Application Of Data Research Articles

Current Practice: Rationale for Screening Children with Hereditary Hemorrhagic Telangiectasia for Brain Vascular Malformations.

Hereditary hemorrhagic telangiectasia is an autosomal dominant vascular dysplasia characterized by mucocutaneous telangiectasias, recurrent epistaxis, and organ vascular malformations including in the brain, which occur in about 10% of patients. These brain vascular malformations include high-flow AVMs and AVFs as well as low-flow capillary malformations. High-flow lesions can rupture, causing neurologic morbidity and mortality. International guidelines for the diagnosis and management of hereditary hemorrhagic telangiectasia recommend screening children for brain vascular malformations with contrast enhanced MR imaging at hereditary hemorrhagic telangiectasia diagnosis. Screening has not been uniformly adopted by some practitioners who contend that screening is not justified. Arguments against screening include application of short-term data from the adult A Randomized Trial of Unruptured Brain Arteriovenous Malformations (ARUBA) trial of unruptured sporadic brain AVMs to children with hereditary hemorrhagic telangiectasia as well as concerns about administration of sedation or IV contrast and causing patients or families increased anxiety. In this article, a multidisciplinary group of experts on hereditary hemorrhagic telangiectasia reviewed data that support screening guidelines and counter arguments against screening. Children with hereditary hemorrhagic telangiectasia have a preponderance of high-flow lesions including AVFs, which have the highest rupture risk. The rupture risk among children is estimated at about 0.7% per lesion per year and is additive across lesions and during a lifetime. ARUBA, an adult clinical trial of expectant medical management versus treatment of unruptured brain AVMs, favored medical management at 5 years but is not applicable to pediatric patients with hereditary hemorrhagic telangiectasia given the life expectancy of a child. Additionally, interventional, radiosurgical, and surgical techniques have improved with time. Experienced neurovascular experts can prospectively determine the best treatment for each child on the basis of local resources. The "watch and wait" approach to imaging means that children with brain vascular malformations will not be identified until a potentially life-threatening and deficit-producing intracerebral hemorrhage occurs. This expert group does not deem this to be an acceptable trade-off.

Role and integration of image processing systems for maritime target tracking

In recent years, maritime traffic has increased, especially in seaborne trade. To ensure safety, security, and environmental protection, various systems have been deployed, often combining data for improved effectiveness. One key application of this combined data is tracking targets at sea, where the Automatic Identification System (AIS) and X-band marine radar are crucial. Recently, there has been growing interest in using visual data from cameras to enhance tracking. This has led to the development of several tracking algorithms based on image processing. While much of the existing literature addresses data fusion, there hasn’t been much focus on why integrating image processing systems is important given the existence of the other systems. In our paper, we aim to analyze these surveillance systems and highlight the benefits of integrating image processing systems. Our main goal is to show how this integration can improve maritime security, offering practical insights into enhancing safety and protection at sea.

Application of Big Data and Analytics to Increase Competitive Advantage

In the digital era the volume of data continues to increase rapidly, big data and analytics are important tools for companies to utilize the data and increase competitive advantage. The purpose of this research is to find out how big data and analytics can be applied to increase competitive advantage. This research uses qualitative research methods. The data collection technique in this research is a literature study. The data that has been collected is then analyzed in three stages, namely data reduction, data presentation and conclusion drawing. The results showed that big data and analytics are important tools for companies to increase competitive advantage in this digital era including through personalization of customer experience, operational optimization, and business strategy development. The implementation of big data and analytics is expected to have an impact on companies so that they can make better decisions, develop new products and services, improve operating efficiency, and strengthen relationships with customers.

Food Sovereignty Towards Rice Sustainability

Malaysia should adhere to the WTO’s policy on agrifood trade liberalization. Agrifood regulations are designed to ensure the protection of farmers and achieve food sovereignty. This research investigates and plots the evolution of agrifood laws that promote food sovereignty and trade liberalization towards rice sustainability. The qualitative study is based on the application of social jurisprudence theory and secondary data. The structure list in the current legislation is mapped to the regulations and standard operations applied in the matrix table. Malaysia’s agrifood regulations are found to reinforce the WTO policy of liberalizing trade for agriculture, which undermines food sovereignty and provides inadequate protection for farmers. The interdependence of supporting sustainable agriculture and empowering small farmers is recognized by SDG2, which aims to address hunger, food security, nutrition, and climate change. The government should provide policies that improve the management of the food system to achieve food sovereignty. A review of the Control of Padi and Rice Act 1994 is necessary for creating new provisions on ‘seed’, which includes a provision on food sovereignty for individuals working in the rice industry. The development of people in agrifood to support their global market can be explained in another provision.

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

Recreational agroecosystem service value evidenced by mobile phone data: implications for incentive enhancement in terraced paddy land

Summary Farmland abandonment contributes to agroecosystem degradation and food crises. Sustainable farmland use requires a well-designed agri-environmental policy to provide farmers with incentives, including agroecosystem services apart from food production. One of these is recreation. Here, we focus on a Japanese terraced paddy land. We assessed seasonal changes in the value of recreational ecosystem services by integrating mobile phone big data of on-site visitors, collected between 2018 and 2020, into a valuation method. The application of mobile data enables the precise and consistent analysis of non-market agroecosystem services. The recreational value of the paddy land varied with season but overall was high. Sustainable farmland use provides social benefits, and we support the validity of agri-environmental policies that relate to economic incentives for agroecosystem conservation. However, the results show that the incentives provided by the public/government may be insufficient in comparison to this recreational value. Our findings provide information regarding the appropriate amount of economic support required to achieve sustainable agricultural land use in this setting.

Critical Concerns of Deploying Blockchain in Internet of Things Applications

The Internet of Things (IoT) and blockchain are both recognized as cutting-edge, popular technologies. In contrast to IoT, which pertains to the spread of linked equipment via supplying information over the Internet, blockchain offers innovative data storage and management avenues. Even as blockchain demands real-time data application and IoT specifies mechanisms to securely store and handle data overflows, a combination of the two seems promising. Blockchain, a technology created with the cryptocurrency Bitcoin, could meet the needs of the IoT. However, combining blockchain with the IoT might present a multitude of issues due to the features of blockchain and IoT technology. Although many articles have been released on the blockchain and the IoT, the concerns with this combination are still vague and dispersed. In light of this, this study seeks to give an overview of the problems that have the biggest impact on blockchain-based IoT by reviewing the pertinent peer-reviewed articles. This article also addresses some suggestions for lessening the impact of these concerns. The study considered peer-reviewed articles published within the last five years, focusing on topics related to blockchain and IoT integration. We identified 44 articles for this review. The study's significance is that it not only expands the scholarly understanding of this complex intersection between blockchain and IoT but also provides actionable insights that can drive innovation and enhance the reliability and security of IoT ecosystems in practical settings.

Model selection for generalized linear models with weak factors

AbstractThe literature has witnessed an upsurge of interest in model selection in diverse fields and optimization applications. Despite the substantial progress, model selection remains a significant challenge when covariates are highly correlated, particularly within economic and financial datasets that exhibit cross‐sectional and serial dependency. In this paper, we introduce a novel methodology named factor augmented regularized model selection with weak factors (WeakFARM) for generalized linear models in the presence of correlated covariates with weak latent factor structure. By identifying weak latent factors and idiosyncratic components and employing them as predictors, WeakFARM converts the challenge from model selection with highly correlated covariates to that with weakly correlated ones. Furthermore, we develop a variable screening method based on the proposed WeakFARM method. Comprehensive theoretical guarantees including estimation consistency, model selection consistency and sure screening property are also provided. We demonstrate the effectiveness of our approach by extensive simulation studies and a real data application in economic forecasting.

Application of Data Mining to Measure the Effectiveness of the Islamic Boarding School’s Independent Curriculum based on Learning Achievement using the Clustering Method

The evolution of educational curricula has been a focal point for institutions aiming to enhance learning outcomes and adapt to students' diverse needs. In this context, Islamic boarding schools, or pesantren, are increasingly exploring independent curricula to better serve their students. This research aims to measure the effectiveness of the independent curriculum at the Al Binaa Bekasi Islamic Boarding School, especially regarding learning achievements in general and Islamic subjects. The method used is data mining clustering to analyze student learning achievement data. In the initial stage, the data collected includes student scores in general subjects (such as Islamic Religious Education, Pancasila Education, Indonesian, English, Mathematics, Science, Social Sciences, Arts, Sports, ICT, Sundanese) and Syar'i (Quran tajwid, hadith, aqidah, fiqh, Hadassah, short). Then, data mining clustering techniques are used to group students based on their achievements in the two subjects. The results of the analysis show that the independent curriculum at Al Binaa Islamic Boarding School effectively increases student learning achievement. The groups formed from data mining clustering show patterns consistent with curriculum objectives, where students in the same group have similar levels of achievement in general and star subjects. This indicates that the independent curriculum has succeeded in leveling student learning achievement. This research contributes to understanding the effectiveness of the independent curriculum in Islamic boarding schools. It can be a basis for further development in designing Islamic boarding school education curricula that are more adaptive and responsive to student needs.

Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.

A new extended Pham distribution for modelling cancer data

In this study, a novel extended Pham model is offered and examined. This model can fit positive, negative, and approximately symmetric data. In addition, it features four alternative hazard rate shapes, namely increasing, decreasing, bathtub, and upside-down bathtub shapes. The new model's main properties, such as quantiles, moments, and mean deviations, are derived. To evaluate the estimation of distribution parameters, two approaches are studied. From a classical standpoint, the maximum likelihood technique is used to determine the point and approximate confidence intervals of all unknown parameters. The Bayesian estimation method, on the other hand, is considered via the Markov chain Monte Carlo technique for obtaining Bayes point estimates alongside Bayes credible intervals. We present various Monte Carlo simulation studies to investigate the efficacy of maximum likelihood and Bayes estimation findings for the model's parameters. Numerical investigations showed that the offered Bayes estimates outperformed those derived using the conventional method. We also analyzed two sets of real data taken from the medical sector; the first represents survival rates for cancer patients who received a combination of radiotherapy and chemotherapy, and the second consists of remission times for bladder cancer patients. These clinical scenarios reveal that the new model outperforms other important competing models based on specific statistical criteria. Ultimately, for both clinical data applications, the proposed extended Pham distribution is the best choice compared to the traditional Pham and the other well-known eight lifetime models.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

Study of Violence Against Women and its Characteristics Using Application of Data Mining Techniques

Study of Violence Against Women and its Characteristics Using Application of Data Mining Techniques

Discovering Latent Topics and Trends in Digital Technologies and Disaster Management Research: A Structural Topic Modeling Approach

The application of Digital Technologies (DTs) in disaster management has recently received much attention. It is imperative to summarize the existing literature in this area to identify the key research topics and possible future directions. Our study is the first of its kind to use an efficient, scalable, data-driven review approach using structural topic modeling to identify the links between DTs and disaster management. The paper has analyzed peer-reviewed Scopus-indexed journal articles on DTs and disaster management published between 2011 and 2023. Nine key research topics were identified, including topics such as technology awareness and education in disaster management, Use of social media in crisis communication, Disaster management interventions through autonomous systems, Communication networks and data applications in disasters, and Disaster management modelling. The topics and future directions represent areas where research and exploration make an important contribution to disaster management by leveraging DTs for better preparedness, response, and recovery in crisis situations.

The powerful lens galaxy cluster PLCK G287.0+32.9 (θE ∼ 43″)

Aims. We present a new high-precision strong-lensing model of PLCK G287.0+32.9, a massive lens galaxy cluster at z = 0.383, with the aim of obtaining an accurate estimation of its effective Einstein radius and total mass distribution. We also present a spectroscopic catalog containing accurate redshift measurements for close to 500 objects up to redshift z = 6, including multiply lensed sources and cluster member galaxies. Methods. We exploited high-quality spectroscopic data from the Multi Unit Spectroscopic Explorer (MUSE), covering a central 3 arcmin2 region of the cluster. We supplemented the spectroscopic catalog by including redshift measurements from VIsible MultiObject Spectrograph (VIMOS) and DEep Imaging Multi-Object Spectrograph (DEIMOS). We identified 129 spectroscopic cluster member galaxies with redshift values of 0.360 ≤ z ≤ 0.405, and mF160W ≤ 21. We complemented this galaxy cluster member sample with 24 photometric members identified with a convolutional neural network (CNN) approach. We also identified 114 multiple images from 28 background sources, of which 84 images from 16 sources are new and the remaining ones have already been identified in previous works. From these, we extracted “golden sample” of 47 secure multiple images and used them, together with the selected cluster member, to build and optimize several strong-lensing models with the software lenstool. Results. The best-fitting lens model shows a root mean square (RMS) separation value between the predicted and observed positions of the multiple images of 0.″75. Using its predictive power, we found three new multiple images and we confirm the configuration of three systems of multiple images that were not used for the optimization of the model. For a source at a redshift of zs = 2, we found a cluster with an Einstein radius of θE = 43.4″ ± 0.1″. This value is in agreement with previous estimates and corresponds to a total mass enclosed in the critical curve of ME = 3.33−0.07+0.02 × 1014 M⊙. Conclusions. The combined application of ancillary Hubble Space Telescope (HST) imaging, VIMOS and DEIMOS data, and the new MUSE spectroscopic observations allowed us to build a new lens model of the galaxy cluster PLCK G287.0+32.9, with an improvement in terms of reconstructing the observed positions of the multiple images of a factor of 2.5 with respect to previous models. The derived total mass distribution confirms this cluster to be a very prominent gravitational lens, with an effective Einstein radius of θE ∼ 43″. We were also able to construct an extensive spectroscopic catalog containing 490 objects, of which 153 are bright cluster members with mF160W ≤ 21, and 114 are multiple images.

Genomic surveillance during the first two years of the COVID-19 pandemic – country experience and lessons learned from Türkiye

BackgroundTürkiye confirmed its first case of SARS-CoV-2 on March 11, 2020, coinciding with the declaration of the global COVID-19 pandemic. Subsequently, Türkiye swiftly increased testing capacity and implemented genomic sequencing in 2020. This paper describes Türkiye’s journey of establishing genomic surveillance as a middle-income country with limited prior sequencing capacity and analyses sequencing data from the first two years of the pandemic. We highlight the achievements and challenges experienced and distill globally relevant lessons.MethodsWe tracked the evolution of the COVID-19 pandemic in Türkiye from December 2020 to February 2022 through a timeline and analysed epidemiological, vaccination, and testing data. To investigate the phylodynamic and phylogeographic aspects of SARS-CoV-2, we used Nextstrain to analyze 31,629 high-quality genomes sampled from seven regions nationwide.ResultsTürkiye’s epidemiological curve, mirroring global trends, featured four distinct waves, each coinciding with the emergence and spread of variants of concern (VOCs). Utilizing locally manufactured kits to expand testing capacity and introducing variant-specific quantitative reverse transcription polymerase chain reaction (RT-qPCR) tests developed in partnership with a private company was a strategic advantage in Türkiye, given the scarcity and fragmented global supply chain early in the pandemic. Türkiye contributed more than 86,000 genomic sequences to global databases by February 2022, ensuring that Turkish data was reflected globally. The synergy of variant-specific RT-qPCR kits and genomic sequencing enabled cost-effective monitoring of VOCs. However, data analysis was constrained by a weak sequencing sampling strategy and fragmented data management systems, limiting the application of sequencing data to guide the public health response. Phylodynamic analysis indicated that Türkiye’s geographical position as an international travel hub influenced both national and global transmission of each VOC despite travel restrictions.ConclusionThis paper provides valuable insights into the testing and genomic surveillance systems adopted by Türkiye during the COVID-19 pandemic, proposing important lessons for countries developing national systems. The findings underscore the need for robust testing and sampling strategies, streamlined sample referral, and integrated data management with metadata linkage and data quality crucial for impactful epidemiological analysis. We recommend developing national genomic surveillance strategies to guide sustainable and integrated expansion of capacities built for COVID-19 and to optimize the effective utilization of sequencing data for public health action.

A comparative study on hydrocarbon detection using cepstrum–based methods

Amplitude anomaly caused by a high–frequency component is attenuated more rapidly than a low–frequency component for a seismic trace in sediments susceptible to gas influence and is always used as a direct indicator of hydrocarbons. Recently, cepstrum–based hydrocarbon detection methods have become an important exploration pathway for detecting amplitude anomalies in the gas–bearing formation more sensitively and accurately. In this study, we compared three cepstrum–based hydrocarbon detection methods. We compared their fluid identification abilities in gas reservoirs and the noise robustness. They were further compared with the traditional absorption coefficient estimation method. We also indicate how the choice of the selection of the sliding–window length influences the performance of the three cepstrum–based hydrocarbon detection methods. Model test results and real data applications show that for thick gas reservoirs, the hydrocarbon detection methods using the Berthil–based cepstrum and the wavelet–based cepstrum can better discriminate the upper and lower interfaces than the hydrocarbon detection method using the Fourier–based cepstrum. For thin gas reservoirs, the hydrocarbon detection method using the wavelet–based cepstrum can identify the lower interface of the gas–bearing reservoir more obviously than the upper interface. The hydrocarbon detection method using the Berthil cepstrum cannot identify the upper and lower interfaces of the gas–bearing reservoir, but it shows higher temporal and spatial resolution compared to the hydrocarbon detection method using the Fourier–based cepstrum. For noise robustness, the hydrocarbon detection method using the wavelet–based cepstrum has the strongest noise robustness property, and the hydrocarbon detection method using the Fourier–based cepstrum is the most sensitive method to the noise. For time–consumption, the hydrocarbon detection method using the wavelet–based cepstrum is the most time–consuming, and the hydrocarbon detection method using the Fourier–based cepstrum is the fastest.

Type I Tobit Bayesian Additive Regression Trees for censored outcome regression

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore, the error is flexibly modeled as a Dirichlet process mixture of normal distributions. An R package is available at https://github.com/EoghanONeill/TobitBART.

Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol.

With the advancement of computer-based testing, log file data has drawn considerable attention from researchers. Although emerging studies have begun to explore log file data, there is a gap in the exploitation of log file data for capturing and understanding participants' cognitive processes. The debate on how to maximize insights from log file data has not yet reached a consensus. Therefore, we present this protocol for a scoping review that aims to characterize the application of log file data in current publications, including the data pre-processing techniques, analytical methodologies, and theoretical frameworks used by researchers. This review will also aim to illuminate how log file data can enhance psychological and educational assessments. Our findings will highlight the opportunities and challenges presented by log file data as an emerging and essential source of evidence for future advancements in psychological and educational assessment.

Talent Training Optimization for Surveying and Mapping Majors in Higher Vocational Colleges Under the Background of Production-Education Integration

In recent years, the surveying and mapping geographic information industry has experienced rapid expansion. The application scenarios for “pan-surveying and mapping” have diversified, and advancements in aerial surveying equipment technology have raised the requirements for data application and expression skills among staff. This has created an urgent need for a team of highly skilled and practically capable surveying and mapping professionals. Higher vocational colleges, as the primary institutions for training surveying and mapping professionals, must align with industry development trends and proactively collaborate with enterprises. Building a training model that integrates production and education is essential to enhancing the quality of surveying and mapping professional training. This paper explores how to optimize the training mode for surveying and mapping professionals in higher vocational colleges within the context of this production and education integration.