detect them. Despite the considerable effort that has been invested in this area, the growing complexity of modern devices always calls for sharper detection methodologies. This paper illustrates a pre-silicon simulation-based technique to detect hardware trojans. The technique exploits well-established machine learning algorithms. The paper introduces all the background concepts and presents the methodology. The validity of the approach has been demonstrated on the AutoSoC CPU, an industrial-grade, safety-oriented, automotive benchmark suite. Experimental results demonstrate the applicability and effectiveness of the approach: the proposed technique is highly accurate in pinpointing suspicious code sections. None of the hardware trojans from the set has been left undetected.

There has been a huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationships with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connection strengths between ROIs. Due to the high-dimensionality and non-Euclidean nature of networks, it is challenging to depict their population distribution and relate them to human traits. Current approaches focus on summarizing the network using either pre-specified topological features or principal components analysis (PCA). In this paper, building on recent advances in deep learning, we develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer their relationships to human traits. We refer to our method as Graph AuTo-Encoding (GATE). We applied GATE to two large-scale brain imaging datasets, the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) for adults, to study the structural brain connectome and its relationship with cognition. Numerical results demonstrate huge advantages of GATE over competitors in terms of prediction accuracy, statistical inference, and computing efficiency. We found that the structural connectome has a stronger association with a wide range of human cognitive traits than was apparent using previous approaches. 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research [1U54MH091657]; McDonnell Center for Systems Neuroscience at Washington University; National Institutes of Health [U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, U01DA041025] Published version Data used in the preparation of this article were obtained from the Human Connectome Project (https://www.humanconnectome.org/) and Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org).The HCP WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) were funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The ABCD study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups/.ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This paper reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.; Dunson and Zhang want to acknowledge support from the National Institutes of Health (NIH) of the United States under award number MH118927. We also want to acknowledge the support from the NVIDIA Corporation with their donation of the Titan-V GPU.

It is necessary and important to develop a simple, accurate, stable, and low-cost quality detection sensor for ensuring the quality of the fish. This paper aims to develop a flexible wireless pH sensor system to detect the freshness of fish. The pH sensor system consists of an indium tin oxide (ITO) electrode, an Ag/AgCl reference electrode, a computing adder, and a wireless communication module. The operational amplifier addition circuit raises the overall potential signal collected by the pH electrode and sends it to the wireless reader from the sensor tag. The sensitivity of the pH electrode is −50.974 mV/pH, and other properties of the electrode, such as reproducibility and response times, are excellent. This paper uses a flexible wireless pH sensor system to continuously monitor fish meat stored at 28 °C for 36 h. The experimental results show that the pH of the fish tissue solution changes with the quality of the fish, and the flexible wireless pH sensor system can achieve accuracy, convenience, and long-term wireless food quality inspection.

This paper introduces reproducible research (RR), and explains its importance, benefits, and challenges. Some important tools for conducting RR in Transportation Research are also introduced. Moreover, the source code for generating this paper has been designed in a way so that it can be used as a template for researchers to write their future journal papers as dynamic and reproducible documents.

Digital inspection twin (DIT) acting as a mirror between the physical and virtual measuring world. In this paper the measurement system with the CMMs ZEISS UMM 500 and DEA-IOTA-2203 was used as a physical twin, and a virtual machine, generated after modelling and configuring in PTC Creo software. Virtual measurement system is developed for inspection of the standard types of tolerances (concentricity, perpendicularity i.e.) and basic inspection features (IF) (plane, cylinder, truncated cone i.e.). The result of this paper is a new approach of the conceptual development of the digital inspection twin, based on CMMs - towards off-line DIT based on CMM by virtue of the control data list (CDL) and .ncl (DMIS) file.

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM., NeuroImage, 245, ISSN:1053-8119, ISSN:1095-9572

The Network Fundamental Diagram (NFD can be used to monitor and alleviate traffic congestion. Theoretically, one needs the trajectories of all vehicles in order to accurately estimate the NFD, which is not feasible. One solution is to construct the NFD using a sample of vehicles serving as probes together with an estimated market penetration rate (MPR). In a previous paper, we built a model to first estimate the MPR and then incorporated it in the NFD estimation. That model used data from probe vehicles (trip travel time, origin, destination, and route) and a few traffic count sensors on a hypothetical grid network. The results indicated that sensor locations have a significant impact on the accuracy of the estimated MPR and NFD. In this paper, we extend the model by adding more spatial criteria and test the approach on a real-world calibrated network, namely downtown Los Angeles. We examine the effect of selecting different probe vehicle samples on the model performance. After identifying the spatial rules for selecting sensor locations and the criteria to select probe vehicles, we present a methodology for estimating the NFD producing an acceptable NFD root mean square error. The significance of this study is that first, the model is validated using a real-world calibrated network; second, the data needed for the estimation are highly available, given that with connectivity the location and speed of a sample of vehicles is available; and third, the computational load to select detector locations and probe vehicles is minimum.

A significant amount of previous research into feature selection has been aimed at developing methods that can derive variables that are relevant to an entire dataset. Although these approaches have revealed substantial improvements in classification accuracy, they have failed to address the problem of explainability of outputs. This paper seeks to address this problem of identifying explainable features using a class-specific feature selection method based on genetic algorithms and the one-vs-all strategy. Our proposed method finds relevant features for each class in the dataset and uses these features to enable more accurate classification, and also interpretation of the outputs. The results of our experiments demonstrate that the proposed method provides descriptive insights into prediction outputs, and also outperforms popular global feature selection techniques in the classifications of high dimensional and noisy datasets. Since there are no known challenging benchmark datasets for evaluating class-specific feature selection algorithms, this paper also recommends an approach for combining disparate datasets for this purpose.

The paper presents an application for virtual didactic laboratory dedicated for instrumentation classes using blockchain technology for saving students measurements data in order to ensure their uniqueness. The application is developed in Labview and it does both, the lab simulation and the data saving using blockchain technology in a local private wallet. The laboratory part is dedicated to a multimeter verification using an adjustable voltage source. The adjustable voltage source is based on a Feussner compensator and on a Weston standard. The blockchain part is connecting to the teacher private wallet and saves the data as a blockchain transaction every time when the student press the button. The application is delivered to the student as an executable program, so he does not know where and how the data is sent. Further, the lab part of the present application can be modified to use remote controlled instruments, except the comparator that can be replaced with a modern adjustable voltage source. The paper presents at the beginning the used blockchain technology, how the transaction are performed, how the traceability can be verified, how the data is organized within a transaction. Further, the lab part of the application is described. Each simulated instrument respects the construction error limits provided in their datasheets, while each individual run of the instrument will generate a uniquely random value within this interval. In the end some demo acquired data and discussions are provided.

Additive manufacturing (AM) is advantageous in terms of lower carbon emissions, less material-wastage, mobility of digital design, product customization and simpler supply chain management. Moreover, during the global COVID-19 crisis, a robust and IT-driven manufacturing technology was highly required to cope with the peak demands of medical and basic emergency supplies. 3D printing (3DP) enabled the manufacturers to fulfill the deficiencies of healthcare equipment for medical professionals and patients throughout the world. In this paper, some emerging applications and challenges incurred by 3D printing during COVID-19 were briefly summarized. 3D printed PLA-based biodegradable mask filters, medical face shields, respiratory protective equipment and 3D bioprinted tissue models for COVID research are some significant contributions of AM. In this paper, a CiteSpace based bibliometric investigation of 3DP-related literature was conducted, to identify the technology evolution pathways for 3D printing, using the data retrieved from Web of Science and published between 1986 and 2021. The main contributing forces including countries, journals, institutions and subject areas were determined. Knowledge clusters were established to identify networks of the most prolific keywords and documents. In addition, a hierarchical knowledge foundation and hotspots of 3DP were recommended for future research. It was revealed that the policy support of the USA, China, England and Germany was significantly favorable for the development of 3DP research. This article provides insights into the COVID-based applications of 3DP and the research dynamics of this field, which can help in the decision-making process to incorporate innovative and emerging technologies in the AM industry.

[EN] A residential load management system equips smart meters (SMs) to measure load utilization at residencies. SM reports electricity usage based on electronic appliances. In this paper, a smart residential load management system is designed by three-fold along with the provisioning of consumer security. Load management encompasses load categorizing by Hopfield neural network, fuzzy-logic based bill payments identification and load state prediction using Markov chain. Residential load is based on three classes: active load, affordable load and inactive load. The estimation of residential load ensures to manage the load at residency either to turn off the load or intimate excess consumption of electricity. The registered consumers of a specific residency are enabled to receive load status by Internet of Things (IoT) devices. SMs at residencies periodically compute the electricity manipulation and those readings are encrypted using hybrid blowfish and elliptic curve cryptography algorithm with considering the behavior information from IoT device and partial key storage assists security, even if the device is theft. This smart residential security assisted load management (SRS-LM) system is developed in network simulator 3 and the results showed improvements in terms of power usage, load power, peak load reduction, total power consumption, power cost and computation time, This work was Supported by National Council of Science and Technology (CONACYT) through the pH.D. scholarship 705018. The authors would like to thank the University of Guadalajara, Mexico, and the Polytechnic University of Valencia, Spain, for all the collaboration and realization of this paper

Eye trackers allow for the collection of eye gaze data. These data can include eye gaze points which are two dimensional Cartesian coordinates ( x , y ) that represent a point on a screen (for screen-based eye trackers) or points in a natural real-world environment (e.g., at home, school or a supermarket). Advances in eye tracking technology have resulted in less intrusive devices being developed that open up opportunities for the Child Computer Interaction community (CCI) to understand how children interact with digital technologies and offer new insights into their cognitive processing. This paper reports the results of a literature review of three digital libraries using the Quorom technique, to identify research on eye tracking that aligns with the core themes of CCI researchers. In total 66 papers were identified that related to the themes of design, education, programming and interaction design. The results highlighted that eye tracking studies with children have relatively small sample sizes (median n = 11 ) and that the median age of the children participating in the studies is 10.3 (inter-quartile range = 3.96). The metrics used within the research studies are predominately fixation duration and fixations within areas of interest. The paper contributes to a critique of existing methodological challenges to ensure quality data is obtained from studies involving children. In addition, the review identified that there are clear opportunities to further understand children’s interaction with digital technology using other metrics such as pupillometry and blinks; these can assist to understand phenomenon including cognitive load and arousal which may be useful for user experience research.