Search

The misuse and abuse of opioids has become a serious public health threat in the United States. The state of California has been hit particularly hard by the opioid epidemic, with a noticeable increase in opioid-related fatalities and hospitalizations. This brief report paper aims to contribute to the growing literature by conducting a geospatial analysis of opioid dispensing patterns in California in 2021. The primary objective was to identify areas characterized by high-risk opioid dispending patterns and explore possible contributing factors. This retrospective study analyzed data from over 7 million records of opioid and benzodiazepine prescriptions dispensed by outpatient pharmacies in California in 2021. A series of generalized linear regression models was employed to assess the impact of neighborhood characteristics on opioid recipients and high-risk opioid dispensing. The study defined high-risk opioid dispensing behavior as: (1) multiple provider episodes, (2) overlapping opioid prescriptions for seven or more days, (3) overlapping opioid and benzodiazepine prescriptions for seven or more days, and (4) a high standardized dosage of opioid prescriptions per month. The study identified variables associated with high-risk opioid dispensing behaviors, including age, population density, income, and housing-related variables, as well as marital status and family-related variables. The study uncovered that there are noticeable disparities in opioid dispensing among different racial and ethnic groups within California. The findings indicated a correlation of high-risk dispensing indicators with certain demographic and socioeconomic factors. There was a substantial regional variation in opioid dispensing practices, with certain rural areas having higher rates of opioid prescriptions than urban areas. [ABSTRACT FROM AUTHOR]

The engine system is critical for a marine vehicle, and its performance significantly affects the efficiency and safety of the whole ship. Due to the harsh working environment and the complex system structure, a marine system is prone to have many kinds of novelties and faults. Timely detection of faults via effective condition monitoring is vital for such systems, avoiding serious damage and economic loss. However, it is difficult to realize online monitoring because of the limitations of measurement and health monitoring methods. In this paper, a marine engine system simulator is set up with enhanced sensory placement for static and dynamic data collection. The test rig and processing for static and dynamic data are described. Then, a physics-based multivariate modeling method is proposed for the health monitoring of the system. Case studies are carried out considering the misfire fault and the exhaust valve leakage fault. In the misfire fault test, the exhaust gas temperature of the misfired cylinder dropped from the confidence interval 100–150 °C to 70–80 °C and the head vibration features decreased from the confidence interval 900–1300 m/s2 to around 200–300 m/s2. For the exhaust valve leakage fault, the engine body vibration main bearing impact RMS increased nearly 10 times. Comparisons between the model-predicted confidence interval and measured data reveal that the proposed model based on the fault-related static and dynamic features successfully identified the two faults and their positions, proving the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with multiple myeloma (MM) who become refractory to three or more lines of therapy (RRMM patients) have few valid therapeutic alternatives. Among them, drugs directed against the BCMA antigen expressed in plasma cells are very appealing. Belantamab-mafodotin (belamaf) is the first antibody-drug conjugate against BCMA ready for clinical use. In this paper, we report the Spanish experience of belamaf monotherapy in 156 patients with RRMM. The overall response rate was 41.8%, with 39.8% of patients achieving a partial response or better. Median progression-free survival was 3.61 months, but interestingly, it increased to 14.47 months in patients achieving a minimal response or better. Treatment was well tolerated, ocular events being the most reported toxicity (87.9%; grade ≥ 3, 33.7%), but only two patients discontinued treatment due to side effects. Overall, our results confirm the safety and efficacy of belamaf in this poor prognosis subset of patients. Belantamab-mafodotin (belamaf) is a novel antibody-drug conjugate targeting B-cell maturation antigen that showed anti-myeloma activity in patients with relapsed and refractory multiple myeloma (RRMM). We performed an observational, retrospective, and multicenter study aimed to assess the efficacy and safety of single-agent belamaf in 156 Spanish patients with RRMM. The median number of prior therapy lines was 5 (range, 1–10), and 88% of patients were triple-class refractory. Median follow-up was 10.9 months (range, 1–28.6). The overall response rate was 41.8% (≥CR 13.5%, VGPR 9%, PR 17.3%, MR 2%). The median progression-free survival was 3.61 months (95% CI, 2.1–5.1) and 14.47 months (95% CI, 7.91–21.04) in patients achieving at least MR (p < 0.001). Median overall survival in the entire cohort and in patients with MR or better was 11.05 months (95% CI, 8.7–13.3) and 23.35 (NA-NA) months, respectively (p < 0.001). Corneal events (87.9%; grade ≥ 3, 33.7%) were the most commonly adverse events, while thrombocytopenia and infections occurred in 15.4% and 15% of patients, respectively. Two (1.3%) patients discontinued treatment permanently due to ocular toxicity. Belamaf showed a noticeably anti-myeloma activity in this real-life series of patients, particularly among those achieving MR or better. The safety profile was manageable and consistent with prior studies. [ABSTRACT FROM AUTHOR]

As an emerging non-contact method of flow monitoring, radar technology compensates for the shortcomings of traditional methods in terms of the efficiency, timeliness, and difficulty in monitoring high floods, and can provide accurate measurement results, making it one of the most promising flow monitoring methods in the future. This paper describes in detail the workflow from radar data acquisition to flow calculation; compares and analyzes the current state-of-the-art signal sampling and its limitations, Doppler spectrum estimation, signal processing and flow inversion; points out the challenges that these technologies may face in the future; and provides corresponding solutions in order to improve the real-time and accuracy of hydrometric as well as promote the development of non-contact flow monitoring technology. [ABSTRACT FROM AUTHOR]

Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction. [ABSTRACT FROM AUTHOR]

Human–robot collaboration has emerged as a prominent research topic in recent years. To enhance collaboration and ensure safety between humans and robots, researchers employ a variety of methods. One such method is physiological computing, which aims to estimate a human's psycho-physiological state by measuring various physiological signals such as galvanic skin response (GSR), electrocardiograph (ECG), heart rate variability (HRV), and electroencephalogram (EEG). This information is then used to provide feedback to the robot. In this paper, we present the latest state-of-the-art methods in physiological computing for human–robot collaboration. Our goal is to provide a comprehensive guide for new researchers to understand the commonly used physiological signals, data collection methods, and data labeling techniques. Additionally, we have categorized and tabulated relevant research to further aid in understanding this area of study. [ABSTRACT FROM AUTHOR]

The main question of this paper is what factors influence willingness to participate in a smartphone-application-based data collection where participants both fill out a questionnaire and let the app collect data on their smartphone usage. Passive digital data collection is becoming more common, but it is still a new form of data collection. Due to the novelty factor, it is important to investigate how willingness to participate in such studies is influenced by both socio-economic variables and smartphone usage behaviour. We estimate multilevel models based on a survey experiment with vignettes for different characteristics of data collection (e.g., different incentives, duration of the study). Our results show that of the socio-demographic variables, age has the largest influence, with younger age groups having a higher willingness to participate than older ones. Smartphone use also has an impact on participation. Advanced users are more likely to participate, while users who only use the basic functions of their device are less likely to participate than those who use it mainly for social media. Finally, the explorative analysis with interaction terms between levels has shown that the circumstances of data collection matter differently for different social groups. These findings provide important clues on how to fine-tune circumstances to improve participation rates in this novel passive digital data collection. [ABSTRACT FROM AUTHOR]

Region-function combinations are essential for smartphones to be intelligent and context-aware. The prerequisite for providing intelligent services is that the device can recognize the contextual region in which it resides. The existing region recognition schemes are mainly based on indoor positioning, which require pre-installed infrastructures or tedious calibration efforts or memory burden of precise locations. In addition, location classification recognition methods are limited by either their recognition granularity being too large (room-level) or too small (centimeter-level, requiring training data collection at multiple positions within the region), which constrains the applications of providing contextual awareness services based on region function combinations. In this paper, we propose a novel mobile system, called Echo-ID, that enables a phone to identify the region in which it resides without requiring any additional sensors or pre-installed infrastructure. Echo-ID applies Frequency Modulated Continuous Wave (FMCW) acoustic signals as its sensing medium which is transmitted and received by the speaker and microphones already available in common smartphones. The spatial relationships among the surrounding objects and the smartphone are extracted with a signal processing procedure. We further design a deep learning model to achieve accurate region identification, which calculate finer features inside the spatial relations, robust to phone placement uncertainty and environmental variation. Echo-ID requires users only to put their phone at two orthogonal angles for 8.5 s each inside a target region before use. We implement Echo-ID on the Android platform and evaluate it with Xiaomi 12 Pro and Honor-10 smartphones. Our experiments demonstrate that Echo-ID achieves an average accuracy of 94.6% for identifying five typical regions, with an improvement of 35.5% compared to EchoTag. The results confirm Echo-ID's robustness and effectiveness for region identification. [ABSTRACT FROM AUTHOR]

The concept of city information modeling (CIM) has become increasingly popular in recent years. A literature review of previous CIM studies is presented in this paper. First, a bibliometric analysis of the current global CIM research is described, revealing that CIM has become a significant research hotspot. Next, three main research areas of the current CIM technique, namely data collection, integration, and visualization, are summarized to describe the characteristics of CIM research. Furthermore, some widely used CIM platforms are compared, and typical application cases of the CIM technique at different stages of the city life cycle are summarized. Finally, the current issues in CIM research are discussed, and future development directions are proposed. The findings of this study are expected to help researchers understand the current state of CIM and identify future development directions, thereby promoting CIM research development. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) tools are increasingly finding their way into education settings. Although their use is still not widespread in educational contexts, the research literature indicates their potential and effectiveness. However, overall and specifically for the education sector there are still numerous research gaps. This study investigates how the use of head-mounted AR displays such as the Microsoft HoloLens can change learning and what needs to be considered from a didactic perspective. The researched sample consists of 18 student teachers with a nature and technology teaching profile of a German-speaking university of teacher education. The data collection included a written questionnaire, video recordings of a teaching unit with HoloLens examining molecular structures, and one-to-one semi-structured interviews. The results of questionnaires and interviews presented in this paper show that all students were highly motivated to work with this technology in teacher education. The usability of the HoloLens was rated very satisfactory, although many students expressed minor problems. Most students attributed a positive impact on learning to the AR device and stated that the usage of the devices increased their motivation for learning the topic. Overall, the results show that the use of AR in teacher education is considered very valuable and should be increasingly employed in the future. [ABSTRACT FROM AUTHOR]

Unmanned-aerial-vehicle (UAV)-aided data collection for Internet of Things applications has attracted increasing attention. This paper investigates motion planning for UAV collecting low-power ground sensor node (SN) data in a dynamic jamming environment. We targeted minimizing the flight energy consumption via optimization of the UAV trajectory while considering the indispensable constraints which cover the collection data demodulation threshold, obstacle avoidance, data collection volume, and motion principle. Firstly, we formulate the UAV-aided data collection problem as an energy consumption minimization problem. To solve this nonconvex optimization problem, we rewrite the original problem by introducing relaxation variables and constructing equivalence constraints to obtain a new relaxation convex problem, which can be solved iteratively using the successive convex approximation (SCA) method. However, SCA is susceptible to initial values, especially in dynamic environments where fixed initial values may lead to a wide range of results, making it difficult to obtain a truly optimal solution to the optimization problem. To solve the initial value problem in dynamic environments, we further propose a communication-flight-corridor(CFC)-based initial path generation method to improve the reliability and convergence speed of the SCA method by constructing reliable communication regions and resilient secure paths in real time. Finally, simulation results validate the performance of the proposed algorithm compared to the benchmark algorithms under different parameter configurations. [ABSTRACT FROM AUTHOR]

In a general wireless sensor network, a sink node collects data from each node successively and then post-processes the data to obtain useful information. However, conventional methods have a scalability problem: the data collection/processing time increases with the number of nodes, and frequent transmission collisions degrade spectrum efficiency. If only statistical values of the data are needed, using over-the-air computation (AirComp) can efficiently perform data collection and computation. However, AirComp also has its problems: when the channel gain of a node is too low, (i) the transmission power of that node will be high, decreasing the lifetime of that node and the entire network, and (ii) sometimes, the computation error still occurs even though the maximal transmission power is used. To jointly solve these two problems, in this paper we investigate the relay communication for AirComp and study a relay selection protocol. The basic method selects an ordinary node with a good channel condition as a relay node, considering both computation error and power consumption. This method is further enhanced to explicitly consider network lifetime in relay selection. Extensive simulation evaluations confirm that the proposed method helps to prolong the lifetime of the entire network and reduce computation errors as well. [ABSTRACT FROM AUTHOR]

This paper considers a wireless sensor network (WSN) assisted by the unmanned aerial vehicle (UAV) in the Internet of Things (IoT). The UAV departs from the data center to the ground node to collect sensor node data as a relay. Under the constraints of battery energy, the UAV will travel to and from the data center repeatedly and transmit the collected sensor node data. The freshness of the node data received by the data center is measured by the Age of Information (AoI) as a performance metric. A genetic algorithm is used to plan the flight trajectory of the UAV. To ensure the data's integrity and accuracy in a single sensor node, the UAV continuously collects sensor node data when the distance from the sensor node is less than the minimum acquisition distance. Through simulation experiments, we analyzed the influence of changing acquisition distance, the initial battery capacity, acquisition success probability, and transmission power on the peak age of information and the average age of information. [ABSTRACT FROM AUTHOR]

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems. [ABSTRACT FROM AUTHOR]

The concept of a metaverse, a virtual world that offers immersive experiences, has gained widespread interest in recent years. Despite the hype, there is still a gap in its practical application, especially in the realm of education. This study presents the design and implementation of a metaverse tailored to the needs of education. The goal of this paper is to demonstrate the feasibility of such a system and evaluate its effectiveness. It is crucial to understand the architecture and implementation of a metaverse to effectively customise it for educational purposes. To assess user experience, a field study was conducted, collecting data through questionnaires and qualitative feedback. The results show that users were pleased with the features, player experience, and ease of use. [ABSTRACT FROM AUTHOR]

In beyond 5G (B5G) non-terrestrial network (NTN) systems, satellite technologies play an important role. Especially for data collection systems (DCS), low-earth orbit satellites have many advantages. Such as global coverage, low latency, and high efficiency. As a miniaturization technology, CubeSat has attracted extensive attention from a large number of scholars. Satellite constellations can coordinate for distributed tasks. This paper proposes a B5G NTN-based data collection system. A CubeSat constellation achieves global coverage as the basic space platform for DCS. The 5G terrestrial network is used as the data bearer network of the gateway station. A traffic load balance strategy is proposed to optimize the system's efficiency. As a unified hardware platform, software-defined radio (SDR) is compatible with various sensor data models. Finally, the design was verified by a series of experiments. [ABSTRACT FROM AUTHOR]

The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model. Experimental results show that on the HPU_WASTE dataset, the proposed model outperforms other models in both data augmentation and waste detection. [ABSTRACT FROM AUTHOR]

More and more activities are being undertaken to implement the Industry 4.0 concept in industrial practice. One of the biggest challenges is the digitization of existing industrial systems and heavy industry operations, where there is huge potential for optimizing and managing these processes more efficiently, but this requires collecting large amounts of data, understanding, and evaluating it so that we can add value back based on it. This paper focuses on the collection, local pre-processing of data, and its subsequent transfer to the cloud from an industrial hydraulic press to create a comprehensive dataset that forms the basis for further digitization of the operation. The novelty lies mainly in the process of data collection and pre-processing in the framework of edge computing of large amounts of data. In the data pre-processing, data normalization methods are applied, which allow the data to be logically sorted, tagged, and linked, which also allows the data to be efficiently compressed, thus, dynamically creating a complex dataset for later use in the process digitization. [ABSTRACT FROM AUTHOR]

In this paper, the novel study of an Internet of Things (IoT) network model with multimodal node distribution and a data-collecting mechanism using mobile clustering nodes is presented. The aim of this work is to introduce the problem of organizing the mobile cluster head IoT network with a heterogeneous distribution node in the service area with multimodal distribution nodes. A new method for clustering a heterogeneous network is proposed, which makes it possible to efficiently identify clusters that differ in terms of the density of nodes. This makes it possible to choose the speed of the mobile cluster head in accordance with the density in each cluster. The proposed method uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. One of the benefits of our proposed model is the increase in the efficiency of using a mobile cluster head. The new solution can be used to organize data collection in the IoT. [ABSTRACT FROM AUTHOR]

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community. [ABSTRACT FROM AUTHOR]

The urgent need to protect user privacy and security has emerged as the World Wide Web has become an increasingly necessary part of daily life. Browser fingerprinting is a very interesting topic in the industry of technology security. New technology will always raise new security issues and browser fingerprinting will undoubtedly follow the same process. It has become one of the most popular topics in online privacy because, to date, there is still no exact solution as to how to stop it entirely. The majority of solutions just aim to reduce the likelihood of obtaining a browser fingerprint. Research on browser fingerprinting is unquestionably required since it is essential to educate users, developers, policymakers, and law enforcement about it so that they can make strategic choices based on knowledge. Browser fingerprinting must be recognised in order to defend against privacy problems. A browser fingerprint is described as data gathered by the receiving server to identify a distant device, and it is different from cookies. Websites frequently utilize browser fingerprinting to obtain information about the type and version of the browser, as well as the operating system, and other current settings. It has been known that even when cookies are disabled, fingerprints can be used to fully or partially identify users or devices. In this communication paper, a new insight into the challenge of browser fingerprint is encouraged as a new venture. Thus, the initial way to truly understand the browser fingerprint is the need to collect browser fingerprints. In this work, the process of data collection for browser fingerprinting through scripting, to offer a complete all-in-one fingerprinting test suite, has been thoughtfully divided into appropriate sections and grouped with key information to be carried out. The objective is to gather fingerprint data with no personal identification information and make it an open source of raw datasets in the industry for any future research purposes. To our best knowledge, there are no open datasets made available for browser fingerprints in the research field. The dataset will be widely accessible by anyone interested in obtaining those data. The dataset collected will be very raw and will be in the form of a text file. Thus, the main contribution of this work is to share an open dataset of browser fingerprints along with its collection methodology. [ABSTRACT FROM AUTHOR]

Underwater acoustic channels are characterized by long propagation delay, limited available bandwidth and high energy costs. These unique characteristics bring challenges to design media access control (MAC) protocol for underwater acoustic sensor networks (UASNs). Especially in data-collection-oriented UASNs, data generated at underwater nodes are transmitted hop-by-hop to the sink node. The traffic loads undertaken by nodes of different depths are different. However, most existing MAC protocols do not consider the traffic load imbalance in data-collection-oriented UASNs, resulting in unfairness in how the nodes transmit their own generated data. In this paper, we propose a novel traffic-aware fair MAC protocol for layered data-collection-oriented UASNs, called TF-MAC. TF-MAC accesses a medium by assigning time slots of different lengths to each layer via different traffic loads to achieve traffic fairness of nodes. To improve throughput and avoid collision in the network, an overlapping time slot division mechanism for different layers and multi-channel allocation scheme within each single layer is employed. Considering the time-varying traffic loads of the nodes, an adaptive packet length algorithm is proposed by taking advantage of the spatial-temporal uncertainty of underwater channels. A sea experiment was conducted to prove the spatial-temporal uncertainty of UASNs, which provides a feasibility basis for the proposed algorithm. Simulation results show that TF-MAC can greatly improve the network performance in terms of throughput, delay, energy consumption, and fairness in the layered data-collection-oriented UASNs. [ABSTRACT FROM AUTHOR]

UAVs have recently become a very popular tool for acquiring geospatial data. Photographs, films, images, and results of measurements of various sensors from them constitute source material for generating, among other things, photographic documentation, visualisation of places and objects, cartographic materials and 3D models. These models are not only material for the visualisation of objects but are also source material for spatial analysis, including the assessment and analyses of the solar potential of buildings. This research aims to benchmark the feasibility of using UAV-derived data acquired from three sensors, namely the DJI Zenmuse P1 camera, the Share PSDK102S v2 multi-lens camera and the DJI Zenmuse L1 laser scanner. The data from these were acquired for the construction of comprehensive and reliable 3D models, which will form the basis for generating solar potential maps. Various sensors, data storage formats, and geospatial data processing capabilities are analysed to determine the most optimal and efficient solution for providing accurate, complete and reliable 3D models of places and objects for the construction of solar potential maps. In this paper, the authors prepare a compilation of the results of the studies from different measurement combinations and analyse the strengths and weaknesses of the different solutions, as well as the integration of the results for an optimal 3D model, which was used to perform solar potential analyses for the selected built-up area. The results of the study show that the parameters for assessing the quality of a 3D model can be statistical parameters that determine the coplanarity of roof slope points (i.e., standard deviation, distances from the plane, and RMS value). The completeness of the model is defined as the percentage of the recorded area by sensors to the total area of the model. [ABSTRACT FROM AUTHOR]

Internal short-circuit (ISC) faults are a common cause of thermal runaway in lithium-ion batteries (LIBs), which greatly endangers the safety of LIBs. Different LIBs have common features related to ISC faults. Due to the insufficient volume of acquired ISC fault data, conventional machine learning models could not effectively identify ISC faults. To compensate for the above deficiencies, this paper proposes a multi-machine learning fusion method to predict ISC faults and to perform faults warning classification under multiple operating conditions using the input of voltage normalization. Firstly, learning data acquisition is captured by experiments and simulation. Secondly, the simulation data are inputted into the ResNet-convolutional neural network (CNN) for pretraining, followed by the transfer learning method to freeze parts of the model layers in the CNN, and part of the experimental data are also inputted into the CNN model for parameter fine-tuning to build a multi-machine learning model. Finally, the degree of ISC faults within the laboratory battery is predicted based on the multi-machine learning model. The results show that the CNN model had a 99.9% prediction accuracy on the simulated dataset, and the multi-machine learning fusion model after transfer learning had a 96.67% prediction accuracy on the laboratory battery dataset, which can accurately identify different levels of ISC faults in batteries and realize the graded warning of ISC faults. [ABSTRACT FROM AUTHOR]

Common Weakness Enumeration (CWE) refers to a list of faults caused from software or hardware. The CWE includes the faults related to programming language and security. We propose a technique to detect the vulnerabilities from incorrect use of a variable in C language. There are various static/dynamic methods to detect the variable vulnerabilities. However, when analyzing the vulnerabilities, a static technique causes a lot of false alarms, meaning that there is no fault in the actual implementation. When monitoring the variable via the static analysis, there is a great overhead during execution, so its application is not easy in a real environment. In this paper, we propose a method to reduce false alarms and detect vulnerabilities by performing static analysis and dynamic verification using machine learning. Our method extracts information on variables through static analysis and detects defects through static analysis results and execution monitoring of the variables. In this process, it is determined whether the currently used variable values are valid and whether the variables are used in the correct order by learning the initial values and permissible range of the variables using machine learning techniques. We implemented our method as VVDUM (Variable Vulnerability Detector Using Machine learning). We conducted the comparative experiment with the existing static/dynamic analysis tools. As a result, compared with other tools with the rate of variable vulnerability detection between 9.17~18.5%, ours had that of 89.5%. In particular, VVDUM detects 'defects out of the range of valid' that are difficult to detect with existing methods, and the overhead due to defect detection is small. In addition, there were a few overheads at run time that were caused during data collection for detection of a fault. [ABSTRACT FROM AUTHOR]

Data collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging. This paper proposes a new procedure for evaluating the utility and privacy of perturbation techniques and an algorithm for comparing perturbation methods. The contribution is significant for those involved in protecting time-series data collected from various sensors as the approach is sensor-type-independent, algorithm-independent, and data-independent. The analysis of the impact of data integrity attacks on the perturbed data follows the methodology. Experimental results obtained using actual data collected from a VW Passat vehicle via the OBD-II port demonstrate the applicability of the approach to measuring the utility and privacy of perturbation algorithms. Moreover, important benefits have been identified: the proposed approach measures both privacy and utility, various distortion and perturbation methods can be compared (no matter how different), and an evaluation of the impact of data integrity attacks on perturbed data is possible. [ABSTRACT FROM AUTHOR]

The research on the application of BIM technology in architectural heritage is increasing. Building information modeling (BIM) makes the realization of architectural heritage assessment, design, and management systematic and effective. However, little is known about the use of China-based BIM in conjunction with architectural heritage. Therefore, this study conducted a systematic literature analysis to determine the application fields and development trends of BIM and architectural heritage research in China. A total of 186 articles were retrieved from CNKI, of which 167 met the inclusion criteria and formed the basis of this systematic literature analysis. With the aid of the literature research visualization analysis software VOSviewer and SATI, a comprehensive literature visualization analysis was carried out. It was determined that the hot fields of BIM research in architectural heritage in China are mainly focused on (a) the perspective of data acquisition and measurement technology, (b) the perspective of model construction based on point cloud data acquisition, and (c) the perspective of BIM model application. Future research trends may focus on the information research of architectural heritage, combined with the application of BIM technology in the whole life cycle management of architectural heritage and other aspects. [ABSTRACT FROM AUTHOR]

Unmanned air vehicle (UAV) systems for performing forestry applications have expanded in recent decades and have great economic benefits. They are validated to be more appealing than traditional platforms in various aspects, such as repeat rate, spatial resolution, and accuracy. This paper consolidates the state-of-the-art unmanned systems in the forestry field with a major focus on UAV systems and heterogeneous platforms, which are applied in a variety of forestry applications, such as wood production, tree quantification, disease control, wildfire management, wildlife conservation, species classification, etc. This review also studies practical applications under multiple forestry environments, including wild and managed forests, grassland, urban green parks, and stockyards. Special forest environments and terrains present customized demands for unmanned systems. The challenges of unmanned systems deployment are analyzed from environmental characterization, maneuverability and mobility improvement, and global regulatory interpretation. To better apply UAV systems into forestry, future directions are analyzed in terms of mobility enhancement and customized sensory adaption, which need to be further developed for synchronizing all possible agents into automatic functioning systems for forestry exploration. [ABSTRACT FROM AUTHOR]

With the increase in biosensors and data collection devices in the healthcare industry, artificial intelligence and machine learning have attracted much attention in recent years. In this study, we offered a comprehensive review of the current trends and the state-of-the-art in mental health analysis as well as the application of machine-learning techniques for analyzing multi-variate/multi-channel multi-modal biometric signals.This study reviewed the predominant mental-health-related biosensors, including polysomnography (PSG), electroencephalogram (EEG), electro-oculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). We also described the processes used for data acquisition, data-cleaning, feature extraction, machine-learning modeling, and performance evaluation. This review showed that support-vector-machine and deep-learning techniques have been well studied, to date.After reviewing over 200 papers, we also discussed the current challenges and opportunities in this field. [ABSTRACT FROM AUTHOR]

The present manuscript describes a case study on the viewpoints of tourism stakeholders on the effect of waste management on a destination′s image. In particular, the study aims to analyze the problems that arose during the summer of 2018 in the touristic image of Corfu because of the waste management on the island. The qualitative method approach through interviews was used to collect the primary data of the survey; online sources were used to collect data to review similar cases. All the stakeholders agree that waste management during the summer of 2018 negatively affected the image of Corfu as a touristic destination. Moreover, based on the interviews′ findings, there is a lack of political will, infrastructure, and information provision from the local authorities on the island of Corfu. Interviews also indicated that an integrated solution to the problem of waste management on the island is the implementation of source separation in households and tourism-related firms with the support of the local authorities. The contribution of this paper is towards identifying the effect that waste management has on the image of a tourist destination. It is the first of its kind conducted in Greece and among the few reported in the literature focusing on the viewpoints of service providers. [ABSTRACT FROM AUTHOR]

The double-end partial discharge (PD) measurement method is the most common method for measuring and localizing PD sources in power cables. The sensitivity of the PD sensor, the processing speed of the data acquisition unit, and the method of the PD localization algorithm are the three main keys to ensuring the accuracy of the PD source localization on power cables. A new multi-end PD localization algorithm known as segmented correlation trimmed mean (SCTM) has recently demonstrated excellent accuracy in the localization of PD sources on power cables. The algorithm, however, is only applicable to multi-end PD measurement methods. In this paper, the mathematical equation of the SCTM algorithm is customized to match the double-end PD measurement method. A MATLAB simulation was conducted to assess the performance of the SCTM algorithm in the double-end PD measurement method. The maximum peak detection (MPD) algorithm, segmented correlation (SC), and SCTM algorithm were compared as PD localization algorithms. The SC algorithms have shown that identifying the correlation bond between two cues instead of the peak of the PD signal in the MPD algorithm significantly increases the PD localization accuracy. The results show that the SCTM algorithm outperforms the MPD and SC algorithms in terms of accuracy. [ABSTRACT FROM AUTHOR]

Sustainable agricultural education is a fundamental base for the sustainable development of the agri-food systems. Sustainable education should provide the necessary skills to the practical world. This paper is focused on skills and competences required by the agri-food stakeholders for their current work and for future sustainable development around the world. An online survey was disseminated for data collection, and quantitative and qualitative analyses were employed to understand the impact of the demographic factors on the variety of skills mentioned by the different stakeholders. As a result, according to all of the stakeholders, the skills of navigating in a changing world, networking and strategic development are relevant for their current work, and technical skills were mentioned as relevant for future sustainable development. The age factor, rather than geography and gender, was found to have a stronger impact on defining the aforementioned skills. [ABSTRACT FROM AUTHOR]

Torque-based impedance control, a kind of classical active compliant control, is widely required in human–robot interaction, medical rehabilitation, and other fields. Adaptive impedance control effectively tracks the force when the robot comes in contact with an unknown environment. Conventional adaptive impedance control (AIC) introduces the force tracking error of the last moment to adjust the controller parameters online, which is an indirect method. In this paper, joint friction in the robot system is first identified and compensated for to enable the excellent performance of torque-based impedance control. Second, neural networks are inserted into the torque-based impedance controller, and a neural adaptive impedance control (NAIC) scheme with directly online optimized parameters is proposed. In addition, NAIC can be deployed directly without the need for data collection and training. Simulation studies and real-world experiments with a six link rotary robot manipulator demonstrate the excellent performance of NAIC. [ABSTRACT FROM AUTHOR]

The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle's On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs). [ABSTRACT FROM AUTHOR]

Internet services have collected our personal data since their inception. In the beginning, the personal data collection was uncoordinated and was limited to a few selected data types such as names, ages, birthdays, etc. Due to the widespread use of social media, more and more personal data has been collected by different online services. We increasingly see that Internet of Things (IoT) devices are also being adopted by consumers, making it possible for companies to capture personal data (including very sensitive data) with much less effort and autonomously at a very low cost. Current systems architectures aim to collect, store, and process our personal data in the cloud with very limited control when it comes to giving back to citizens. However, Personal Data Stores (PDS) have been proposed as an alternative architecture where personal data will be stored within households, giving us complete control (self-sovereignty) over our data. This paper surveys the current literature on Personal Data Stores (PDS) that enable individuals to collect, control, store, and manage their data. In particular, we provide a comprehensive review of related concepts and the expected benefits of PDS platforms. Further, we compare and analyse existing PDS platforms in terms of their capabilities and core components. Subsequently, we summarise the major challenges and issues facing PDS platforms' development and widespread adoption. [ABSTRACT FROM AUTHOR]

Big Data is a phenomenon that affects today's world, with new data being generated every second. Today's enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users' processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform's efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments. [ABSTRACT FROM AUTHOR]

Deep geothermal energy can aid in the decarbonization of heat within the UK; this is required to meet net zero carbon emissions targets by 2050. The Cheshire Basin represents a significant opportunity for the development of deep geothermal resources; there are vast quantities of high permeability sandstones in hydraulic continuity, with temperatures favorable for direct heat use and, potentially, for power generation. Newly produced basal temperature maps in this study indicate the likely maximum temperatures for the basin, with the hottest temperature expected to be between 100 and 131.2 °C in the Crewe area. There have also previously been a range of estimates highlighting a geothermal resource within the basin to be in the region of 44.1 to 75 × 1018 J; however, previous estimates for heat in place are limited to simple volumetric or geometrical constraints. Therefore, this paper uses digitized depth and temperature maps to provide new estimates for the heat in place. Results suggest the resource has been underestimated and there is a need for more detailed evaluation. Depending on the geothermal gradient, the resource could be between 91 and 144 × 1018 J (1.26 to 1.45 × 1017 J/km2). Although there is a significant amount of heat in place, geological issues preventing development remain, such as the uncertainty in the quality of the reservoir at depth due to data limitations and the lateral continuity of the Manchester Marls Formation, which could act as a barrier to flow. Nevertheless, further regional assessment of the basin and data acquisition is required to build confidence in the reservoir quality and reduce uncertainty. This could unlock the basin for geothermal development. [ABSTRACT FROM AUTHOR]

For newly developed measuring systems, it is easy to estimate type-B uncertainties based on technical data from the measuring modules applied. However, it is difficult to estimate A-type uncertainties due to the unknown type and level of interferences infiltrating the measuring system. This is a particularly important problem for measurements carried out in the presence of typical of power grid disturbances. The aim of the research was to develop a method and a measurement stand for experimental assessment of uncertainties in a measuring system that makes use of data acquisition modules containing analog-to-digital converters (ADCs). The paper describes, in detail, the design of a completed test stand. It presents an original application in the LabVIEW environment, which enables testing the dependence of the uncertainties with the quantity of the measurements averaged in a series, for different kinds and levels of interferences infiltrating the measuring path. The results of tests for several popular measuring modules are presented. An analysis of the determined uncertainties was carried out in relation to the parameters of the tested measurement modules and for various levels of interferences. It is proved that an increase in the number of averaged measurements to approx. 100–200 always results in a decrease in uncertainty for each tested module and under all conditions. However, a further increase in the quantity of measurements, even up to 1000 averaged measurements, proved reasonable only for high-accuracy modules, in particular with a high level of interferences. An excessive increase in the quantity of averaged measurements proved a low effect for modules characterised by a low resolution and with a low level of interferences. The measurement results also proved that when estimated, uncertainties in the interference probability distribution are significant, especially if they deviate from normal distribution. [ABSTRACT FROM AUTHOR]

Modern digitization technologies have created an increasing number of possibilities for capturing the physical dimensions and appearance of archaeological artifacts and sites in 3D. The usage of such data is usually targeted to the research, study, and documentation of our cultural heritage. At the same time, the increasing quality of the produced digitizations has opened new possibilities for the further exploitation of digitization outcomes in a wider context than initially expected. A pioneer in this direction was the gaming industry, where photogrammetry has been recently employed to achieve extreme photorealism. Of course, challenges still exist, especially when digitization accuracy is of importance, such as in the case of large-scale archaeological sites. Further challenges regard the need to combine indoor and outdoor scenes that pose requirements in the selection of the appropriate digitization modalities and post-processing strategies. In more detail, the challenges relate to the appropriate usage of existing technologies, organization issues in terms of digitization visits, the combination and registration of data, data acquisition, and data processing methodologies, etc. In this paper, we demonstrated a methodology for the digitization of archaeological sites that can be used for creating digital assets suitable for various scenarios including research, education, and entertainment. [ABSTRACT FROM AUTHOR]

The development of innovative information technologies has improved the geological mapping process through the use of smart and portable equipment to collect field data, build a geological database and produce geological maps. This revolution has also brought great influence and challenges to practical training in field geology. In this paper, we present our workflow and application of the Digital Geological Survey System (DGSS) during field geology training for undergraduates in Zhoukoudian. The DGSS employs a Point-Routing-Boundary (PRB) model to reform the methods of geological section survey and geological mapping in terms of data collection and map-making and provides a pipelined solution from field data collection to map-making. The experiences of data collection, geological mapping, cross-section survey, and production of stratigraphic histograms and cross-section maps prove that DGSS can save time and reduce labor intensity for undergraduates during learning field geology. Based on the field practice of undergraduates in Zhoukoudian, the influence of the DGSS in promoting field geological teaching and the students' feedbacks to DGSS are discussed. Overall, the DGM system is more popular than the conventional notebook and toolbox. The experience in Zhoukoudian proves that digital devices are efficient and useful for geological practical training of field geology for undergraduates. [ABSTRACT FROM AUTHOR]

Background: Being subjected to or witnessing coercive measures in mental health services can have a negative impact on service users, carers and professionals, as they most often are experienced as dehumanising and traumatic. Coercion should be avoided, but when it does happen, it is important to understand how the experience can be processed so that its consequences are managed. Method: A systematic review and meta-ethnography was used to synthesise findings from qualitative studies that examined service users', staff's and relatives' experiences of recovery from being exposed to coercive measures in mental health care settings. We identified, extracted and synthesised, across 23 studies, the processes and factors that were interpreted as significant to process the experience. Results: Recovery from coercion is dependent on a complex set of conditions that support a sense of dignity and respect, a feeling of safety and empowerment. Being in a facilitating environment, receiving appropriate information and having consistent reciprocal communication with staff are the means through which these conditions can be achieved. People employ strategies to achieve recovery, both during and after coercion, to minimise its impact and process the experience. Conclusions: The findings point to the importance of mental health care settings offering recovery-oriented environments and mental health professionals employing recovery-oriented practices, that would empower service users to develop strategies for managing their mental distress as well as their experiences in mental health care in a way that minimises traumatisation and fosters recovery. [ABSTRACT FROM AUTHOR]

In the contemporary era, smart buildings, characterized by their integration of advanced technologies to enhance energy efficiency and user experience, are becoming increasingly prevalent. While these advancements offer notable benefits in terms of operational efficiency and sustainability, they concurrently introduce a myriad of privacy concerns. This review article delves into the multifaceted realm of privacy issues associated with energy-efficient smart buildings. We commence by elucidating the potential risks emanating from data collection, storage, and analysis, highlighting the vulnerability of the personal and behavioral information of inhabitants. The article then transitions into discussing the rights of occupants, emphasizing the necessity for informed consent and the ability to opt-out of invasive data collection practices. Lastly, we provide an overview of existing regulations governing the intersection of smart buildings and privacy. We evaluate their effectiveness and present gaps that necessitate further legislative action. By offering a holistic perspective on the topic, this review underscores the pressing need to strike a balance between harnessing the benefits of technology in smart buildings and safeguarding the privacy of their occupants. [ABSTRACT FROM AUTHOR]

High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Voxel Selection-based Refining Segmentation (VS-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjøntegaard Delta rate (BD-rate) gain of −1.58% in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%. [ABSTRACT FROM AUTHOR]

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas. [ABSTRACT FROM AUTHOR]

In the era of the Fourth Industrial Revolution, cybercriminals are targeting critical infrastructures such as traffic light systems and smart grids. A major concern is the security of such systems, which can be broken down into a number of categories, such as the authentication of data collection devices, secure data transmission, and use of the data by authorized and authenticated parties. The majority of research studies in the literature have largely focused on data integrity and user authentication. So far, no published work has addressed the security of a traffic light system from data collection to data access. Furthermore, it is evident that the conventional cloud computing architecture is incapable of analyzing and managing the massive amount of generated data. As a result, the fog computing paradigm combined with blockchain technology may be the best way to ensure data privacy in a decentralized manner while reducing overheads, latency, and maintaining security. This paper presents a blockchain-based authentication scheme named VDAS using the fog computing paradigm. The formal and informal verifications of the proposed solution are presented. The evaluation of the proposed scheme VDAS showed that it has low communication and computation costs compared to existing lightweight authentication techniques. [ABSTRACT FROM AUTHOR]

As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users' signs and symptoms from their devices for analysis. However, the process of data collection may pose a risk of privacy leakage without a trusted third party. To address this issue, we propose a medical data collection based on local differential privacy and Count Sketch (MDLDP). The algorithm first uses a random sampling technique to select only one symptom for perturbation by a single user. The perturbed data is then uploaded using Count Sketch. The third-party aggregates the user-submitted data to estimate the frequencies of the symptoms and the mean extent of their occurrence. This paper theoretically demonstrates that the designed algorithm satisfies local differential privacy and unbiased estimation. We also evaluated the algorithm experimentally with existing algorithms on a real medical dataset. The results show that the MDLDP algorithm has good utility for key-value type medical data collection statistics in the IoMT. [ABSTRACT FROM AUTHOR]

In order to improve the agility of college students, this paper designs a distributed agility training system. The system includes an upper computer and nine lower computers, in which the lower computer realizes the functions of data acquisition and communication with the upper computer and calculates the reaction time. The Android-based system software was installed in the upper computer to complete the functions of network connection, setting training times and showing the exercise time. In order to test the effectiveness of the equipment, nine university students were invited to complete agility training over 8 weeks with the help of agility training equipment in preparatory, enhancement and special stages. A t-test (Student's t test) was conducted on the test results at different positions on the front and middle and back areas of the court before and after the training. The results show that the agility of the experimental objects was significantly improved after training, from the midpoint to any point at the front, middle and back court (p < 0.01). This shows that using equipment designed to develop agility for long-term training can promote the sensitive quality in badminton learners. [ABSTRACT FROM AUTHOR]

ML-based applications already play an important role in factories in areas such as visual quality inspection, process optimization, and maintenance prediction and will become even more important in the future. For ML to be used in an industrial setting in a safe and effective way, the different steps needed to use ML must be put together in an ML pipeline. The development of ML pipelines is usually conducted by several and changing external stakeholders because they are very complex constructs, and confidence in their work is not always clear. Thus, end-to-end trust in the ML pipeline is not granted automatically. This is because the components and processes in ML pipelines are not transparent. This can also cause problems with certification in areas where safety is very important, such as the medical field, where procedures and their results must be recorded in detail. In addition, there are security challenges, such as attacks on the model and the ML pipeline, that are difficult to detect. This paper provides an overview of ML security challenges that can arise in production environments and presents a framework on how to address data security and transparency in ML pipelines. The framework is presented using visual quality inspection as an example. The presented framework provides: (a) a tamper-proof data history, which achieves accountability and supports quality audits; (b) an increase in trust by protocol for the used ML pipeline, by rating the experts and entities involved in the ML pipeline and certifying legitimacy for participation; and (c) certification of the pipeline infrastructure, the ML model, data collection, and labelling. After describing the details of the new approach, the mitigation of the previously described security attacks will be demonstrated, and a conclusion will be drawn. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption. [ABSTRACT FROM AUTHOR]

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users' charging requirements, the EV aggregator's power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users' requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users' requirements, and improving the system frequency stability. [ABSTRACT FROM AUTHOR]