Specific Data Research Articles

Problems on copper data above 10 MeV in ENDF/B-VIII.0 and JEFF-3.3

We performed a benchmark test for the recent nuclear data libraries of copper data using the integral experiment at JAEA/FNS. So far, we had pointed out that the copper data might have wrong cross section data of elastic scattering and/or capture reactions around a few keV because the calculated results sensitive to lower energy neutrons with JENDL-4.0, ENDF/B-VIII.0 and JEFF-3.3 showed a tendency to underestimate the experimental ones drastically. Recently, we found out another problem that the calculated results with ENDF/B-VIII.0 and JEFF-3.3 copper nuclear data underestimated and overestimated the experimental ones related to high energy neutrons above 10 MeV such as the 93Nb(n,2n)92mNb reaction rate, respectively. These tendencies did not appear in the calculation results with their previous versions, ENDF/B-VII.1 and JEFF-3.2. In this study, we compared the 63Cu and 65Cu data in ENDF/B-VIII.0 and JEFF-3.3 with those in ENDF/B-VII.1 and JEFF-3.2, respectively, and clarified how specific reaction data caused the underestimation and overestimation by replacing them from the current ones to the previous ones. The copper data in ENDF/B-VIII.0 and JEFF-3.3 should be re-evaluated by reference to this study.

First Morpho-Functional Assessment of Immature Stages of Pelecocera Species (Diptera: Syrphidae) Feeding on False Truffles.

With 14 species, Pelecocera Meigen, 1822 is a scarce and small genus of hoverflies (Diptera: Syrphidae: Rhingiini) from the Holarctic Region. Apart from the finding of larvae of Pelecocera (Chamaesyrphus) japonica (Shiraki, 1956) in fungi in Japan, the larval biology of these hoverflies is virtually unknown. The early stages of all Pelecocera species are undescribed. The adults of Pelecocera (Pelecocera) tricincta Meigen, 1822 and Pelecocera (Chamaesyrphus) lugubris Perris, 1839 are found in Palearctic conifer forests with sand dunes. We here report the first morphological evidence of the immature stages of Pelecocera (P. lugubris and P. tricincta), as well as specific data on their breeding sites. Larvae of both species were collected feeding on the hypogean basidiomycete Rhizopogon luteolus Fr. & Nordholm, 1817 in Denmark in 2021. The first larval stage and second larval stage of P. tricincta, the third larval stage of P. lugubris, the anterior respiratory process, and the posterior respiratory process of the puparia of these two species were analyzed and studied using stereomicroscope and scanning electron microscope techniques. The chaetotaxy of the puparium of each species is also described and illustrated. A taxonomic diagnosis of the larvae of the genus Pelecocera is proposed to separate them from the larvae of other genera of the tribe.

Unifying Structured Data as Graph for Data-to-Text Pre-Training

Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2T generation tasks as graph-to-text generation. To effectively exploit the structural information of the input graph, we propose a structure-enhanced pre-training method for D2T generation by designing a structure-enhanced Transformer. Concretely, we devise a position matrix for the Transformer, encoding relative positional information of connected nodes in the input graph. In addition, we propose a new attention matrix to incorporate graph structures into the original Transformer by taking the available explicit connectivity structure into account. Extensive experiments on six benchmark datasets show the effectiveness of our model. Our source codes are available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/unid2t.

Valuing the economic benefits of species recovery programmes

Abstract Accounting for the values placed on nature by the public is key to successful policies in reversing ongoing biodiversity declines. However, biodiversity values are rarely included in policy decisions, resulting in poorer outcomes for people and nature. Our paper addresses an important evidence gap related to the non‐availability of values for appraising large‐scale policies and investment programmes for species recovery and habitat improvement at the national level. We use a stated preference choice modelling approach to estimate household preferences and Willingness to Pay for species recovery and habitat improvement over a wide range of habitats in England. The framing of our stated preference study is crucial to the evidence we develop. Within the study, we define species recovery as incremental improvements to habitat quality and present respondents with choices between conservation policy options that improve different habitat types. We then use the response data to estimate values for habitat quality improvements, and the associated improvements to species presence and abundance. We are thus able to estimate economic benefits for ‘wild species recovery’ simultaneously across a wide range of habitat types. Willingness to pay values for habitat improvement was found to be highest for improvements from ‘moderate’ to ‘full’ species recovery by 2042; and for habitat types which have relatively low current extents in England, such as lowland fens. Policy Implications: biodiversity policy designers can make use of stated preference methods to guide decisions over which aspects of biodiversity targets to focus more resources on, since this enables policy to reflect public preferences, and thus engages higher public support for conservation. In our specific data and context, this implies prioritising the restoration of species recovery to high levels and focussing resources on scarcer rather than more abundant habitat types. Read the free Plain Language Summary for this article on the Journal blog.

PENGUKURAN KINERJA ASRI MEDICAL CENTER DALAM PERSPEKTIF BALANCED SCORECARD

The company performance is a reflection of an activity results and condition of the company. The results of the activities of the company being analyzed, where the results will show the condition of the company's management during the period of performance analysis done. Asri Medical Center (AMC) is one of Muhammadiyah University of Yogyakarta units in health care, education and research belong to da’wa infrastructures. To realize its goal in providing quality health services for people, AMC has to make a performance appraisal regularly and continuously. The objective of this research is to measure the performance of AMC based on the Balanced Scorecard with reference to the performance standards in the AMC. This research is an eksploratoris case study that measured management performance with the Balanced Scorecard, which is assessing the performance not only from the financial aspect but also from non-financial aspects. The subjects of this research are AMC managers, and the data used is the data of 2010 and 2011. The data used are the primary data i.e field studies in the form of interview and observation and also secondary data in the form of general data and specific data on the AMC which includes financial and non financial data. AMC has been running the Balanced Scorecard stages, that is strategy formulation arranged as a result of the macro, industry environments analysis as well as SWOT analysis and has formulated a vision, mission, purpose, basic beliefs, basic values and strategy. The next phase, AMC has developed a strategic plan that includes strategic goal, target and strategic initiatives. The strategic targets have been formulated considering the financial perspective, customer perspective, business process perspective and learning and growth perspective. AMC has to define planning into work program with key performance indicators that can be measured and in accordance with performance standards for each medical center.

Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROCPyTorch_EHR = 0.911, AUROCLR = 0.857, AUROCLGBM = 0.892). External validation with 393,713 patient events from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset in Boston confirms its superior accuracy (AUROCPyTorch_EHR = 0.859, AUROCLR = 0.816, AUROCLGBM = 0.838). Our model effectively stratifies patients into high-, medium-, and low-risk categories, potentially optimizing antimicrobial therapy and reducing unnecessary MRSA-specific antimicrobials. This highlights the advantage of deep learning models in predicting MRSA positive cultures, surpassing traditional machine learning models and supporting clinicians’ judgments.

Perceptions of University Lecturers and Students Regarding the Manifestation of Life Values among Pedagogical Students from Vietnam

In the dynamic landscape of education, the exploration of life values has emerged as a cornerstone of personal and societal development. This study delves into the shared perceptions of life values between university lecturers and pedagogical students in Vietnam. It seeks to understand the depth of their agreement and the potential ramifications for pedagogical practices. Additionally, this research investigates the multifaceted manifestations of life values among pedagogical students, as perceived by both students and lecturers. A questionnaire was meticulously designed, featuring two vital components: (1) the conceptual awareness of “life value”, and (2) manifestations of life values of pedagogical students. A diverse array of participants, consisting of 816 pedagogical students and 98 lecturers from prominent institutions, was involved in the survey. The collected data underwent rigorous mathematical and statistical analysis. Descriptive and inferential statistical methods were employed, unveiling compelling insights. The perceptions of life values among participants underscored their significance. Both students (86.0%) and lecturers (78.6%) held the perspective that life values encompassed meaningful and beneficial elements. Furthermore, their convergence on the importance of life values in guiding various life aspects is evident. Regarding the manifestations of life values among pedagogical students, nuanced perspectives emerged. Students displayed attributes such as self-care, positive attitudes, willingness to help, and emotional control. While this study offers valuable insights, limitations such as sample specificity and self-reported data bias should be acknowledged. Nevertheless, it lays the foundation for understanding the role of life values in education and informs potential interventions to enrich the educational journey for students and educators alike.
 
 Received: 27 October 2023 / Accepted: 23 February 2024 / Published: 5 March 2024

Challenges in data-based reactor modeling: A critical analysis of purely data-driven and hybrid models for a CSTR case study

In this study, we critically examine the performance of hybrid and purely data-driven models in reactor systems using catalytic CO2 methanation in a continuously stirred tank reactor as a representative case. Our comparative analysis includes four models: one purely data-driven model and three hybrid models. These hybrid models blend data-driven and mechanistic approaches, using data-driven submodels for specific process parts and data correction for mechanistic inaccuracies. The models are evaluated on simulated data to assess their accuracy, training effort, and reliability. Our results show that hybrid models do not consistently outperform the purely data-driven model. This highlights the need for careful model selection, taking into account the specifics of the problem. The choice between hybrid and pure data-driven models requires a balanced evaluation of effort and potential benefits, emphasizing the importance of systematic analysis in model selection.

Research On the Uneven Distribution of Educational Resources in China

Taking the current situation of education in China as the starting point, firstly this paper points out the healthy changes such as educational reform. It focuses on the analysis of the uneven distribution of educational resources in China's current education, which focuses on the uneven distribution of educational resources in the eastern and western regions and the uneven development of education in urban and rural area. Finally, the article concludes the imbalance of China's education development through the analysis of specific data such as the average salary of high school teachers and the overall national education expenditure. One of the most intuitive problems caused by the difference in educational resources is the huge difference in the illiteracy rate between the east and the west of China, with the illiteracy rate in most eastern provinces being less than 7%, while that in some western provinces is as high as 40%. It also puts forward suggestions for increasing investment in education expenditure, increasing teachers' salaries, and strengthening supervision.

Data Protection Issues in Automated Decision-Making Systems Based on Machine Learning: Research Challenges

Data protection issues stemming from the use of machine learning algorithms that are used in automated decision-making systems are discussed in this paper. More precisely, the main challenges in this area are presented, putting emphasis on how important it is to simultaneously ensure the accuracy of the algorithms as well as privacy and personal data protection for the individuals whose data are used for training the corresponding models. In this respect, we also discuss how specific well-known data protection attacks that can be mounted in processes based on such algorithms are associated with a lack of specific legal safeguards; to this end, the General Data Protection Regulation (GDPR) is used as the basis for our evaluation. In relation to these attacks, some important privacy-enhancing techniques in this field are also surveyed. Moreover, focusing explicitly on deep learning algorithms as a type of machine learning algorithm, we further elaborate on one such privacy-enhancing technique, namely, the application of differential privacy to the training dataset. In this respect, we present, through an extensive set of experiments, the main difficulties that occur if one needs to demonstrate that such a privacy-enhancing technique is, indeed, sufficient to mitigate all the risks for the fundamental rights of individuals. More precisely, although we manage—by the proper configuration of several algorithms’ parameters—to achieve accuracy at about 90% for specific privacy thresholds, it becomes evident that even these values for accuracy and privacy may be unacceptable if a deep learning algorithm is to be used for making decisions concerning individuals. The paper concludes with a discussion of the current challenges and future steps, both from a legal as well as from a technical perspective.

Feasibility of quality indicators on prehospital advanced airway management in a physician-staffed emergency medical service: survey-based assessment of the provider point of view

ObjectiveWe aimed to determine the feasibility of quality indicators (QIs) for prehospital advanced airway management (PAAM) from a provider point of view.DesignThe study is a survey based feasibility assessment following...

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

Searchable Blockchain-Based Healthcare Information Exchange System to Enhance Privacy Preserving and Data Usability.

Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.

Research on New Electric Power System Risk Assessment Based on Cloud Model

A large number of new energy power electronic equipment is connected to the new power system, and the high proportion of new energy brings huge volatility and randomness, so it is necessary to establish a systematic risk assessment system to adapt to the new power system. This paper has improved the neglect of environmental benefits and supply and demand balance in the existing indicator system and established an indicator system for the risk of the new power system from the five perspectives of safety, supply and demand, sufficiency, cleanliness, and flexibility through the analysis of the evaluation indicator system of the large power grid and the influencing factors of the new power system. The index weight of a new power system is established by means of the entropy weight–critic combination weighting method, and the comprehensive evaluation is carried out by combining it with the cloud model. Finally, the specific data of the new power system in Fujian Province from 2020 to 2022 are taken as an example to verify the feasibility of combining the combined weighting method and the cloud model, and it is concluded that the new power system in Fujian Province has been in a “low risk” operation state for the recent three years.

Identifying failure types in cyber-physical water distribution networks using machine learning models

Abstract Water cyber-physical systems (CPSs) have experienced anomalies from cyber-physical attacks as well as conventional physical and operational failures (e.g., pipe leaks/bursts). In this regard, rapidly distinguishing and identifying a facing failure event from other possible failure events is necessary to take rapid emergency and recovery actions and, in turn, strengthen system's resilience. This paper investigated the performance of machine learning classification models – support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) – to differentiate and identify failure events that can occur in a water distribution network (WDN). Datasets for model features related to tank water levels, nodal pressure, and water flow of pumps and valves were produced using hydraulic model simulation (WNTR and epanetCPA tools) for C-Town WDN under pipe leaks/bursts, cyber-attacks, and physical attacks. The evaluation of accuracy, precision, recall, and F1-score for the three models in failure type identification showed the variation of their performances depending on the specific failure types and data noise levels. Based on the findings, this study discussed insights into building a framework consisting of multiple classification models, rather than relying on a single best-performing model, for the reliable classification and identification of failure types in WDNs.

Screening asthmatics for atopic status using the ALergy EXplorer (ALEX2) macroarray

Background Screening asthma patients for atopy facilitates management. Since 2010, the core biomarker for screening asthma subjects for atopic status has been the qualitative Phadiatop. multi-aeroallergen screen. A more quantitative macroarray, the Allergy Explorer (ALEX2), shows promise as an alternative. Objective The study’s goal was to examine the pros and cons of the use of ALEX2 in the screening of asthma patients for atopic status. Methods We evaluated the atopic (IgE-sensitization) status in asthmatic Amish and Hutterite farm children using the ImmunoCAP and ALEX2 assays in Phadiatop equivocal and positive subjects. Results All 42 asthmatic children were analyzed by Phadiatop and total serum IgE. Of these, 22 had a negative Phadiatop (<0.1 kUa/L) and total IgE <100 kU/L which defined them as non-atopic and they were excluded from ALEX2 testing. Of six children with equivocal Phadiatops (0.1–0.2 kUa/L-Group 1) and three children with a negative Phadiatop but total IgE >100 kUa/L (group 3), 44% (n = 4) had detectable IgE antibody by ALEX2 to mite, tree pollen, and other allergens not detected by Phadiatop, but confirmed by allergen-specific ImmunoCAP testing. In 11 Phadiatop positive subjects (>0.2 kUa/L–group 2), all but one were positive by ALEX2. IgE antibody specific for mold and rabbit aeroallergens matched their agricultural and pet exposure history. Three children were positive for IgE antibody to allergens in the profilin, nsLTP, or PR-10 cross-reactive protein families. Conclusion Judicious use of ALEX2’s enhanced specificity data not provided by the Phadiatop can aid in the interpretation of sensitization patterns and planning management of atopic asthmatics, but sensitization relevance must be confirmed by the patient’s clinical history.

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process.

The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment's operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R2) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

Who cares about data? Data care arrangements in everyday organisational practice

ABSTRACT The increasing datafication of social life has led to a growing body of research on data work which focusses on new data practices like self-tracking, new professions like data analysts or new occupational roles. In these new types of data work, people develop strong affective relations to data, and caring for data (quality) is key. This attention to ‘spectacular’ data work however leads to an important oversight: Much of the data work in everyday organisational practice is done by workers ‘somehow’, in addition to and alongside their existing tasks. This article sets out to better understand this mundane data work. It asks: Why and how do people in organisations care for data? Based on two qualitative case studies, we present the concept of data care arrangements. Data care arrangements are configured through the ascription of values to (specific) data sets and the work of generating, maintaining, and repairing data. This data care work is not necessarily homogeneous in organised settings but can become stabilised in data care arrangements. Thus, the notion of data care arrangements underlines that data in everyday organisational practice are not an object of care per se, but that data care is an accomplishment.

Do Women and Minority Orthopaedic Residents Report Experiencing Worse Well-being and More Mistreatment Than Their Peers?

Do Women and Minority Orthopaedic Residents Report Experiencing Worse Well-being and More Mistreatment Than Their Peers?

Many nonnormalities, one simulation: Do different data generation algorithms affect study results?

Monte Carlo simulation studies are among the primary scientific outputs contributed by methodologists, guiding application of various statistical tools in practice. Although methodological researchers routinely extend simulation study findings through follow-up work, few studies are ever replicated. Simulation studies are susceptible to factors that can contribute to replicability failures, however. This paper sought to conduct a meta-scientific study by replicating one highly cited simulation study (Curran et al., Psychological Methods, 1, 16-29, 1996) that investigated the robustness of normal theory maximum likelihood (ML)-based chi-square fit statistics under multivariate nonnormality. We further examined the generalizability of the original study findings across different nonnormal data generation algorithms. Our replication results were generally consistent with original findings, but we discerned several differences. Our generalizability results were more mixed. Only two results observed under the original data generation algorithm held completely across other algorithms examined. One of the most striking findings we observed was that results associated with the independent generator (IG) data generation algorithm vastly differed from other procedures examined and suggested that ML was robust to nonnormality for the particular factor model used in the simulation. Findings point to the reality that extant methodological recommendations may not be universally valid in contexts where multiple data generation algorithms exist for a given data characteristic. We recommend that researchers consider multiple approaches to generating a specific data or model characteristic (when more than one is available) to optimize the generalizability of simulation results.