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Background: This study aims to identify trends, research gaps, leading journals, institutions, countries, and authors by examining the top 100 cited Yttrium-90 Radioembolization (Y90) research articles. The insights gained will assist funding and collaboration efforts in Y90 research. The team analyzed 981 articles from 36 peer-reviewed journals, gathering data on authors, publication year, journal, citations, affiliations, keywords, and references. Author affiliations were scrutinized to pinpoint institutions and countries. p values < 0.05 were deemed statistically significant. The most cited paper (703 citations) was authored by Salem et al. (2010), and the journal with the most publications (16) was Journal of Vascular and Interventional Radiology. There was no significant correlation between journal impact factor and citation metrics. A decreasing trend in the number of top 100 articles was observed since the peak period (2008–2013). Most papers were published in high-impact factor (IF > 3, n = 31/35) and SCImago Q1 journals (86/100), with Northwestern University, Clínica Universidad de Navarra, and Ludwig Maximilian University of Munich being the leading institutions. The most prolific authors were Salem, R., Lewandowski, R.J., and Mulcahy, M.F., with Salem, R. as the most influential. A statistically significant positive correlation between collaborative links and published articles was also discovered. Short conclusion: The top 100 cited articles were primarily published in high-impact journals, and Northwestern University and the USA showed greater productivity and collaboration. These findings have crucial implications for researchers, policymakers, and institutions, aiding in the improvement of Y90 application and understanding in clinical practice. [ABSTRACT FROM AUTHOR]

Background: The study tests the effects of data collection modes on patient responses associated with the multi-item measures such as Patient-Reported Outcomes Measurement System (PROMIS®), and single-item measures such as Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE), and Numerical Rating Scale (NRS) measures. Methods: Adult cancer patients were recruited from five cancer centers and administered measures of anxiety, depression, fatigue, sleep disturbance, pain intensity, pain interference, ability to participate in social roles and activities, global mental and physical health, and physical function. Patients were randomized to complete the measures on paper (595), interactive voice response (IVR, 596) system, or tablet computer (589). We evaluated differential item functioning (DIF) by method of data collection using the R software package, lordif. For constructs that showed no DIF, we concluded equivalence across modes if the equivalence margin, defined as ± 0.20 × pooled SD, completely surrounds 95% confidence intervals (CI's) for difference in mean score. If the 95% CI fell totally outside the equivalence margin, we concluded systematic score difference by modes. If the 95% CI partly overlaps the equivalence margin, we concluded neither equivalence nor difference. Results: For all constructs, no DIF of any kind was found for the three modes. The scores on paper and tablet were more comparable than between IVR and other modes but none of the 95% CI's were completely outside the equivalence margins, in which we established neither equivalence nor difference. Percentages of missing values were comparable for paper and tablet modes. Percentages of missing values were higher for IVR (2.3% to 6.5% depending on measures) compared to paper and tablet modes (0.7% to 3.3% depending on measures and modes), which was attributed to random technical difficulties experienced in some centers. Conclusion: Across all mode comparisons, there were some measures with CI's not completely contained within the margin of small effect. Two visual modes agreed more than visual-auditory pairs. IVR may induce differences in scores unrelated to constructs being measured in comparison with paper and tablet. The users of the surveys should consider using IVR only when paper and computer administration is not feasible. [ABSTRACT FROM AUTHOR]

Purpose: To give an overview of the multicenter Prostate Cancer Outcomes (PCO) study, involving paper-based and web-based collection of patient-reported outcome measures (PROM) in patients undergoing local treatment for prostate cancer in certified centers in Germany. The PCO study is part of the larger Movember-funded TrueNTH Global Registry. The article reports on the study's design and provides a brief progress report after the first 2 years of data collection. Methods: Prostate cancer centers (PCCs) certified according to German Cancer Society requirements were invited to participate in collecting patient-reported information on symptoms and function before and at least once (at 12 months) after treatment. The data were matched with disease and treatment information. This report describes progress in patient inclusion, response rate, and variations between centers relative to online/paper use, and also data quality, including recruitment variations relative to treatment in the first participating PCCs. Results: PCC participation increased over time; 44 centers had transferred data for 3094 patients at the time of this report. Patient recruitment varied widely across centers. Recruitment was highest among patients undergoing radical prostatectomy. The completeness of the data was good, except for comorbidity information. Conclusions: The PCO study benefits from a quality improvement system first established over 10 years ago, requiring collection and harmonization of a predefined clinical dataset across centers. Nevertheless, establishing a PROM routine requires substantial effort on the part of providers and constant monitoring in order to achieve high-quality data. The findings reported here may be useful for guiding implementation in similar initiatives. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

Digital transformation in healthcare improves the safety of health systems. Within our health service, a new digital hospital has been established and two wards from a neighbouring paper-based hospital transitioned into the new digital hospital. This created an opportunity to evaluate the impact of complete digital transformation on medication safety. Here we discuss the impact of transition from a paper-based to digital hospital on voluntarily reported medication incidents and prescribing errors. This study utilises an interrupted time-series design and takes place across two wards as they transition from a paper to a digital hospital. Two data sources are used to assess impacts on medication incidents and prescribing errors: (1) voluntarily reported medication incidents and 2) a chart audit of medications prescribed on the study wards. The chart audit collects data on procedural, dosing and therapeutic prescribing errors. There are 588 errors extracted from incident reporting software during the study period. The average monthly number of errors reduces from 12.5 pre- to 7.5 post-transition (p < 0.001). In the chart audit, 5072 medication orders are reviewed pre-transition and 3699 reviewed post-transition. The rates of orders with one or more error reduces significantly after transition (52.8% pre- vs. 15.7% post-, p < 0.001). There are significant reductions in procedural (32.1% pre- vs. 1.3% post-, p < 0.001), and dosing errors (32.3% pre- vs. 14% post-, p < 0.001), but not therapeutic errors (0.6% pre- vs. 0.7% post-, p = 0.478). Transition to a digital hospital is associated with reductions in voluntarily reported medication incidents and prescribing errors. [ABSTRACT FROM AUTHOR]

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼ 10 times faster data acquisition and achieving ∼ 10 times reduction in data size. [ABSTRACT FROM AUTHOR]

The paper is focused on the spatial differentiation of the quality of life at a district level, drawing data from the World Value Survey Round 7 (2017–2022). Spatial quality of life research in the traditional sense is based on the premise that quality of life is experienced by the individual in a physical geographical space, and therefore the research focuses on the objective, spatial dimension of quality of life, measured using the objective indicators, usually from statistical sources. The novelty of the paper lies in the premise that a spatial investigation of the subjective dimension of quality of life is also possible, focusing on the question of where people are satisfied with their lives. The indicator of this dimension is a self-related evaluation, obtained using the face-to-face method or the internet. In the case of the World Value Survey 7, data collection was done using the Computer-Assisted Personal Interviewing (CAPI) technique. The aim of the paper is to conceptualize the subjective dimension of quality of life, apply the concept using data from WVS 7 and to find out which of the selected variables—also contained in WVS 7—are predictors of quality of life. [ABSTRACT FROM AUTHOR]

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS should deal with data responsibly, guaranteeing nondiscrimination with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed. [ABSTRACT FROM AUTHOR]

As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed. [ABSTRACT FROM AUTHOR]

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

With the development of industry, more and more crucial mechanical machinery generate wildness demand of effective fault diagnosis to ensure the safe operation. Over the past few decades, researchers have explored and developed a variety of approaches. In recent years, fault diagnosis based on deep learning has developed rapidly, which achieved satisfied results in the filed of mechanical equipment fault diagnosis. However, there is few review to systematically summarize and sort out these special image deep learning methods. In order to fill this gap, this paper concentrates on reviewing comprehensively the development of special image deep learning for mechanical equipment fault diagnosis in past 5 years. In general, a typical image fault diagnosis based on fault image deep learning generally consists of data acquisition, signal processing, model construction, feature learning and decision-making. Firstly, the method of signal preprocessing is introduced, and several common methods of converting signals into images are briefly compared and analyzed. Then, the principles and variants of deep learning models are expounded. Further more, the difficulties and challenges encountered at this stage are summarized. Last but not least, the future development and potential trends of the work are concluded, and it hoped that this work will facilitate and inspire further exploration for researchers in this area. [ABSTRACT FROM AUTHOR]

To improve the effect of multi-target tracking, this paper proposes an indoor trajectory tracking method based on particle filtering, which automatically completes the construction of WiFi fingerprint database and performs fusion positioning without offline data collection. Moreover, this paper designs a tracking method based on particle filtering, which can estimate the current location of the user based on the map restriction information and the user's movement information at the same time. This paper designs an improved trajectory tracking algorithm, which can effectively accelerate the convergence speed of the particle filter by fusing the WiFi positioning results. In addition, this paper combines machine vision technology to research and analyze multi-target tracking technology, builds an intelligent analysis model, and combines experimental research to study multi-target tracking technology. Through simulation experiment research, we know that the multi-target tracking technology based on machine vision proposed in this paper has good results. [ABSTRACT FROM AUTHOR]

This paper examines the status of information ecology research through studying the published papers on the topic of information ecology included in Social Sciences Citation Index and Science Citation Index database from 1992 to 2013. It applies bibliometrics and knowledge mapping to analyze the changes in the number of published papers as time goes on, in terms of country and geographic area, research topics, research methods, funding sources, hot research spots, and research trends. In addition, this paper summarizes the origin and the evolution of information ecology research and introduces institutions that conduct information ecology research. The results indicate that information ecology is an emerging field with vigorous development in recent years, and information ecology research is a multi-disciplinary subject. The research also reveals that information ecology research mainly focuses on information ecosystems, information ecology in e-commerce, and information ecology in a network. This paper calls for wider and deeper research on information ecology, in order to explore information ecology issues caused by the rapid development of new technologies. [ABSTRACT FROM AUTHOR]

Entrepreneurship shows regional differences through countries' history, economic environments, society and habits of individuals. Youngsters can think globally and have the highest skills to access information and know-how. This paper collects data from students' project work from different countries and extract that information, which can give interesting input to describe regional effects on entrepreneurial propensity through a quantified semantic analysis. Youngsters from 12 countries received the same instructions to present their national economies and then create a business there. The fundamental question of the paper is that are there any definable attributes from the economic environment, which effect youngsters' entrepreneurial propensity? How can we describe these effects? We linked values and made a numerical evaluation of youngsters' business concepts, which gives not only a range but also the grade of the presence of local or regional elements. The results show that the more exact special elements of a national economy mentioned, the more barriers participants are aware of. The research concept and the results may give useful inputs in knowledge transfer and education of entrepreneurship. [ABSTRACT FROM AUTHOR]

Matrix metalloproteinases (MMPs) had a variety of subtypes, which may be related to tumor invasion and angiogenesis, and the polymorphisms from MMPs have been also associated with the susceptibility to a variety of tumors, including prostate cancer (PCa). However, previous studies have not systematically analyzed the association between MMP and prostate cancer, so we conducted systematic data collection and analyzed to evaluate the relationship among polymorphisms in MMPs and PCa susceptibility. We searched PubMed, Web of Science, Embase and Google Scholar for all papers published up to Apr 3rd, 2023, and systematically analyzed the relationship among MMP1-1607 2G/1G, MMP2-1306 T/C, MMP2-735 T/C, MMP7-181 G/A, MMP9-1562 T/C and PCa susceptibility using multiple comparative models and subgroup analyses. We found that MMP2-1306 T/C polymorphism showed associations with PCa susceptibility, with the Ethnicity subgroup (Asian) being more pronounced. Similarly, MMP9-1562 T/C has also had associations with PCa susceptibility. Our current study found that the polymorphisms of, MMP2-1306 T/C, and MMP9-1562 T/C had strong associations with PCa risk. [ABSTRACT FROM AUTHOR]

Objective: Energy usage in has been increased due to the rising demand of cloud infrastructure. The government policy has been focused on building the green IT data center. The energy data need to be collected in order to monitor the energy usage. However, in an old typical data center, the building has been built with no support of such data collection. In this research, we aim to design the energy data collection system for our existing data center, a case study of data center in Thailand at the university. Based on the collected data, an energy usage monitoring system and prediction can be developed. Methods: In the case study of Kasetsart University data center, the building and electric layouts were predetermined. The building layout and existing IT hardware were investigated. We designed the meter types and the number of meters to be installed for the building the energy data collection system. The corresponding database system was also designed for data logging, data visualization and analysis purpose. Results: As a result, 25 installed meters along with the add-on network system were installed for logging data. A data usage example was demonstrated by building the data visualization and analysis. The presented 1 year dataset collected showed the changes of energy usages which can be used to compare with real activities happening in the campus. This encourages the integration of other related environment data such as outside temperature which may affect the electric billing cost. The dataset can be used for prediction of the electric usage; thus, the policy for reducing the electric billing cost could be established. In this paper, as a data note, we focus on the methodology of data collection required for data center. [ABSTRACT FROM AUTHOR]

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

Context: The landscape provides not only a living space for all life forms, including humans, but also a spatial base and set of resources for the implementation of individual human activities. Inappropriate implementation of human activities, disrespecting the properties of the landscape's natural resources, causes the degradation of natural resources and, consequently, the human living. Objectives: The aim of this paper is to develop new methodological procedures and algorithms for effective assessment of natural capital based on the geosystem approach. Methods: Each territorial unit (geosystem) represents a unique combination of natural assets that create a certain potential for the development of individual activities and eco-stabilization functions. In this study, we developed a new approach and algorithms to assess the natural capital of landscapes for sustainable use. This involves selecting indicators and their functional interpretation, as well as collecting available spatial data and statistics for GIS analysis, synthesis, and modeling. Results: The methodological procedure consists of the determination of indicators for natural capital assessment, the determination of their functional values and weighting coefficients, the determination of the suitability of the geosystem for the implementation of individual activities based on the value of natural capital, and the determination of restrictions and limiting factors. The set of data on landscape assets can be categorized into abiotic, land cover and biotic, and socio-economic indicators, which can either support human activities or limit them. Options for sustainable use of natural capital were split into two groups of potential activities: (I) natural capital for landscape planning activities and (II) specific activities or functions (e.g., natural capital for energy use, recreation, regulation services). The modeling of eco-stabilizing natural capital in Trnava LTSER pointed to low spatial ecological stability, mainly in the central part of the district. Discussion: Discussion pointed to strength, novelty and opportunities of implementing methodological approach to natural capital assessment. Conclusions: As an output of this methodological approach, a comprehensive digital spatial database of landscape-ecological data for the assessment of natural capital and the suitability of its use for socio-economic activities has been created in Slovakia. The database represents a set of consistent spatial information on natural capital assets and other indicators, including land cover and socio-ecological indicators. The methodological approach can be applicable to any territory on the basis of a modification. [ABSTRACT FROM AUTHOR]

Participatory mapping approaches have raised increasing interest in a variety of fields of research and practice during the past two decades. However, their use in modeling and studying individual mobility and activity spaces is rather recent and limited. This paper focuses on the use of public participation GIS (PPGIS) data in activity space modeling and analysis and aims to draw more scholarly attention to the existing research potentials in this area. While reviewing the pros of using PPGIS for activity space studies, this paper also discusses the existing limitations and outlines how they can be addressed in future research. PPGIS effectively enables collecting data from larger samples, making it possible to conduct more comprehensive geospatial and statistical analyses on the data. Additionally, the strong link between individuals and places in PPGIS data facilitates more person-based approaches in investigating person-environment relationships. However, use of PPGIS for activity space studies comes with analytical and data quality challenges that need to be thoroughly taken into consideration. Particularly, future research needs to seek new ways of including the temporal dimension in PPGIS and find new methods of using the data for activity space modeling and analysis. [ABSTRACT FROM AUTHOR]

In this paper, we present a new proposal on how to measure quark/gluon jet properties at the LHC. The measurement strategy takes advantage of the fact that the LHC has collected data at different energies. Measurements at two or more energies can be combined to yield distributions of any jet property separated into quark and gluon jet samples on a statistical basis, without the need for an independent event-by-event tag. We illustrate our method with a variety of different angularity observables, and discuss how to narrow down the search for the most useful observables. [ABSTRACT FROM AUTHOR]

Conducting a vulnerability and risk assessment (VRA) is a critical step in adaptation planning and implementation. This research paper has assessed how the co-developed approach helped the Government of Nepal and stakeholders in Nepal to agree on and operationalize the vulnerability and risk assessment framework and what works best for the country's context. The methodological framework consists of eight steps including (i) scoping, (ii) reviewing the framework (iii) identifying data sources (iv) exploring data sources, nature, and character (v) data collection, tabulation, filtration, and normalization (vi) weightage and composite value (vii) analysis of the data (viii) identifying climate change impact, vulnerability, and risk. The output of the vulnerability and risk assessment in Nepal presents a compilation of impacts, vulnerabilities, and risks and a description of their context, root causes, and trends. These research-based assessment can be used to draw some possible adaptation options and improved decision-making at the national and sub-national levels. However, there were some challenges faced in analyzing the vulnerability and risks based on the indicators. There was a lack of a multi-year, complete, and uniform database, and difficulties in developing scenarios of hazards due to unclarity on climate change attribution. The lessons from this paper will be important for designing a more practical and country-driven VRA framework and methodology for other countries. [ABSTRACT FROM AUTHOR]

With the development of intelligent factories, collaborative robotic arms are becoming the critical equipment for flexible production lines, and digital twin is the best way to realize intelligent and digitized collaborative robotic arm. This paper designs and develops a collaborative robotic arm monitoring system based on digital twin and proposes a six-dimensional system architecture. Key technologies such as equipment twin modeling for the assembly process, multi-source heterogeneous data acquisition, and digital twin–driven real-time monitoring are studied. The system is driven by twin models and real-time data, and based on the key physical characteristics of the collaborative robotic arm. The model and data are effectively integrated to realize real-time monitoring of the assembly process and provide support for intelligent decision-making. The effectiveness and feasibility of the system are verified by running the application on the UR5 and UR10 collaborative robotic arm assembly test benches. [ABSTRACT FROM AUTHOR]

Digital twin technology is a key technology to realize cyber-physical system. Owing to the problems of low visual monitoring of the blade-rotor test rig and poor equipment monitoring capabilities, this paper proposes a framework based on the digital twin technology. The digital-twin based architecture and major function implementation have been carried out form five dimensions, i.e. Physical layer, Virtual layer, Data layer, Application layer and User layer. Three key technologies utilized to create the system including underlying equipment real-time communication, virtual space building and virtual reality interaction have been demonstrated in this paper. Based on RS-485 and other communication protocols, the data acquisition of the underlying devices have been successfully implemented, and then the real-time data reading has been achieved. Finally, the rationality of the system has been validated by taking the blade-rotor test rig as the application object, which provides a reference for the monitoring and evaluation of equipment involved in manufacturing and experiment. [ABSTRACT FROM AUTHOR]

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database. Measurement(s) battery capacity • Voltage • electrical conductivity • Faraday efficiency • energy • Chemical Properties Technology Type(s) digital curation • computational modeling technique Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12646277 [ABSTRACT FROM AUTHOR]

In this paper, we concentrate on a non-orthogonal multiple access (NOMA)-enabled UAV data collection network for Internet of Things devices (IoTDs), where a unmanned aerial vehicle (UAV) is deployed as an aerial base station. During its flight period, the UAV can collect data from IoTDs and take advantage of the simultaneous wireless information and power transfer technology to charge the batteries of IoTDs. With the aid of NOMA, spectrum efficiency has been improved. We aim to prolong the lifetime of the IoT network, via jointly optimizing the UAV trajectory, the time allocation for information communication and wireless power transfer, the IoTDs' transmit power, as well as the IoTDs' group scheduling for NOMA. Then, we use the block coordinate decent and successive convex approximation techniques to tackle the non-convexity of the formulated problem. Numerical results show that the proposed solution increases the residual energy of the IoTDs, thus prolonging the lifetime of the network. [ABSTRACT FROM AUTHOR]

Vision-based defect recognition in the metal industry is an important and challenging task. Many mainstream methods based on supervised learning need a large amount of annotated defect data. However, the data acquisition in industrial scenarios is quite difficult and time-consuming. To alleviate the problem, in this paper, we propose a few-shot defect recognition (FSDR) method for metal surfaces by attention-embedding and self-supervised learning. The proposed method includes two stages,pre-training and meta learning. In the pre-training stage, an attention embedding network (AEN) is designed for better learning the defect local correlation of multi-receptive field and reducing the background interference, and to train a robust AEN without any annotations, a multi-resolution cropping self-supervised method (MCS) is developed for better generalizing the few-shot recognition task. Then, in the meta learning stage, to generate the embedding features, the defect images are encoded by the pre-trained AEN, and we also design a query-guided weight (QGW) to address the bias of embedding feature vector. The classification information is gained by computing the distance of the embedding feature vector of each category. We evaluate the proposed FSDR on the NEU dataset and the experiments show competitive results in 1-shot and 5-shot defect recognition tasks compared with the mainstream methods. [ABSTRACT FROM AUTHOR]

Responsible AI is concerned with the design, implementation and use of ethical, transparent, and accountable AI technology in order to reduce biases, promote fairness, equality, and to help facilitate interpretability and explainability of outcomes, which are particularly pertinent in a healthcare context. However, the extant literature on health AI reveals significant issues regarding each of the areas of responsible AI, posing moral and ethical consequences. This is particularly concerning in a health context where lives are at stake and where there are significant sensitivities that are not as pertinent in other domains outside of health. This calls for a comprehensive analysis of health AI using responsible AI concepts as a structural lens. A systematic literature review supported our data collection and sampling procedure, the corresponding analysis, and extraction of research themes helped us provide an evidence-based foundation. We contribute with a systematic description and explanation of the intellectual structure of Responsible AI in digital health and develop an agenda for future research. [ABSTRACT FROM AUTHOR]

Background: To assess quality of life and unmet needs after stroke, patient-reported outcome measures (PROMs) have gained increasing attention. However, patients' perspectives on assessing PROMs remain unclear, potentially hindering implementation into clinical practice. Therefore, this study explored patients' preferences on assessing PROMs after ischemic stroke. Methods: A paper-based questionnaire was sent to stroke survivors treated at the Department of Neurology, University of Leipzig, Germany. Health-related quality of life (HRQoL, EQ-5D-5L) and preferences regarding different aspects of data collection to assess PROMs were investigated and linked to socio-demographic and medical characteristics. Results: 158 persons were contacted and 80 replies were subsequently analyzed. Mean age was 70.16 years and mean HRQoL was 68.79 (visual analogue scale with a theoretical maximum of 100). Participants showed positive attitudes towards PROMs as they saw potential to improve care of other patients (n = 66/79; 83.54%) or to improve their own situation (n = 53/74; 71.62%). Participants preferred an annual interview after stroke (n = 39/80; 48.75%) and would preferably spend 15–30 min (n = 41/79; 51.90%) to answer a written survey (n = 69/80; 86.25%). The initially treating clinic was preferred as initiator of such surveys (n = 43/79; 54.43%). Stratification revealed that participants with more than 1 h of daily digital media usage preferred email as way of communication. Conclusions: For the first time, this study showed individual preferences on assessing PROMs after ischemic stroke, focusing on the way, time interval, duration, and initiation site of surveys. These insights might help to successfully implement PROMs after stroke and subsequently detect unmet needs and deficits in stroke care. [ABSTRACT FROM AUTHOR]

Augmented Reality in education can support students in a wide range of cognitive tasks–fostering understanding, remembering, applying, analysing, evaluating, and creating learning-relevant information more easily. It can help keep up engagement, and it can render learning more fun. Within the framework of a multi-year investigation encompassing primary and secondary schools across Europe, the ARETE project developed several Augmented Reality applications, providing tools for user interaction and data collection in the education sector. The project developed innovative AR learning technology and methodology, validating these in four comprehensive pilot studies, in total involving more than 2,900 students and teachers. Each pilot made use of a different Augmented Reality application covering specific subjects (English literacy skills, Mathematics and Geography, Positive Behaviour, plus, additionally, an Augmented Reality authoring tool applied in a wide range of subjects). In this paper, we introduce the datasets collected during the pilots, describe how the data enabled the validation of the technology, and how the approach chosen could enhance existing augmented reality applications in data exploration and modelling. [ABSTRACT FROM AUTHOR]

The focus of this paper is to review the qualitative case methods that have been used in technology transfer research over the last 20 years from 1996 to 2015. Case methods allow for more in-depth analyses and provide the opportunity to place research into a certain context due to the selection of e.g. specific sectors, institutions, countries, etc. Using a systematic literature review of five of the top journals in the field of technology transfer research, namely Journal of Technology Transfer, Research Policy, Science and Public Policy, R&D Management and Technovation, it yielded 107 articles using the search terms: 'Technology Transfer' AND ('Case Study' OR 'Case Method' OR 'Qualitative'). Our findings indicate a clustering of themes using qualitative case methods around technology transfer mechanisms and TTOs, academic entrepreneurship, university-industry collaboration, commercialization as well as R&D and firm knowledge transfer. We also identify trends in case method technology transfer research with respect to authorship, location of papers, sectoral contexts, data collection, numbers of cases and data analysis software. We conclude our paper discussing the implications of trends and themes and suggest that researchers need to reflect on used terminology and their utilization and postulate a need for more plurality of data collection methods. [ABSTRACT FROM AUTHOR]

3D orthogonal woven composites are receiving increasing attention with the ever-growing market of composites. A current challenge for these materials' development is how to improve their damage tolerance in orthogonal and layer-to-layer structures under extreme loads. In this paper, a damage reduction strategy is proposed by combining structural and electromagnetic properties. An integrated experimental platform is designed combining a power system, a drop-testing machine, and data acquisition devices to investigate the effects of pulse current and impact force on woven composites. Experimental results demonstrate that pulse current can effectively reduce delamination damage and residual deformation. A multi-field coupled damage model is developed to analyze the evolutions of temperature, current and damage. Parallel current-carrying carbon fibers that cause yarns to be transversely compressed enhance the mechanical properties. Moreover, the microcrack formation and extrusion deformation in yarns cause the redistribution of local current among carbon fibers, and its interaction with the self-field produces an obvious anti-impact effect. The obtained results reveal the mechanism of damage reduction and provide a potential approach for improving damage tolerance of these composites. In this work, the authors demonstrate that pulse current can effectively be used to reduce delamination damage and residual deformation in 3D orthogonal woven composites, enhancing mechanical properties and damage tolerance. [ABSTRACT FROM AUTHOR]

Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption. [ABSTRACT FROM AUTHOR]

Background: Heterodyne interferometry offers an exciting solution for achieving sub-nanometer or ten-picometer-order mechanical displacement measurements because of its high resolution and noise immunity. Objective: In this paper, we investigate the stability and resolution of a conventional displacement-measuring heterodyne interferometer using a single phase-locked loop (PLL) at a sub-nanometer or 10-pm level over an µm-scale measurement regime in normal air. Methods: In the experiment, we perform the beat frequency reduction of the heterodyne signals to take advantage of a low-cost frequency sampling data acquisition (DAQ). The primary calculations of the single phase-locked loop (PLL) algorithm are implemented in a point-by-point (pt-pt) construction, providing pt–pt low-pass filters (LPFs) with high noise suppression. The output is appended into an array of N points and averaged to reduce the noise of the outgoing signal again. Using the PLL phase meter with the beat frequency reduction can ensure a mdeg-order phase measurement with Allan deviation stability of ~ 10 µdeg over 1000 s. We consider a combination of the miniaturization of optical paths in air and the PLL phase meter integrated with low-cost devices while ensuring mdeg-order measurement stability to achieve the interferometer's pm-order measurement resolution and stability. Results: The measurement results show that the interferometer can achieve a resolution of 26 pm over a 5 μm-range mechanical displacement measurement. The Allan deviation stability of the interferometer reaches ~ 10 pm over a sampling time of 30 s, and the displacement noise floor is 10 pm/Hz1/2 above 1 Hz. Conclusions: The new signal-processing PLL phase meter provides higher noise reduction than the old one presented in our previous work. With low cost but high resolution and stability, the PLL phase meter is incredibly suitable for pm-order displacement-measuring heterodyne interferometry. Combining a well-reduced PLL phase meter and a conventional heterodyne interferometer with a stable, isolated configuration and the minimization of optical paths in the air can give the measurement system a high resolution and stability. The measurement system can have a more significantly improved resolution of a few tens of pm than interferometers presented in previous publications and stability comparable to the modified heterodyne interferometers shown in earlier works over the sampling time of 30 s (possibly up to 100 s). A conventional heterodyne interferometer with a single PLL is expected to perform large-range displacement measurements with resolution at ten picometer levels in air. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for initial camera pose estimation from just a single image which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoids in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method. This is achieved with very little effort in terms of training data acquisition—a few hundred calibrated images of which only three need manual object annotation. Code and models are released at https://gitlab.inria.fr/tangram/3d-aware-ellipses-for-visual-localization. [ABSTRACT FROM AUTHOR]

Nowadays, millions of people use Online Social Networks (OSNs) like Twitter, Facebook and Sina Microblog, to express opinions on current events. The widespread use of these OSNs has also led to the emergence of social bots. What is more, the existence of social bots is so powerful that some of them can turn into influential users. In this paper, we studied the automated construction technology and infiltration strategies of social bots in Sina Microblog, aiming at building friendly and influential social bots to resist malicious interpretations. Firstly, we studied the critical technology of Sina Microblog data collection, which indicates that the defense mechanism of that is vulnerable. Then, we constructed 96 social bots in Sina Microblog and researched the influence of different infiltration strategies, like different attribute settings and various types of interactions. Finally, our social bots gained 5546 followers in the 42-day infiltration period with a 100% survival rate. The results show that the infiltration strategies we proposed are effective and can help social bots escape detection of Sina Microblog defense mechanism as well. The study in this paper sounds an alarm for Sina Microblog defense mechanism and provides a valuable reference for social bots detection. [ABSTRACT FROM AUTHOR]

Correction to: Environmental Modeling & Assessment https://doi.org/10.1007/s10666-022-098... The second affiliation of Jorge Davalos reflected in the published article is incorrect. N. Pecastaing: data collections" Instead, it should read: "J.M Montaud: CGE modelling and writing of the paper. N. Pecastaing: data collection and writing of the paper" The original article has been corrected. [Extracted from the article]

With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of rescheduling policies in the event of both rework and reconditioning in job shop manufacturing systems. Since these events occur in unplanned and disrupting manner, to address them properly, it is required to manage real-time information and to have flexible reaction capacity. These capabilities, of data acquisition and robotics, are provided by Industry 4.0 Technologies. However, to take full advantage of those capabilities, it is imperative to have efficient decision-making processes to deliver adequate corrective actions. In this sense, we propose an event-driven rescheduling approach. This approach consists of an architecture that integrates information acquisition, optimization process, and rescheduling planning. We study the performance of the system with several algorithms with two performance criteria, namely, (i) relative performance deviation (RPD) in terms of objective function and (ii) schedule stability. We also propose a hybrid policy that combines full rescheduling regeneration with stability-oriented strategies aimed to balance both criteria. We conducted extensive computational tests with instances from the literature under different scenarios. The results show that a sophisticated algorithm can obtain better quality schedules in terms of the objective function but at the expense of sacrificing stability. Finally, we analyze and discuss the results and provide insights for its use and implementation. [ABSTRACT FROM AUTHOR]

Supercomputers are the most powerful computing machines available to society. They play a central role in economic, industrial, and societal development. While they are used by scientists, engineers, decision-makers, and data-analyst to computationally solve complex problems, supercomputers and their hosting datacenters are themselves complex power-hungry systems. Improving their efficiency, availability, and resiliency is vital and the subject of many research and engineering efforts. Still, a major roadblock hinders researchers: dearth of reliable data describing the behavior of production supercomputers. In this paper, we present the result of a ten-year-long project to design a monitoring framework (EXAMON) deployed at the Italian supercomputers at CINECA datacenter. We disclose the first holistic dataset of a tier-0 Top10 supercomputer. It includes the management, workload, facility, and infrastructure data of the Marconi100 supercomputer for two and half years of operation. The dataset (published via Zenodo) is the largest ever made public, with a size of 49.9TB before compression. We also provide open-source software modules to simplify access to the data and provide direct usage examples. [ABSTRACT FROM AUTHOR]

Background: The use of patient-reported outcome measures (PROM) and patient-reported experience measures (PREM) provide health providers with valuable feedback on how to improve clinical care and patient outcomes. This paper describes a qualitative study that was conducted to learn about factors influencing the well-being of people living with HIV (PLHIV) in Finland. The findings will be used to develop themes for HIV-specific PROM and PREM questions. Methods: PROMs and PREMs were developed by the Finnish Institute for Health (THL) as a part of a project to develop a national quality-of-care registry for HIV. The study aimed to identify issues and concerns among people living with HIV (PLHIV) that influence their well-being (PROMs) and their experiences in the healthcare system (PREMs). The data were collected through face-to-face in-depth interviews and focus group discussions based on open-ended and semi-structured questions. The data were analyzed using thematic analysis. Results: The assessment identified the following PROMs of concern: psychological well-being, concerns about stigma, physical health, social well-being, sexual well-being, medication uptake, managing other medications with antiretrovirals (ARVs), and growing old. The assessment identified the following PREMs: helping patients understand their own health status, proving an opportunity for patients to discuss physical health, psychological and sexual well-being, supporting the uptake of ARVs, assisting patients with medication use, showing compassion towards patients, and empowering patients against stigma. Conclusion: These findings of the study can be used to develop domain-specific PROM and PREM questions for the national HIV quality care register. [ABSTRACT FROM AUTHOR]

April 15th, 2019: Notre-Dame Cathedral in Paris was burning, the spire collapsed on the nave, vaults crumbled and most of the timber roof was gone. In the post-disaster context, the authenticity and the monitoring of the archaeological remains are crucial for their potential reuse during reconstruction. This paper analyzes the collapsed transverse arch from the nave of Notre-Dame as a case study of reconstruction, using the digital twin framework. We propose four facets for the digital twin experiment—physical anastylosis, reverse engineering, spatio-temporal tracking of assets, and operational research—that are described in detail, while being assembled to support a hybrid reconstruction hypothesis. The digital twin can realize the parallel unfolding of physical-native and digital-native processes, while acquiring and storing heterogeneous information as semantically structured data. The results demonstrate that the proposed modeling method facilitates the formalization and validation of the reconstruction problem and increases solutions performances. As result, we present a digital twin framework application ranging from acquisition to data processing that informs a successful hybrid reconstruction hypothesis. [ABSTRACT FROM AUTHOR]

In the work presented in this paper, we use data collected from mobile users over several weeks to develop a neural network-based prediction model for the power consumed by a smartphone. Battery life is critical to the designers of smartphones, and being able to assess scenarios of power consumption, and hence energy usage is of great value. The models developed attempt to correlate power consumption to users' behavior by using power-related data collected from smartphones with the help of specially designed logging tool or application. Experiences gained while developing the model regarding the selection of input parameters to the model, the identification of the most suitable NN (neural network) structure, and the training methodology applied are all described in this paper. To the best of our knowledge, this is the first attempt where NN is used as a vehicle to model smartphones' power, and the results obtained demonstrate that NNs models can provide reasonably accurate estimates, and therefore, further investigation of their use in this modeling problem is justified. [ABSTRACT FROM AUTHOR]

Track and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine. [ABSTRACT FROM AUTHOR]

The fundamental purpose of the decennial Census is an enumeration of the U.S. population at a particular 'Census moment' for the purpose of apportionment. The 'Census moment' for the 2010 Census occurred at 11:59 p.m. on April 1, 2010. This means that, ideally, all persons alive and living in the United States at that moment are included in the Census count, while any person not alive at that moment is excluded. In reality, this goal is challenging to achieve. Since the actual date of data collection often varies widely, it is possible that individuals are included, or excluded, in the count due to this discrepancy between the Census Day and the date of data collection. In this paper, I explore how the Census Bureau addresses this issue specifically when dates of birth after Census Day are encountered. First, I describe the three methods of data collection (Self-administered questionnaires, enumerator-administered questionnaires, and Telephone Questionnaire Assistance/Coverage Follow-Up operator-administered questionnaires), and how dates of birth after Census Day are addressed in each of these methods. Next, I explore related findings from the 2010 Census, including how many dates of birth after Census Day were found in the 2010 Census data, how they were processed according to data collection method, and how this impacted the final Census count. Finally, I discuss the performance of the new procedures related to dates of birth after Census Day that were implemented in the 2010 Census, along with implications for moving forward into future Censuses. [ABSTRACT FROM AUTHOR]

Digital restoration is a rapidly growing methodology within the field of heritage conservation, especially for early cinematic films which have intrinsically unstable dye colourants that suffer from irreversible colour fading. Although numerous techniques to restore film digitally have emerged recently, complex degradation remains a challenging problem. This paper proposes a novel vector quantization (VQ) algorithm for restoring movie frames based on the acquisition of spectroscopic data with a custom-made push-broom VNIR hyperspectral camera (380–780 nm). The VQ algorithm utilizes what we call a multi-codebook that correlates degraded areas with corresponding non-degraded ones selected from reference frames. The spectral-codebook was compared with a professional commercially available film restoration software (DaVinci Resolve 17) tested both on RGB and on hyperspectral providing better results in terms of colour reconstruction. [ABSTRACT FROM AUTHOR]

The digitalization of traditional Palmyra manuscripts, such as Lontar, is the government's main focus in efforts to preserve Balinese culture. Digitization is done by acquiring Lontar manuscripts through photos or scans. To understand Lontar's contents, experts usually carry out transliteration. Automatic transliteration using computer vision is generally carried out in several stages: character detection, character recognition, syllable recognition, and word recognition. Many methods can be used for detection and recognition, but they need data to train and evaluate the resulting model. In compiling the dataset, the data needs to be processed and labelled. This paper presented data collection and building datasets for detection and recognition tasks. Lontar was collected from libraries at universities in Bali. Data generation was carried out to produce 400 augmented images from 200 Lontar original images to increase the variousness of data. Annotations were performed to label each character producing over 100,000 characters in 55 character classes. This dataset can be used to train and evaluate performance in character detection and syllable recognition of new manuscripts. Measurement(s) accuracy • Precision • Recall • F1-score Technology Type(s) Python Factor Type(s) bounding box • frequency • spatial location • dimensional size Sample Characteristic - Organism lontar • manuscript • glyphs Sample Characteristic - Environment balinese glyphs Sample Characteristic - Location Bali Province [ABSTRACT FROM AUTHOR]

Aiming to illuminate the effects of enforced confinements on people's lives, this paper presents a novel dataset that measures human behaviour holistically and longitudinally during the COVID-19 outbreak. In particular, we conducted a study during the first wave of the lockdown, where 21 healthy subjects from the Netherlands and Greece participated, collecting multimodal raw and processed data from smartphone sensors, activity trackers, and users' responses to digital questionnaires. The study lasted more than two months, although the duration of the data collection varies per participant. The data are publicly available and can be used to model human behaviour in a broad sense as the dataset explores physical, social, emotional, and cognitive domains. The dataset offers an exemplary perspective on a given group of people that could be considered to build new models for investigating behaviour changes as a consequence of the lockdown. Importantly, to our knowledge, this is the first dataset combining passive sensing, experience sampling, and virtual assistants to study human behaviour dynamics in a prolonged lockdown situation. Measurement(s) 'human behaviour during the COVID-19 outbreak', 'human behaviour', 'physical behaviour', 'social behaviour', 'emotional behaviour', 'cognitive behaviour' Technology Type(s) 'smartphone device', 'activity tracker', 'digital questionnaires' Sample Characteristic - Organism 'human beings' Sample Characteristic - Location 'The Netherlands', 'Greece' [ABSTRACT FROM AUTHOR]