Merging Data Research Articles

Genetic overlap and causal inferences between diet-derived antioxidants and small-cell lung cancer.

Several studies have reported that antioxidants exert both preventive and inhibitory effects against tumors. However, their causal effects on small-cell lung cancer (SCLC) remain controversial. Herein, we explored the causal effects of 6 antioxidants on SCLC by combining a genome-wide association study database and the Mendelian randomization (MR) approach. We obtained antioxidant genetic variance data for 6 exposure factors: carotene, vitamin A (retinol), selenium, zinc, vitamin C, and vitamin E, from the genome-wide association study database. The instrumental variables for exposure factors and SCLC outcomes were integrated by screening instrumental variables and merging data. Two-sample MR was used to analyze the causal relationship between exposure and outcomes. Finally, we examined the heterogeneity and horizontal pleiotropy of the MR analysis by performing multiple sensitivity analyses. We found a causal relationship between carotene and SCLC using two-sample MR analysis and sensitivity analysis (P = .02; odds ratio = 0.73; 95% confidence interval: 0.55-0.95). In contrast, there was no causal relationship between other examined antioxidants and SCLC. We found that diet-derived circulating antioxidants could afford protection against SCLC, and carotene is the causal protective factor against SCLC.

Merging Ontologies and Data from Electronic Health Records

The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired. This article presents an approach that accurately relates data in EHRs to ontologies in the medical realm. Thanks to ontologies, clinicians can be assisted when writing or analysing health records, e.g., our solution promptly suggests rigorous definitions for scientific terms, and automatically connects data spread over several parts of EHRs. The first step of our approach consists of converting selected data and keywords from several EHR formats into a format easier to parse, then the second step is merging the extracted data with specialised medical ontologies. Finally, enriched versions of the medical data are made available to professionals. The proposed approach was validated by taking samples of medical records and ontologies in the real world. The results have shown both versatility on handling data, precision of query results, and appropriate suggestions for relations among medical records.

Accuracy Enhancement of Orthoimage Generation for Urban Areas

This research addresses the problems of conventional orthoimages created from aerial imagery of urban areas, especially relief displacements, occlusions and information loss caused by man-made objects. An approach is presented to generate more accurate orthoimages based on elevation models that exactly describe the entire surface of the observed region. The approach utilizes the digital building model, in addition to the digital terrain model, in the orthorectification process. Occluded areas are identified and filled in the final orthoimage by merging data from conjugate images. The data used in the research consists of a small block of high resolution aerial images covering an urban area. The images are captured recently with the digital aerial camera UltraCam-D. The image pixel size is 9 m by 9 m yielding a nearly 10-cm ground sample distance. The results reached by applying the proposed approach on the test images have been promising. They have proven the validity of the approach in generating more proper orthoimages.

Air conditioning compressor crankshaft roundness measurement based on a laser profiler

Crankshafts are key components of air conditioning compressors. The good roundness of a crankshaft has positive effects on the operating stability, service life, working efficiency, and noise radiation of the compressor. This study proposes a line point cloud data merging method for measuring crankshaft roundness using a laser profiler. First, a crankshaft roundness measurement system for air conditioning compressors is developed, and a high-accuracy 2D laser displacement sensor is employed to collect the line point cloud data. Second, the steps of the merging method are divided into two parts: (1) the radial merging to eliminate the effects of eccentricity; (2) the circumferential merging to remove overlapping data points of two adjacent profiles. Then, a pin gauge is used to select the point cloud region of the profile line for data merging. Experimental results show that the measurement repeatability of the proposed system can be controlled at the micrometer level. Besides, the roundness measured from the merged data is very close to the roundness measuring instrument measurements, which verifies the accuracy of the measurement system and the validity of the line point data merging algorithm.

Seafloor morphology and potential gas hydrate distribution in the offshore Niger Delta

Bottom simulating reflectors (BSRs) and seismic pipe features have been used as proxies for defining the distribution of gas hydrate sediments in the offshore Niger Delta. This is the most extensive mapping of gas hydrate sediments in the Delta as of today. The seismic data merge comes from multiple surveys acquired with different parameters and seismic resolutions over the course of decades of oil and gas exploration in the region. Indicated gas hydrate distribution generally follows the structural fabric of the Niger Delta with BSRs occurring along the apexes of the thrust-related ridges that have bathymetric relief on the seafloor. The presence of swarms of seismic pipe features landwards of BSR locations suggests hydrates occur beyond BSR locations. The potential gas hydrates sediment acreage in offshore Niger Delta is 17600 sq-km, representing 20% of the area with a thickness of the gas hydrate stability zone reaching 440 m in the more outboard regions of the Delta. Total gas hydrates sediment coverage likely exceeds this value as BSRs become indistinguishable from sediment strata in regions of flat dips. The presence of double BSRs further suggests the presence of thermogenic gas hydrates in the region and allows to extend the thickness of the potential hydrate zone to 550 m in the outboard regions of the Delta.

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.

Fuzzy Cash Flow Analysis of Fluctuations in Interest Rates at the State Bank of Pakistan using Present Worth Criterion

A systematic approach is required to analyze present worth by incorporating annual interest rate fluctuations. This is a key factor in attracting local and foreign investors and maintaining a level of economic advancement. This study primarily focuses on the domain of the State Bank of Pakistan (SBP) and contributes towards achieving three major objectives. The first objective includes modeling interest rate values as triangular fuzzy numbers, and using the Analytic Hierarchy Process (AHP), a Multi-criteria Decision-Making technique (MCDM). Second, the triangular fuzzy interest rates are forecasted over a future timespan. Subsequently, the last objective includes the use of the Present Worth Analysis (PWA) to calculate the final net present value. The results demonstrate a trend in interest rates with net present values of specific future years. This research is a good initiative for the SBP and helpful for both foreign and local investors. The study is novel in that it involves the merging of qualitative and quantitative data. Furthermore, the combination of the forecasting technique, the AHP, fuzzy set theory, and present worth criteria renders a unique approach to formulating present worth. There is no previous research of this magnitude that has been conducted on this topic in a developing country such as Pakistan. This study delves into the annual interest rate fluctuations, providing valuable insights for present worth calculations.

Examining Treatment Attendance Dynamics: Role of Immigrant Status and Neighborhood Factors

In the United States, where one in every seven residents identifies as an immigrant, this study explores the impact of both individual and neighborhood-level factors on treatment attendance, with a particular emphasis on immigrant status. The aim is to identify the drivers and obstacles contributing to disparities in treatment attendance. Hierarchical Linear Modeling (HLM) was employed to integrate individual and neighborhood-level variables and to assess interactions between immigrant status and neighborhood characteristics. Neighborhood factors like Neighborhood Immigrant Density and Neighborhood Concentrated Disadvantage did not show a significant association with treatment attendance. However, immigrant status was significantly associated with higher attendance rates, a trend that persisted even after controlling for other individual and neighborhood factors. Age, identification as Black, and preference for the English language also emerged as significant influences on attendance. This study utilized secondary data merging publicly available census information with electronic medical records (EMRs) from an integrated care clinic. Data integration was achieved using ArcGIS software. The findings underscore the predictive role of immigrant status in treatment attendance, shedding light on the potential challenges immigrants face. This research improves our understanding of the relationship between treatment attendance and both individual and neighborhood factors, guiding healthcare providers in creating targeted interventions for equal treatment access. Although neighborhood attributes yielded no significant associations, the study underscores the need for further exploration. Ultimately, these findings equip healthcare providers with insights into diverse influences on attendance, contributing to efforts aimed at addressing health disparities.

Semantic segmentation of urban land classes using a multi-scale dataset

ABSTRACT The use of remote sensing imagery for land cover and land use classification has made significant advancements in recent years. However, it becomes particularly challenging to enhance the semantic representation of high-resolution networks while dealing with uneven land categories and merging multi-scale data without compromising the accuracy of semantic segmentation. To tackle this challenge, this paper presents a novel method for classifying high-resolution remote sensing images based on a deep neural network that performs semantic segmentation of urban construction lands into five categories: vegetation, water, buildings, roads, and bare soil. The network incorporates a U-shaped high-resolution neural network and the advanced high-resolution network (HRNet) framework. The parallel storage of feature maps with different resolutions enables the exchange of information between them. The data pre-processing module addresses the issue of data imbalance in the semantic segmentation of urban construction lands, resulting in an increase in Intersection over Union (IoU) values for different land types by 3.75%-12.01%. Additionally, a target context representation module is introduced to enhance the feature representation of pixels by calculating the relationship between pixels and multiple target regions. Moreover, a polarization attention mechanism is proposed to extract the characteristics of geographical objects in all directions and achieve a stronger semantic representation. This method provides a novel approach to accurately and effectively extract information on construction lands and advance the development of monitoring algorithms for urban construction lands. To validate the proposed U-HRNet-OCR+PSA network, a comparative analysis was conducted with six classical networks, including DeepLabv3+, PSPNet, U-Net, U-Net++, HRNet, and HRNet-OCR, as well as the relatively new ViT-adapter-L, Oneformer and InternImage-H. The experiments demonstrate that the U-HRNet-OCR+PSA network achieves higher accuracy compared to the aforementioned networks. Specifically, the corresponding IoU values for the buildings, roads, vegetation, bare soil, and water in the multi-scale dataset are 89.79%, 90.05%, 94.89%, 85.91%, and 88.36%, respectively.

Evaluasi Pembelajaran Nontes Dalam Mata Pelajaran Akhlak di SMA MUH 1 Karanganyar

This research aims to determine the implementation of non-test evaluation on Akhlak learning at SMA MUH 1 Karanganyar. The method used is a qualitative method (qualitative description) with the aim of describing, analyzing phenomena, events and evaluating Aqidah Moral learning activities carried out by teachers for class 11 students at SMA MUH 1 Karanganyar. The data collection techniques used were observation, interviews with teachers, interviews with school principals, documentation, field notes, and triangulation. Triangulation is a data collection technique that combines several existing data merging techniques data source. The results of the research show that in general the quality of Akhlak learning at MUH 1 Karanganyar Vocational School has been running well. Likewise, the learning outcomes are also good results. However, in implementing this learning there are also things that need to be done aimed at improving learning programs.

Merging weather radar data and opportunistic rainfall sensor data to enhance rainfall estimates

Accurate rainfall estimates with high spatiotemporal resolution are vital for urban drainage applications, and if adjusted to ground observations, weather radar data has potential for these applications. The density of meteorological rain gauges for adjustment is, however, often sparse and non-uniformly distributed in space. Opportunistic rainfall sensors provide an increased density of ground observations but often with reduced or unknown accuracy for each individual station. This paper demonstrates the merging of data from weather radar, personal weather stations, and commercial microwave links into an integrated rainfall product. Merging opportunistic rainfall estimates is shown to improve the accuracy of the opportunistic rainfall observations through a quality control algorithm. In this study, we show that the accuracy of rainfall estimates is significantly improved by merging opportunistic rainfall data and weather radar data when compared to the accuracy of each rainfall product without merging. Nash-Sutcliffe efficiency (NSE) values up to 0.88 are obtained for daily accumulated merged rainfall products, while the NSE-values of the individual rainfall products range from −7.44 to 0.65, and similar tendencies are observed for root mean squared error (RMSE) values. For merging weather radar and opportunistic rainfall data, a novel approach, i.e., “moving median bias adjustment” is presented. Applying this approach, a high-performing rainfall product is derived independently from conventional high-quality rain gauges, which in this study are only used for independent validation. In addition, it is demonstrated that accurate rainfall estimates can be obtained by sub-daily merging, underscoring the importance of merging in nowcasting and near real-time applications.

Do local governments tax homeowner communities differently?

AbstractThis article investigates whether and how strongly the share of homeowners in a community affects residential property taxation by local governments. Different from renters, homeowners bear the full property tax burden, irrespective of local market conditions, and the tax is more salient to them. “Homeowner communities” may hence oppose high property taxes in order to protect their housing wealth. By merging granular spatial data from a complete housing inventory in the 2011 German Census with historical homeownership rates and housing damages during the Second World War as sources of exogenous variation in local homeownership, we provide empirical evidence that otherwise identical jurisdictions charge significantly lower property taxes when the share of homeowners in their population is higher. This result is invariant to local market conditions, which suggests tax salience is the key mechanism behind this effect. Moreover, we find positive spatial dependence on tax multipliers, indicative of property tax mimicking by local governments.

Volatility, Uncertainty, and Option Pricing

Amidst global and Chinese uncertainties, this paper delves into stock market volatility and option pricing within the current economic policy context. Focusing on China's stock market, it calculates returns and volatility changes in key indices, analyzing differences among indices and funds, along with temporal variations. Shared data characteristics and reflections on new market trends emerge. Employing methods such as the GARCH model, Black-Scholes formula, and Monte Carlo algorithm, it analyzes volatility and option pricing using extensive annual and monthly data. Visualizations showcase market change patterns. The study defines volatility as a measure of financial asset price fluctuation extent, reflecting asset risk. Higher volatility indicates pronounced price fluctuations and uncertainty, while lower volatility signifies smoother fluctuations and greater certainty. Merging data with volatility's significance, the study probes China's securities market uncertainty, investigating the link between option pricing and volatility. It concludes by identifying the connection between volatility, uncertainty, and option pricing, pointing to future research directions and challenges. Future work will track the latest market trends to enrich understanding.

Certified Registered Nurse Anaesthetists’ and Critical Care Registered Nurses’ perception of knowledge/power in teamwork with Anaesthesiologists in Sweden: a mixed-method study

Efficient teamwork is crucial to provide optimal health care. This paper focuses on teamwork between Anaesthesiologists (ANES), Certified Registered Nurse Anaesthetists’ (CRNA) and Critical Care Registered Nurses (CCRN) working in challenging environments such as the intensive care unit (ICU) and the operating room (OR). Conflicts are common between physicians and nurses, negatively impacting teamwork. Social hierarchies based on professional status and power inequalities between nurses and physicians plays a vital role in influencing teamwork. Foucault was a famous thinker especially known for his reasoning regarding power/knowledge. A Foucauldian perspective was therefore incorporated into this paper and the overall aim was to explore CCRN/CRNA perception of knowledge/power in teamwork with ANES.Methods A mixed-method approach was applied in this study. Data was collected using a web-based questionnaire containing both closed-end and open-ended questions. A total of 289 CCRNs and CRNAs completed the questionnaire. Data analysis was then conducted through five stages as outlined by Onwuebugzie and Teddlie; analysing quantitative data in SPSS 27.0 and qualitative data with a directed content analysis, finally merging data together in ATLAS.ti v.23.Results The result reveals a dissonance between quantitative and qualitative data; quantitative data indicates a well-functioning interdisciplinary teamwork between CCRN/CRNA and ANES - qualitative data highlights that there are several barriers and inequalities between the two groups. Medicine was perceived as superior to nursing, which was reinforced by both social and organisational structures at the ICU and OR.Conclusion Unconscious rules underlying current power structures in the ICU and OR works in favour of the ANES and biomedical paradigm, supporting medical knowledge. To achieve a more equal power distribution between CCRN/CRNAs and ANES, the structural hierarchies between nursing and medicine needs to be addressed. A more equal power balance between the two disciplines can improve teamwork and thereby reduce patient mortality and improve patient outcomes.

Revolutionizing Data Management through Cloud-Based Data Fusion Platforms in Distributed Network Architectures

Data management is developing rapidly, and we need solutions that can handle massive volumes of diverse data. Especially for cloud-based data fusion and global network designs. Our research offers a fresh solution. Each difficult formula in this manner improves the system. Standardizing, matching, translating, and merging data from several sources is the fundamental strategy for data integration and management. We found that this alternative is superior to standard data management systems for growing, working fast, consistently, securely, and accurately integrating data, as well as cost-effectiveness. Data's visual presentation enhances the method's advantages and shows its potential. This research proves the technique works and illustrates how it may be utilized to advance the field. Supporting today's sophisticated data systems is a major advance. It's a solid, scalable data management solution that can evolve.

A Model for Predicting IoT User Behavior Based on Bayesian Learning and Neural Networks

To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. The model is based on Bayesian learning and neural networks and is designed to provide insights into the future behavior of users, allowing for the allocation of resources in advance. In this paper, the data are preprocessed by data merging and format processing, and then the association rules are mined by association rules analysis. Finally, the data are utilized to train the behavioral prediction model of the short‐duration memory network via Bayesian optimization. The experimental results showed that the average running time of the research model was 1.682 s, the average accuracy was 96.77%, the average root‐mean‐square error was 0.382, and the average absolute error was 0.315. The designed behavior prediction model is capable of effectively predicting the user behavior of the Internet of Things, thereby enabling the reasonable allocation of energy and resources in the Internet of Things system.

A Reinforcement-Learning-based Parameter Tuning Methodology for Traction Control in the Holistic Railway Digital Twin System

Holistic railway digital twin systems can optimize railway performance by merging diverse models and data for monitoring, diagnostics, and prognostics. In such systems, traction control is critical to safe and efficient train operations. However, due to the dynamic and non-linear railway operations, achieving the optimal parameter configuration for traction control algorithms remains complex. Conventional approaches to parameter tuning often rely on experience-based manual adjustments or simple optimization algorithms, failing to capture the intricate interplay between various parameters and their impact on overall system performance. To address this, we present a novel methodology that leverages deep rein- forcement learning (RL) techniques to autonomously fine-tune traction control parameters within the holistic railway digital twin system. The core idea involves modeling the traction control in a co-simulation of a well-built multibody dynamic model and a traction controller. Through iterative learning, the RL agent refines its policy, converging toward parameter configurations that enhance both operational efficiency and safety. The RL- based tuning methodology shows significant traction control improvement over conventional methods. The RL-based methodology showcases its adaptability to varying conditions, which is essential for modern railway systems that operate in dynamic and unpredictable environments.

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

AbstractThe dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis.

The application of the propensity score matching method in stock prediction among stocks within the same industry.

Stock price prediction is crucial in stock market research, yet existing models often overlook interdependencies among stocks in the same industry, treating them as independent entities. Recognizing and accounting for these interdependencies is essential for precise predictions. Propensity score matching (PSM), a statistical method for balancing individuals between groups and improving causal inferences, has not been extensively applied in stock interdependence investigations. Our study addresses this gap by introducing PSM to examine interdependence among pharmaceutical industry stocks for stock price prediction. Additionally, our research integrates Improved particle swarm optimization (IPSO) with long short-term memory (LSTM) networks to enhance parameter selection, improving overall predictive accuracy. The dataset includes price data for all pharmaceutical industry stocks in 2022, categorized into chemical pharmaceuticals, biopharmaceuticals, and traditional Chinese medicine. Using Stata, we identify significantly correlated stocks within each sub-industry through average treatment effect on the treated (ATT) values. Incorporating PSM, we match five target stocks per sub-industry with all stocks in their respective categories, merging target stock data with weighted data from non-target stocks for validation in the IPSO-LSTM model. Our findings demonstrate that including non-target stock data from the same sub-industry through PSM significantly improves predictive accuracy, highlighting its positive impact on stock price prediction. This study pioneers PSM's use in studying stock interdependence, conducts an in-depth exploration of effects within the pharmaceutical industry, and applies the IPSO optimization algorithm to enhance LSTM network performance, providing a fresh perspective on stock price prediction research.

Geometric digital twins of long-living assets: uncertainty-aware 3D images from measurement and CAD data

Geometric digital twins (gDT) of long-living assets are formed from a multitude of different data sources like CAD or measurement data imaging as-designed to sensory-based acquired as-is states. Change detection techniques allow merging 3D data pairwise, deriving a geometric digital twin of the physical asset. However, positional uncertainties must be considered to account for the data’s reliability when facing different kinds of data sources. The contribution of this work provides the ability to specify (an-)isotropic positional uncertainty quantities for meshes and even sparse point clouds in advance, enabling uncertainty-aware change detection. In the case of sensory-based as-is data, uncertainty is defined by the measurement process, while in the case of as-designed CAD data, the uncertainty relates, e.g., to manufacturing tolerances. Instead of testing on equal means during point-wise comparison as in M3C2, we utilize the Bhattacharyya distance measure to quantify the (dis)similarity between correspondences during change detection.