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The safe operation of the fluid machine is greatly affected by fault states. With the development of data collection technology in process industrial systems, data-based methods are widely applied in fault diagnosis. The observed data of the fluid machine belongs to typical multivariable time series, so the Euclidean features related to the observed timestamps and the non-Euclidean features related to the observed variables need to be extracted simultaneously by the fault diagnosis method. However, the existing diagnostic studies do not involve the explainability analysis of the diagnostic process, which makes it hard to evaluate the contribution of these features to the accurate diagnosis. An explainable diagnosis model based on temporal and graph convolutional neural network is proposed. The class activation map algorithm is improved to perform explainability analysis of the diagnosis process related to the observed variables and timestamps. Using the simulation data of the fluid machine, features related to observed variables and timestamps of six operating states are fully extracted. Through data experiments, this method can be utilized to achieve high-precision fault diagnosis, and can intuitively display the contribution of each observed variable and its each timestamp to network decision-making. This helps to trace system faults and has significant benefits for process safety assurance. [ABSTRACT FROM AUTHOR]

Complex canopy cover conditions often challenge the accurate measurement of many individual tree attributes that are pivotal to the sustainable management of forest resources. Advances in drone laser scanning (DLS) and mobile laser scanning (MLS) have enabled the acquisition of high-density point clouds with the potential to better resolve detailed tree structures. Yet, the quality of DLS and MLS data can be limited by occlusions and environmental complexities. To quantify the impacts of canopy cover on the tree attribute estimation, this study investigated the utility of DLS and MLS data both individually and combined. Considering the scanning characteristics, we examined direct fusion and a new strategy using a relative weighting scheme based on the probability density of vertical point distribution. We compared the accuracy of seven tree attributes derived from quantitative structure models (QSMs) based on (1) DLS, (2) MLS, (3) fused, and (4) weighted point clouds under low, moderate, and high canopy cover levels. We found that the weighted data improved the modelling efficiency of QSMs by ∼ 20% on average, compared to fused and MLS data. Across canopy cover levels, the fused and weighted data achieved comparable results and outperformed DLS/MLS data in estimating tree attributes. Specifically, diameter at breast height and crown base height were accurately extracted from the fused, weighted, and MLS data under low canopy cover with the concordance correlation coefficient (CCC) > 0.80. As canopy cover increased, they were best estimated using the fused data (CCC > 0.90, RRMSE < 22%). Height was accurate regardless of canopy cover, which was independent of data collection platforms (CCC > 0.80, RRMSE < 16%). The crown diameter was also well estimated by fused, weighted, and MLS data across canopy cover levels (CCC > 0.82, RRMSE < 19%). The total, stem, and branch volumes could be best modelled by the fused data with increasing canopy cover. Overall, the fusion of DLS and MLS point clouds allowed the retrieval of comprehensive tree-level information. However, forestry practitioners still need to evaluate the trade-offs in selecting the most appropriate platform for laser scanning data based on their needs. Future studies should also enhance the modelling of trees with complex branching structures to strengthen the extraction of diverse attributes. [ABSTRACT FROM AUTHOR]

Since the existing control strategy for electro-hydraulic composite braking (EHB) system is essentially "vehicle-centered", it is liable to cause incompatibility with current driving behavior, torque fluctuation and inconsistent braking feeling occur, which affects braking safety. In view of the above issue, a personalized MPC control strategy in accordance with the design methodology of characteristic E is presented. To do this, the data collection platform for characterizing driving behavior is constructed under typical vehicle-following conditions. Then, a generalized radial basis function (GRBF) neural network is adopted to accurately identify braking intensity of different driving behaviors. Next, an optimization model for the maximum energy recovery of EHB system is established in terms of required braking torque and motor speed, the distribution coefficient of braking torque is optimized by applying the adaptive particle swarm optimization (APSO) algorithm. Finally, the proposed personalized MPC control strategy is verified under different driving behaviors, the results display that: (1) the personalized MPC controller possess superiority of acquiring stable braking feeling, the torque tracking error is decreased by 96.8 %; (2) energy recovery for EHB system with optimized torque distribution is increased by 34.47 % under FTP-75 cycle conditions, and the response amplitude of braking feeling is increased by 5.6 %. • Braking torque distribution coefficients of EHB system is optimized for different driving behaviors. • The parameter setting issue of driving behaviors characteristics and MPC controller is addressed. • A personalized MPC controller in accordance with driving behavior characteristic E is presented. [ABSTRACT FROM AUTHOR]

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation. [ABSTRACT FROM AUTHOR]

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results. To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching. To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience. Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data. • Visual analytics platforms lack support for handling mobility data in ITS scenarios. • We present a visual-based toolkit to support mobility data analytics for ITS. • The solution allows practitioners to perform complex analyses without writing code. • The solution is open-source and integrated within the KNIME Analytics Platform. • The effectiveness of the solution is assessed in a real-world ITS analytics scenario. [ABSTRACT FROM AUTHOR]

Completing continuous circular capsulorhexis (CCC) requires the operator to perform fine operations, which is difficult to do accurately when continuous fine actions are out of balance in the classification of CCC procedures. Multimodal deep learning can improve the classifier's performance, but the recognition accuracy of inferior classes is difficult to improve. To solve these problems, a bidirect-gate recurrent unit (Bi-GRU)-attention-based multimodal, multi-timescale data fusion network (BiMNet) is proposed, which contains a data extraction module called a skip-concatenate gate recurrent unit (SC-GRU), a bimodal data fusion attention computation, and a decoder module. The combination of these modules can fully extract the features of different temporal scales in multimodal action data and fuse them effectively. The model is validated using the ophthalmologist CCC multimodal maneuver dataset, which was collected by the data collection platform constructed in this research, achieving an accuracy of 0.9124 ± 0.0125 in continuous action sequence segmentation and improving the F1-score of minority class recognition to over 80%, making it more effective than baseline algorithms. • A Bi-GRU-attention-based multimodal data fusion network (BiMNet) is proposed. • The skip-concatenate GRU module and a bimodal data fusion module are proposed. • BiMNet can improve the classification effect of dealing with imbalanced data. • Established a multimodal maneuver dataset of continuous circular capsulorhexis. [ABSTRACT FROM AUTHOR]

• Truck drivers' working conditions: from working schedules to time away from family. • The load represents a main safety concern imposing careful storage on the vehicle. • Truck driver's attitudes and behaviours related to in-vehicle Technologies. • Truck Platooning and the new skills imposing new training needs. The platooning technology allows for two or more trucks running in convoy at a pre-defined distance between each other, being virtually connected using connectivity technology and automated driving support systems. It is recognized that truck platooning systems bring economical and environmental advantages. Thus, it is time for a transition from the existing truck freight activity towards truck platooning systems. This requires an important improvement in terms of in-vehicle technology, together with infrastructure improvement and truck drivers' acquisition of new technology-related skills. A holistic approach is previewed to identify both the requirements for the development of truck platooning services and the requests for their safe deployment in the real world. Then, qualitative data were collected from truck drivers working for two different Portuguese freight companies using Focus Groups (FG). Thus, three FG sessions were organized and carried out with a total of 22 truck drivers. Considering that age and experience on the job are important factors to take into consideration for technological changes on the job, their potential impact on truck drivers' activity was addressed on the focus group discussions. Anyway, the potential users' attitudes regarding any innovation on the job were addressed as a prevention of further negative attitudes or misuse. Having safety in mind as a permanent attitude toward on job innovation is actually the most important factor toward success. [ABSTRACT FROM AUTHOR]

Collecting accurate travel data is vital for transportation planning purposes. Regional travel demand forecasts as well as transportation system analyses depend on datasets that provide origins and destinations of travel for various modes, purposes of travel, socio-economic characteristics of the system users, and other attributes critical for understanding travel demand. GPS-based household travel surveys emerged as a state-of-the-practice method to collect travel data with increased accuracy and detail. The Maricopa Association of Governments conducted a survey utilizing Future Mobility Sensing (FMS) technology. One hundred percent of the sample was collected with the FMS technology platform that combines mobile sensing through a smartphone app with machine learning and a user interface. The technology enables detailed, multi-day, multimodal, user-verified travel and activity behavior data to be obtained with a reduced burden on participants. The data collected through the survey was analyzed together with a comparable dataset obtained through traditional recall-based collection methods during the same time period. The broad conclusions are that the 100% GPS-based surveys with the FMS technology platform provide greater accuracy, detail and completeness of data, as well as greater flexibility than traditional data collection approaches that rely on participant recall. Emphasis was made on comparative analyses between traditionally collected data and the GPS survey with the FMS technology. The paper systematically identifies and explains differences and provides original analyses that can inform future decision making relevant to similar data collection exercises. The method is particularly applicable for monitoring mobility in the ongoing conditions of rapidly changing travel behavior, especially due to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• Nine supervised machine-learning models were trained and compared. • A hyperspectral data collection platform was developed for field data collection. • Effectiveness of MCC and F1 scores were compared for imbalanced dataset. • Quadratic discriminant classifier with F1 score 0.95 & MCC 0.85 was chosen. Weed infestation and their management are a critical production challenge in agricultural fields. Palmer amaranth has created management challenges because it has multiple emergence pattern and has evolved resistant to nine unique herbicide sites of action. Effective Palmer amaranth detection and positive identification in field conditions will help to improve Palmer amaranth control. A field-based hyperspectral imaging system was developed to record Palmer amaranth in soybean fields. The data were pre-processed applying Savitzky-Golay 2nd derivative, Multiplicative Scatter Correction, and Standard Normal Variate in a forward feed manner. Recursive feature elimination, SelectFromModel, sequential forward selection, and backward elimination were used to select significant wavebands from the available 224 bands. Later, supervised machine-learning models were generated to classify soybean and Palmer amaranth using the selected wavebands. Matthew's correlation coefficient (MCC), F1 score, precision, and recall were considered as the most significant parameters to evaluate the models' performance. The highest result was obtained by quadratic discriminant analysis with a prediction accuracy of 93.95%, a precision of 90.30%, a recall of 90.29%, an F1 score of 0.95, and an MCC score of 0.85. The findings of this study showed that the combination of hyperspectral imaging and machine-learning is a potential technique for real-time weed detection in the open field condition. [ABSTRACT FROM AUTHOR]

Scene texts are the type of texts that we see in our surroundings when we are outside. They have custom fonts, sizes, shapes and colors that are often different from the fonts or styles typically found in documents. Scene text recognition has grown in popularity due to its significance in the development of smarter automated systems. Despite the fact that numerous types of research on scene text detection and recognition have been conducted in different languages, similar work on the Bengali language is limited by the lack of large-scale usable datasets. In this paper, we propose a multipurpose system for Bengali Scene text that allows for easy dataset collection via crowdsourcing as well as annotation, classification and detection tasks to be performed on the same platform. It comes with an Android-based application that can be used to capture or load images from the device, select Bengali texts over the images and annotate them. The images and labels are stored in the cloud. A Python-based script is used to perform real-time data processing, analysis, and detection tasks. The proposed system has also been tested on a crowdsourced Bengali scene text dataset collected by this system. The classification model achieved an accuracy score of 97%, and the detection model achieved a Mean Average Precision score of 92 on the on these crowd contributed dataset. [ABSTRACT FROM AUTHOR]

Detection of gravity change over time has been used to better understand magmatic activity at volcanoes for decades, but the technique is not commonly applied to forecasting eruptions. In contrast, other tools, notably seismic, deformation, and gas monitoring have made exceptional strides in the past several decades and form the foundation for eruption forecasting, especially during the final buildup to an eruption. Reasons for this gap include the high cost and fragile nature of gravity instruments, and the ambiguous nature of many results. But this is changing. Instrumentation is becoming more robust and accurate, expenses may soon diminish thanks to technological advances, and the record of success in tracking subsurface mass change (either from magma or hydrothermal fluids) in volcanic areas is growing. Here we review how gravity change can be applied to forecasting volcanic eruptions across a variety of spatial and temporal scales. We argue that microgravity has untapped potential as a forecasting tool in three specific ways: constraining probabilistic assessments, detecting long-term mass change that may occur prior to the onset of vigorous seismicity and deformation, and identifying transient activity that indicates magma ascent or other changes that immediately precede new eruptions or changes in ongoing eruptions. As with any volcano-monitoring method, microgravity has strengths and weaknesses, but the varied forms of data collection—for instance, campaign versus continuous, and relative versus absolute—offer the potential to record a broad range of signals at volcanoes with a diversity of magmatic systems. The technique is currently underutilized; additional attention, investment, and application at more volcanoes could help to realize its promise. • Microgravity provides a powerful tool for forecasting of volcanic eruptions. • Campaign gravity data can help to constrain probabilistic forecasts. • Long-term magma accumulation is accompanied by gravity increase. • Continuous gravity can provide short-term indication of magma ascent. • Microgravity needs attention and investment to realize its promise in forecasting. [ABSTRACT FROM AUTHOR]

In the Asian region, no international organ transplantation registry exists. Individual centers maintain their own database, or some countries developed a national registration system. To promote collaboration among Asian transplantation societies, the Asian Society of Transplantation (AST) has developed an international transplantation registry for the Asian countries that has been named as the Asian Society Transplant Registry (ASTREG). In 2017, the AST council formed a registry committee to develop 2 kinds of databases: ASTREG-N (nationwide level), which collects yearly aggregated data of participating countries, and ASTREG-H (hospital level), which collects the data of transplant recipients and donors from individual centers. ASTREG-N collects each country's aggregate data of solid-organ transplantation, such as the total number of transplantations and deceased donors. ASTREG-H collects 5 transplant domains, namely recipient baseline characteristics, immunosuppression, post-transplant event, annual post-transplant evaluation, and donor traits. For the ASTREG-H project, South Korea, Philippines, Mongolia, and Myanmar are the current participants. A web-based secure data entry platform with real-time data visualization and automated data verification systems is currently available. Any participating centers can run this platform as their own data collection system. The ASTREG is a collaborative project that will be the representative solid-organ transplantation database in the Asian region. It can aid in the harmonization of transplantation data in the Asian region. [ABSTRACT FROM AUTHOR]

Contrary to many previous studies on population protocols using the uniformly random scheduler, we consider a more general non-uniform case. Here, pair-wise interactions between agents (moving and communicating devices) are assumed to be drawn non-uniformly at random. While such a scheduler is known to be relevant for modeling many practical networks, it is also known to make the formal analysis more difficult. This study concerns data collection , a fundamental problem in mobile sensor networks (one of the target networks of population protocols). In this problem, pieces of information given to the agents (e.g., sensor readings) should be eventually delivered to a predefined sink node without loss or duplication. Following an idea of the known deterministic protocol TTF solving this problem, we propose an adapted version of it and perform a complete formal analysis of execution times in expectation and with high probability (w.h.p.). We further investigate the important issue of energy consumption. The goal is to improve TTF in terms of energy complexity, while still keeping good time complexities (in expectation and w.h.p.). Namely, we propose a new parameterized protocol for data collection, called lazy TTF, and present a study showing that an appropriate choice of the protocol parameters can considerably improve energy performances (compared to TTF), at a slight expense of time performance. [ABSTRACT FROM AUTHOR]

• Individual FTG rates can give a good characterization of the zone. • Zonal FTG rates can be obtained from individual FTG rates. • Number of employees was found to be the only representative variable for models. • Combining the stated methodologies lead to a suitable FTG framework for Latin America. Ever-growing urban areas and global population movement towards urbanization lead to major concerns regarding urban logistics and last mile operations. In Latin America, the problem becomes critical since volatile emerging economies and unstable political situations, which are common in the region, introduce additional limitations for strong logistics solutions. In the city of Quito (Ecuador), traffic regulations only consider time-schedule restrictions for vehicle mobility without any other policies that benefit urban goods movement. This is in part because there is a lack of knowledge on freight flows, mainly related to the difficulties to retrieve data to that purpose. This paper proposes a freight trip generation analysis in Quito, based on a methodology included in "MIT's Better Cities for Logistics Toolkit", which defines specific zones observation and data collection campaigns. More precisely, a procedure combining observation-based and declarative data collection processes is proposed. First, the opportunities of combining both observed and declared data to characterize freight trip generation are addressed on the basis of a literature review. Main issues of combining establishment based surveys and observations are addressed to generalize the proposed framework. Finally, application implications in a transferability perspective to other Latin American countries are addressed. [ABSTRACT FROM AUTHOR]

The International Organization for Standardization (ISO) recently released technical specifications about minimum reporting requirements in soundscape studies and methods for data collection (ISO/TS 12913-2:2018). The document provides an informative annex with three alternative methods: two are based on soundwalks and questionnaires, whilst the third refers to narrative interviews. This study was conducted in accordance with the ISO/TS 12913-2 technical specifications. Its aim was testing whether compatible results could be obtained from the first two methods, during the same data collection session. For this purpose, a soundwalk was organised in a university campus with two groups of students using the two methods separately, while experiencing the same acoustic environments. Results show that for this case study the two methods returned soundscape categorisations that are similar and have strong statistically significant associations (p =.023). In particular, in 7 out of 8 locations, the two methods would categorise positive and negative soundscapes identically. However, since the correspondence was not perfect, further studies should investigate the topic of methodological comparisons in soundscape research, and possibly consider revising or integrating the two methods. [ABSTRACT FROM AUTHOR]

Passive acoustic technology has potential as a surrogate method for monitoring coarse bedload transport in rivers and streams. This has been demonstrated by both laboratory experiments and field studies where it was found that acoustic energy generated by particle collisions was proportional to the amount of bedload in transport. One factor that has inhibited further development of the technique is that comparison of acoustic data is complicated by the wide variety of hardware used. A second factor is the need for each research group to assemble hardware, which is often expensive and designed for laboratory use. In addition, the hardware used in the field is rarely calibrated. In order to demonstrate how an entire acoustic data acquisition system should be calibrated for coarse bedload data collection, this manuscript presents the assembly and calibration of an inexpensive acoustic system comprised of readily available equipment. The following are presented here: assembly of the system, key operating parameters and suggested specifications, system calibration, and experiences from multiple deployments of the example system. [ABSTRACT FROM AUTHOR]

High efficiency data collection remains a great challenge for HPC reliability and resilience, yet may pave the way to overcome the barrier before fault prediction. Not only for increasing scalability up to exascale systems but even for contemporary supercomputer architectures does it require substantial efforts to efficiently collect and analyze data that contains system fault information within the framework of faults prediction. In this term, the article mainly focuses on efficient data collection and data preprocessing, preferring to optimize an effective framework to improve the efficiency of data collection in petascale system. The core of our framework includes a data collection acceleration layer scheduled by H2FS, a further detailed information get by performance analysis tool, as well as a new method for log template extraction, which all attribute to a more efficient and convenient framework for the real-time data collection. Hereafter, we conducted extensive tests based on a petascale system to verify the solution, and the experimental results demonstrate to be effectiveness and scalability of our framework. [ABSTRACT FROM AUTHOR]

As the product environmental footprint (PEF) has a profound impact on the environmental sustainability, it is crucial to estimate PEF in the product life cycle. The existing researches lack the prescriptiveness and environmental footprint-specific accounting guidance to produce consistent PEF, thus most of the existing PEF models fail for product life cycle. The paper is devoted to PEF assessment for product life cycle. After the introduction of PEF, the calculation model for each of the 14 kinds of PEF is proposed. PEF assessment process is then put forward in detail, which includes 10 steps, i.e. functional unit selection, system boundary determination, impact types selection, data collection, impact types classification, impact types characterization, impact types of PEF calculation, impact types standardization, impact types grouping and weighting, PEF assessment result. PEF assessment process of an agricultural picking robot is used to demonstrate the effectiveness of the proposed methodology. Image 1033 • Product environmental footprint model is proposed. • Each of product environmental footprints for product lifecycle is proposed. • Establishment of product environmental footprint in the product lifecycle. [ABSTRACT FROM AUTHOR]

Highlights • The four-layer architecture of the data management system developed in SNERDI. • The "name-value separation" method to model and store the data. • Interfaces mechanism for data collection from software and documents. • Document-conversion and content-restoration of database for full-text search. • The plant information model configuration method. Abstract The data management system (DMS) developed in SNERDI is a central platform for data collection, storage, control, configuration, display, and application of all engineering data, serving for project design and management. The four-layer architecture of the DMS is proposed, and considering the specialty of nuclear power design data, key mechanisms on data modeling and storage, data collection, full text search, data visualization, and plant information model configuration are studied and solutions are presented. Specifically, the data is stored by a "name-value separation" method in which metadata and value is separately modeled and stored. Design data is collected through interfaces which acquire data either by standard template or by data extraction from engineering documents and 3D models. For full text search, all the documents are converted into plain text files, and the DMS database is "content-restored" into JSON-formatted files. Data visualization solves the problem of data display friendly and graphically, as well as 3D models navigation. The plant information model is configured by a three-step cascade style. [ABSTRACT FROM AUTHOR]

In many of the Internet of Things (IoT) applications, a network of sensors that are wirelessly connected is vital because it provides a better picture of remotely sensed environments. However, traditional network data collection consumes a high amount of energy because of data packets that need to be routed on a hop-by-hop basis to the base station. To alleviate this problem, Unmanned Aerial Vehicles (UAV) have been used to travel over the sensed environment to collect data. The time and motion constraints of UAVs mean that they require the shortest and smoothest paths to address these constraints. So, the shortest path is planned based on Travelling Sales Problem (TSP), and to smoothen the path, we used Bezier curves to convert paths that are flyable. The proposed algorithm exhibits faster data collection in comparison with other solutions, while achieving a high delivery rate of packets and a low energy usage. [ABSTRACT FROM AUTHOR]

Following an integrated data analytics framework that includes descriptive analysis and multiple automatic content analysis, we examined 265 projects that have been funded by the National Science Foundation (NSF) under the Smart and Connected Health (SCH) program. Our analysis discovered certain characteristics of these projects, including the distribution of the funds over years, the leading organizations in SCH, and the multidisciplinary nature of these projects. We also conducted content analysis on project titles and automatic analysis on the abstracts of the projects, including term frequency/word cloud analysis, clustering analysis, and topic modeling using Biterm method. Our analysis found that five main research areas were explored in these projects: system or platform development, modeling or algorithmic development for various purposes, designing smart health devices, clinical data collection and application, and education and academic activities of SCH. Together we obtained a comparatively fair understanding of these projects and demonstrated how different analytic approaches could complement each other. Future research will focus on the impact of these projects through an analysis of their publications and citations. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) is a promising technology for the construction sector, as it addresses multiple risks, supports decision-making and enhances value. However, its technological and contractual novelties introduce some new risks. To observe the BIM-driven risk transformation, this paper performs a thorough analysis involving international experts and practitioners. Data is collected in the form of open-ended interviews and typeset questionnaire along with case studies of running projects. Results reveal that BIM eliminates a majority of significant risks. Further, the findings fuel a new research problem; the lack of a dedicated BIM plugin for risk management. Responding to it, a theoretical framework is developed to automate the risk management process and improve overall project management practice. It is concluded that construction projects can greatly benefit from an automated risk management system and investment in developing a dedicated plugin is recommended, ensuring an effective penetration of BIM in the construction industry. [ABSTRACT FROM AUTHOR]

In this paper, an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception are presented. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which is capable of processing the sensor data from multi-CAVs and extracting the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multi-CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high-definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, to refine trajectories from the object tracking algorithms, a post-processing method is proposed. Given the objects' information from the object detection and tracking and the world model, a Kalman filter and Chi-square test method are applied to reduce the noise and remove the outlier in the trajectories. Aiming at tackling the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories belonging to the same object. Then, a vehicle-kinematics-based trajectory prediction method is used, and a forward–backward-smoothing technique is applied to reconstruct the trajectory between the discontinuous trajectories. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the improvements by the post-processing algorithm in terms of noise level and outlier removal are discussed, which confirm the functionality and effectiveness of the proposed holistic data collection and processing platform. In another aspect, the extracted objects' information and generated HD maps can be used for several purposes in the transportation research community and ADS development community: analyzing the interaction between human-driven vehicles and ADS-equipped vehicles, car-following behavior analysis of ADS-equipped vehicles, traffic flow status analysis and modeling, and scenario generation for ADS testing. • An automated driving system data processing platform using cooperative perception is designed. • This platform leverages sensor data from connected automated vehicles. • Results of cooperative object detection and tracking in a late fusion scheme is presented. • Postprocessing algorithm is proposed to reduce noise level and outlier removal. [ABSTRACT FROM AUTHOR]

• Permutation importance scores were used to interpret the results of Census outreach. • "Direct engagement (1-on-1)" and "single events" have the highest impact. • Outreach activities were found not as important as socio-demographic features. • Careful data collection should be performed to ensure meaningful analysis. • The results, methods, and findings are relevant for public campaigning efforts. [ABSTRACT FROM AUTHOR]

Real-world data (RWD) generated during the pre-approval phase could be supplementary to primary clinical trial outcomes; however, as we discuss here, a data collection framework is needed to ensure the validity and applicability of these data. [ABSTRACT FROM AUTHOR]

Mineral exploration and mining are data-driven industries. Here, we emphasize the role of “ data upcycling ” as a significant contributor to modern exploration. Upcycling can take three basic forms, all aimed at enhancing the veracity of data: (1) re-collection of data, (2) collection of complimentary data, and (3) assessment and innovative portrayal/integration of data. Upcycling of previously collected (or legacy) data allows information to be integrated into modern datasets. This paper offers perspectives and examples on how data upcycling benefits mineral exploration, with short case studies from Western Australia highlighting the role of government, service providers and resource explorers. [ABSTRACT FROM AUTHOR]

Crop growth models can be useful tools to evaluate potential scenarios, yet the limited field data is an obstacle for providing significant insights at the local level. This certainly applies to several regions in Africa, where crop models built regional scenarios based on low resolution field data, leading to unsupported agricultural interventions on smallholders' systems. This review provides a synthesis analysis of crop modeling efforts in Africa using Agricultural Production Systems Simulator (APSIM) studies as a case-study. This research aims to highlight the value of standardized protocols to collect, store and deploy field data, and highlights the critical issue of limited data accessibility of published manuscripts and unavailability of a data sharing platform.Investment in local-level data collection and data sharing platforms is critical to guarantee the advancement of science and to provide reliable assessments to address complex challenges of food, nutrition, and climate security in Africa. [Display omitted] • Limited field data represents a challenge for crop models to provide useful insights. • This study showcases the need for investments on open data sources. • A systematic review was executed to collect crop modeling studies within Africa. • There is a need of standardization for field data collection and its accessibility. • Investment in local-level data is critical to provide reliable food assessments. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) technology can be used to analyze the mechanism and evaluate the size of the heat-affected zone (HAZ) during laser cutting carbon fiber-reinforced plastics (CFRP). A hardware acquisition platform and data collection system for collecting AE signals were first built. Experiments of 17 groups with different laser repetition rates and 13 groups with different galvanometer scanning speeds were carried out in CFRP laser processing. Through the spectrum analysis of fast Fourier transform, it can be observed that the frequency distribution of the AE signal was located only at the integer multiple of the laser repetition rate, and the maximum frequency of the AE signal was the product of the laser repetition rate and the number of AE oscillation cycles between two adjacent laser pulses; The critical state of laser ablation and denudation (plasma impact) was described by root-mean-square (RMS) characteristics of the AE signal. When the laser repetition rate is 50 kHz and the scanning speed is 2500 mm/s, laser ablation and denudation are both very intense, which is helpful to improve the processing efficiency; As the laser repetition rate increases, the RMS value of AE increases first and then decreases with the synchronization of the HAZ. By monitoring the RMS value, the value of HAZ can be obtained clearly. [ABSTRACT FROM AUTHOR]

The indicator framework established for the monitoring of SDG4 is unambiguous on the need to advance in the production of global learning data. However, the production of SDG4 learning metrics has been riddled with technical and political difficulties. Drawing on a combination of documentary analysis, interviews and non-participant observation, this paper reconstructs the process of negotiation of the data suppliers, statistical routines and reporting standards necessary for the production of SDG4 learning metrics. The paper thus offers insight into the mechanics of global quantification, and on the transformative impact of such processes on the agendas and relationships of partaking organizations. • With the advent of SDG4, the production of globally-comparable learning data has become an institutional priority for UIS. • The production of globally comparable learning data presents technical as well as political difficulties related to the collective nature of indicator-making. • The relative success of the UIS in the area of learning measurement owes much to its efforts to posit itself as an honest broker and accomodate different data sources and harmonization mehods. • UIS' centrality is endangered by the existence of parallel measurement projects and an uncertain rapport with UNESCO. • The production of SDG4 learning data has been shaped by a tension between the principles of in-built comparability and country ownership. [ABSTRACT FROM AUTHOR]

While there is increased interest among engineers and scientists to collaborate with environmental justice organizations (EJOs), and while there are well-known programs that facilitate collaborations between engineers, scientists, and underserved communities, little is known systematically about the engineering and scientific challenges faced by EJOs. We lay the foundation for such a systematic understanding through exploratory research in which we conducted 47 semi-structured interviews with EJO staff across the United States to ask what their engineering and scientific challenges to combating environmental injustice are. Using deductive-inductive coding we discover that EJOs are looking for support to engineering and scientific challenges that fit specific topical areas and methods. Using the words of our informants, we then unpack the qualitative themes of select topical areas (air quality, public and environmental health, fossil fuel infrastructure, clean and just energy transitions, environmental restoration, and indigenous groups) and methods (data collection, data dissemination, data analysis, spatial analysis, online platform building, networking of experts). We explore the patterns of how these themes overlap with five different strategies (policy change, utilizing the media, educating the public, providing legal advice, and developing collaborative projects) used by the EJOs in our study. We show that EJOs want to collaborate with engineers and scientists to solve technical problems, mitigate harm to their communities, and create positive futures. Our research informs the conception of strategic efforts to expand the impact of engineering and scientific work done to address EJ challenges, particularly given the limited resources that currently exist to support collaborative efforts in this space. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the purchase willingness of Chinese consumers for pork, beef, lamb, and rabbit meat. A conceptual framework and focus group discussions were adopted to design the questionnaire which included experience, eating habits, purchase drivers, purchase location, seasonality, and willingness to purchase red meat among Chinese consumers. Data collection was undertaken using an online platform questionnaire, with 4039 meat consumers participating in this study. By constructing the partial least squares – path modeling (PLS-PM) to explore the concerns of Chinese consumers while purchasing various types of red meats as well as the effect of these questions on consumers' willingness intensity to purchase red meat. Willingness intensity to purchase lamb is significantly affected by experience, seasonality, purchase location, eating habits, willingness, purchase drivers, and personal information; beef is affected by experience, seasonality, purchase location, eating habits, willingness, and personal information; pork is mainly affected by eating habits and purchase drivers; while eating habits, purchase location, and personal information have positive impacts on willingness intensity to purchase rabbit meat. This study developed the orthogonal matching pursuit – stochastic gradient descent (OMP-SDG) model, which integrated the questionnaire collecting personal information with the questionnaire on Chinese consumers' perceptions of red meat. The program can more precisely forecast which types of red meat people like and their degree of willingness. Finally, Chinese consumers preferred beef, lamb, pork, and rabbit meat in that order. They also prefer local meat, believing it to be of high quality and providing a satisfactory sensory experience, appearance, and saturation. • PLS-PM models identified factors affecting red meat willingness. • The OMP-SGD method was proposed to analyze fused data. • Data fusion and simultaneous measurement of multiple questionnaires were used. [ABSTRACT FROM AUTHOR]

Life cycle inventory (LCI) data gathering is one of the most time-consuming and data-intensive tasks in life cycle assessment (LCA) studies. Until now, it has mainly been described at a theoretical level and is associated with non-specific guidelines in LCA reference documents, thus leaving LCA practitioners with limited guidance prone to inefficient, not transparent, and sometimes inconsistent steps taken in the LCI phase. To tackle this problem, a stepwise data collection guidance is developed based on a critical review of existing guidelines and LCA case studies published in peer-reviewed scientific journals. The guidance encompasses three steps: (i) planning of data collection, including the outlining of LCI blocks, which represent unit processes in an aggregation level that is technology-wise appropriate; (ii) data gathering using the LCI blocks; and (iii) LCI blocks finalization. A customizable, generic LCI template for data collection is provided to facilitate the conduct of steps (i)-(iii). A real-life data collection case of lithium-ion batteries manufacturing is used to demonstrate the guidance's applicability. LCI block datasets were produced using the customized LCI template and the proposed guidance. Cooperation with the case data provider thus enabled to fill the data collection template, which was then reviewed iteratively. Although this case evidences the guidance's operability and its benefits in facilitating and strengthening LCA practice, several constraints undermining reproducibility and transparency could be identified, in particular issues related to data confidentiality. Despite these limitations, which require further efforts from practitioners and more transparency from stakeholders involved in the commission of LCA studies, the authors call for applying the proposed stepwise guidance to enhance future LCA practice. [Display omitted] • Literature review of guidance documents and 300+ LCA case studies. • Establishment of state-of-the-art on data collection practices in LCA. • Gaps: undetailed guidelines leading to LCI data transparency and reporting issues. • 3-step guidance + customizable generic LCI template developed to bridge the gaps. • Guidance use to increase LCI data harmonization, transparency, and accessibility. [ABSTRACT FROM AUTHOR]

This article presents a dataset collected from the subterranean (SubT) environment with a current state-of-the-art sensors required for autonomous navigation. The dataset includes sensor measurements collected with RGB, RGB-D, event-based and thermal cameras, 2D and 3D lidars, inertial measurement unit (IMU), and ultra wideband (UWB) positioning systems which are mounted on the mobile robot. The overall sensor setup will be referred further in the article as a data collection platform. The dataset contains synchronized raw data measurements from all the sensors in the robot operating system (ROS) message format and video feeds collected with action and 360 cameras. A detailed description of the sensors embedded into the data collection platform and a data collection process are introduced. The collected dataset is aimed for evaluating navigation, localization and mapping algorithms in SubT environments. This article is accompanied with the public release of all collected datasets from the SubT environment. Link: Dataset • The paper presents a dataset from the SubT environment with state-of-the-art sensors. • The dataset employs RGB, RGB-D, event-based and thermal cameras, lidars, IMU and UWB. • The dataset provides recorded data from all the sensors in ROS bag files. [ABSTRACT FROM AUTHOR]

We consider a scenario where mobile agents are required to collect measurements from a geographically dispersed set of sensors and return them to a base. An agent faces a risk of destruction while traversing the environment to reach the sensors, and gets a reward for gathering a sensor measurement only if it successfully returns to base. We first consider the single agent scenario and characterize several properties of the optimal policy for the agent based on a quantity that we term the "reward-to-risk" ratio of the tasks. We provide the optimal policy when the risk of destruction is sufficiently high, and then extend our analysis to multiple (heterogeneous) agents, with potentially different costs and survival probabilities. In particular, when the risk of destruction is sufficiently high, we show that the multi-agent task allocation problem has a submodular structure, and accordingly provide a greedy algorithm that is guaranteed to return an allocation that is within a 1 2 factor of optimal. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel end-to-end system called Human–Machine Collaborative Inspection (HMCI) to enable collaboration between inspectors with Mixed Reality (MR) headsets and a robotic data collection platform (robot) for structural inspections. We utilize the MR headset's holographic display and precise head tracking to allow inspectors to visualize and localize information (e.g., structural defect) on the real scenes, which are gathered by the robot and processed by an offsite computational server. The primary use case of HMCI is to enable the inspector to visualize, supervise, and improve results produced by automated defect detection algorithms in near real-time. The workflow in HMCI starts with collecting images and depth data to generate 3D maps of the site from the robot. A technique called single-shot localization is developed to create visual anchors for real-time spatial alignment between the robot and the MR headset. The 3D map and images are then sent to the computational server for analysis to detect defects and their locations. Then, the information is received by the MR headset and overlaid on the actual scenes to visualize it with spatial context. An experimental study is conducted in a lab environment to demonstrate HMCI using Microsoft HoloLens 2 (HL2) as the MR headset and Turtlebot2 as the robot. We start with the reconstruction of a 3D environment using a 3D depth sensor (Azure Kinect) on Turtlebot2 and visually detect fiducial markers as regions-of-interest (replicating structural damage) along a predefined inspection path. Then, regions-of-interest are successfully anchored to the real scene and visualized through HL2. To our knowledge, HMCI is one of the first human–machine collaborative systems that can integrate robots and inspectors with the MR headset, which has been developed, tested, and presented for structural inspection. [ABSTRACT FROM AUTHOR]

Children living with parents who have childhood maltreatment histories tend to develop compromised behavioral health outcomes, suggesting the intergenerational effects of child maltreatment. Driven by attachment theory, this study aims to identify the pathways from parents' childhood maltreatment to their child's behavioral problems and functioning. The sample of parent participants (N = 202) was recruited from an online data collection platform – Amazon Mechanical Turk. Structural equation modeling with the estimator of maximum likelihood was used to examine the intergenerational effects of child maltreatment. This study identified two important mediators, i.e., parents' emotion regulation and parental mentalization. Parental childhood maltreatment had a positive association with parents' difficulties in emotion regulation (β = 0.45, p < 0.001), which in turn was positively associated with parental prementalizing mode (β = 0.47, p < 0.001). Parental prementalizing mode was then positively associated with child behavioral problems (β = 0.32, p < 0.001). The second significant path was from parental childhood maltreatment to their child functioning, which was mediated directly through parents' difficulties in emotion regulation (β = - 0.26, p < 0.05) without any sequential mediation through parental mentalization. This study advances our existing understanding of the complexities of child maltreatment and informs an intergenerational approach in terms of screening and intervention, so professionals can effectively work for families to alleviate the enduring negative effects of child maltreatment, to reduce prohibitive social expenditures of mental health services, and to promote social justice. [ABSTRACT FROM AUTHOR]

For grid-connected photovoltaic (PV) system, monitoring is considered as a crucial aspect for observing the stability and performance of the system. The simplest method is to have the data collected and transmitted across data cables. Due to the cost and technical limitations of the data cable, the monitoring station needs to be located reasonably close to the monitored plant. Apart being inconvenient, the use of data cable often adds capital and maintenance cost to the system. In this research project, a Zigbee-based wireless monitoring system is developed for online monitoring of a grid-connected photovoltaic system. Parameters like temperature, irradiation, PV power output and grid inverter power output are monitored. The implementation process, including design and development of the hardware and software, is explained in detail. A user-friendly web-application is also developed, such that the monitored data is easily accessible via internet. To validate the performance, the system has been implemented on 1.25 kW p grid-connected photovoltaic system. [ABSTRACT FROM AUTHOR]

Real-time performance optimization of thermal systems is crucial for energy conservation but also challenging because of the high system complexity and time sensitivity. Herein, an online parameter identification and real-time optimization platform for thermal systems is developed, and a gas-steam combined cycle cogeneration system is used to demonstrate its capability. The platform integrates data collection, parameter identification, system simulation, and real-time optimization modules. The data preprocessing and parameter identification modules collects real-time operation parameters for optimization. The system simulation module applies the heat current method to model the cogeneration system precisely, and a high-efficiency simulation procedure is proposed using the hierarchical and categorized (H&C) algorithm. The system optimization module introduces the artificial neural network technology to ensure the response time of real-time optimization. Meanwhile, the H&C algorithm and genetic algorithm are combined to update the database to improve the optimization performance gradually. The platform is first validated on three typical conditions. It is further deployed at a power plant, where a field test is conducted for the practical verification. Field test results show that the standard coal consumption of the cogeneration system could be reduced by 0.415 g/kWh by using the platform, which proves its practicability. • Propose a real-time optimization platform for thermal systems. • Develop online parameter identification technique for model accuracy. • Integrate heat current method, genetic algorithm, and neural network in platform. • Optimize a gas-steam combined cycle cogeneration system as an example. • Reduce the standard coal consumption by 0.415 g/kWh in the field test. [ABSTRACT FROM AUTHOR]

To overcome the limitations of the state-of-the-art influenza surveillance systems in Europe, we established in 2008 a European-wide consortium aimed at introducing an innovative information and communication technology approach for a web-based surveillance system across different European countries, called Influenzanet. The system, based on earlier efforts in The Netherlands and Portugal, works with the participation of the population in each country to collect real-time information on the distribution of influenza-like illness cases through web surveys administered to volunteers reporting their symptoms (or lack of symptoms) every week during the influenza season. Such a large European-wide web-based monitoring infrastructure is intended to rapidly identify public health emergencies, contribute to understanding global trends, inform data-driven forecast models to assess the impact on the population, optimize the allocation of resources, and help in devising mitigation and containment measures. In this article, we describe the scientific and technological issues faced during the development and deployment of a flexible and readily deployable web tool capable of coping with the requirements of different countries for data collection, during either a public health emergency or an ordinary influenza season. Even though the system is based on previous successful experience, the implementation in each new country represented a separate scientific challenge. Only after more than 5 years of development are the existing platforms based on a plug-and-play tool that can be promptly deployed in any country wishing to be part of the Influenzanet network, now composed of The Netherlands, Belgium, Portugal, Italy, the UK, France, Sweden, Spain, Ireland, and Denmark. [ABSTRACT FROM AUTHOR]

State-of-charge (SOC) is the key parameter for battery management, and the accurate estimation of SOC is pretty important for the safe and stable operation of lithium batteries. This paper investigates a regularized extreme learning machine trained with the spectral Fletcher–Reeves algorithm and tuned with the beetle antennae search algorithm (BAS-SFR-RELM) for intelligent and robust SOC estimation. In the experiment section, the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile are performed on a battery test platform for data collection. In the simulation section, the root mean squared error (RMSE) and the mean absolute error (MAE) are adopted to evaluate the performance of the model. Compared with the linear regression (LR), the back propagation (BP) network, the multi-layer perceptron (MLP), and the long short-term memory (LSTM) network, the BAS-SFR-RELM method can efficiently obtain the optimal regularization coefficient to effectively prevent overfitting with faster convergence speed. Increasing the number of hidden neurons in the BAS-SFR-RELM appropriately can improve the SOC estimation precision. Implementing the BAS-SFR-RELM with the noise-added data set gives high robustness for SOC estimation • A novel model is constructed to estimate the SOC of lithium batteries. • The BAS algorithm is applied to optimize the regularization coefficient then overfitting is prevented. • The SFR algorithm is introduced to tune the output weights of the RELM. • The UDDS data set and the LA92 data set are used for model training, validation and testing. [ABSTRACT FROM AUTHOR]

• Review work on rumour detection is presented. • A novel taxonomy has been developed based on earlier state-of-the-arts for Rumour analysis. • The crucial factors used for analysis have been identified. • The publicly available rumour datasets have been outlined. • The data collection approaches on social media platforms have been discussed. In the present era, the social network is used as an important medium for sharing thoughts and opinions of an individual. The main reason behind this is, it provides a fast-spreading of information among the public easily, requiring a very low cost of access. This leads to having online social media as one of the stepping stones to encourage false content and influencing public opinion and its decision. Rumour is one of the prominent forms of misleading information on social media and should be detected as early as possible for avoiding their significant effects. Due to these reasons, the researchers have put their keen interest in developing an effective rumour detection framework in the last years. In this paper, we mainly focused on six main aspects. Firstly, we discuss rumours from a definition perspective that have been considered in the state-of-the-art and describe the generalized model of rumour detection. Secondly, we discuss how to get access to data from different social media platforms, and presents various state-of-the-art methods to gather these data, as well as publicly available datasets. Third, we describe a different set of features that have been considered in rumour detection approaches. Fourth, we provide deep insight into the various methods used to employ rumour detection and its veracity assessment on multimedia data (Text and Images) with some practical implications. Whereas in the fifth aspect, the constraints of the study have been discussed. Finally, we concluded with useful findings and suggested future directions. [ABSTRACT FROM AUTHOR]

Construction workers' posture-related data is closely connected with their safety, health, and productivity performance. The importance of posture-related data has drawn the attention of researchers in construction management and other fields. Accordingly, many data collection methods have been developed and applied to collect posture-related data. Despite the importance of workers' posture-related data, there lacks a review of previous data collection methods in the construction industry. This paper fills the research gap by reviewing previous methods to collect posture-related data for construction workers via 1) summarizing working principles and applications of posture-related data collection in construction management, which demonstrates the extensive use of motion sensors and Red-Green-Blue (RGB) cameras in posture-related data collection, 2) comparing the above methods based on data quality and feasibility on construction sites, which reveals the reason why motion sensors and RGB cameras have been prevalent in previous studies, 3) revealing research gaps of posture-related data collection tools and applications, and providing possible future research directions. • Pose-related data collection methods in construction were compared based on data quality and applicability. • The review demonstrated the extensive use of motion sensors and RGB cameras and revealed the reasons. • Research gaps of pose-related data collection tools and applications were revealed. • Possible future research directions were suggested. [ABSTRACT FROM AUTHOR]

• A pointer meter interconnect and automatic data collection frame using machine vision and WSN technologies. • A time-efficient and robust intelligent pointer dial reading algorithm in farmlands. • The new algorithm has a better performance by virtue of common characters of pointer dials and statistic analysis method. • A solution for non-digital data collection in farmlands. Some non-digitized measuring instruments such as pointer meters are utilized by some farmlands by virtue of their high stability and accuracy, and the data collection depending on the human movements requires considerable time and effort and has a low technology level. Therefore, an intelligent pointer meter indication reading and automatic collection method is proposed using machine vision and wireless sensor network (WSN) technology. An intelligent pointer meter indication reading device consisting of an image acquisition module, an intelligent control center, a wireless transmission module, and a power module was developed, which could accomplish intelligent meter indication reading and automatic data uploading. Furthermore, a pointer meter indication reading algorithm for a resource-limited environment is proposed. Firstly, the pointer and the circle axis were recognized by using edge detection and the geometrical characteristics of the pointer meter, and then, the scales and scale values were determined using the statistical characteristics of the circles around the pointer's vertex. Next, by utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we adjusted the scales and the scale values, taking full advantage of the pointer's characteristic that the scales' rotation angles changed linearly with the scale values, which were classified by an SVM taking in the HOG feature as the input. Finally, the indication was computed in terms of the relative position of the pointer between the adjacent scale values, and the accuracy was 94.2%. The proposed method was evaluated by comparing it with some newly published algorithms. The pointer detection method had stronger robustness and anti-interference capability than the image segmentation method and the skeleton extraction method. The Θ(n) value, indicating the complexity of the scale detection algorithm, was considerably smaller than the Θ(n2) value, denoting the modified central mapping method used mostly for scale detection. The energy consumption by each module was measured, and the results showed that the image acquisition module accounted for the largest energy consumption, 87.7%, which was more than eight times as much as the second-largest energy consumption, 10.3% (that of the wireless transmission module); the problem of power supply could be solved by using solar panels. [ABSTRACT FROM AUTHOR]

• Review of building power consumption datasets. • Data collection platforms. • Power consumption anomaly detection dataset. • Improving datasets collection and exploitation. • Visualization of power consumption micro-moments. In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from several sources, including ambient conditions, user occupancy, weather conditions and consumer preferences. Thus, a proper understanding of the available datasets will result in a strong basis for improving energy efficiency. Starting from the necessity of a comprehensive review of existing databases, this work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date. Furthermore, data collection platforms and related modules for data transmission, data storage and privacy concerns used in different datasets are also analyzed and compared. Based on the analytical study, a novel dataset has been presented, namely Qatar university dataset, which is an annotated power consumption anomaly detection dataset. The latter will be very useful for testing and training anomaly detection algorithms, and hence reducing wasted energy. Moving forward, a set of recommendations is derived to improve datasets collection, such as the adoption of multi-modal data collection, smart Internet of things data collection, low-cost hardware platforms and privacy and security mechanisms. In addition, future directions to improve datasets exploitation and utilization are identified, including the use of novel machine learning solutions, innovative visualization tools and explainable mobile recommender systems. Accordingly, a novel visualization strategy based on using power consumption micro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage. [ABSTRACT FROM AUTHOR]

The abortion process at later gestational ages—from placement of dilators, to completion of procedure—can have a duration of up to three days. Coupled with mandatory waiting periods in many states, later-abortion procedures often place undue burdens on patients (increased travel, accommodation, and administrative costs). We present evidence on the safety of later-abortion protocols that are carried out in one day, which can result in reducing client burden. We conducted a retrospective review of 379 patient medical charts from two different clinics (in four states) that provide abortions for gestational ages 18–24 weeks, in one day. Data was collected using an online data collection platform. We used Stata 15 to clean and perform preliminary descriptive analyses. We found that, on an average, complications occurred in 0.79% (n = 3) of the total procedures. Literature suggests that for D&E procedures occurring between 14 and 24 weeks, complications occur in 4% of cases (Autry et. al, 2002). In our study, cervical tear was the only incident. All the incidents were resolved in the procedure room on the day of the procedure. One-day procedures result in a low occurrence of complications—lower than what has been reported for longer procedures and have not needed hospital admission. By adopting shorter procedures, financial burden on patients, especially those traveling long distances, can be reduced, and access to later abortion care can be expanded. [ABSTRACT FROM AUTHOR]

Weight discrimination is a well-established risk factor for disordered eating cognitions and behaviors. However, little is known about what may account for this association. Recent research suggests that anticipated weight stigma may explain the relationship between weight discrimination and non-eating disorder related health outcomes; the present study seeks to replicate this premise and extend it to the disordered eating realm. In a non-clinical sample of adults in the United States (N = 297) we test the hypothesis that weight discrimination has an indirect association with eating disorder symptomatology through anticipated stigma. At a single timepoint, participants recruited from the online data collection platform SocialSci completed self-report, online surveys of weight discrimination in day-to-day life, anticipated weight stigma, eating disorder symptoms, and demographic information. As hypothesized, weight discrimination was indirectly associated with greater disordered eating symptoms via its association with anticipated weight stigma. This pattern of results held when controlling for gender, body mass index, and self-perceived weight status. These findings suggest that anticipated stigma is relevant in the association between weight discrimination and greater disordered eating. This premise deserves additional attention using methodological approaches that can facilitate stronger causal claims. We discuss the potential for this line of research to inform clinical interventions. [ABSTRACT FROM AUTHOR]

• This technique supports engineers by providing automated capabilities to make decisions in the field. • Improving consistency and accelerating decisions, an imperative after a major disruptive event. • Automated post-event data collection to facilitate the data analysis process and reduce bias in the results. In post-event reconnaissance missions, engineers and researchers collect perishable information about damaged buildings in the affected geographical region to learn from the consequences of the event. A typical post-event reconnaissance mission is conducted by first doing a preliminary survey, followed by a detailed survey. The objective of the preliminary survey is to develop an understanding of the overall situation in the field, and use that information to plan the detailed survey. The preliminary survey is typically conducted by driving slowly along a pre-determined route, observing the damage, and noting where further detailed data should be collected. This involves several manual, time-consuming steps that can be accelerated by exploiting recent advances in computer vision and artificial intelligence. The objective of this work is to develop and validate an automated technique to support post-event reconnaissance teams in the rapid collection of reliable and sufficiently comprehensive data, for planning the detailed survey. The focus here is on residential buildings. The technique incorporates several methods designed to automate the process of categorizing buildings based on their key physical attributes, and rapidly assessing their post-event structural condition. It is divided into pre-event and post-event streams, each intending to first extract all possible information about the target buildings using both pre-event and post-event images. Algorithms based on convolutional neural networks (CNNs) are implemented for scene (image) classification. A probabilistic approach is developed to fuse the results obtained from analyzing several images to yield a robust decision regarding the attributes and condition of a target building. We validate the technique using post-event images captured during reconnaissance missions that took place after hurricanes Harvey and Irma. The validation data were collected by a structural wind and coastal engineering reconnaissance team, the National Science Foundation (NSF) funded Structural Extreme Events Reconnaissance (StEER) Network. [ABSTRACT FROM AUTHOR]