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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]

Purpose: Patient-reported outcome (PRO) measures are essential for assessing subjective patient experiences. Interactive voice response (IVR) data collection provides advantages for clinical trial design by standardizing and centralizing the assessment. Prior to adoption of IVR as a mode of PRO administration in the Testosterone Trials (TTrials), we compared IVR to paper versions of the instruments to be used.Methods: IVR versions of the FACIT-Fatigue scale and Psychosexual Daily Questionnaire, Question 4, were developed. In one pilot study, IVR versions of these scales were compared to paper versions in 25 men ≥ 65 years at each of two clinical sites. In another study, IVR versions of the SF-36 Vitality Scale (SF-36), Positive and Negative Affect Scale, and Patient Health Questionnaire were evaluated in comparison with previously validated paper versions in 25 men at two clinical sites. Both paper and IVR versions of each instrument were administered in counterbalanced order, and test-retest reliability was evaluated by repeated administration of the test. Bland-Altman plots were used to assess the degree of agreement. Test-retest correlations for each measure were also determined.Results: Satisfactory agreement was observed between IVR and paper versions of each study measure. Specifically, linear and highly positive associations were observed consistently across the study for IVR and paper versions of all study measures. These ranged from r = 0.91-0.99. Test-retest reliability for all measures was acceptable or better (r = 0.70-0.90).Conclusions: The IVR versions of TTrials endpoints in these two studies performed consistently well in comparison with paper versions. [ABSTRACT FROM AUTHOR]

In this work is presented and described an optical system specifically designed and implemented to be applied for the study of the ink–paper interaction at the paper surface, considering the two main directions of the paper sheet (machine and cross directions). This work is particularly concerned with the system description, complete characterization, test, and its usage under real conditions. Concerning the system characterization, the main parameters were addressed and set having in mind its optimization. Relatively to the test of the system, several measurements using specific object samples with known dimensions were carried out to evaluate the accuracy of the obtained results. In terms of its usage under real conditions, an experiment was carried out using a commercial ink and common copy paper to dynamically collect data of its interaction. The collected data, after processing, enabled the determination of the main results of the analyzed ink–paper interaction, which allowed to reach the following conclusions; (1) the implemented system offers two different analysis approaches (qualitative and quantitative), both showing to be in agreement; and (2) the system also shows to be important to identify variations of the ink–paper interaction, manifested in the machine and cross directions of the paper. [ABSTRACT FROM AUTHOR]

Global science is set to experience different times in the 2020s. China surpasses the USA in terms of the number of scientific papers in 2020 in most scientific databases. This scenario is expected to have implications not only in East Asia but also beyond the region. Against this backdrop, this paper investigates the scientific influence of the US-China competition on six major Muslim-majority science systems. Multiple data sources are used to collect data for the bibliometric analyses, which include the trend analysis of collaboration patterns, discipline-based collaborations, authorship patterns for responsibility in collaborations, and citation premium of collaborations with the USA versus China. All the analyses consistently demonstrate that the USA is losing its scientific influence on the selected Muslim-majority science systems to China. Analysis results indicate an astonishing increase in collaborations with China-based scientists in the last decade. Half of the selected Muslim-majority science systems collaborate more with China-based scientists, whereas the other half collaborate more with the USA in 2021, indicating a fifty-fifty split. The collaborations with China-based authors garner higher citation premiums for the selected Muslim-majority science systems than the collaborations with the USA. [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]

The present study was an attempt to determine whether rates of probability discounting would vary as a function of the type of data-collection method, mode of task administration, type of data analysis, and/or type of outcome being discounted. Some of the 650 participants completed the task using the fill-in-the-blank method (FITB) while others completed it using a version of the multiple-choice (MC) method. Some participants completed the task in paper-pencil format and others did so online. All participants discounted seven different outcomes, and the data were analyzed using a hyperbolic equation and a hyperbolic-like equation, and by calculating the area under the discounting curve. Steeper rates of probability discounting were observed for the FITB than the MC method and for participants who completed the task online than in paper-pencil format. The different methods of analyzing the data sometimes led to different conclusions. Lastly, different rates of probability discounting were observed across the different outcomes being discounting. Implications and recommendations for researchers who study probability discounting are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a literature review that aimed at analyzing the use of Problem Structuring Methods in Social-Ecological Systems. These systems are formed at the interface between social and ecological systems, whose complexity is a feature and, therefore, complex problems are quite common. In the Operational Research realm, there is a class of methods that have been developed to deal with this type of problem, the Problem Structuring Methods. They seek to generate a shared understanding of complex problems from the perception of the involved actors, aiming at structuring them before solving them. Thus, it is reasonable to understand that these methods are suitable for handling complex problems in Social-Ecological Systems. This paper verified this statement through a literature review of 28 peer-reviewed papers. It contributes to both extant literature on Problem Structuring Methods and Social-Ecological Systems presenting where the interventions took place; the complex problems addressed; the Problem Structuring Methods applied, and the purpose of these methods in the interventions; the approaches used for collecting input data; the types of participants that participated of the interventions; as well as the main benefits and limitations of these methods in the interventions. It is concluded that Problem Structuring Methods are approaches that can be useful in dealing with complex problems in Social-Ecological Systems, their use should be encouraged, and some advances presented here can improve their application in these types of intervention. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [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]

The increasing frequency and sophistication of cyber-attacks pose significant threats to organizational entities and critical national infrastructure, leading to substantial financial and operational consequences. Detecting such attacks early and accurately remains a complex endeavour, compounded by challenges in intrusion detection system (IDS) design, the exploitation of zero-day attacks, and issues of reliability and resiliency in physical systems. This research addresses these challenges through a two-fold approach: firstly, implementing input data fusion from diverse and heterogeneous sources, and secondly, fusing classifiers from multiple deep learning (DL)-based algorithms. The success of machine learning (ML) and DL models for IDS relies on meticulous data collection and classifier selection. The paper underscores the limitations of relying on single datasets and ML/DL algorithms, emphasizing potential biases and training restrictions. Rigorous experiments were conducted to identify optimal DL architectures, ensuring the creation of models that exhibit robust generalization on new traffic instances, leading to trusted and unbiased results. The study demonstrates the efficacy of the proposed models through comprehensive evaluations and metrics. Results indicate that the fusion of data and classifiers significantly improves model generalization. The paper also outlines key challenges and future trends in data fusion, emphasizing its role in enhancing IDS performance for securing critical infrastructure. [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]

Online crowdsourcing platforms have rapidly become a popular source of data collection. Despite the various advantages these platforms offer, there are substantial concerns regarding not only data validity issues, but also the ethical, societal, and global ramifications arising from the prevalent use of online crowdsourcing platforms. This paper seeks to expand the dialogue by examining both the "internal" aspects of crowdsourcing research practices, such as data quality issues, reporting transparency, and fair compensation, and the "external" aspects, in terms of how the widespread use of crowdsourcing data collection shapes the nature of scientific communities and our society in general. Online participants in research studies are informal workers who provide labor in exchange for remuneration. The paper thus highlights the need for researchers to consider the markedly different political, economic, and socio-cultural characteristics of the Global North and the Global South when undertaking crowdsourcing research involving an international sample; such consideration is crucial for both increasing research validity and mitigating societal inequities. We encourage researchers to scrutinize the value systems underlying this popular data collection research method and its associated ethical, societal, and global ramifications, as well as provide a set of recommendations regarding the use of crowdsourcing platforms. [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]

To understand how the digitalization of higher education influences the inter-relationship between students, teachers, and their broader contexts, research must account for the social, cultural, political, and embodied aspects of teaching and learning in digital environments. Digital ethnography is a research method that can generate rich contextual knowledge of online experiences. However, how this methodology translates to higher education is less clear. In order to explore the opportunities that digital ethnography can provide in higher education research, this paper presents a methodological review of previous research, and discusses the implications for future practice. Through a systematic search of five research databases, we found 20 papers that report using digital ethnographies to explore teaching and learning in higher education. The review synthesizes and discusses how data collection, rigour, and ethics are handled in this body of research, with a focus on the specific methodological challenges that emerge when doing digital ethnographic research in a higher education setting. The review also identifies opportunities for improvement—especially related to participant observation from the student perspective, researcher reflexivity in relation to the dual teacher-researcher role, and increased diversity of data types. This leads us to conclude that higher education research, tasked with understanding an explosion of new digital practices, could benefit from a more rigorous and expanded use of digital ethnography. [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]

For pedestrian detection in all weather conditions, multispectral imagery is the preferred solution for multimodal data acquisition. Due to the complementarity of multispectral data, the performance of pedestrian detection has been continuously improved. However, it is precise because of the diversity of data that the complexity of the model is increased. How to obtain a simple and lightweight high-performance network is the primary problem that the industry needs to solve. To solve this problem, this paper firstly proposes a lightweight high-performance network multi-receptive field and attention mechanism for multispectral pedestrian detection (MAPD). MAPD is a new scalable network based on multi-receptive field and attention mechanism. It cleverly combines multi-receptive field module and convolutional block attention module (CBAM) attention module to obtain multi-receptive field and attention module (MA). The module can be easily embedded into other network structures. After that, we analyze the proportion of pedestrian objects in the image to determine the receptive field range of the target, and use this range to design the multi-receptive field module of the network to obtain a network model suitable for detecting different object tasks. The MAPD network proposed in this paper has a very low parameter amount. Ablation studies on KAIST and CVC-14 datasets show that our method is effective and achieves state-of-the-art detection performance. [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 aim of this paper is to further understanding on the ethical and conceptual challenges associated with researching human trafficking in Edo state, Nigeria. The contribution addresses perceptions of these phenomena among some local actors in the state. Based on five years' research on human trafficking from Nigeria to Europe, this paper puts into perspective local perceptions, and some dynamics of data collection. Also considered here are ethical challenges bordering on trust and suspicion of the researcher's intent, informed consent and suspicion of researcher's motive especially those linked to funding. The paper concludes by recommending ways of navigating as well as overcoming the associated challenges in researching human trafficking in Edo state. [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]

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be insufficient resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey. Supplementary materials accompanying this paper appear on-line. [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]

Using a novel hybrid-fusion neural network technique and cloud computing environment, this paper examines the relationship between individual flexibility, workplace interpersonal problems, and construction worker safety. The population is made up of construction firms based in Mashhad, Iran. This is an applied study, and the data collection approach used is a descriptive survey. The data was obtained using cloud-based storage, data analysis techniques, and data processing tools. The primary objective is to investigate the critical nature of personal safety on construction sites and techniques for minimizing construction accidents. The statistical population consisted of Mashhad, Iran-based construction firms. Due to the impossibility of studying the entire research population, a sample of n = 200 participants was chosen for this study. Following questionnaire distribution, 151 valid questionnaires were gathered. The primary study instrument was a 28-item questionnaire devised by the researcher based on the Questionnaire on the Experience and Evaluation of Work and occupational health. On a Likert scale, the things were rated. Cronbach's alpha coefficient was also used to determine the questionnaire's validity. SPSS-26 software was used to analyze the data in a MATLAB environment. The findings indicate that interpersonal conflict, workplace stress, supervisor involvement, physical injuries, dangerous incidents, and coworker participation are all connected with worker safety at a significant level of p 0.05. One of the research's advances is the use of new hybrid-fusion neural networks. Cloud computing has performed admirably in this study, taking into account the massive volume of data for speedy analysis and the development of a mechanism as an appropriate application. Additionally, the study was motivated by a dearth of comprehensive research on individual flexibility and interpersonal conflicts among construction workers. [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 introduction to the special issue 'The Geography of Innovation and Entrepreneurship' in the Annals of Regional Science surveys a collection of nine papers which consider agglomeration economies and spatial heterogeneity of regions and firms through the lenses of innovation and entrepreneurship. They all make use of extensive and detailed data sources that enable models to provide a richer picture of how firms, industries and regions are affected by innovation and entrepreneurship but also how these entities shape and foster renewal. These factors include spatial concentration, industry composition, labor market characteristics, immigration, firm characteristics, R&D activities and R&D collaboration. The papers add to the understanding of the geography of innovation and entrepreneurship by suggesting alternative ways of identifying spillovers, combing and integrating internal and external knowledge sources, and by estimating the impact on innovation, new firm formation and growth. [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]

With increase in the demand of better data collection from various IoT devices, researchers are showing more interests in providing the enhanced data collection methods to the users. Many data collection methods have proposed, but still more efficiency and better results are required. In this paper, a novel and secure data collection method from IoT devices has proposed. In this paper, SEED (Secure and energy efficient data-collection) method has discussed. It includes creation of aggregator nodes and path discovery algorithms. Integrity of data is analysed by using MD5 hashing technique. This hashing technique is one of the most usable techniques as compared to other. In the regard of implementation of this novel secure and energy data collection method, MD5 is used to just hash the processing data in the entire process. In earlier scenarios, there were various issues regarding fault tolerance, congestion, energy wastage, path discovery and load balancing in a network. To resolve these issues, updating of the aggregator node is done after each failure or transmission. Transmission of huge amount of data can create different challenges in network. This research is completely different from other existing researches of data collection with routing as it deals with sink node by using a unique path discovery algorithm. This proposed mechanism provides the better results for all of the nodes and network in terms of energy efficiency and throughput. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a steady growth of time-use research, driven by the increased research and policy interest in population activity patterns and their associations with long-term outcomes. There is recent interest in moving beyond traditional paper-administered time diaries to use new technologies for data collection in order to reduce respondent burden and administration costs, and to improve data quality. This paper presents two novel diary instruments that were employed by a large-scale multi-disciplinary cohort study in order to obtain information on the time allocation of adolescents in the United Kingdom. A web-administered diary and a smartphone app were created, and a mixed-mode data collection approach was followed: cohort members were asked to choose between these two modes, and those who were unable or refused to use the web/app modes were offered a paper diary. Using data from a pilot survey of 86 participants, we examine diary data quality indicators across the three modes. Results suggest that the web and app modes yield an overall better time diary data quality than the paper mode, with a higher proportion of diaries with complete activity and contextual information. Results also show that the web and app modes yield a comparable number of activity episodes to the paper mode. These results suggest that the use of new technologies can improve diary data quality. Future research using larger samples should systematically investigate selection and measurement effects in mixed-mode time-use survey designs. [ABSTRACT FROM AUTHOR]

Workarounds, or practices that deviate from the official pathway to a target, are frequent phenomena in the organisational context. With respect to collaboration, they highlight an area of mismatch between normative versus lived work practices, and therefore depict a relevant research area deeply rooted in computer supported cooperative work (CSCW). Building on the theory of hierarchical opposition by Louis Dumont and empirical data collected through ethnographic research at a company classified as a small- and medium-sized enterprise (SME) in the German metal industry, this paper addresses the emergence of workarounds in collaborative work processes by setting them into the wider organisational context. The organisational layer of analysis reveals that workarounds emerge to cater for inversed information power relations and information asymmetries in the shop floor setting, which require communication to flow against the hierarchical slope between planning and execution functions. By applying an organisational lens to the concept of workarounds, this paper contributes a novel empirical analysis that confirms the value of workarounds as a source of insight into collaborative practices. [ABSTRACT FROM AUTHOR]

When approving plant protection products, exposure data are required for risk analysis. Exposure data can be collected for various exposure pathways, such as dermal, inhalation or ground sediment. For measuring exposures, pyranine, a fluorescent dye, and a collector can be used. However, the choice of collector material depends on the specific exposure pathway. This study aims to determine the most suitable collector, in combination with the tracer pyranine, for recording exposure through different pathways in practical trials. Seven different collectors (Tyvek®, labels, plastic patches, paper patches, nylon filters, fibreglass filters, petri dishes) were subjected to laboratory and field tests to assess various quality parameters. Blank values, recovery rates, storability, and fluorescence degradation under UV-radiation were measured. Based on the results, a matrix was created summarizing which collector might be best suited to capture each exposure pathway. Almost all collectors demonstrated high recovery rates (Tyvek® 100%; labels 100%; plastic patches 100%; paper patches 100%; nylon filters 95%; fiberglass filters 60.9%) as well as good storability. Furthermore, all plastic-based collectors (labels, Tyvek®, plastic patches, petri dishes) showed a very good recovery rate above 95% when exposed to UV-radiation. However, nylon filters were not suitable for utilization under field conditions due to the rapid degradation of fluorescence under UV-radiation (recovery rate: 20–56%). Nevertheless, nylon filters showed stable recoveries under protected conditions and can be used to assess inhalation exposures under these conditions. Fibreglass filters, with recovery rates of 84–86%, were well-suited for testing inhalative exposures outdoors when a correction factor was applied. Tyvek® was the most suitable material for detecting total dermal exposure under field conditions. This is due to its quality characteristics and availability in a variety of sizes. Finally, petri dishes were ideal for collecting ground sediments. [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]

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test cases. However, existing automated tools are not mature enough to be widely used by software testing groups. This paper conducts an empirical study on the state-of-the-art automated tools for Java, i.e., EvoSuite, Randoop, JDoop, JTeXpert, T3, and Tardis. We design a test workflow to facilitate the process, which can automatically run tools for test generation, collect data, and evaluate various metrics. Furthermore, we conduct empirical analysis on these six tools and their related techniques from different aspects, i.e., code coverage, mutation score, test suite size, readability, and real fault detection ability. We discuss about the benefits and drawbacks of hybrid techniques based on experimental results. Besides, we introduce our experience in setting up and executing these tools, and summarize their usability and user-friendliness. Finally, we give some insights into automated tools in terms of test suite readability improvement, meaningful assertion generation, test suite reduction for random testing tools, and symbolic execution integration. [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]

The traditional monitoring method of water resources environment mainly relies on manual sampling and laboratory analysis, which is limited in time and space and can not realize the comprehensive monitoring of water resources environment. With the continuous development of computer technology and network communication technology, real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis has gradually become a new solution. This paper aims to propose a real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis, in order to achieve accurate and efficient monitoring of water resources environment, timely acquisition and analysis of key data of water resources environment, and provide scientific basis for relevant decisions. In this paper, the key parameters of water quality, water quantity and water ecology are collected by using computer remote data acquisition and image analysis. Using computer image analysis technology, the collected image data are processed and analyzed, and the related characteristics and indicators of water resources environment are extracted. The results show that the real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis can monitor the data changes of water resources environment accurately and efficiently. Through the analysis and processing of the real-time data, the problems in the water resources environment can be found and warned in time, and the corresponding measures can be taken to regulate and manage. [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]

The theory and application of the Taylor Dispersion technique for measuring diffusion coefficients in binary systems is reviewed. The theory discussed in this paper includes both the ideal Taylor–Aris model and the estimation of corrections required to account for small deviations from this ideal associated with a practical apparatus. Based on the theoretical treatment, recommendations are given for the design of practical instruments together with suggestions for calibration, data acquisition and reduction, and the rigorous estimation of uncertainties. The analysis indicates that relative uncertainties on the order of 1% are achievable in practice. [ABSTRACT FROM AUTHOR]

Purpose: Human trafficking occurs when a person uses force, fraud, or coercion to obtain an act or service from another person. While trafficking is often depicted as a crime perpetrated by strangers, recent literature has found family members can also be traffickers. Only four peer reviewed studies have specifically examined familial trafficking. Method: The current study builds upon this sparse literature by reviewing medical charts of patients in a large hospital system who were identified as survivors of familial trafficking between October 2017 and September 2021. Descriptive statistics are reported for all 39 identified patients. Results: Majority of patients were female (92.3%) and under 36 years old (56.4%; M = 34.2, SD = 11.6). Despite most patients now being adults, all except one were minors at the time the trafficking began. Parents were the most common perpetrator (66.7%) followed by aunt/uncle (12.8%). Nine cases were chosen, given level of detail in patient charts, to be highlighted in a case series to further describe the experiences of these survivors. Cases examined included labor trafficking, parents as perpetrators, intersection of substance use, and males as victims. Force was the most common means of control used and often manifested as physical or sexual abuse that preceded the exploitation. Another reoccurring theme was substance use being a factor when parents were perpetrators. Conclusion: The findings from this paper can help inform screening techniques by encouraging providers to inquire about familial relationships. Findings may also be helpful for providers working with this population and future research endeavors. [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]

Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments. However, the irregular seismic acquisition is accompanied by the lack of acquisition data, which requires high-precision regularization. The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal, involving sparse transform base optimization and threshold modeling. First, this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency. Second, an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy. Test results show that the method has good adaptability to different seismic data and sparse transform bases. The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency. The parallel computing strategy of curvelet transform combined with OpenMP is further proposed, which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy. Finally, the actual acquisition data are used to verify the proposed method. The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently. [ABSTRACT FROM AUTHOR]

This paper is based on the first author's PhD research data collection experiences conducted with his country people who work and live as migrant workers in the production sector in Malaysia. The data collection process is a crucial component in doctoral studies as there is a need to ensure that the data collected is representative, reliable and unbiased. The main objective of this PhD data collection was to understand the economic and social well-being of the migrant workers in the light of the economic and social well-being approaches. In exploring those issues, there were many challenges that the first author faced during data collection (between October 2016 and April 2017). This paper divulges these practical experiences and limitations of this data collection. The notable challenges and limitations were difficult to identify study locations and undocumented migrant workers, time constraint, the difficulty of sampling from a large number of the estimated population, matter of confidentiality and rapport building, lack of cooperation, high field cost, the possibility of false data, language barrier and scared of harassment and personal protection. In addition, the paper discusses the strategies undertaken for better capture of the economic and social well-being. The experiences learnt from this data collection process can be a valuable guideline for the researchers who intend to do further research in a similar context. [ABSTRACT FROM AUTHOR]

The energy consumption from the building sector accounts for more than 21% of the total in China, where the energy-use intensity of public buildings could reach 3 times that of residential buildings. Energy efficiency improvement (EEI) is considered to be one of the most rapid and cost-effective means for the building sector to achieve carbon neutrality. The Chinese government has carried out a series of policy tool exploration to promote the energy efficiency improvement efforts by public building owners since over a decade ago, but encountered various problems and obstacles. This paper investigated and compared the current status challenges, and problems in the implementation of EEI policy tools in China and developed countries through literature review, field investigations and interviews with experts and stakeholders, and finally also put forward some possible coping strategies for Chinese government. Results indicated that energy grading and labeling, energy benchmarking, carbon-emission trading and energy-consumption quota management were the main 4 EEI policy tools designed based on measured energy consumption data, where the first 3 ones had been widely used over the developed countries but the last one was unique to China. Difficulty in large-scale collection of building energy-consumption data, lack of scientific methods on building energy-use level evaluation, limited data application and feedback, undesirable association between stakeholders, etc., were the main obstacles for achieving the long-term effectiveness of the EEI policy tools. Cooperating with energy supply enterprises to collect data, establishing an effective energy grading system and a closed loop of policy implementation, exploring a new model of building carbon-emission trading and a differential pricing mechanism, combined with innovative financial supports, might be the solutions. The main contribution of this study is that a comprehensive investigation and analysis on the current development status, obstacles, and possible solutions of the public building EEI policy tools in China was carried out, which is expected to provide a reference for the government of China and even the world. [ABSTRACT FROM AUTHOR]

DataPipe (https://pipe.jspsych.org) is a tool that allows researchers to save data from a behavioral experiment directly to the Open Science Framework. Researchers can configure data storage options for an experiment on the DataPipe website and then use the DataPipe API to send data to the Open Science Framework from any Internet-connected experiment. DataPipe is free to use and open-source. This paper describes the design of DataPipe and how it can help researchers adopt the practice of born-open data collection. [ABSTRACT FROM AUTHOR]

The ongoing study of blast waves and blast wave mitigation continues to play an essential role in protecting structures and personnel. The methodology, however, for capturing far-field blast waves in large-scale tests has remained largely unchanged for three decades, relying on large arrays of pressure transducers connected by hundreds of meters of cabling and requiring a considerable amount of time to set up. This paper evaluates the use of a modern low-cost microprocessor with high computational power to capture blast waves with sufficient fidelity to provide scientists and engineers with credible data. The system utilizes an ARM Cortex M7 processor as an experimental data acquisition (DAQ) system for measuring far-field blast waves in an open-air blast arena at sampling speeds of up to 1.8 Msps (megasamples per second). The experimental system's performance was evaluated by comparing it to a traditional commercial system used for measuring blast waves. The comparison showed an average Spearman correlation coefficient r of 0.928 between the two systems, suggesting a low variance between the commercial and experimental DAQ systems. This suggests that, despite its simplicity, the experimental system is an effective and low-cost alternative for accurately measuring blast waves. [ABSTRACT FROM AUTHOR]

Introduction: A new national survey has been carried out by the Italian Centers for Cognitive Disorders and Dementias (CCDDs). The aim of this new national survey is to provide a comprehensive description of the characteristics, organizational aspects of the CCDDs, and experiences during the COVID-19 pandemic. Methods: A list of all national CCDDs was requested from the delegates of each Italian region. The online questionnaire is divided in two main sections: a profile section, containing information on location and accessibility, and a data collection form covering organization, services, treatments, activities, and any service interruptions caused by the COVID-19 outbreak. Results: In total, 511 out of 534 (96%) facilities completed the profile section, while 450 out of 534 (84%) CCDDs also completed the data collection form. Almost half of the CCDDs (55.1%) operated for 3 or fewer days a week. About one-third of the facilities had at least two professional figures among neurologists, geriatricians and psychiatrists. In 2020, only a third of facilities were open all the time, but in 2021, two-thirds of the facilities were open. Conclusion: This paper provides an update on the current status of CCDDs in Italy, which still shows considerable heterogeneity. The survey revealed a modest improvement in the functioning of CCDDs, although substantial efforts are still required to ensure the diagnosis and care of patients with dementia. [ABSTRACT FROM AUTHOR]