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The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks. [ABSTRACT FROM AUTHOR]

This paper is a product purity study of recycled Li-ion batteries with a focus on hydrometallurgical recycling processes. Firstly, a brief description of the current recycling status was presented based on the research data. Moreover, this work presented the influence of impurities such as Cu, Fe and Mg on recovered cathode materials performance. The impact of the impurities was described depending on their form (metallic or ionic) and concentration. This work also reviewed hydrometallurgical recycling processes depending on the recovered material, obtained purity and recovery methods. This purity data were obtained from both research and battery industry actors. Finally, the purity study was completed by collecting data regarding commercial battery-grade chemical compounds and active lithium cathode materials, including required purity levels and allowed impurity limitations. [ABSTRACT FROM AUTHOR]

Industry 4 (I4) was a revolutionary new stage for technological progress in manufacturing which promised a new level of interconnectedness between a diverse range of technologies. Sensors, as a point technology, play an important role in these developments, facilitating human–machine interaction and enabling data collection for system-level technologies. Concerns for human labour working in I4 environments (e.g., health and safety, data generation and extraction) are acknowledged by Industry 5 (I5), an update of I4 which promises greater attention to human–machine relations through a values-driven approach to collaboration and co-design. This article explores how engineering experts integrate values promoted by policy-makers into both their thinking about the human in their work and in their writing. This paper demonstrates a novel interdisciplinary approach in which an awareness of different disciplinary epistemic values associated with humans and work guides a systematic literature review and interpretive coding of practice-focussed engineering papers. Findings demonstrate evidence of an I5 human-centric approach: a high value for employees as "end-users" of innovative systems in manufacturing; and an increase in output addressing human activity in modelling and the technologies available to address this concern. However, epistemic publishing practices show that efforts to increase the effectiveness of manufacturing systems often neglect worker voice. [ABSTRACT FROM AUTHOR]

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model's hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker's collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers. [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. [ABSTRACT FROM AUTHOR]

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. [ABSTRACT FROM AUTHOR]

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments. [ABSTRACT FROM AUTHOR]

With the increasing number of households owning pets, the importance of sensor data for recognizing pet behavior has grown significantly. However, challenges arise due to the costs and reliability issues associated with data collection. This paper proposes a method for classifying pet behavior using cleaned meta pseudo labels to overcome these issues. The data for this study were collected using wearable devices equipped with accelerometers, gyroscopes, and magnetometers, and pet behaviors were classified into five categories. Utilizing this data, we analyzed the impact of the quantity of labeled data on accuracy and further enhanced the learning process by integrating an additional Distance Loss. This method effectively improves the learning process by removing noise from unlabeled data. Experimental results demonstrated that while the conventional supervised learning method achieved an accuracy of 82.9%, the existing meta pseudo labels method showed an accuracy of 86.2%, and the cleaned meta pseudo labels method proposed in this study surpassed these with an accuracy of 88.3%. These results hold significant implications for the development of pet monitoring systems, and the approach of this paper provides an effective solution for recognizing and classifying pet behavior in environments with insufficient labels. [ABSTRACT FROM AUTHOR]

The revolution of the Internet of Things (IoT) and the Web of Things (WoT) has brought new opportunities and challenges for the information retrieval (IR) field. The exponential number of interconnected physical objects and real-time data acquisition requires new approaches and architectures for IR systems. Research and prototypes can be crucial in designing and developing new systems and refining architectures for IR in the WoT. This paper proposes a unified and holistic approach for IR in the WoT, called IR.WoT. The proposed system contemplates the critical indexing, scoring, and presentation stages applied to some smart cities' use cases and scenarios. Overall, this paper describes the research, architecture, and vision for advancing the field of IR in the WoT and addresses some of the remaining challenges and opportunities in this exciting area. The article also describes the design considerations, cloud implementation, and experimentation based on a simulated collection of synthetic XML documents with technical efficiency measures. The experimentation results show promising outcomes, whereas further studies are required to improve IR.WoT effectiveness, considering the WoT dynamic characteristics and, more importantly, the heterogeneity and divergence of WoT modeling proposals in the IR domain. [ABSTRACT FROM AUTHOR]

This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case. [ABSTRACT FROM AUTHOR]

LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology. [ABSTRACT FROM AUTHOR]

Suter curves for the Wh and Wm characteristics and four-quadrant (4Q) diagrams of 11 radial pump–turbine models with different specific speeds (nq = 24.34, 24.8, 27, 28.6, 38, 41.6, 41.9, 43.83, 50, 56, and 64.04) are presented for the first time in this paper, as well as Suter curves for two pump models (nq = 25 and 41.8) previously published in the literature. All of these curves were analyzed to establish a certain universal law of behavior, depending on the specific speed. To determine such a law, a fitting procedure using regression and spline methods was carried out. This paper provides details of a research plan and structures (including data collection for four-quadrant diagrams for pump–turbine and pump models under different specific speeds nq), a procedure for re-calculating four-quadrant diagrams of the models as Suter curves for the Wh and Wm characteristics, definitions of the optimal points for pump and turbine operating modes in pump–turbine models under different specific speeds, and the development of numerical models in MATLAB to obtain a universal equation for the Wh and Wm characteristics. The scientific contribution of this paper is that it is the first to publish original mathematical curves using universal equations for the Wh and Wm characteristics of radial pumps and pump–turbines. The applicability of the equations is demonstrated by considering a pumping station in which two radial pumps were installed, for which the calculation of transient processes was performed using a numerical model developed in MATLAB by the authors. The transition process results are compared for two cases: first, when input data in the numerical model are used with the values of the Suter curves for the Wh and Wm characteristics obtained by re-calculating the four-quadrant operating characteristics (Q11, n11, M11) at a given specific speed, and second, when the values of the Suter curves for the Wh and Wm characteristics are obtained from the universal equations. [ABSTRACT FROM AUTHOR]

The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV's navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection. [ABSTRACT FROM AUTHOR]

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development. [ABSTRACT FROM AUTHOR]

With the development of the social economy and the continuous growth of the population, emergencies within field stations are becoming more frequent. To improve the efficiency of emergency evacuation of field stations and further protect people's lives, this paper proposes a method based on improved YOLOv5s target detection and Anylogic emergency evacuation simulation. This method applies the YOLOv5s target detection network to the emergency evacuation problem for the first time, using the stronger detection capability of YOLOv5s to solve the problem of unstable data collection under unexpected conditions. This paper first uses YOLOv5s, which incorporates the SE attention mechanism, to detect pedestrians inside the site. Considering the height of the camera and the inability to capture the whole body of the pedestrian when the site is crowded, this paper adopts the detection of the pedestrian's head to determine the specific location of the pedestrian inside the site. To ensure that the evacuation task is completed in the shortest possible time, Anylogic adopts the principle of closest distance evacuation, so that each pedestrian can leave through the exit closest to him or her. The experimental results show that the average accuracy of the YOLOv5s target detection model incorporating the SE attention mechanism can reach 94.01%; the constructed Anylogic emergency evacuation model can quickly provide an evacuation plan to guide pedestrians to leave from the nearest exit in an emergency, effectively verifying the feasibility of the method. The method can be extended and applied to research related to the construction of emergency evacuation aid decision-making systems in field stations. [ABSTRACT FROM AUTHOR]

Deep learning models have revolutionized research fields like computer vision and natural language processing by outperforming traditional models in multiple tasks. However, the field of time series analysis, especially time series forecasting, has not seen a similar revolution, despite forecasting being one of the most prominent tasks of predictive data analytics. One crucial problem for time series forecasting is the lack of large, domain-independent benchmark datasets and a competitive research environment, e.g., annual large-scale challenges, that would spur the development of new models, as was the case for CV and NLP. Furthermore, the focus of time series forecasting research is primarily domain-driven, resulting in many highly individual and domain-specific datasets. Consequently, the progress in the entire field is slowed down due to a lack of comparability across models trained on a single benchmark dataset and on a variety of different forecasting challenges. In this paper, we first explore this problem in more detail and derive the need for a comprehensive, domain-unspecific overview of the state-of-the-art of commonly used datasets for prediction tasks. In doing so, we provide an overview of these datasets and improve comparability in time series forecasting by introducing a method to find similar datasets which can be utilized to test a newly developed model. Ultimately, our survey paves the way towards developing a single widely used and accepted benchmark dataset for time series data, built on the various frequently used datasets surveyed in this paper. [ABSTRACT FROM AUTHOR]

With the ubiquitous use of digital technologies and the consequent data deluge, official statistics faces new challenges and opportunities. In this context, strengthening official statistics through effective data governance will be crucial to ensure reliability, quality, and access to data. This paper presents a comprehensive framework for digital data governance for official statistics, addressing key components, such as data collection and management, processing and analysis, data sharing and dissemination, as well as privacy and ethical considerations. The framework integrates principles of data governance into digital statistical processes, enabling statistical organizations to navigate the complexities of the digital environment. Drawing on case studies and best practices, the paper highlights successful implementations of digital data governance in official statistics. The paper concludes by discussing future trends and directions, including emerging technologies and opportunities for advancing digital data governance. [ABSTRACT FROM AUTHOR]

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability. [ABSTRACT FROM AUTHOR]

Existing approaches for the 3D modeling of tunnels suffer from several problems, such as highly difficult data acquisition, redundancy of model data, large computational burden, and the inability of the resulting models to be monolithic. Therefore, solutions to the tunnel network modeling problem for complex structures need to be proposed and elaborated in detail. In this paper, a construction method for a three-dimensional tunnel general monomer model based on parallel pathfinding is proposed. Widely used tunnel CAD drawings are analyzed and read, a disordered arc ensemble intersection trend decision method is developed, and an automatic path extraction solution algorithm for unidirectional modeling of tunnel centerlines is constructed. By constructing and splicing the surface elements of the 3D model, a monomeric 3D tunnel model representing the complex network structure is finally obtained. Moreover, the modeling of shafts is realized based on the monomer model, allowing for the three-dimensional topological relationships between different sub-levels of the tunnel and the ground to be established. The automatic modeling method proposed in this paper is applied to the digital twin platform of a filling project in a mining area in Gansu province, China. The experimental results demonstrate that the 3D tunnel models constructed in this way have a smaller data volume, higher modeling accuracy, and more stable growth of modeling speed. [ABSTRACT FROM AUTHOR]

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks. [ABSTRACT FROM AUTHOR]

This paper describes the workflow followed to survey and monitor the interior and exterior of a large and complex metal wreck, the SS Thistlegorm in the Red Sea, using digital photogrammetry. Utilizing a simple single-camera rig and off-the-shelf software, this study presents a cost-effective and easily replicable method for monitoring change in metal wrecks that meets professional standards and can involve non-specialists in data collection. This paper details the background of the SS Thistlegorm, the equipment used, and the photogrammetric surveys conducted in 2017 and 2022. It outlines the importance of having a clearly thought-out data-management system when working on a large target recorded over a limited series of dives. In particular, this paper considers testing the accuracy of the data obtained and describes the post-processing workflow in detail. The conclusion underscores the feasibility of achieving an accurate geo-referenced baseline 3D survey with readily available equipment and how these data can contribute to historical research and ongoing monitoring efforts. [ABSTRACT FROM AUTHOR]

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions. [ABSTRACT FROM AUTHOR]

Malaysia's energy intensity (GWh/GDP) shows an increasing trend since the 1990s, leading to the government's efforts to promote energy efficiency via policies such as the National Energy Efficiency Action Plan (NEEAP), which includes the Energy Performance Contracting (EPC) initiative. This paper reviews recent publications in industrial Combined Heat and Power (CHP) with a focus on international case studies relevant to Malaysian industries that use industrial steam and highlights trends within the research area. It also provides the basis for more case studies to be performed in the Malaysian industry to improve energy efficiency while also supporting further academic research in the area. Additionally, the paper documents the importance of data collection and analysis as well as demand forecasting, not only for a better understanding of industrial energy systems but also to increase profitability since system loads may vary throughout a typical year. A multi-criteria and comprehensive approach is recommended in future case studies to ensure energy efficiency, economic returns and environmental impact are considered to ensure long-term sustainability. A summary of barriers to CHP implementation in the industry is also included to provide a broad understanding of industrial CHP. [ABSTRACT FROM AUTHOR]

The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real-world networks is multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related datasets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the visualization publication dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a bag-of-words index and a word similarity matrix, all derived from the initial corpus and metadata. [ABSTRACT FROM AUTHOR]

Rock quality designation (RQD) is a parameter that describes rock mass quality in terms of percentage recovery of core pieces greater than 10 cm. The RQD represents a basic element of several classification systems. This paper studies scale effects for RQD measurements using synthetic rock masses generated using discrete fracture network (DFN) models. RQD measurements are performed for rock masses with varying fracture intensities and by changing the orientation of the simulated boreholes to account for orientation bias. The objective is to demonstrate the existence of a representative elementary length (REL, 1D analogue of a 3D representative elementary volume, or REV) above which RQD measurements would represent an average indicator of rock mass quality. For the synthetic rock masses, RQD measurements were calculated using the relationship proposed by Priest and Hudson and compared to the simulated RQD measurements along the boreholes. DFN models generated for a room-and-pillar mine using mapped field data were then used as an initial validation, and the conclusion of the study was further validated using the RQD calculation results directly obtained from the depth data collected at an iron cap deposit. The relationship between rock mass scale and assumed threshold length used to calculate RQD is also studied. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally. [ABSTRACT FROM AUTHOR]

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The diverse and multi-manufacturer equipment in weaving workshops exacerbates the complexity of multi-source heterogeneous data. Moreover, traditional data collection methods, which are mostly based on fixed frequencies, increase the network load during real-time high-frequency data reception, making stable, long-term operation difficult. Conversely, low-frequency collection might miss important state changes, thus affecting the quality of weaving big data. To address these issues, this paper proposes a service-oriented Digital Twin modeling method for weaving workshops. This method combines OPC Unified Architecture (OPC UA) with a state change-based data collection approach, utilizing a sliding time window (STW) to identify anomalous data and employing median interpolation to correct these anomalies. The goal is to enhance the representation capability of the Digital Twin in the weaving workshop by improving the data quality. For a specific service of predicting the warp-out time of 288 air-jet looms in a workshop, the average error of the predicted warp-out time using the dynamic data set proposed in this study was reduced from 0.85 h to 0.78 h compared to the static data set based on fixed frequency, an improvement of 8.2%, thereby validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of CNC (Computer Numerical Control) working time through the strategic positioning and grouping of cutting tools. Our analysis primarily focused on evaluating the previous arrangement of tools in the magazine and exploring the potential for repositioning them to enhance CNC operational efficiency. The work methodology is based on the collection of direct data from CNC as well as through CAD/CAM simulations. The collected data are analyzed with the Python programming language, which is one of the most used and efficient languages now. By grouping the tools according to the same function, tools were repositioned next to each other, which we obtained. After collecting the data and processing them in the Python programming language, we made a visualization of the data. Working time is reduced from 27.44 s to 26.46 s and results are analyzed in the Python Programming language. The working time is reduced by 0.98 s, which is approximately a 3.57% reduction in the working time. This indicates a significant improvement in efficiency due to the optimization of the cutting tool positions in the magazine. [ABSTRACT FROM AUTHOR]

Orbital flybys have been extensively studied for spacecraft missions, resulting in effective mathematical and physical models. However, these models' applicability to natural encounters involving asteroids has not been explored. This paper examines the applicability of two such theories, patched conics (PC) and the Keplerian map (KM), to asteroid encounters. A review of the two methods will be provided, highlighting their assumptions and range of applicability. Simulations of asteroid–asteroid encounters will then be performed to evaluate their effectiveness in these scenarios. The simulation parameters are set by collecting data on actual asteroid–asteroid encounters, hereby presented, generally characterised by high close approach distances and small masses of the perturbing bodies, if compared to those used to build the flyby theories. Results show that the PC theory's effectiveness diminishes with increasing approach distances, aligning with its assumptions. Moreover, the prediction of the model is better in the geometric configurations where the flyby has major effects on the orbital energy change. The KM theory has shown good effectiveness for encounters occurring outside the sphere of influence of the perturbing body, even for very high distances. This research investigates flyby models' strengths and weaknesses in asteroid encounters, offering practical insights and future directions. [ABSTRACT FROM AUTHOR]

Helichrysum italicum (Roth) G. Don is a typical Mediterranean plant, with limited distribution on the islands of Sardinia, Corsica, and the Iberian Peninsula, as well as the islands of the Adriatic Sea and the Balkan Peninsula. In these regions, H. italicum is mainly collected from spontaneous nature, while in recent years, there has been a pronounced cultivation trend due to increased demand and market requirements for constant quality of raw materials. Bearing in mind that biological activity is linked with chemical composition, this review aimed to collect data from different scientific databases (Scopus, PubMed, Web of Science, and Google Scholar) on the antimicrobial activity of essential oil and its chemical composition. A total of 20 papers investigating the antibacterial, antibiofilm, and antifungal activities of H. italicum essential oil were found. Furthermore, in these samples, several compounds occurred as dominant: neryl acetate, α-pinene, and γ-curcumene. These compounds are known for their antimicrobial properties, which likely contribute to the essential oil's efficacy against various microbial strains. [ABSTRACT FROM AUTHOR]

Introduction: Digitalization in medicine is steadily increasing. Complex treatments, scarce personnel resources and a high level of documentation are a constant burden on healthcare systems. The balancing between correct manual documentation in the digital records and limited staff resources is rarely successful. The aim of this study is to evaluate the adherence between documentation and lack of documentation in the treatment of critical care patients. Material and Methods: For the evaluation, data from the hospital information system (HIS) of several intensive care units (ICU) were examined in conjunction with data collected from a checklist. All boluses of sedatives, analgesics and catecholamines were documented paper based across all shifts and all weekdays and compared with corresponding digital data from the HIS (2019–2022) of previous years. Results: 939 complete digital patient records revealed a massive under-documentation of the medication administration compared to that applied according to the checklist. Only 12% of all administered catecholamines, 11% of α2-agonists, 33% of propofol, 92% of midazolam and 46% of opioids were found in the digital recordings. In comparison, the effect was more pronounced on weekdays compared to weekends. In addition, the highest documentation gap was found in the comparison of early shifts. Comparing neurosurgical vs. internal vs. anesthesiologic ICUs there was a highly significant difference between anesthesiologic ICUs compared with other disciplines (p < 0.0001). Discussion: Our data shows that there is a remarkable documentation gap and incongruence in the area of applied boli. Automated documentation by connecting syringe pumps that enter data directly into the HIS can not only reduce the workload, but also lead to comprehensive and legally required documentation of all administered medication. [ABSTRACT FROM AUTHOR]

Expansion joints render bridge structures highly vulnerable to damage during strong ground motions. Failures of expansion joints triggered by earthquakes not only jeopardize the post-earthquake serviceability of the bridge but also have a significant impact on the bridge's overall seismic performance. Despite extensive investigations and efforts to integrate these measures into design specifications aimed at mitigating the consequences of relative movements between adjacent bridge spans, major earthquakes have still revealed instances of damage related to expansion joints. On 6 February 2023, strong earthquake sequences occurred in Kahramanmaraş, Turkey, with magnitudes of M7.7 and M7.6. The fault lines and epicenters of these shallow earthquakes were near the city and town centers and caused severe structural damage to buildings and infrastructures. There are approximately 1000 railway and highway bridges in the earthquake-affected region. Although both highway and railway bridges have generally performed well, some bridges experienced structural damage during the Kahramanmaraş earthquakes. A large number of damage on the bridges is due to pounding and opening relative movements in expansion joints. This paper presents a comprehensive seismic evaluation of expansion joint failure mechanisms on bridges without viscous dampers during the 2023 Kahramanmaraş earthquake sequences and an in-depth investigation into the seismic performance of bridge expansion joints equipped with viscous dampers and shock transmission unit devices are implemented utilizing the strong ground motion data collected throughout the earthquake sequences. It can be stated that the near-fault induced significant directivity and fling effects, resulting in notable velocity pulses and permanent tectonic deformations, and that these effects contributed to the failures of expansion joints, viscous damper devices, pot bearings, and shear keys. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

This paper explores the results of a study conducted in collaboration with the homelessness sector in central London and an academic team. Data were collected on 134 women who used homelessness services in an area of central London during a nine-day window. In addition, fully anonymised service history records, covering an average period of 85 months, were reviewed with the consent of another 59 women with lived experience of homelessness. Nine women also agreed to in-depth interviews. Five key stakeholders in policy and practice were also interviewed. The research supports the findings of earlier research into the gender dynamics of homelessness. The results highlight the presence of a high-cost, high-risk population of women who are characterised by sustained and recurrent experience of homelessness, housing exclusion, and deprivation and who make repeated and sustained use of homelessness and other services without escaping homelessness. Strong associations between domestic abuse and women's homelessness are evident in the results of the research, again echoing the results of earlier work. The possibilities of developing new strategic responses to women's homelessness, including specialised forms of Housing First, are considered. [ABSTRACT FROM AUTHOR]

Identifying underperforming photovoltaic (PV) modules is crucial to ensure optimal energy production and financial returns, as well as preventing potential safety hazards in case of severe damage. To this aim, current–voltage (I-V) curve tracing can be employed as in situ monitoring technique for the early detection of faults. In this paper, we introduce a novel low-cost, microcontroller-based I-V tracer for the diagnosis of individual PV modules. The tool features a unique power conditioning circuit, facilitating accurate data acquisition under static conditions as well as the even distribution of the measured points along the I-V curve. A specific active disconnecting circuit enables in situ and on-line measurement without interrupting the string power generation. The designed prototype is used to characterize a set of PV modules under real operating conditions. The measured I-V curves exhibit expected trends, with the measured data closely matching theoretical values and an estimated mean relative error less than 3%. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time–domain Gaussian fitting method is proposed in this paper. The concept of a time–domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time–domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time–domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms. [ABSTRACT FROM AUTHOR]

The results of traditional vegetation-measuring methods are mostly two-dimensional data, which can only convey limited information. The greening situation of many cities or regions in the world cannot be fully assessed by these results. In this regard, this paper proposes the use of the air–ground integrated point cloud data acquisition mode for measuring vegetation. This mode combines a backpack-mounted laser scanning system, a vehicle-mounted laser scanning system, and UAV tilt photography technology to collect greening data in a comprehensive park and along a municipal road in Guangzhou, China. To classify the collected greening data, we propose the BiFPN-KPointNet-CBAM model, which was derived from PointNet. The model was introduced to analyze the distribution of green plants in study areas. The experimental findings indicate that our model achieved a notable enhancement in the overall accuracy by approximately 8% compared with other state-of-the-art models. Compared with the traditional greening survey method, this method obtained three-dimensional and more accurate greening data, and thus, provides higher quality greening data for urban managers. [ABSTRACT FROM AUTHOR]

Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem—missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE) and moving average filter (MAF) to perform accurate missing data imputation. First, convolution and gated recurrent unit (GRU) are applied to the encoder of the ADAE, while the decoder still utilizes the fully connected layers to form an asymmetric network structure. The ADAE extracts the local periodic and temporal features from monitoring data and then decodes the features to realize the imputation of the multi-type missing. On this basis, according to the continuity of power data in the time domain, the MAF is utilized to fuse the prior knowledge of the neighborhood of missing data to secondarily optimize the imputed data. Case studies reveal that the developed method achieves greater accuracy compared to existing models. This paper adopts experiments under different scenarios to justify that the MAF-ADAE method applies to actual power equipment monitoring data imputation. [ABSTRACT FROM AUTHOR]

Space weather science has been a growing field in Africa since 2007. This growth in infrastructure and human capital development has been accompanied by the deployment of ground-based observing infrastructure, most of which was donated by foreign institutions or installed and operated by foreign establishments. However, some of this equipment is no longer operational due to several factors, which are examined in this paper. It was observed that there are considerable gaps in ground-based space-weather-observing infrastructure in many African countries, a situation that hampers the data acquisition necessary for space weather research, hence limiting possible development of space weather products and services that could help address socio-economic challenges. This paper presents the current status of space weather science in Africa from the point of view of some key leaders in this field, focusing on infrastructure, situation, human capital development, and the research landscape. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Geographies" titled "Perspectives on Advanced Technologies in Spatial Data Collection and Analysis." The editorial discusses the rapid changes that have occurred in the field of geographical information science and systems in recent decades. It highlights various advanced technologies that have contributed to these changes, such as volunteered geographic information (VGI), wearable devices, social media and networking apps, GIS cloud computing, mobile networks and communication techniques, GeoAI, and blockchain. The editorial also mentions the collection of new types of spatial and spatiotemporal data and invites contributions on topics including geospatial open-source software, big data analysis, GeoAI, and geovisual analytics. The authors acknowledge the authors of the papers in the special issue, the anonymous referees, and the editorial staff for their contributions. [Extracted from the article]

Within the integration and development of data-driven process models, the underlying process is digitally mapped in a model through sensory data acquisition and subsequent modelling. In this process, challenges of different types and degrees of severity arise in each modelling step, according to the Cross-Industry Standard Process for Data Mining (CRISP-DM). Particularly in the context of data acquisition and integration into the process model, it can be assumed with a sufficiently high degree of probability that the acquired data contain anomalies of various kinds. The outliers must be detected in the data preparation and processing phase and dealt with accordingly. If this is sufficiently implemented, it will positively impact the subsequent modelling in terms of accuracy and precision. Therefore, this paper shows how outliers can be identified using the unsupervised machine learning methods autoencoder, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest (iForest), and One-Class Support Vector Machine (OCSVM). Following implementing these methods, we compared them by applying the Numenta Anomaly Benchmark (NAB) and sufficiently presented the individual strengths and disadvantages. Evaluating the correctness, distinctiveness and robustness criteria described in the paper showed that the One-Class Support Vector Machine was outstanding among the methods considered. This is because the OCSVM achieved acceptable anomaly detections on the available process datasets with comparatively little effort. [ABSTRACT FROM AUTHOR]

Simple Summary: We provide a data classification and analysis method to estimate fire risk using facility data for thermal power plants. Experimental analysis is conducted on the data classified by the proposed method for 500 megawatt (MW) and 100 MW thermal power plants. In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states—Steady, Transient, and Anomaly—categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities. [ABSTRACT FROM AUTHOR]