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Background Clinical guidelines on infection control strategies in healthcare workers (HCWs) play an important role in protecting them during the severe acute respiratory syndrome coronavirus 2 pandemic. Poorly constructed guidelines that are incomprehensive and/or ambiguous may compromise HCWs’ safety. Objective The objective of this study was to develop and validate a tool to appraise guidelines on infection control strategies in HCWs based on the guidelines published early in the coronavirus disease 2019 pandemic. Design, setting, and outcomes A three-stage, web-based, Delphi consensus-building process among a panel of diverse HCWs and healthcare managers was performed. The tool was validated by appraising 40 international, specialty-specific, and procedure-specific guidelines along with national guidelines from countries with a wide range of gross national income. Results Overall consensus (≥75%) was reached at the end of three rounds for all six domains included in the tool. The Delphi panel recommended an ideal infection control guideline should encompass six domains: general characteristics (domain 1), engineering recommendations (domain 2), personal protective equipment (PPE) use (domain 3), and administrative aspects (domain 4-6) of infection control. The appraisal tool performed well across the six domains, and the inter-rater agreement was excellent for the 40 guidelines. All included guidelines performed relatively better in domains 1–3 than in domains 4–6, and this was more evident in guidelines originating from lower income countries. Conclusion The guideline appraisal tool was robust and easy to use. Engineering recommendations aspects of infection control, administrative measures that promote optimal PPE use, and HCW wellbeing were generally lacking in assessed guidelines. This tool may enable health systems to adopt high-quality HCW infection control guidelines during the severe acute respiratory syndrome coronavirus 2 pandemic and may also provide a framework for future guideline development.

Motivation Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug’s mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. Results In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet’s ability to use mechanistic insight to identify a drug’s mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug–target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method’s utility in providing mechanistic insight into repurposing predictions it provides. Availability and implementation The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). Supplementary information Supplementary data are available at Bioinformatics online.

Natural dyes extracted from natural sources are likely to be less harmful and more eco-friendly than synthetic dyes. In this study, cotton fabric was dyed with natural dyes extracted from Java plum fruit ( Syzygium cumini). Dyeing was accomplished with or without mordanting agents using simultaneous and post-mordanting methods. The color of the treated substrates was investigated in terms K/S and CIELAB values. In addition, the attenuation co-efficient and solubility in organic solvents of the extracted dyes were determined. The wash and rubbing fastness of the dyed fabrics were good to excellent. Deep shades were obtained by post mordanting method with ferrous sulfate, whereas alum treated fabrics produced brighter shades with excellent fastness. Java plum fruit is available in many countries and offers a possible substitute for synthetic dyes.

The ‘EcoLab’ is a smart living research laboratory currently being developed by the University of Suffolk (UoS), in partnership with BT. The purpose of this short paper is to present the scheme and share some of our early work, specifically how the reach of the project extends beyond the technical performance of smart technology to consider how to embed the end-user in every element of the building’s design.

Background Inequities in access to health services are a global concern and a concern for Canadian populations living in rural areas. Rural children hospitalized at tertiary children’s hospitals have higher rates of medical complexity and experience more expensive hospitalizations and more frequent readmissions. The 2 tertiary pediatric hospitals in Alberta, Canada, have already been operating above capacity, but the pediatric beds at regional hospitals are underused. Such imbalance could lead to poor patient safety and increased readmission risk at tertiary pediatric hospitals and diminish the clinical exposure of regional pediatric health care providers, erode their confidence, and compel health systems to further reduce the capacity at regional sites. A Telemedicine Rounding and Consultation for Kids (TRaC-K) model was proposed to enable health care providers at Alberta Children’s Hospital to partner with their counterparts at Medicine Hat Regional Hospital to provide inpatient clinical care for pediatric patients who would otherwise have to travel or be transferred to the tertiary site. Objective The aim of this study is to identify perceived barriers and enablers to implementing the TRaC-K model. Methods This study was guided by the Theoretical Domains Framework (TDF) and used qualitative methods. We collected qualitative data from 42 participants from tertiary and regional hospitals through 31 semistructured interviews and 2 focus groups. These data were thematically analyzed to identify major subthemes within each TDF domain. These subthemes were further aggregated and categorized into barriers or enablers to implementing the TRaC-K model and were tabulated separately. Results Our study identified 31 subthemes in 14 TDF domains, ranging from administrative issues to specific clinical conditions. We were able to merge these subthemes into larger themes and categorize them into 4 barriers and 4 enablers. Our findings showed that the barriers were lack of awareness of telemedicine, skills to provide virtual clinical care, unclear processes and resources to support TRaC-K, and concerns about clear roles and responsibilities. The enablers were health care providers’ motivation to provide care closer to home, supporting system resource stewardship, site and practice compatibility, and motivation to strengthen tertiary–regional relationships. Conclusions This systematic inquiry into the perceived barriers and enablers to the implementation of TRaC-K helped us to gain insights from various health care providers’ and family members’ perspectives. We will use these findings to design interventions to overcome the identified barriers and harness the enablers to encourage successful implementation of TRaC-K. These findings will inform the implementation of telemedicine-based interventions in pediatric settings in other parts of Canada and beyond. International Registered Report Identifier (IRRID) RR2-10.1186/s12913-018-3859-2

The article analyzes the sources on the history of Saints Peter and Paul Garrison Church (the former Jesuit Church) discovered in the funds of Vasyl Stefanyk National Scientific Library of Ukraine in Lviv. In particular, the documents that are of practical importance for the process of church revitalization as the building remained closed from 1946 until 2011 are distinguished. The significant corpus of sources concerning the church history is the archive of Vasyl Stefanyk National Scientific Library of Ukraine in Lviv, the depository of which was located in the church until the end of 2011. With these documents, it is possible to trace the history of the church building during the Soviet time and the changes that happened during this period. Moreover, the collection of photographic materials with the depiction of different parts of the church constitutes a significant corpus of sources. This information is valuable for the conservation professionals working on the renewal of church exteriors and interiors. Particularly, the photographs that show the process of the roof restoration in 1959 offer an opportunity to assess the degree of the ceiling destruction during the Second World War. Photographs of the frescoes in the main nave demonstrate the state of the ceiling and frescos as of the beginning of the 20th century and therefore give an insight into the progress of conservation works during the interwar period. The analysis of the photograph of the altar of Saint Stanislaw Kostka makes it possible to understand what one of the three chapels in the church looked like. The other two chapels (of the Virgin Mary and Saint Stanislaw Kostka) will be restored in course of time. As for the third chapel of Saint Benedict the Martyr, important documents concerning the restoration of this chapel at the beginning of the 20th century are stored in the funds of the library. With these documents, it is possible to specify the appearance of the chapel and the peculiarities of its decorations, as the chapel does not belong to the church nowadays. To sum up, it can be stated that the funds of Vasyl Stefanyk National Scientific Library of Ukraine in Lviv contain a relatively small amount of materials on the history of Saints Peter and Paul Garrison Church (the Jesuit Church). At the same time, some of them are important for the studies of the history of the church, so they deserve attention from the researchers., У статті проаналізовано джерела до історії Гарнізонного храму святих апостолів Петра і Павла (колишній костел єзуїтів), виявлені у фондових колекціях Львівської національної наукової бібліотеки ім. Василя Стефаника. Зокрема окремо виділено ті документи, які мають практичне значення у процесі ревіталізацїі храму, який був зачинений із 1946 до 2011 р. Важливим корпусом джерел, які стосуються історії храму, є архів самої бібліотеки ім. В. Стефаника, фондосховище якої розміщувалось у будівлі церкви до кінця 2011 р. Ці документи дають змогу, зокрема, простежити історію будівлі в радянський час та зміни, які з нею відбувались протягом цього періоду. Також вагомим комплексом джерел є колекція фотоматеріалів із зображеннями різних частин храму. Наприклад, аналіз зображення вівтаря св. Станіслава Костки дає можливість краще зрозуміти вигляд однієї з трьох храмових каплиць, дві з яких (Богородиці та власне св. Станіслава Костки) вдасться відновити з часом. Також у збірках бібліотеки зберігаються важливі документи, які стосувались реставрації третьої каплиці – св. Бенедикта-мученика – на початку ХХ ст. Завдяки їм є можливість дізнатись про вигляд та особливості декорації каплиці, приміщення якої в наші дні не належить храму.Зазначимо, що у фондових колекціях Львівської національної наукової бібліотеки імені В. Стефаника зберігається порівняно небагато матеріалів до історії Гарнізонного храму святих апостолів Петра і Павла (костелу єзуїтів). Водночас деякі з них є достатньо важливими для студій з історії храму, тому вони варті уваги дослідників.

Inhomogeneous linear ordinary differential equations (ODEs) and systems of ODEs can be solved in a variety of ways. However, hardware circuits that can perform the efficient analog computation to solve them are rarely in the literature. To address such problems, this paper proposes a general method of using a memristor-capacitor (M-C) circuit to solve inhomogeneous linear ODEs and systems of ODEs of any order in initial value problems. The M-C circuit can match the coefficients of the equations sought by adjusting the memristor resistance value according to the coefficient formula proposed in the paper, which has higher programmability. Then, some ODEs and systems of ODEs are given in the paper as examples to evaluate the proposed method. According to the comparison results based on MATLAB software simulation and the simulation based on OrCAD software, the designed M-C circuit has an effective improvement in speed and the accuracy exceeds 99.95% in software simulation. Based on practical verification, this paper gives the actual M-C circuit experiment based on PCB. Moreover, the proposed method can be used to quickly solve the object motion state in the spring mass damping system in actual engineering, and the accuracy can reach 99.98%.

In this research paper, CBIR algorithm development using RGB histogram-based block contour method to improve the retrieval performance is presented along with the simulation results. CBIR to extract the visual content of an image automatically like color, texture, size, direction, distance, nearness or its shape using HSV colour RGB histogram & block contour methods is proposed here in this paper. The work presented in this paper is a new methodology of the picture retrievals by the combination of different parameters of the query image with the help of RGB histograms & the block contours. Simulations are carried out in the Matlab environment and the simulation results are observed. The CBIR based system is going to be tested using different types of test images for varieties & finally the nearest test outcomes will be got & be tabulated neatly. Here, in this proposed work using different sizes of the blocks & the no. of characteristics & the time obtained for extracting those realistic parameters could be found out & also other parameters such as the recalling, precision, characteristics were calculated and compared with existing systems. The precision and recall are used to calculate the performance of the designed system. The results presented gives precision values & the values of the recall parameter of the developed & existing CBIR systems, which shows the efficacy of the proposed methodology that is superior. Here, the observation is – the developed methodology will be highly unique, its performance will be more superior to the existing systems. Finally, to conclude, it could be iterated that this contributory work was developed to show about the RGB histogram & block contour concept involvement to extract the different features of the images and carry out the performance of system (retrieval). Finally, the results of the simulation gives the improved performance of the system (retrieval) over the others, thus showing the profoundness or the effectiveness of the research work done.

This paper highlighted on a new improvement of the salp swarm algorithm (SSA) that is taken from the behavior of salp fishes. This shows a promising result, however it suffered from some drawbacks that limited its ability. The proposed improvement of SSA is called SSA 2. In this paper the design system where the ZigBee protocol received temperature, humidity and gas from the sensors. The base station collects received data and sends it to platform in order to process these data via SSA2. This system enables the ability to monitor and track any fault that may be occurring. A new term has been developed to make the algorithm more targeted in finding the optimal answer. Moreover, the calculation results and simulation results in Matlab shows great enhancement in the accuracy and precision of the results at very fast speed for convergence compared with the original salp swarm algorithm (SSA).

In this paper, a new dataset, the driver emotion facial expression (DEFE) dataset for drivers spontaneous emotions analysis is introduced. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a specic emotion, each participant completed the driving tasks in the same driving scenario and rated his/her emotional responses during the driving processes from the aspects of dimensional emotion method and discrete emotion method. The study also conducted classication experiments to recognize the scales of arousal, valence, dominance, as well as the emotion category and intensity to establish baseline results for the proposed dataset. Furthermore, this paper compared emotion recognition results difference through facial expressions between dynamic driving and static life scenarios. The results showed signicant differences in the AUs presence of facial expressions between dynamic driving and static life scenarios, Therefore, to accurately recognize the drivers emotions to establish a reliable emotion-aware human-machine interaction system, thereby improving driving safety and comfort, publishing a human emotion dataset specically for the driver is necessary. The proposed dataset will be publicly available so that researchers worldwide can use it to develop and examine their driver emotion analysis methods.

In this paper, a hybrid Modular multilevel converter (MMC) topology is proposed, in which two of the submodules (SMs) are based on SiC MOSFET, while others are based on Si IGBT. In the proposed hybrid topology, the capacitor voltage of SiC SM is controlled to be half of that of Si SM, which reduces the requirement for the SiC devices. Whats more, this paper improves the modulation scheme to move most of the switching actions from Si SMs to the two SiC SMs, which strengthens the merits and avoids the shortcomings of SiC MOSFET. Besides, to solve the voltage imbalance problem caused by the change of topology and modulation scheme, a specialized voltage balancing scheme is proposed. Finally, the simulation and experiment result proves the feasibility of the hybrid MMC and control scheme, and the comprehensive analysis verifies that the hybrid MMC can achieve a better balance among cost, loss and feasibility compared with others.

This paper studies the delay-dependent conditions for finite-time extended dissipativity based non-fragile feedback control for neural networks with mixed interval time-varying delays. By applying Jensen’s inequality, an extended Jensen’s double integral inequality, and a free matrix form inequality to the Lyapunov–Krasovskii functional (LKF), delay-dependent conditions are derived and solved by the Matlab control toolbox in terms of linear matrix inequalities (LMIs). By stability criteria, this paper is less conservative than the other works. In addition, we demonstrate the advantage of our obtained methods by five numerical examples. One practical example shows a real-world approach: the quadruple-tank process system (QTPS).

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In order to make the learned policies more transparent, we propose in this paper a policy iteration scheme that retains a complex function approximator for its internal value predictions but constrains the policy to have a concise, hierarchical, and human-readable structure, based on a mixture of interpretable experts. Each expert selects a primitive action according to a distance to a prototypical state. A key design decision to keep such experts interpretable is to select the prototypical states from trajectory data. The main technical contribution of the paper is to address the challenges introduced by this non-differentiable prototypical state selection procedure. Experimentally, we show that our proposed algorithm can learn compelling policies on continuous action deep RL benchmarks, matching the performance of neural network based policies, but returning policies that are more amenable to human inspection than neural network or linear-in-feature policies.

Mutation analysis assesses a test suites adequacy by measuring its ability to detect small artificial faults, systematically seeded into the tested program. Mutation analysis is considered one of the strongest test-adequacy criteria. Mutation testing builds on top of mutation analysis and is a testing technique that uses mutants as test goals to create or improve a test suite. Mutation testing has long been considered intractable because the sheer number of mutants that can be created represents an insurmountable problemboth in terms of human and computational effort. This has hindered the adoption of mutation testing as an industry standard. For example, Google has a codebase of two billion lines of code and more than 150,000,000 tests are executed on a daily basis. The traditional approach to mutation testing does not scale to such an environment; even existing solutions to speed up mutation analysis are insufficient to make it computationally feasible at such a scale. To address these challenges, this paper presents a scalable approach to mutation testing based on the following main ideas: (1) mutation testing is done incrementally, mutating only changed code during code review, rather than the entire code base; (2) mutants are filtered, removing mutants that are likely to be irrelevant to developers, and limiting the number of mutants per line and per code review process; (3) mutants are selected based on the historical performance of mutation operators, further eliminating irrelevant mutants and improving mutant quality. This paper empirically validates the proposed approach by analyzing its effectiveness in a code-review-based setting, used by more than 24,000 developers on more than 1,000 projects. The results show that the proposed approach produces orders of magnitude fewer mutants and that context-based mutant filtering and selection improve mutant quality and actionability. Overall, the proposed approach represents a mutation testing framework that seamlessly integrates into the software development workflow and is applicable to industrial settings of any size.

Localization forms the heart of various autonomous mobile robots. For efficient navigation, these robots need to adopt effective localization strategy. This paper, presents a comprehensive review on localization system, problems, principle and approaches for mobile robots. First, we classify the localization problems in to three categories based on the information of initial position of the robot. Next, we discuss on robot position update principles. Then, we discuss key techniques to localize the mobile robot such as: probabilistic approach, autonomous map building and radio frequency identification (RFID) based scheme. In the probabilistic localization section, we discuss the Markov localization and Kalman filter along with its extended versions. Autonomous map building focuses on the widely used simultaneous localization and mapping (SLAM) approach. This section also discusses on applying SLAM to localize brain-controlled mobile robots. Next, we discuss on applying evolutionary approaches to estimate optimal position. The RFID scheme addresses on effective utilization of RFID tags to track objects and position the robot. We then analyze on position and orientation errors occurred by different localization strategies. We conclude this paper by highlighting future research possibilities.

Data/tasks of industrial internet of things (IIoT) are extremely private and valuable to computing. Edge computing facilitates IIoT services by offering platforms of supportive facilities and functionalities. However, the convergence of intelligent edge computing and IIoT platforms has been held back by the need to develop systems for maintaining security and privacy. In this paper, we propose a new framework for securely and confidentially sending, storing, and computing IIoT tasks. In this paper, we proposed a new framework (SPTM-EC) for securely sending, storing, and computing IIoT tasks that does not breach confidentiality. This framework employs a lightweight encryption scheme alongside modified ElGamal encryption and digital signature schemes. We analyzed the robustness of this framework in terms of security and privacy, and assessed its performance through simulations. Ultimately, the proposed framework outperformed the existing models in terms of time complexity, latency and energy consumption.

RESUMO A predominância do ingles como lingua franca nas ciencias e a pressao para elevar o ‘ranking’ internacional das universidades brasileiras intensificaram a demanda por apoio ao desenvolvimento de uma serie de letramentos academicos em ingles, por exemplo, em areas como a apresentacao de pesquisas em conferencias internacionais. Os desafios para fornecer apoio direcionado ao desenvolvimento de tais letramentos incluem espaco limitado no curriculo, negociacao de horarios regulares para reunioes entre estudantes e instrutores ocupados e o fato de que grandes instituicoes geralmente tem campus geograficamente dispersos. O ‘blended learning’ oferece uma possibilidade de enfrentar esses desafios atraves da flexibilidade oferecida pelo modo de ministrar o curso, que e parcialmente presencial e parcialmente ‘on-line’ e assincrono. Este artigo relata o desenho, o ensino e a avaliacao de duas edicoes de um curso em habilidades de apresentacao em conferencias, oferecido a estudantes de pos-graduacao na Faculdade de Medicina da Universidade de Sao Paulo em 2018-19. O artigo discute as possibilidades, limitacoes e desafios continuos do ‘blended learning’ no apoio a letramentos academicos em um curso projetado para aumentar as habilidades de apresentacao em ingles dos estudantes de medicina. ABSTRACT The predominance of English as a scientific lingua franca and the pressure to raise the international ranking of Brazilian universities have increased the demand for support in developing a range of academic literacies in English, for example in areas such as presenting one’s research at international conferences. The challenges to delivering targeted support for the development of such literacies include limited space in the curriculum, negotiating regular meeting times for busy students and instructors, and the fact that large institutions often have geographically dispersed campuses. Blended learning offers a possible means of addressing these challenges in the flexibility afforded by course delivery that is partly face-to-face and partly online and asynchronous. This paper reports on the design, delivery and evaluation of two editions of a course in conference presentation skills, delivered to postgraduate students in the Faculty of Medicine at the University of Sao Paulo in 2018-19. The paper discusses the affordances, limitations and continuing challenges of blended learning in support of academic literacies designed to enhance medical students’ presentational skills in English.

Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme.

Abnormal violent behavior by people with mental disorders is common. When patients with mental disorders make some abnormal behaviors in public places, they may cause physical and mental harm to others and themselves. Thus, it is necessary to monitor the behavior of human with mental disorders under surveillance video. However, it is a comprehensive challenge to detect abnormal behavior of human (especially patients with mental disorders) based on abnormal detection and motion recognition technology. To address these issues, in this paper, we propose an end-to-end abnormal detection framework from a new perspective in conjunction with the Graph Convolutional Network (GCN) and a 3D Convolutional Neural Network (CNN). Specifically, we first train a one-class classifier to extract features and forecast abnormal scores in the framework. To improve the performance in abnormal behavior detecting, GCN is used to start modeling toward the similarity between video clips for the modification of noise labels. Then, based on this framework, GCN will recognize the normal behavior clips in the abnormal video and delete them, while the clips identified as abnormal behavior are retained. Finally, we use 3D CNN to extract video features and classify abnormal behaviors. In order to better detect the violent behavior of patients with mental disorders, the paper focuses on the UCF-Crime dataset of violent behavior. By experimenting with the dataset, the classification accuracy reaches 37.9%, which is 9.50% higher than that of the current state-of-the-art approaches. This proves the feasibility of classifying abnormal behaviors with this framework.

Dual-active-bridge (DAB) based DC-DC converter is usually chosen for energy conversion. In this paper, there exists many coupled relationships such as the controversy between local minimum current stress and transmission power, which influence efficiency promotion greatly. To analyze complicated coupled relationship and obtain the global minimum values, after unifying backflow power and transmission power into one integral function, a novel extended-phase-shift (EPS) control based on complete differential algorithm is proposed, which can not only eliminate the backflow power completely, but can realize a type of multi-objective optimizations including improvement of transmission power and decrease of current stress. Through the proposed method in this paper, the efficiency can be improved gradually. Moreover, the coupled relationships among variables are clarified clearly, which can avoid power loss increase during optimization. A DAB-based prototype is established and the experimental results verify the correctness and effectiveness of the proposed method.

Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022)​​​​​​​. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

Vehicle speed is known as one of the most important parameters in the various goals for driving. Although the most critical goal of driving is safety, each driver can consider another secondary goal, such as reducing travel time, economical driving, green driving, passenger comfort, etc. In this paper, we propose a smartphone-based application that utilizes the cloud/fog computing service infrastructure in order to recommend speed according to primary safety goal and another secondary objective. Due to the strategic nature of the driving speed selection issue, we have modeled the problem as a game that the drivers are the players, and the speed of the vehicle is their strategy. We solve the proposed game via evolutionary dynamics with appropriate convergence time, and the resulting equilibrium profile is announced to the drivers as the recommended speed at specified time intervals. Finally, the experimental results obtained from the simulation of the proposed scheme confirm that the deviation from the proposed equilibrium velocity is not profitable for the offending player. Indeed, deviation from equilibrium conditions leads to the negative impacts on the formal evaluation parameters presented in this paper.

In this paper, a novel lane keeping control method for automated vehicles based on human-simulated intelligent control (HSIC) is proposed, which is inspired by human expert drivers' steering characteristics including good foresight, precise execution and notable intermittency. The novelty of the paper is to introduce the HSIC concept into lateral control of vehicles, which is a multi-mode control scheme implemented by the combination of the feedforward control for curve tracking and act-and-wait control for intermittent error correction. Theoretically, the stabilization problem of the HSIC method is investigated based on the switched system related method. Experiments on the joint simulation platform of PreScan and CarSim show that the newly presented HSIC scheme has better matching performance to the expert driver and good robustness. For automated lane keeping systems, the HSIC method could provide human-like qualities, which may be one of the essential points to determine whether the driver is comfortable or not when the driver hands over the steering authority, improve the transition smoothness in the scenario of human vehicle co-piloting, and eliminate the potential conflicts between manual driving and automated driving vehicles in the future mixed traffic flow.

In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

LoRa (Long-Range) has become the Deoxyribo Nucleic Acid (DNA) of the Internet of things (IoT) for equipping smart solutions. Home automation is responsible for providing a safe and stylish home. This paper proposes a capable architecture of home automation for both short-range and long-range utilizing multiple communication technologies, namely LoRaWAN, server-based LoRa gateway, and Bluetooth connectivity. This integrated system effectively controls distinct types of home appliances and keeps smart management among all the electronics components. A regular user can easily manage these unified systems by using an Android application. This paper also presents experimental data analysis. The results and discussion section provide a set of experiments like estimated transmission delay calculation for LoRa, Wi-Fi, and Bluetooth, a coverage area calculation for LoRa with RSSI and SNR values, and a System Usability Scale (SUS). The scheme has achieved a SUS score of 93%. However, the proposed architecture can be called an outright package for smart home and will be very workable, abuzz, and handy.

Although the number of aspect-oriented software development techniques and tools proposed by the scientific literature have been increasing since the late 80′s, the evidence about the benefits that the aspect-oriented paradigm have reached in real-world settings is scarce. Our objective is to identify and classify the aspect-oriented software development methodologies used to reduce the effort and costs of moving from traditional approaches to the aspect-oriented approach in real-world settings. We conducted a systematic mapping study (SMS). Our search strategies retrieved a set of 3212 papers out of which 115 were selected as relevant studies. We defined eight categories to classify these studies: aspect-oriented methodologies proposed for early aspects; development early phases covered; notations; modeling techniques; supporting tools; aspect-oriented methodologies used in real-world settings; benefits reported and unsolved issues. As a result, 39 named methodologies were reported; they cover the business modeling (14 papers), requirements (93 papers), test cases (1 paper) and design (41 papers) phases of the software development life cycle (SDLC); we found 36 different notations, with UML as the most mentioned (66 papers); 22 modeling techniques were found, where the use cases were the ones that appeared the most, on 43 occasions; 43 support tools of which none was repeated in more than 3 articles; 15 applications in real-world settings; 17 benefits, modularization being the most mentioned with 4 occurrences; and 15 pending improvement opportunities. Finally, we have obtained conclusions: the literature analyzed demonstrates that there are no prevailing standards on methodologies and notations, beyond that the most employed are those belonging to the OMG; we have not found any methodology that includes all phases of the SDLC; the diagrams corresponding to OMG’s standards account for 78% of the results in the facet of modeling techniques; there are no tools that have the greatest predilection and the most used is mentioned only three times; in addition, 41% of the articles do not mention any tool; the evidence of aspect-oriented methodologies application in real-world settings reached just the 10%, although the declared benefits are coincident with those promised in the aspect-orientated literature. The mentioned pending issues can guide new studies (RQ8).

A single input dual output (SIDO) converter with a coupled inductor is a widely used converter to generate two outputs from a single input. The analysis presented in the literature is for continuous conduction mode (CCM), where the converters are operating in heavily loaded conditions. While operating, the load may reduce, leading to the discontinuous conduction mode (DCM) of the converter. A coupled inductor SIDO (CI-SIDO) boost converter is analyzed in this paper. As this converter combines two boost converters with a common input and a coupled inductor, the DCM operation of one boost affects the other. Due to coupling, it is found that the body diode of the MOSFET turns ON in the DCM operation of the CI-SIDO boost converter. This paper finds the conditions to turn ON body diode and its effect on the average values of the inductor current, diode current, and input current. The paper also analyses the CI-SIDO boost when the body diode is not turned ON. This paper finds the effect of change in input voltage, load current, and duty ratios on the DCM operation of the CI-SIDO boost. Simulations verify all the obtained conditions in MATLAB/Simulink and experiments using a laboratory prototype.

This paper presents the dynamic modeling and analysis of an inverted pendulum robot with an adaptable overcoming wheel. Stairways are inevitable in environments where humans and robots operate and interact. The conventional wheel cannot overcome an obstacle that is higher than the radius of the wheel of the inverted pendulum robot. This paper presents a solution for a problematic situation in which a balancing robot climbs steps with a pair of transformable wheels. Dynamic modeling of the varying radius of the wheel was performed using the Lagrangian approach, and a simple gain controller was selected. Simulations were carried out using MATLAB and Simulink to confirm the dynamic model. In future work, the wheel should be tested on a robot model.

Mobile Crowdsensing (MCS) is a newly-emerged sensing paradigm where a large group of mobile workers collectively sense and share data for real-time services. However, one major problem that hinders the further development of MCS is the potential leakage of workers’ data privacy. In this paper, we integrate Federated Learning (FL) with MCS and introduce a novel sensing system called Federated Mobile Crowdsensing (F-MCS). In F-MCS, the workers can optimize the global model while keeping all the sensitive training data locally, thus ensuring their data privacy. Nevertheless, there are still two major issues in F-MCS. The first issue is that in F-MCS services, the workers are heterogeneous in terms of computational capacities and data resources. Hence, qualified workers should be appropriately selected to improve the efficiency of the training process. The second issue is that F-MCS is a cross-device FL system where the platform will finally get the global model after multiple training rounds. However, most privacy-preserving techniques are designed for cross-silo FL platforms, which cannot be applied to real-world F-MCS scenarios. To tackle the above problems, in this paper, we propose a privacy-preserving scheme for F-MCS, namely FedSky. Mainly, by extending the classic FedAvg algorithm, FedSky selects qualified workers based on constrained group skyline (CG-skyline) and securely aggregates model updates based on the homomorphic encryption technique. Comprehensive security analysis demonstrates the privacy-preservation of FedSky. Extensive experiments are conducted on an image classification task, where the comparison results validate the proposed scheme’s efficiency and effectiveness.

The capacitor voltage balancing algorithm (VBA) is an important issue to maintain the regular operation of modular multilevel converter (MMC). However, it requires a higher switching frequency to obtain a better balancing performance. In this paper, the VBA based on the reassignment of switching times is proposed to improve the balancing performance of capacitor voltages with a low operating frequency. The mechanism of the reduced switching frequency voltage balancing algorithm (RSFVBA) is presented. Based on that, the relationship among switching times, switching frequency, and capacitor voltage deviation is deduced. And then, the voltage deviations in the positive and negative intervals of the arm current are analyzed in detail. The RSFVBA proposed in this paper adjusts the switching times of SMs in different intervals of arm current to reduce the voltage deviation. Moreover, the proposed RSFVBA can also provide a low operating frequency because the reassignment of switching times does not change the total switching times in a line cycle. The feasibility of the proposed strategy is verified by simulation results in the professional tool Matlab/Simulink.

In this paper, the output regulation problem of fully distributed linear heterogeneous multi-agent systems via event-triggered communication is investigated. A novel information acquisition scheme is proposed, which can effectively save the computing resources of the controller. The time-invariant matrix S is obtained directly through completely distributed communication. Therefore, the computational burden caused by the introduction of matrix observer is reduced. At the same time, to save communication resources, this paper adopts a communication scheme and designs a Zeno-free event-triggered mechanism for each agent. The trigger mechanism in this paper can avoid the continuous monitoring of the system state, and does not need to supplement the self-triggering algorithm. In addition, the control strategy designed in this paper is fully distributed with fixed parameters, and the use of global information can be avoided without using dynamic parameters in the controller. Finally, a numerical example is given to verify the effectiveness of the proposed method.

A design for an asynchronous bundled-data barrel shifter that incorporates a deterministic completion detection circuitry is proposed. A bundled-data asynchronous circuit is inherently free from challenges associated with clock signals, but it is devoid of information pertaining to propagation delay through the circuit. An obvious solution is to produce the acknowledge signal at least δ delay units after the arrival of the request signal, where δ is the maximum propagation delay through the circuit. However, similar to synchronous circuits, such bundle-data asynchronous approach does not take advantage of times where the propagation delay through the circuit is less than δ. This paper proposes a design for a deterministic completion detection scheme for an asynchronous bundled-data barrel shifter. Once the request signal is active, the shifting operation begins, and the acknowledge signal is generated after a delay that matches the actual shifting delay associated with the shift amount using a deterministic completion detection scheme. The shifter can reduce the idle time between the time its output is ready and the start of the next shift cycle by using a deterministic completion detection circuit. In this paper, the proposed design of the asynchronous barrel shifter and its average delay is analyzed. Its performance is also compared against its synchronous counterpart.

This paper is concerned with a communication-efficient algorithm update scheme for solving distributed convex optimization problems by introducing a distributed event-triggered approach. Compared with real-time consensus-based distributed optimization algorithms in the literature, this paper focuses on extending the real-time gradient tracking scheme and proposes a novel distributed event-triggering condition to reduce the frequency of information exchange between agents in a network. The proposed event-triggered approach for consensus-based distributed optimization algorithms not only avoids the real-time consecutive communication and the coordinated computation between agents but reduces the computation load of algorithm execution. Furthermore, the proposed event-triggering condition depends on only local states from neighbors at only their event times and does not require a global and homogeneous sampling period. In addition, this paper analytically shows that the proposed consensus-based distributed optimization algorithm based on an event-triggered approach can also converge to the exact optimal solution with a linear convergence rate even if the real-time consecutive communication between agents is replaced with sporadic communication.

This paper studies the privacy preserving average consensus (PPAC) of wireless sensor networks (WSNs). Note that most of the PPAC schemes only focus on the consensus of one-dimensional state, which is not suitable for the actual scenarios. In view of this, the multi-dimensional privacy-preserving average consensus (MPPAC) problem is considered in this paper, where the nodes are divided into two types, the sink nodes and the ordinary ones. A novel MPPAC algorithm is proposed by introducing the super-increasing sequence as well as the RSA algorithm, where the super-increasing sequence plays a key role in tackling the multi-dimensional measurement of the sensors, and the RSA algorithm realizes the privacy preserving average consensus among sink nodes. Simulation results illustrate the effectiveness of this proposed scheme.

This paper focuses on the cooperative fault diagnosis of fuzzy time-delay fractional order multi-agent system(FOMAS) over directed networks, whose fractional order belongs to (0; 1): Due to the interconnections, for each agent’s estimator, when estimating the local state and error parameters, whose purpose is to further consider faulty effects on others. Firstly, by the interconnection relationships between each agent, a new formulation scheme is proposed so that the shared signals are achieved by correctly constructing auxiliary graphs of a directed network. Then, unlike the existed papers dealing with normal fuzzy multiagent systems, new admissible criteria of fuzzy FOMASs are proposed with time-varying. Finally, to illustrate the method proposed above is available, a practical application example of unmanned aerial vehicles is given.

The Model Predictive Control (MPC) framework has shown great potential for the control of Variable Speed Limits (VSLs) and Ramp Metering (RM) installations. However, the implementation to large freeway networks remains challenging. One major reason is that, by considering the VSLs to be discrete decision variables, an extremely difficult Mixed Integer Nonlinear Programming (MINLP) optimization problem has to be solved within every controller sampling interval. Consequently, many related papers relax the MINLP problems by considering the VSLs to be continuous variables. This paper proposes two novel MPC algorithms for coordinated control of discrete VSLs and continuous RM rates that do not make this relaxation. The proposed algorithms use a distributed control architecture and an alternating optimization scheme to relax the MINLP optimization problems but still consider the VSLs as discrete variables and, hence, offer a trade-off between computational complexity and system performance. The performance of the proposed algorithms is evaluated in a case study. The case study shows that relaxing the VSLs to be continuous variables with a distributed architecture results in a significant performance loss. Furthermore, both proposed algorithms have a lower computational complexity than the more conventional centralized approach and, as a result, they do manage to solve all optimization problems within the sampling intervals. Moreover, one of the proposed algorithms has a system performance that is remarkably similar to the optimal performance of the centralized approach.

This paper investigates the secure consensus tracking problem for continuous-time nonlinear multiagent systems with sensor attacks. By designing a secure data selector, the unattacked output data is extracted from a group of output measurements under sparse sensor attacks. Then, by virtue of the obtained data and neural networks, a state observer is constructed to estimate the unavailable system states, where the convex combination theory is introduced to reduce the difficulty of solving observation gains. To utilize the limited communication resources reasonably, a novel saturated threshold event-triggered control strategy is proposed to reduce control updates, and then each update is encoded into a binary signal (0 or 1) to further reduce the occupation of communication bandwidth. The designed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded, and its effectiveness is verified via a simulation of attitude control of unmanned aerial vehicles.

This paper proposes a modeless prediction scheme to estimate the coupling effect for wireless power transfer (WPT) systems. To address the impact of the variable coupling effect for WPT systems, the acquirement of the coupling effect has been gradually one of the most important research topics in recent years. However, the mutual inductance cannot be directly measured, since it is not only determined by the distance but also by various external factors. Although there are several efforts for the prediction of the mutual inductance which have been made in previous studies, the practicability of existed schemes mostly suffers from limitations caused by the communication link, the complex calculation and the measurement accuracy. Accordingly, this paper proposes a modeless prediction scheme using neural network (NN) to dynamically estimate the varying coupling effect caused by practical working conditions like the variation of relative position between the transmitting and the pickup coils. In this paper, the simulated and experimental results are both given to verify the feasibility of the proposed modeless prediction scheme as well as its salient advantages of communication-free, calculation-simple, and measurement-convenient.

The motivation behind 15 years of continuous development within the topic of the Tensor Product (TP) model transformation is that the greater the number of parameters or components of the Takagi Sugeno (TS) fuzzy model one can manipulate, the larger the complexity reduction or control optimization one can achieve. The paper proposes a radically new type of extension to the TP model transformation: while earlier variants of the TP model transformation focused on how the antecedent - consequent fuzzy set system of a given TS fuzzy model could be varied, the present work, in contrast, focuses on how the number of inputs to a given TS fuzzy model can be manipulated. The proposed extension is capable of changing the number of inputs or transforming the non-linearity between the fuzzy rules and the input dimensions. These new features considerably increase the modeling power of the TP model transformation, allowing for further complexity reduction and more powerful control optimisation to be achieved. The paper provides two examples to show how the proposed extension can be used in a routine-like fashion.

Liquid Crystal Display (LCD) defects detection on module-level is increasingly important for flat-panel displays (FPD) industry to increase the production capacity via machine vision technology. However, it is an overwhelmingly challenging issue due to various difficulties. This paper discloses a practical automatic optical inspection (AOI) system consisting of hardware structure and software algorithm to detect module-level defects. The AOI system is the core component to build a distributed integrated inspection system with the help of the Internet of Things (IoT). Starting from the analysis of the challenges encountered in module-level defects inspection, a delicate photograph scheme is proposed to reveal different kinds of defects. In order to robustly work on the module-level defects detection with complex situations, a novel framework based on YOLOV3 detection unit is proposed in this paper, including the preprocessing module, detection module, defects definition module and interferences elimination module. To the best of our knowledge, this is the first work that designs a practical automatic optical inspection system for module-level defects detection. In order to demonstrate the effectiveness of the proposed method, extensive experiments have been conducted on the manufacturing lines. Evaluation of the detection performance of the AOI system in comparison with a manual scheme indicates that the proposed system is practical for module-level defects detection. Currently, the proposed system has been deployed in a real-world LCD manufacturing line from a major player in the world.

The Delphi Technique is a group judgement method which is typically used to reach agreement from a group of people with expertise in a particular area. It is an iterative process where panel members complete questionnaires over several rounds, often rating their agreement/disagreement against a statement, with changes made in later rounds based on the feedback received. It has been used widely in pharmacy-related studies relevant to education, research and practice. This paper provides a critical analysis of the various design choices which researchers may consider when planning a Delphi namely the panel of participants, the use of the Likert scale, the effect of feedback, what constitutes consensus and the number of rounds. It also gives an overview of the development and origins of the Delphi, and discusses the advantages and disadvantages of the technique. Advantages include that the Delphi can be conducted with panel members in different geographical locations in their own time, however the technique can therefore take longer to conduct and lacks face-to-face discussion. Patient experts may be less comfortable participating in a relatively complex survey, however the anonymous nature of the process can be more inclusive in allowing participants to feedback candidly. This paper shows the importance of careful planning of the design choices to ensure the reliability and validity of the Delphi.

This essay reviews the following works: Political Landscapes: Forests, Conservation, and Community in Mexico. By Christopher R. Boyer. Durham, NC: Duke University Press, 2015. Pp. xix + 337 pages. $27.95 paper. ISBN: 9780822358329. Dictablanda: Politics, Work, and Culture in Mexico, 1938–1968. Edited By Paul Gillingham and Benjamin T. Smith. Durham, NC: Duke University Press, 2014. Pp. vii + 444. $28.95 paper. ISBN: 9780822356370. The War Has Brought Peace to Mexico: World War II and the Consolidation of the Post-Revolutionary State. By Halbert Jones. Albuquerque: University of New Mexico Press, 2014. Pp. ix + 296. $55.00 cloth. ISBN: 9780826351302. Mexico’s Once and Future Revolution: Social Upheaval and the Challenge of Rule since the Late Nineteenth Century. By Gilbert M. Joseph and Jurgen Buchenau. Durham, NC: Duke University Press, 2013. $23.95 paper. Pp. x + 252. ISBN: 9780822355328. Instituting Nature: Authority, Expertise, and Power in Mexico’s Forests. Andrew S. Mathews. Cambridge, MA: MIT Press, 2011. Pp. xii + 304. $32.00 paper. ISBN: 9780262516440. The Logic of Compromise in Mexico: How the Countryside Was Key to the Emergence of Authoritarianism. By Gladys I. McCormick. Chapel Hill: University of North Carolina Press, 2016. Pp. xiv + 284. $32.95 paper. ISBN: 9781469627748. Myths of Demilitarization in Postrevolutionary Mexico, 1920–1960. By Thomas Rath. Chapel Hill: University of North Carolina Press, 2013. Pp. vii + 256. $29.95 paperback. ISBN: 9780807839294.

This essay reviews the following works: Rethinking Race in Modern Argentina. Edited by Paulina Alberto and Eduardo Elena. New York: Cambridge University Press, 2016. Pp. xviii + 373. $120.00 cloth. ISBN: 9781107107632. Reimagining Black Difference and Politics in Brazil: From Racial Democracy to Multiculturalism.By Alexandre Emboaba Da Costa. New York: Palgrave Macmillan, 2014. Pp. vii + 233. $105.00 cloth. ISBN: 9781137386335. Racial Subordination in Latin America: The Role of the State, Customary Law, and the New Civil Rights Response. By Tanya Kateri Hernandez. New York: Cambridge University Press, 2013. Pp. vii + 247. $35.99 paper. ISBN: 9781107695436. The Color of Love: Racial Features, Stigma, and Socialization in Black Brazilian Families.By Elizabeth Hordge-Freeman. Austin: University of Texas Press, 2015. Pp. ix + 311. $29.95 paper. ISBN: 9781477307885. Blackness in the Andes: Ethnographic Vignettes of Cultural Politics in the Time of Multiculturalism. By Jean Muteba Rahier. New York: Palgrave Macmillan, 2014. Pp. ix + 243. $110.00 cloth. ISBN: 9781137272713. Land of the Cosmic Race: Race Mixture, Racism, and Blackness in Mexico. By Christina A. Sue. New York: Oxford University Press, 2013. Pp. ix + 234. $24.95 paper. ISBN: 9780199925506. Pigmentocracies: Ethnicity, Race, and Color in Latin America. By Edward Telles and the Project on Ethnicity and Race in Latin America. Chapel Hill: University of North Carolina Press, 2014. Pp. xiii + 320. $29.95 paper. ISBN: 9781469617831.

This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the dynamic window method (DWA) with better planning effect in local path planning problems, we propose a fusion algorithm named DACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.

Distributed storage can store data in multiple devices or servers to improve data security. However, in today’s explosive growth of network data, traditional distributed storage scheme is faced with some severe challenges such as insufficient performance, data tampering, and data lose. A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage. Under this scheme, the following improvements have been made in this paper. This paper first analyzes the problems faced by distributed storage. Then proposed to build a new distributed storage blockchain scheme with sharding blockchain. The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology, which can improve the efficiency of data verification between nodes. In addition, this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS. This new scheme is one of the foundations for building our improved distributed storage blockchain scheme. Compared with the previous scheme, our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening. The security of VSS can be further improved. Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs.

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient’s safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints.

Stateful logic through memristor is a promising technology to build Computing-in-Memory (CIM) systems. However, aging-induced degradation of memristors' threshold voltage imposes a major challenge to the reliability and guardbands estimation of memristive CIM systems, especially the Material Implication (IMPLY) logic based CIM systems. In this paper, a novel in-situ aging-aware error monitoring scheme for memristor-based IMPLY logic is proposed. The proposed in-situ error monitoring scheme can achieve faster error detection speed and higher detection accuracy than the straightforward program-verify monitoring scheme. Simulation results under Monte-Carlo simulation show that the proposed monitoring scheme can effectively detect the major operation failures existing in IMPLY logic operations with a detection accuracy up to 99.95%. Moreover, a case study of error monitoring design of 4-bit IMPLY-based adder is carried out. The analysis result exhibits that the proposed in-situ monitoring scheme can achieve 75.2% improvement on the detection speed against the program-verify scheme. Further analysis on a convolution filter in VGG-11 based Binarized Neural Network shows that 74% improvement on the detection speed can also be achieved by using the proposed monitoring scheme, which suggests that the proposed in-situ error monitoring scheme is an efficient solution to improve the reliability of IMPLY-based memristive CIM systems.