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The widespread use of mobile devices has intensified the demand for literature translation in both academic research and personal learning. However, existing translation applications face significant challenges regarding offline mode and data synchronization. Ensuring translation quality and efficient data synchronization, particularly under unstable or disconnected network conditions, has emerged as a critical issue. Current research predominantly focuses on local storage and update mechanisms in offline mode. However, limitations in storage capacity and update strategies often hinder translation effectiveness. Additionally, data synchronization techniques have been primarily studied in stable network environments, with insufficient attention to strategies that address poor network quality or intermittent connectivity. To address these gaps, this study explores the offline mode and data synchronization technologies in literature translation applications on mobile devices. The study proposes optimized storage and update strategies for the offline mode while enhancing the efficiency and reliability of data synchronization, ultimately improving the usability and user experience of translation applications. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules. [ABSTRACT FROM AUTHOR]

Through-wall radar systems with single transmitter and receiver have the advantages of portability, simplicity, and independent operation; however, they cannot accomplish two-dimensional (2D) localization and tracking of targets. This paper proposes distributed wireless networking for through-wall radar systems based on a portable single transmitter and single receiver radar. Moreover, a target joint positioning method is proposed in this study, which can balance system portability, low cost, and target 2D information estimation. First, a complementary Gray code transmission waveform is utilized to overcome the issue of mutual interference when multiple radars operate simultaneously in the same frequency band, and each radar node communicates with the processing center via wireless modules, forming a distributed wireless networking radar system. In addition, a data synchronization method combines the behavioral cognition theory and template matching, which identifies identical motion states in data obtained from different radars, realizing slow-time synchronization among distributed radars and thereby eliminating the strict hardware requirements of conventional synchronization methods. Finally, a joint localization method based on Levenberg-Marquardt is proposed, which can simultaneously estimate the positions of radar nodes and targets without requiring prior radar position information. Simulation and field experiments are performed, and the results reveal that the distributed wireless networking radar system developed in this study can obtain 2D target positions and track moving targets in real time. The estimation accuracy of the radar’s own position is less than 0.06 m, and the positioning accuracy of moving human targets is less than 0.62 m.

Conventional angular acceleration estimation methods generate non-negligible errors when the control surface rapidly deflects; hence, the estimated angular acceleration cannot be used for aerodynamic parameter identification directly. This paper proposes an angular acceleration estimation method based on angular velocity equivalent model. The angular acceleration of the angular velocity equivalent model, whose angular velocity response is consistent with the flight test data, is used as the estimated angular acceleration. Firstly, the equivalent angular velocity model is established by combining the generic aerodynamic model with the rotational dynamic equations of aircraft. Secondly, the angular velocity data and the control surface deflection data are synchronized. Finally, the angular velocity response of the equivalent model is used as the predictor, and the estimated angular acceleration is obtained by extended Kalman filter. The aerodynamic parameter identification and validation are carried out using the estimated angular acceleration of a large civil aircraft flight test data. The results show that the angular acceleration obtained through the proposed angular acceleration estimation method meets the accuracy requirement of aerodynamic parameter identification. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing popularity of contact-free smart sensing has contributed to the development of the Artificial Intelligence of Things (AIoT). The contact-free sensory data has great potential to mine and analyze the hidden information for AIoT-enabled applications. However, due to the limited storage resource of contact-free smart sensing devices, data is naturally stored in the cloud, which is at risk of privacy leakage. Cloud storage is generally considered insecure. On one hand, the openness of the cloud environment makes the data easy to be attacked, and the complex AIoT environment also makes the data transmission process vulnerable to the third party. On the other hand, the Cloud Service Provider (CSP) is untrusted. In this article, to ensure the security of data from contact-free smart sensing devices, a Cloud-Edge-End cooperative storage scheme is proposed, which takes full advantage of the differences in the cloud, edge, and end. Firstly, the processed sensory data is stored separately in the three layers by utilizing well-designed data partitioning strategy. This scheme can increase the difficulty of privacy leakage in the transmission process and avoid internal and external attacks. Besides, the contact-free sensory data is highly time-dependent. Therefore, combined with the Cloud-Edge-End cooperation model, this article proposes a delta-based data update method and extends it into a hybrid update mode to improve the synchronization efficiency. Theoretical analysis and experimental results show that the proposed cooperative storage method can resist various security threats in bad situations and outperform other update methods in synchronization efficiency, significantly reducing the synchronization overhead in AIoT. [ABSTRACT FROM AUTHOR]

The rapid development of Internet technology has enabled mobile devices to play a more significant role in people's lives. A user may possess multiple devices to meet various needs such as office work, socializing, and entertainment. In practical applications, multiple devices owned by the same user also face many data synchronization requirements to support distributed applications across devices (e.g., cross-device video playback continuation). However, there is currently limited research on endto- end data synchronization across multiple devices. Therefore, this paper proposes a data push strategy, called Push After Delay (PAD), suitable for application synchronization between multiple mobile terminals of users. The strategy adopts a differentiated approach to active and sleeping devices and flexibly delays the transmission to sleeping devices. The delay decision is based on the dynamic adaptive adjustment method of AIMD (Additive Increase Multiplicative Decrease) and the scheduling enhancement mechanism oriented to application access frequency, which adaptively pushes synchronization according to user usage habits. Experimental results show that compared with the undifferentiated push synchronization scheme, the PAD push strategy can significantly reduce the number of wake-ups of data synchronization on mobile devices while ensuring a low data access error rate and achieving a balance between consistency and synchronization overhead. [ABSTRACT FROM AUTHOR]

This study presents a newly developed edge computing platform designed to enhance connectivity between edge devices and the cloud in the agricultural sector. Addressing the challenge of synchronizing a central database across 850 remote farm locations in various countries, the focus lies on maintaining data integrity and consistency for effective farm management. The incorporation of a new edge device into existing setups has significantly improved computational capabilities for tasks like data synchronization and machine learning. This research highlights the critical role of cloud computing in managing large data volumes, with Amazon Web Services hosting the databases. This paper showcases an integrated architecture combining edge devices, networks, and cloud computing, forming a seamless continuum of services from cloud to edge. This approach proves effective in managing the significant data volumes generated in remote agricultural areas. This paper also introduces the PAIR Mechanism, which is a solution developed in response to the unique challenges of agricultural data management, emphasizing resilience and simplicity in data synchronization between cloud and edge databases. The PAIR Mechanism's potential for robust data management in IoT and cloud environments is explored, offering a novel perspective on synchronization challenges in edge computing. [ABSTRACT FROM AUTHOR]

The display platform of train provides the train driver with the key data of the running train and receives control command input. Its operation reliability is very important for safe driving. In order to quickly locate and response to the fault of the display platform and reduce the impact of the fault on train operation, the paper proposes a scheme for real-time diagnosis and data synchronization of train display platform operation status based on redundant display platform with two display units. Firstly, the self-diagnosis of the display unit was realized to monitor and diagnose the equipment status in real time based on the application layer, and to quickly locate the problems such as application exit, system crash and anomalies in resource utilization. Secondly, based on the RS422 communication link, a reliable communication protocol was defined to achieve the mutual diagnosis and data synchronization of operation status between multiple display units, and solve the problems of self-diagnosis failure in abnormal state of the unit and failure to obtain key data of faulty unit. A function verification experiment was carried out based on the train redundant display platform to verify the effectiveness of this scheme. The experimental results indicate that on the one hand, this scheme provides comprehensive self-diagnosis and redundant display unit state mutual diagnosis from the application layer, providing a basis for fault analysis and localization. On the other hand, it provides a way for two display units to work coordinately through key data synchronization and storage functions.

Data synchronization and content delivery services are key to supporting healthcare dataflows built by organizations. These types of services must prepare and process the data to accomplish mandatory non-functional requirements, such as security and reliability. This is a challenge as multiple applications, infrastructures, and platforms participate in healthcare dataflows. This paper presents FedFlow, a federated content distribution platform to build infrastructure-agnostic health data sharing and synchronization services to support healthcare dataflows. FedFlow creates secure and efficient data sharing and synchronization patterns for intra-dataflows and inter-dataflows by using implicit parallel data preparation schemes. A prototype of FedFlow was developed to conduct a case study about the building of inter-dataflows for delivering synchronized health data to multiple organizations by using combinations of non-functional requirements algorithms to accomplish governmental rules related to health data management. The experimental evaluation in a multi-cloud federated environment showed that FedFlow is around 90% faster than a traditional pipeline implementation, around 40% faster than Jenkins workflow management, and almost 30% faster than duplicity. [ABSTRACT FROM AUTHOR]

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives. [ABSTRACT FROM AUTHOR]

This study presents a newly developed edge computing platform designed to enhance connectivity between edge devices and the cloud in the agricultural sector. Addressing the challenge of synchronizing a central database across 850 remote farm locations in various countries, the focus lies on maintaining data integrity and consistency for effective farm management. The incorporation of a new edge device into existing setups has significantly improved computational capabilities for tasks like data synchronization and machine learning. This research highlights the critical role of cloud computing in managing large data volumes, with Amazon Web Services hosting the databases. This paper showcases an integrated architecture combining edge devices, networks, and cloud computing, forming a seamless continuum of services from cloud to edge. This approach proves effective in managing the significant data volumes generated in remote agricultural areas. This paper also introduces the PAIR Mechanism, which is a solution developed in response to the unique challenges of agricultural data management, emphasizing resilience and simplicity in data synchronization between cloud and edge databases. The PAIR Mechanism’s potential for robust data management in IoT and cloud environments is explored, offering a novel perspective on synchronization challenges in edge computing.

Nonlinear complex systems are widely used in various industries, and the complexity and dynamism of the systems pose enormous challenges to data synchronization and interaction. This study proposes a method based on signal synchronization for data synchronization and interaction in nonlinear complex systems. The synchronization interaction path of data was analyzed, and signals were used as the carrier for data synchronization. The data synchronization interaction was carried out by combining the main synchronization signal and the auxiliary synchronization signal. Furthermore, mean filtering was introduced for signal filtering, and the main synchronization process was divided into two parts: coarse and fine tuning. The experimental results showed that the main synchronization frequency deviation of the research method was 303 Hz when the main synchronization completion rate was 100% in a −15 dB signal-to-noise ratio environment. In the analysis of data synchronization success rate, the research method achieved a highest success rate of 99.7% when the data transmission density was 50 pieces per minute. The experiment shows that the research method can effectively improve the quality and efficiency of data synchronization and interaction in nonlinear complex systems.

Aiming at the problem of data interaction and synchronization of driver display unit based on MVB (multifunction vehicle bus) and Ethernet redundant communication in the network system of current power centralized train, this paper presents a solution. A normalization processing method of MVB data and Ethernet data is realized by establishing one-to-one mapping relationship between MVB and Ethernet data.On this basis, the existing data (including pulse and level) transmission methods are improved, which taking a variety of complex pulse (including bit pulse, byte pulse and word pulse ) transmission requirements into full consideration, and the data transmission synchronization of multiple redundant displays is realized, which effectively solves the problem of inconsistent data transmission of multiple redundant display units in the networked control system under redundant communication conditions. This design method is effectively compatible with the MVB data exchange method, and ensures the consistency of MVB data processing and Ethernet data processing by using function overload, and effectively prevents data analysis errors; the pulse is processed in a unified way, and the multi type pulse transmission only needs simple configuration without coding, which improves the development efficiency; the perfect synchronization method effectively ensures the correctness of data synchronization. This design method has been verified and applied in the power centralized dual-source locomotive project, and all kinds of data interaction and synchronization are correct. This design method can be completely reused for subsequent projects with similar application scenarios, which only needs simple configuration.

Ubiquitous networks comprise a diverse set of homogeneous or heterogeneous networks to perform ubiquitous computing operations. In the real-time application context, users can access data from any place and at any time by using mobile devices/nodes such as computer/laptop, mobile, tablet and so on. Nowadays, mobile computing paradigms have an impact on the spread of mobile computing devices, resulting in the transmission of data across ubiquitous networks. To avoid uncertainty, it is critical to maintain data consistency across all devices. This research work has proposed a context aware data synchronization using cognitive agents as a case study on shopping environments. The proposed data synchronization scheme detects network changes, resolves any potential conflicts, and updates various synchronizing devices with different device attributes. Cognitive agents are used to track the data across multiple devices. Context aware ubiquitous networks respond to customer requests based on their personal profiles, which often include individual preferences while considering the technological restrictions of the mobile device and the context of operating environment. The proposed work is compared with other existing methods in terms of data accuracy, data synchronization time, and computation and communication overhead. [ABSTRACT FROM AUTHOR]

The output-only modal analysis is ubiquitously used for structural health monitoring of civil engineering systems. The measurements for such applications require the use of multiple data acquisition systems (DAS) to avoid complicated meshes of cables in high-rise buildings, avoid traffic constriction on a bridge during measurements, or to avoid having limited channels in a single DAS. Nevertheless, such requirements introduce time synchronization problems which potentially lead to erroneous structural dynamic characterization and hence misleading or inconclusive structural health monitoring results. This research aims at proposing a system-identification-based time synchronization algorithm for output-only modal analysis using multiple DAS. A new procedure based on the compensation of the phase angle shifts is proposed to identify and address the time synchronization issue in ambient vibration data measured through multiple DAS. To increase the robustness of the proposed algorithm to the inherent inconsistencies in these datasets, the kernel density function is applied to rank multiple time-shift estimates that are sometimes detected by the algorithm when inaccuracies exist in the data arising from low signal-to-noise ratio and/or presence of colored noise in the ambient excitations. First, the synchronized ambient vibration dataset of a full-scale bridge is artificially de-synchronized and used to present a proof of concept for the proposed algorithm. Next, the algorithm is applied to ambient vibration data of a 30-story, reinforced concrete building, where the synchronization of the data could not be achieved using two DAS despite best efforts. The application of the proposed time synchronization algorithm is shown to both detect and correct the time synchronization discrepancies in the output-only modal analysis. [ABSTRACT FROM AUTHOR]

Sensor-based sorting techniques offer the potential to improve ore grades and reduce the amount of waste material processed. Previous studies show that sensor-based sorting can reduce energy, water and reagent consumption and fine waste production by discarding waste prior to further processing. In this literature review, recent investigations of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated to inform optimal sensor selection. Additionally, the fusing of data from multiple sensing techniques to improve characterization of the sensed material and hence sorting capability is investigated. It was found that the key to effective implementation of sensor-based sorting is the selection of a sensing technique which can sense a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classes of potential sensor fusion sorting applications in mineral processing are proposed and illustrated with example cases. It was also determined that the main holdup for implementing sensor fusion is a lack of correlatable data on the response of multiple sensing techniques for the same ore sample. A combined approach of experimental testing supplemented by simulations is proposed to provide data to enable the evaluation and development of sensor fusion techniques. [ABSTRACT FROM AUTHOR]

When a short circuit occurs in the power supply area of a distribution network with a high-permeability distributed generation, the line current will increase, the voltage will drop sharply, and the fault characteristics will be more complex. Therefore, the automatic, quick, and accurate location of fault sections is of great significance to the reliability of power supply. In order to prevent large-scale power outages in the power supply area caused by the failure of feeders and transformers, this paper proposes a novel method to locate fault sections in active distribution networks by taking into account the data of the power supply area. On the basis of the synchronization of calculated and measured time and the observability of the fault state, a limited number of intelligent terminals are reasonably arranged in the distribution network feeder and power supply area. Additionally, the fault location equation is established based on the three-phase voltage change values of the nodes before and after the fault collected by intelligent terminals, so that the fault section is determined by comparing the residuals. Finally, the proposed method is verified by the improved IEEE 37-node and IEEE 123-node three-phase distribution networks. The results show that it has high accuracy in locating fault sections in multiple fault scenarios. [ABSTRACT FROM AUTHOR]

During a clinical study the AEA (Acoustic Emission Analysis) of arthritic defects in the knee joint was enhanced by the addition of kinetic measurement data. This enhanced AEA based method permits a non-invasive diagnosis and assessment of arthritic joint damage at an early stage. The diagnostic procedure includes three separate measurements that contribute in different ways to an extended diagnosis of the disease pattern [1, 2, 3]. During a series of three knee bends a force plate provides data of the ground force while a video-based gait analysis records the corresponding movement and the angles of hip-, knee-, and ankle joints. At the same time AEA detects the acoustic anomalies of damaged cartilage and the absolute angle of the system. The patterns of the kinetic data were analyzed to define the instants of time to correlate the data of the 3 measurement systems. The analysis of the force data yields a pattern with 8 phases. By means of the stance phase between the knee bends the instants of time are used to synchronize force and video based data. In the second step the synchronization of video based data was done by means of the absolute angle of the AEA system [4, 5]. The superposition of kinetic data and the acoustic emission permits a preliminary graphic representation and assessment of the measurement data. The procedure will be applied for the analysis of patients in a clinical study

Summary: The high cost of conventional monitoring systems limits their application range. In this paper, microelectromechanical system (MEMS) accelerometers are adopted together with wireless nodes developed in the Faculty of Engineering, University of Porto (FEUP), in the continuous dynamic monitoring of an arch bridge, and their ability for automated tracking of the bridge modal properties is evaluated. In order to optimize the system autonomy and make the installation more flexible, the wireless nodes do not include hardware for online time synchronization. Therefore, the data synchronization has to be achieved in the data processing. In this context, the present paper includes the following features: (1) analysis of the influence of lack of synchronization on operational modal analysis; (2) proposal of a procedure for automatic estimation of modal properties using an index named pole similarity index to distinguish mathematical poles from physical modes and density‐based spatial clustering; and (3) the use of a new data synchronization method to be applied together with system identification algorithms. After the presentation and validation of the devised processing tools, it performed a comparison of the identification results obtained by the wireless MEMS system and a high‐cost commercial data acquisition system to evaluate the capability of the developed hardware for continuous dynamic monitoring. It is demonstrated that with the help of the proposed algorithms, the wireless data acquisition system meets the requirements for continuous dynamic monitoring. [ABSTRACT FROM AUTHOR]

With the advancement of structural health monitoring (SHM) technology, operational modal analysis (OMA) has played an indispensable role in finite-element model updating, damage detection, and wind resistance design. As a result of combined factors such as sensor errors and equipment failures, multiple-channel response signals often exhibit slight or significant asynchrony, leading to unforeseeable uncertainty and phase deviation during OMA. Additionally, both clock-based wireless sensor networks and cable-based wired SHM systems cannot guarantee complete data synchronization. This paper presents a novel approach for detecting and synchronizing SHM data with arbitrary time lags in the post-processing stage. The approach focuses on phase variations across multiple modes and converts time lags into phase period differences within a specified bandwidth. To improve the accuracy and automation of frequency-domain analysis, the variational mode extraction (VME) algorithm is employed in the study, which provides a robust solution by extracting fundamental mode components. The feasibility is validated utilizing a linear time-invariant simulation system with non-proportional damping. Finally, the proposed approach is implemented in the SHM system with actual time lags at the Shanghai Tower, the tallest building in China. The relative time lags between vibration response channels are successfully estimated, revealing that the data asynchronization of channels can be attributed to the misaligned timestamps between data acquisition substations. This finding mitigates current challenges in estimating the mode shape of the Shanghai Tower and assists in reducing uncertainty during the OMA process. • A novel approach for structural health monitoring data synchronization is proposed • Variational mode extraction algorithm is used to improve accuracy and automation • Robustness in non-proportional damping system with arbitrary time lags is verified • Approach is successfully applied to the Shanghai Tower SHM data with actual lags • Paper reveals laws in data asynchronization and identifies mode shapes of the tower [ABSTRACT FROM AUTHOR]

RPKI Relying party needs to synchronizing data from repository periodically to verify information.In general, Rsync and RRDP are the two common means to complete data synchronization, however, each of them has related problems.In order to solve these problems, through analyzingthe way for synchronizing data from repository by the relying party, a mathematical model was established.Furthermore, based on the current problems faced by the two synchronization means, an improved RPKI data synchronization scheme was proposed.The advantages and disadvantages of the improved scheme were analyzed in detail, as well as the applicable scenarios.The improved scheme could provide a reference for optimizing the deployment and application of RPKI.

RPKI Relying party needs to synchronizing data from repository periodically to verify information. In general, Rsync and RRDP are the two common means to complete data synchronization, however, each of them has related problems. In order to solve these problems, through analyzingthe way for synchronizing data from repository by the relying party, a mathematical model was established. Furthermore, based on the current problems faced by the two synchronization means, an improved RPKI data synchronization scheme was proposed. The advantages and disadvantages of the improved scheme were analyzed in detail, as well as the applicable scenarios. The improved scheme could provide a reference for optimizing the deployment and application of RPKI.

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized — (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions. [ABSTRACT FROM AUTHOR]

The structure of a power energy system is becoming more distributed than before. It becomes challenging to manage such a system in a centralized way, because a central authority may not exist or may not be trusted by all parties. Blockchain is a promising tool to address this challenge, by enabling trusted collaboration in the absence of a trusted central authority. Its use in the energy sector has been pioneered by several pilot projects. However, to date the energy sector has not seen large-scale deployment of blockchain, partly because the founders of those pilot projects, the public, and utilities have not reached consensus on the values and limitations of blockchain in energy. This perspective aims to bridge this gap. First, the philosophy and unique values of blockchain are discussed. Second, some promising blockchain-based applications in energy systems are presented. Third, some common misunderstandings of blockchain in energy are discussed. Last, some frequently-asked questions from utilities are discussed. Hopefully this perspective can help advance large-scale deployment of blockchain in energy systems. [ABSTRACT FROM AUTHOR]