For years, effort has been devoted to establishing an effective bull breeding soundness evaluation procedure; usual research on the subject is based on BBSE (Bull Breeding Soundness Examination) methodologies, which have significant limitations in terms of their evaluation procedure, such as high cost, time-consuming and administratively difficult. This research focuses on the creation of a prediction model to supplement and/or improve the BBSE approach, through the study of two algorithms, Clustering and Artificial Neural Network (ANN), to find the optimum Machine Learning (ML) approach for our application, with an emphasis on data categorization accuracy. The tool is designed to assist veterinary medicine and farmers in identifying key factors and increasing certainty in their decision-making during the selection of bulls for breeding purposes, providing data from a limited number of factors generated from a deep pairing study of bulls. Zebu Bulls, European Bulls, and Crossbred Bulls were the general groupings. The data utilized in the model's creation (N =359) considers five variables that influence improvement decisions. This approach enhances decision-making by 12%. ANN obtained an accuracy of 90%; Precision was 97% for satisfying, 92% for unsatisfying, and 85% for Bad.

As the problem of air pollution becomes more urgent, the precise determination of the characteristics of air pollution and the associated mechanisms of transport of pollutants becomes an increasingly urgent task. The simulation results of the Copernicus Atmospheric Monitoring Service (CAMS) [18] were used as initial data sets. The area of the Crimean Peninsula (within coordinates 44.50 latitude, 33.50 longitude) in 23 - 26 January, 2019 was chosen for the study. That time concentrations of pollutants (aerosol particles with a diameter of 2.5 (PM2.5) and 10 micrometers (PM10)) steadily increased and reached the maximum value. Four kinds of meteorological data sets were used in the work. The first and second data sets were the global NCEP/NCAR and (ERA-5) re-analyses. The third and fourth meteorological data sets were obtained using the Weather tracking and Forecasting model (WRF) as domains on high-resolution grids: 9 km and 2 km. The analysis of meteorological data sets was carried out to analyze the nature of the air masses movement in the studied region. Using the HYSPLIT model simulation was carried out to determine area of the sources of negative impact. The emissions transfer from the supposed sources of negative impact on the Crimea region is analyzed. The sensitivity of simulation the inverse and direct trajectories of air masses transfer was studied based a set of simulation results. On the basis of HYSPLIT model the simulation was carried out separately using meteorological data sets having different spatial and temporal resolutions. In general, the simulation results allowed us to conclude that the HYSPLIT model in combination with high-resolution meteorological input data of the WRF model are good to identify the sources of possible negative effects on atmospheric air located at long distances from the study site.

$\phi$CDM models provide an alternative to the standard $\Lambda$CDM paradigm, while being physically better motivated. These models lead to a time-dependent speed of sound for dark energy that is difficult to replicate by $w$CDM parametrizations. We review the most up-to-date status of observational evidence for the $\phi$CDM models in this paper. We start with an overview of the motivation behind these classes of models, the basic mathematical formalism, and the different classes of models. We then present a compilation of recent results of applying different observational probes to constraining $\phi$CDM model parameters. Over the last twenty years, the precision of observational data has increased immensely, leading to ever tighter constraints. A combination of the recent measurements favors the spatially flat $\Lambda$CDM model, but a large class of $\phi$CDM models is still not ruled out.

Epidemiological surveillance and phylogenetic studies rely nowadays on processing and analysing huge volumes of data. Processing tasks consist on running and refining a series of intertwined computational tasks. And, despite of existing several web applications for data processing and interactive visualization for phylogenetic studies, integrating many different tools and algorithms, their execution is total or partially on the client side, making them unsuitable for dealing with huge volumes of data. Studies are often also not easy to reproduce. On the other hand, in recent years, data-centric workflow systems have been proposed, allowing to deal better with increasingly larger datasets. The integration of these systems within phylogenetic tools will allow to scale them as required, and will contribute also to promote studies reproducibility. We propose then the FLOWViZ middleware for facilitating the integration of a state of the art data-centric workflow system, Apache Airflow, within web applications for phylogenetic analyses. This framework abstracts contracts and a core API for defining tools and workflows, where tools are assumed to be containerized. FLOWViZ has been tested and evaluated within the PHYLOViZ web application, a tool supporting phylogenetic inference and data visualization.

(1) Background: The aim of the present study was to evaluate the effect of ETS homologous factor (EHF) in malignant breast cancer cells. (2) Methods: Overexpression and knockdown of the EHF gene in human and mouse breast cancer cells were performed, and the TCGA dataset and Q-omics were analyzed. (3) Results: We found that the tumor suppressor NDRG2 is correlated with EHF gene expression in triple-negative breast cancer cells, that EHF overexpression results in reduced cell proliferation and that apoptosis is promoted by chemotherapeutic reagent treatment of EHF-overexpressing cells. By EHF overexpression, senescence-associated β-galactosidase activity and p21WAF1/CIP1 expression were increased, suggesting that EHF may induce cellular senescence. In addition, overexpression of EHF reduced the migratory ability and inhibited epithelial-mesenchymal transition (EMT). Furthermore, EHF inhibited the phosphorylation of STAT3. Overexpression of EHF also reduced tumor size, and lung metastasis in vivo. At the tumor site, β-galactosidase activity was increased by EHF. Finally, Kaplan-Meier-plotter analysis showed that TNBC patients with high expression of EHF had a longer relapse-free survival rate. (4) Conclusions: Our findings demonstrated that EHF inhibits breast tumor progression by inducing senescence and regulating EMT in TNBC cells.

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.

This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) Manufacturing Company B's Electricity consumption data and meteorological data in Naju, Jeollanam-do, South Korea are collected and preprocessed. (2) The preprocessed data proposes the Prophet model in the first step for seasonality and event handling prediction. (3) In the second step, seven multivariate data are experimented with GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15-minute intervals. (4) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The experimental results demonstrate that the proposed method outperforms the conventional Prophet model by more than 23 times and the modified GRU model by more than 2 times in terms of MAPE.

With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

This research aims to thoroughly understand the delicate interplay between stakeholders' perspectives, biometric data protection policies, and the deployment of face recognition technologies in Indonesia's dynamic digital world. Adopting a qualitative methodology, the study conducts Focus Group Discussions (FGDs) with various stakeholders, including government agencies, technology developers, regulatory agencies, civil society groups, and data subjects as end-users. Face-to-face group discussions facilitate an in-depth exploration of participants' perspectives, yielding rich qualitative data that reveals nuanced insights into the delicate balance between technological innovation, ethical considerations, and socioeconomic repercussions associated with implementing face recognition technology. The research comprehensively explains the difficulties and opportunities associated with responsible technological progress and biometric data security. The findings from the FGDs influence the development of strategies that effectively balance technical advancement with individual rights, privacy, and social well-being within the growing digital landscape of Indonesia. The regulatory function of the government seeks to bridge the gap between public expectations and technological advancements. Collaboration between academic institutions, government agencies, the private sector, and data subjects as end-users emerges as a crucial element, fostering an all-encompassing strategy integrating research, law, technology deployment, and user empowerment. This holistic approach is necessary to ensure that biometric data and facial recognition technology are utilized responsibly, thereby laying the way for Indonesia's technologically sophisticated and ethically aware digital future.

With an ever increasing amount of research data available, it becomes constantly more important to possess data literacy skills to benefit from this valuable resource. An integrative course was developed to teach students the fundamentals of data literacy through an engaging genome sequencing project. Each cohort of students performed planning of the experiment, DNA extraction, nanopore sequencing, genome sequence assembly, prediction of genes in the assembled sequence, and assignment of functional annotation terms to predicted genes. Students learned how to communicate science through writing a protocol in the form of a scientific paper, providing comments during a peer-review process, and presenting their findings as part of an international symposium. Many students enjoyed the opportunity to own a project and to work towards a meaningful objective.

Abstract—Annually, approximately 500,000 Merger and Ac- quisition (M&A) transactions are disclosed globally, each trans- action inciting substantial perturbations to the associated com- panies’ equity prices. The probability of an M&A transaction’s closure, as perceived by the public, inherently influences the stock price of the target company leading up to the proposed date of the deal. Given the recent advancements in the realm of Natural Language Processing (NLP), we propose an empirical investigation into the correlation between digital dialogue sur- rounding M&A transactions and consequent movements in the stock prices of involved companies. Utilizing transformer-based encoder-only architectures, we fine tune a stance detection model on an extensive dataset, amassed from digital communication platforms, featuring public discourse related to five historical M&A transactions. Ultimately, we achieved 70% accuracy on deal-completion stance detection using the Roberta-base model. We subsequently employ the aggregated the public sentiment towards the completion or termination of a proposed M&A transaction to model stock price movement. Utilizing a multitude of time-series based approaches, we achieve a mean absolute error of 2.29 USD for next-day price prediction and 3.40 USD for next-week price prediction. Ultimately, we find an existing but tenuous relationship between online discourse and the price trajectory of target companies, ultimately highlighting the complex social and economic phenomena behind M&A deals.Index Terms—Mergers and Acquisitions, Stock prediction, su- pervised learning, neural networks, Recurrent Neural Network, LSTM, stance detection, transformers

Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data for studying land cover and land use change (LCLUC) over the past four decades. However, the availability of cloud-, shadow-, and snow-free observations varies spatially and temporally due to climate and satellite data acquisition schemes. Spatio-temporal heterogeneity poses a major issue for some time-series analysis approaches, but can be addressed with pixel-based compositing that generates temporally equidistant cloud-free or near-cloud free synthetic images. Although much consideration is given to methods identifying the ‘best’ pixel value for each composite, determining the aggregation period receives less attention and is often done arbitrary, or based on expert intuition. Here, we evaluated data compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel‑2 time series across Europe. We considered separate and joint use of both data archives and analyzed spatio-temporal availability of composites during each calendar year and pixel-specific growing season. We reported mean annual composites’ availability investigating differences among biogeographical regions, checked feasibility of pan‑European analyses for three LCLUC applications based on annual, monthly and 10-day composites, and analyzed the shortest feasible compositing window ensuring ≥50% temporal data availability and interpolation of the remaining composites for individual years and across a variety of medium- and long‑term time windows. Our results highlighted low data coverage in the 1980s, 1990s, and in 2012, as well as spatial variability in data availability driven by climate and orbit overlaps, which altogether impact spatio-temporal consistency of medium- and long-term time series, limiting feasibility of some LCLUC analyses. We demonstrated that prior to 2011 monthly composites ensured overall 50-62% data coverage for each calendar year, and ~75% afterwards, with further increase to ~82% when Landsat and Sentinel-2 were combined. Temporal consistency of monthly composites was overall low and temporal interpolation augmenting up to 50% missing data each year and across a time window of interest, ensured feasibility of analyses. Applications based on shorter than monthly composites were challenging without joining Landsat and Sentinel‑2 archives after 2015, and beyond the Mediterranean biogeographical region. Using pixel-specific growing season data typically boosted data availability in most geographies and diminished most of the latitudinal differences, but feasibility of complete time series with sub-monthly compositing windows was still restricted to the most recent years, and required data interpolation. Overall, our analyses provided a detailed assessment of Landsat and Sentinel-2 data availability over Europe, and based on selected application examples, highlighted often lacking spatio-temporal consistency of time series with sub-monthly compositing windows and long-time periods, which might hinder feasibility of some LCLUC applications.

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese-Kazakh (Latin script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes is large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses enhanced momentum contrast loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrastive visual-language based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and F1 values by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual-language has improved the performance of tail classes and reduced the accuracy difference between majority and minority classes.

The Genotype-Phenotype Working Group was established in November 2021 as part of the AgBioData Consortium (https://www.agbiodata.org) with the goal of identifying current challenges in annotating and integrating large-scale genotype and phenotype data. Over the course of the year, the members of this working group identified different types of data sets, explored experimental platforms and methods for data generation, and examined how these data are annotated including the metadata requirements. We conducted a thorough review of publicly funded repositories for raw and processed data for each data type. We also examined several secondary databases and knowledgebases that enable the integration of heterogeneous data types in the context of the Genome Browser, Pathway Networks and tissue-specific gene expression. The review revealed a need for additional infrastructural support, standards, and tools to connect Genotype to Phenotype data and enhance data interoperability for knowledge synthesis and to foster translational research.

This work estimates yield losses due to drought events in mountain grasslands in north-eastern Italy, laying the groundwork for index-based insurance. Given the high correlation between Leaf Area Index (LAI) and grassland yield, we exploit LAI as a proxy for yield. We estimate LAI by the Sentinel-2 biophysical processor and we compare different gap-filling methods, including time-series interpolation and fusion with Sentinel-1 SAR data. We derive a Forage Production Index (FPI) as the growing season cumulate of the daily product between LAI and a meteorological water stress coefficient. Finally, we calculate the drought index as the anomaly of FPI. The validation of Sentinel-2 LAI with ground measurements showed RMSE of 0.92 [m2 m-2] and R2 of 0.81, on average over all the measurement sites. The comparison between FPI and yield showed R2 of 0.56 at the pixel scale and R2 of 0.74 at the parcel scale. The developed prototype FPI index was used at the end of the growing season of the year 2022 for calculating the payments of an experimental insurance scheme that was proposed to a group of farmers in Trentino-South Tyrol.

Falls, especially those left unattended, are fatal for the elderly. Several efforts have been made to use the Internet of Things and Machine Learning algorithms to detect falls. All such systems have issues such as (a) sensor placement on the torso and thigh which will be uncomfortable for the elderly. (b) Predictions made on the cloud- the result of the prediction is then sent back to the end device to raise an alert. This is prone to network connectivity and latency issues. We have built an end/device that is wrist-worn and has multiple Inertial Measurement Unit sensors and a heart sensor. We have developed three novel ensemble algorithms (a)Stack(A) (b) Variable weighted Ensemble Voting algorithm-B(VWE(B)), and (c) Variable weighted Ensemble Voting algorithm-C (VWE(C)). Since the ensemble algorithm is run on the end-device built around Qualcomm Snapdragons 820c, we do both feature extraction and selection to reduce data dimensionality. We have used multiple methods such as (a) Identifying features that have maximum impact (b) Principal Component Analysis (PCA) (c) Shapley’s values (d) Cross-Correlation combined with relliefF. We got an accuracy of 97% and specificity of 99%. In this paper, we present the analysis of the system with and without pruning.

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural networks and an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

Migratory movements are a political, social and public health issue on a global scale. Access to sexual and reproductive health services for irregular migrant women (IMW) is a public health issue. The aim of this study is to identify qualitative evidence on IMW's experiences of sexual and reproductive health care in emergency and primary care settings. Methods: meta-synthesis of qualitative studies. Synthesis includes assembling and categorising findings based on the simili-arty in meaning. The search was conducted between January 2010 and June 2022 using PubMed, WOS and CINAHL databases. Results: of 131 articles found in the initial search, only 9 articles met the criteria and were included in the review. Four main themes were established: (1) The need to focus emergency care on sexual and reproductive health, (2) Unsatisfactory clinical experiences, (3) Forced reproduction, (4) Alternating between formal and informal healthcare services. Con-clusions: IMW’s attitudes towards sexual and reproductive health are influenced by culture, ed-ucational level, fear, barriers and the attitude of healthcare providers. Healthcare institutions need to be aware of the IMW’s experiences to understand the specific difficulties they face. IMW call for socially and culturally sensitive health care, cultural mediators, improved communication and safe environments that ensure confidentiality.

As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

Artificial Intelligence (AI) is used in our day-to-day life. AI for journalism is a reality. Just like other vital aspects of our life, AI has also entered the world of journalism. Many reputed news organizations have adopted AI in journalism to perform various newsroom tasks. In today’s digital world, several technologies are powering journalism. One such technology that is transforming the journalism field is artificial intelligence. Accuracy is a core value of journalism. With AI and machine learning systems, there’s a statistical element of uncertainty which means it’s impossible to guarantee 100 percent accuracy. AI tools such as OpenAI’s ChatGPT, Microsoft’s Bing chatbot, and Google’s Bard have been subject to debate now. AI tools have the potential to assist in a variety of tasks, such as scraping PDF files, writing code, and translating languages. AI can be a helpful tool for journalism students and media researchers. A main concern among computational journalists is that AI sometimes hallucinates data. This review paper highlights how artificial intelligence can help journalists.

In recent years, Cloud Computing and Big Data have been considered the most attractive areas that are revolutionizing the IT world. Cloud Computing paradigm has recently appeared that allows running proprietary or difficult portable applications outside their original software environment on one or more virtual hardware platforms. Therefore, we are to developing such techniques which make it possible to secure communication between the communicating Cloud entities. These techniques must take into account several factors due to the data transmitted in this type of environment is proprietary and of significant size. Conventional data security techniques are not suitable for today's cloud usage. Hence, the main research of this thesis is to define an adaptable architecture with the aim to propose a scalable system that supports cloud services. We will define feasible security solutions dedicated to the Cloud computing context in order to robustly protect data stored in the Cloud. We are more precisely looking for working on NoSQL databases. We also intend to propose a secure solution based on the blockchain that has powerful features like decentralization, autonomy, security, reliability, and transparency.

Soil moisture modeling is necessary for many hydrometeorological and agricultural applications. One of the ways in which modeling of soil moisture (SM) can be improved is by assimilating SM observations to update the model states. Remotely sensed SM observations are prone to being riddled with data discontinuities, namely in the horizontal and vertical spatial, and temporal dimensions. A set of synthetic experiments were designed in this study to assess how much impact each of these individual components of spatiotemporal gaps can have on the modeling performance of SM as well as streamflow. Results show that not having root-zone SM estimates from satellite derived observations is most impactful in terms of modeling performance. Having temporal gaps and horizontal spatial gaps in the satellite SM data also impacts modeling performance, but to a lesser degree. Real-data experiments with the remotely sensed Soil Moisture Active Passive (SMAP) product generally brought improvements to the SM modeling performance in the upper soil layers, but not so much in the bottom soil layer. The updating of model SM states with observations also resulted in some improvements in the streamflow modeling performance during the synthetic experiments, but not during the real-data experiments.

Loop detectors are probably the widest-used technology for traffic state estimation. Previous research has shown that loop detector positions within the link significantly affect the estimation of the macroscopic fundamental diagram (MFD) of a given network. This paper examines the biases produced by the positioning of loop detectors on the MFD, using both analytical and simulation methods, as well as empirical data from UTD-19. We confirm earlier results that a uniform distribution of loop detector positions reduces the bias. For non-uniform distribution, we found that: (i) the subsets of the MFD by the loop detector position help estimate whether the loop detector MFD will have a bias; (ii) if the detectors in the network are positioned more downstream with a larger variation, the loop detector MFD is more likely to have a discrepancy in position subsets of the MFD; (iii) a lower ratio of link length to green signal time elevates the MFD bias as well. This research opens the possibility for the bias of MFD induced by the loop detector data to be corrected by only using itself.

Management of tumor lysis syndrome (TLS) associated with cancer chemotherapy for malignant tumors is important because of its potentially fatal course. The use of rasburicase, a recombinant urate oxidase, is recommended for TLS; however, because rasburicase is an enzymatic drug, one should be cautious of anaphylaxis during administration. Using claims data in Japan, we investigated the rate of rasburicase re-administration and the occurrence of anaphylaxis during re-administration in patients with hematopoietic malignancies in a multicenter setting. Re-administration of rasburicase was defined as administration after an interval of 21 days from the first dose. Of 373 patients, 18 were re-administered with rasburicase (re-administration rate: 4.8%). No patient developed anaphylaxis. The median number of days from the first to the last dose of rasburicase was 256.5 days (interquartile range: 138.8–455.8 days). The median daily dose was 7.5 mg (4.5–11.3 mg), and the median total dose was 33.8 mg (19.1–64.1 mg). This claims database analysis revealed that the re-administration rate of rasburicase was low in Japanese patients with hematopoietic malignancies, suggesting that rasburicase was being used appropriately, and that associated anaphylaxis was not observed.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

Recently, deep learning-based image quality enhancement models have been proposed to improve the perceptual quality of distorted synthesized views impaired by compression and Depth Image Based Rendering (DIBR) process in multiview video systems. However, due to the lack of multi-view video plus depth data, the training data for quality enhancement models is small, which limits the performance and progress of these models. Augmenting the training data to enhance the Synthesized View Quality Enhancement (SVQE) models is a feasible solution. In this paper, we suggest a deep learning-based SVQE model using more synthetic Synthesized View Images (SVIs). To simulate the irregular geometric displacement of DIBR distortion, a random irregular polygon-based SVI synthesis method is proposed based on existing massive RGB/RGBD data, and a synthetic synthesized view database is constructed, which includes synthetic SVIs and DIBR distortion masks. Moreover, to further guide the SVQE models to focus more precisely on DIBR distortion, the DIBR distortion mask prediction network which could predict the position and variance of DIBR distortion is embedded into the SVQE models. The experimental results demonstrate that by pretraining on the synthetic SVI database, the performance of the existing SVQE models could be greatly promoted. In addition, by introducing the DIBR distortion mask prediction network, the SVI quality could be further enhanced.

This material presents the results of a comparative analysis of the fungal diversity in the world system of microbial culture collections on one side with a variety of known fungal producers on the other side. The main VKM databases used are Fungal DC and Metabolites of Fungi, the central point of analysis - the fungal ability to synthesize promising metabolites for applied use. It indicates that the option of obtaining new promising strains from the collection funds is still underestimated by the scientific community. In particular it is shown that not more than 3% of the total fungal species fund contained in culture collections are used practically. The database Fungal DC developed in VKM is available on-line in www.vkm.ru and www.mycobank.org. It is possible their use will considerably expand the range of studied strains and lead to the acquisition of new scientifically significant data.

Automated software bots infiltrate online surveys and corrupt data integrity, not to mention waste researcher time and budgets. Although resources exist to help keep bots out and identify bots when they do evade survey barriers, bot attacks may be a persistent problem for online surveys for a long time to come. Bots are evolving -- even as survey designers try ever more sophisticated methods to fend them off and weed their answers out. Vigilance needs to be high and the bot generators should not be under-estimated. We recount here some bot features we encountered after our own survey was attacked that helped to identify them, and that have not been detailed elsewhere. We also discuss reasons why commonly recommended strategies for how to keep bots out may not be feasible for many scientific researchers.

In ref. [1], we pointed out that certain anomalies observed in the published data for ac magnetic susceptibility of a room temperature superconductor reported in Nature 586, 373 (2020) [2] would be cleared up once the measured raw data were made available. Part of the measured raw data were recently posted in arXiv:2111.15017 [3]. Here we report the results of our analysis of these raw data and our conclusion that they are incompatible with the published data. Implications of these results to the claim that the material is a room temperature superconductor are discussed.

3D city models integrate heterogeneous urban data from multiple sources in a unified geospatial representation, combining both semantics and geometry. Although in the last decades, they are predominantly used for visualization, today they are used in a large range of tasks related to exploration, analysis, and management across multiple domains. The complexity of urban processes and the diversity of urban environment bring challenges to the implementation of 3D city models. To address such challenges, this paper presents the development process of a 3D city model of a single neighborhood in Sofia city based on CityGML 2.0 standard. The model represents the buildings in LOD1 with a focus on CityGML features of related to the buildings like building part, terrain intersection curve and address. Similar building models of 18 cities provided as open datasets are explored and compared in order to extract good modeling practices. As a result, workflows for generation of 3D building models in LOD1 are elaborated and improvements in the feature modeling are proposed. Two options of building model are examined: modeling of a building as a single solid and modeling of a building with separate building parts. Finally, the possibilities for visualization of the model in popular platforms such as ArcGIS Pro and Cesium Ion are explored.

Room temperature superconductivity has recently been reported for a carbonaceous sulfur hydride (CSH) under high pressure by Snider et al [1]. The paper reports sharp drops in magnetic susceptibility as a function of temperature for five different pressures, that are interpreted as signaling a superconducting transition. Here I question the validity and faithfulness of the magnetic susceptibility data presented in the paper by comparison with the measured raw data reported by two of the authors of ref. [2]. This invalidates the assertion of the paper [1] that the susceptibility measurements support the case for superconductivity in this compound.

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

The goal is to reduce poverty, the method used is a literature study to see the effect of using Financial Technology (Fintech) on financial inclusion, method to see the effect of financial inclusion on poverty. From the results of the tests, the use of Fintech can increase financial inclusion, thereby encouraging poverty alleviation. It can be seen that the Financial Inclusion variable has a significant and negative effect on the poverty variable. This shows that countries that have high levels of financial inclusion are generally estimated to have low poverty rates. Increasing a country's financial inclusion can reduce poverty.

Convolutional Neural Networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the Discrete Wavelet Transform and the other on the Constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across three benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art performance across all three data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification (code available at https://github.com/LorisNanni).

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

The link between ICTs, freight transport, and CO₂ emissions has not received much explicit examination by researchers, despite freight transportation being an egregious contributor to environmental degradation. This paper investigates how ICTs can affect environmental quality when interacting with freight transport activities in 43 countries over the period 2002-2014, using carbon emissions as a proxy for environmental damages. ICTs are measured in terms of internet, telephone, and mobile phone technologies. Using GMM methodology, the results show that ICTs contribute to dampening environmental degradation when interacting with freight transport activities. Specifically, a 10% increase in the interaction between ICTs and freight transportation will decrease carbon emissions by between 1.27% and 3.02%. The results further suggest that fixed and smartphone technologies are the main contributors to reducing emissions when adopted in some specific transport sectors (i.e. road, rail, and inland), while the internet is the most efficient technology when interacting with air transport activity. In addition, the interaction between ICTs and multimodality accelerates environmental quality. The policy implications of these findings are discussed.

This paper shows the proposal of the use of linked data for smart metering systems. The aim of linked data in smart grid applications such as smart metering systems is to give semantic knowledge of the energy transactions between all participators in the power markets. In combination with other technologies like blockchain, the linked data could improve decentralization and security in the world's energetic future.

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

High-resolution K-shell photoionization cross-sections for the C-like atomic nitrogen ion (N+) are reported in the 398 eV (31.15 Å) to 450 eV (27.55 Å) energy (wavelength) range. The results were obtained from absolute ion-yield measurements using the SOLEIL synchrotron radiation facility for spectral bandpasses of 65 meV or 250 meV. In the photon energy region 398 eV - 403 eV, 1s⟶2p autoionizing resonance states dominated the cross section spectrum. Analyses of the experimental profiles yielded resonance strengths and Auger widths. In the 415 eV - 440 eV photon region 1s⟶1s2s22p2 4Pnp and 1s⟶1s2s22p2 2Pnp resonances forming well-developed Rydberg series up n=7 and n=8 , respectively, were identified in both the single and double ionization spectra. Theoretical photoionization cross-section calculations, performed using the R-matrix plus pseudo-states (RMPS) method and the multiconfiguration Dirac-Fock (MCDF) approach were bench marked against these high-resolution experimental results. Comparison of the state-of-the-art theoretical work with the experimental studies allowed the identification of new resonance features. Resonance strengths, energies and Auger widths (where available) are compared quantitatively with the theoretical values. Contributions from excited metastable states of the N+ ions were carefully considered throughout.

The purpose of this paper is to evaluate the acceptance and the utilization of GPS/GPRS-based telematics technology in road transport companies registered in Poland. Telematics technologies are essential for management of energy saving and emissions reduction in road transport. It is in line with the European Union policy of sustainable transportation. The evaluation is based on a survey designed and carried out in 2020. The issues concerning the scope of telematics systems utilization as well as the internal and external factors affecting their use are analysed. The methodology is based on Technology Acceptance Model (TAM) and Structural Equation Modelling (SEM). The results are checked for robustness. Based on the results, it can be reasoned that as a result of the COVID19 pandemic, the companies started to use telematics systems more widely than they did before. Furthermore, the companies employing more people recognize the higher usefulness of telematics systems and are motivated to have the systems more than smaller enterprises; however, TAMs estimated separately for small and medium-sized enterprises did not reveal any significant differences in the parameter estimates.

In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

— Big Data analytics has come a long way since its inception. This field is growing day by day. With the advent of large handling capacity of computational analysis of modern computing systems as well as Internet of Things (IoT), this field has revolutionized the way we think about data. It has influenced the major domains such as healthcare, automobile, computing, climatology, and space communications. Of late, the health care sector has been largely influenced by this. This communication deals with the areas of healthcare where big data analytics has been largely influential. Encompassing the basics of Big Data Analytics (BDA) driven by IoT, the applications of it in healthcare sector are outlined, accompanied by future expectations. Additionally, it also presents a comprehensive analysis of recent application with special reference to Covid-19 in this sector.

The e-commerce platform in the digital economy era has evolved into a data platform ecosystem built around data resources and data mining technology systems. The most typical applications of big data are also concentrated in the field of e-commerce. E-commerce companies should first grasp the interactive relationship among the three major factors of data, technology and innovation, e-commerce platform operation is a multidisciplinary research field. It is not easy for researchers to obtain a panoramic view of the knowledge structure in this field. Knowledge graph is a kind of graph that shows the development process and structure relationship of knowledge with the field of knowledge as the object. It is not only a visual knowledge mapping, but also a serialized knowledge pedigree, which provides researchers with a quantitative research method for the development trend of statistics and academic status. The purpose of this research is to help researchers understand the key knowledge, evolutionary trends and research frontiers of current research. This study uses Citespace bibliometric analysis to analyze the data of the Science Net database and finds that: 1) The development of the research field has gone through three stages, and some representative key scholars and key documents have been recognized; 2) the common knowledge mapping of literature The co-occurrence of citations and keywords shows research hotspots; 3) The results of burst detection and central node analysis reveal research frontiers and development trends. Today, the visualization of big data brings different challenges. The abstraction between the world and today's data visualization occurs when the data is captured. Every user sees his own visualization data generated by standardized calculations. At the same time, there are still many controversies in the theoretical model, structure and structural dimensions. This is the direction that future researchers need to further study.

A new discrete distribution for count data called extended biparametric Waring (EBW) distribution is developed. Its name is related to the fact that, in a specific configuration of its parameters, it can be seen as a biparametric version of the univariate generalized Waring (UGW) distribution, a well-known model for the variance decomposition into three components: randomness, liability and proneness. Unlike the UGW distribution, the EBW can model both overdispersed and underdispersed data sets. In fact, the EBW distribution is a particular case of a UWG distribution when its first parameter is positive; otherwise, it is a particular case of a Complex Triparametric Pearson (CTP) distribution. Hence, this new model inherits most of their properties and, moreover, it helps to solve the identification problem in the variance components of the UGW model. We compare the EBW with the UGW by a simulation study, but also with other over and underdispersed distributions through the Kullback-Leibler divergence. Additionally, we have carried out a simulation study in order to analyse the properties of the maximum likelihood parameter estimates. Finally, some application examples are included which show that the proposed model provides similar or even better results than other models, but with fewer parameters.

The proportion of area under various crops at a given point in time, known as a cropping pattern, plays an essential role in determining the level of agricultural production. In this study, cropping patterns of three sub-counties in Murang’a County, a typical African smallholder farming area in Kenya, were mapped. Specifically, we compared the performance of eight classification scenarios for mapping cropping patterns; namely using (i) only Sentinel-2 reflectance bands (S2), (ii) S2 and S2 derived vegetation indices (VIs); (iii) S2 and S2 vegetation phenology (VP); (iv) S2 and Sentinel-1 radar backscatter data (S1); (v) S2, VIs, and S1; (vi) S2, VP, and S1; (vii) S2, VIs and VP, and (viii) S2, VIs, VP and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The guided regularized random forest (GRRF) algorithm was used to select the optimum variables and to perform the respective classification for each scenario. The most accurate result of the overall accuracy of 93.16% was attained from the scenario (viii) S2, VIs, VP, and S1. The McNemar’s test of significance did not show significant differences (p≤0.05) among the tested scenarios. Our study demonstrated the strength of GRRF and the synergetic advantage of S2 and S1 derivatives to map cropping patterns in a heterogeneous landscape where high resolution imagery are inaccessible. Our cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions.

The business planning process can be considered as a strategic phase of any business. Given that the business plan is a management accounting tool, there are countless approaches that can be adopted to prepare it since there is no legal requirement, as opposed to obligations relating to financial accounting. However, in general, every business plan consists of a numerical part (budget) and a narrative part. In this research, the author highlights, on the basis of experiences and commonly used theories, a standard process that can be adaptable to the business plan of any type of activity. The use of big data is highlighted as an essential part of feeding the data of almost all the steps of the budget. The author then manages to determine a generally applicable standard process, indicating all the data necessary to prepare an accurate and reliable business plan. A case study will provide adequate support to the demonstration of the immediate applicability of the proposed model.

The origin of SARS-CoV-2 is debated, even after 18 months into the COVID-19 pandemic and a special investigation conducted by the World Health Organization. The RaTG13 sequence has been a highlight of discussions surrounding the origin of SARS-CoV-2. Here we express our opinion about the need for better reporting standards for information about sequencing data, especially for pathogens, citing our findings with the reported RaTG13 genome.

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatic tools and resources, and advocate for greater openness, interoperability, accessibility and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a clear and present need for a fit-for-purpose, open source SARS-CoV-2 contextual data standard. As such, we have developed an extension to the INSDC pathogen package, providing a SARS-CoV-2 contextual data specification based on harmonisable, publicly available, community standards. The specification is implementable via a collection template, as well as an array of protocols and tools to support the harmonisation and submission of sequence data and contextual information to public repositories. Well-structured, rich contextual data adds value, promotes reuse, and enables aggregation and integration of disparate data sets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19.

This paper addresses a method to transmit streaming data via rolling shutter camera-based optical camera communications (OCC). Since the amount of data that can be contained within one frame is limited, and the continuity of received data cannot be guaranteed due to OCC environmental variations, we introduce the concept of dividing the streaming data into several fragmented sets that are transmitted sequentially. We propose a superframe to contain sequential packets of fragmented data and corresponding indexes, so that sequential packets for streaming data can be continuously collected. When redundant frame transmission is considered, any packet lost due to OCC environmental conditions can be recovered. Experimental results show that the proposed method can be successfully used to transmit streaming data, with the number of redundant frames required to acquire all data packets based on image resolution. In addition, we describe how to identify missing packets from a network point of view to reduce the number of redundant frames needed to acquire all the data. This paper presents baseline results of communication performance when sending streaming data via a rolling shutter-based OCC.

Recently, the intensities of natural disasters have increased significantly owing to climate change and various other environmental factors, causing unprecedented damage. Measures must be established to reduce damage from large-scale natural disasters caused by the rapidly changing environment. The Japanese government published a hazard map manual in 2015 and obligates the creation of a hazard map as a measure to reduce high-scale storm surges. This manual presents a typhoon model based on a parametric model that is used to create a hazard map. The Myers model assuming concentric circles, which is primarily used in East Asia, is disadvantageous as it cannot consider geographic characteristics. Therefore, a new parametric model is necessary to calculate wind and pressure fields, which change according to geographic characteristics. To improve this limitation of the Myers model, we calculated the wind and pressure fields considering geographical effects by combining the Holland model, which can consider the size of a developing typhoon, and the Mascon model, which changes by geographic characteristics. To determine the gradient coefficient of the Holland model, the coefficient that changes every moment was calculated using grid point value data. The result indicated excellent reproducibility of storm surge height according to the geographic characteristics.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

A novel software engineering platform called the Dynamic Nuchwezi Architecture Platform (DNAP) is introduced, specified and its novelties explained. The unique features of this platform are explained and several new concepts and abstractions upon which its implementation, usage, and analysis are hinged also elaborately discussed. The motivations for this new approach to building especially tools used in data engineering are spelled out and the platform is contrasted against other existing technologies of a similar kind. Finally, it is shown what known limitations DNAP suffers, as well as what room for further research and improvement there is in this field.

Storing scientific data on the file system in a meaningful and transparent way is no trivial task. In particular when the data have to be accessed after their originator has left the lab the importance of a standardized file structure cannot be underestimated. It is desirable to have a structure that allows for the unique categorization of all kinds of data from experimental results to publications. It has to be accessible to a broad variety of workflows, e.g., via graphical user interface as well as via command line, in order to find widespread acceptance. Furthermore, the inclusion of already existing data has to be as simple as possible. We propose a three-level structure to organize and store scientific data that incorporates the full chain of scientific data management from data acquisition to analysis to publications. Metadata are saved in a standardized way and connect original data to analyses and publication as well as to their originators. A simple software tool to check a file structure for compliance with the proposed structure is presented.

On 25 May 2018, the General Data Protection Regulation (GDPR)Article 17, the Right to Erasure (‘Right to be Forgotten’) came into force making it vital for organisations to identify, locate and delete all Personally Identifiable Information (PII) where a valid request is received from a data subject to erase their PII and the contractual period has expired. This must be done without undue delay and the organisation must be able to demonstrate reasonable measures were taken. Failure to comply may incur significant fines, not to mention impact to reputation. Many organisations do not understand their data, and the complexity of a hybrid cloud infrastructure means they do not have the resources to undertake this task. The variety of available tools are quite often unsuitable as they involve restructuring so there is one centralised data repository. This research aims to demonstrate compliance with GDPR’s Article 17 Right to Erasure (‘Right to be Forgotten’) is achievable in a Hybrid cloud environment by following a list of recommendations. However, 100% retrieval, 100% of time will not be possible, but we show that small organisations running an ad-hoc Hybrid cloud environment can demonstrate that reasonable measures were taken to be Right to Erasure (‘Right to be Forgotten’) compliant.

Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this manuscript we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.

In this study, we investigate spectral structure of conformable Sturm-Liouville problems and with this end, we obtain representation of solutions under different initial conditions and asymptotic formulas for eigenfunctions, eigenvalues, norming constants and normalized eigenfunctions. Consequently, we prove the existence of infinitely many eigenvalues. Also, we compare the solutions with graphics with different orders, different eigenvalues, different potentials and so, we observe the behaviors of eigenfunctions. We give an application to the α-orthogonality of eigenfunctions and reality of eigenvalues for conformable Sturm-Liouville problems defined by [15] in the last section.

Since World War I, the commercial aviation industry has seen many improvements that now allow people and goods to reach the other side of the world in few hours, consuming much less fuel than in recent decades. Improvements in cargo capacity and energy efficiency were significant and, in this scenario, commercial airlines were able to thrive and bring great benefits to world economy. However, this sector is facing environmental challenges due to the intensive use of aviation fuel. Brazil is one of the largest domestic air passenger markets in the world and still has great growth potential, considering its economic potential and territorial dimensions: roughly the same size as the US and twice the size of the European Union. This paper discusses partial productivity of jet fuel in Brazilian domestic aviation and proposes an econometric method to support public regulators and airlines decisions. The proposed model uses variables such as aircraft size, route characteristics and idle flight capacity in a panel data analysis. The results show that reducing idle capacity is one of the best ways to achieve better short-term fuel efficiency and therefore will reduce environmental impacts and have positive economic effects on commercial air transport activities.

Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. In practice, due to sensor failures or to communication errors, in the long-time running, some data may be lost. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples, which besides can be very long. Data completion is a critical step, which must be done with the utmost rigour in order to not propagate errors in the rest of the processing chain stages. Some Big Data techniques do not work if the data series are incomplete, due to the loss of some data. When this occurs it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad-hoc method to replenish the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company that present patterns on a daily and a weekly scale. A method based on tensors is used to take advantage of the data structure. A specially designed \textit{tensorization} is employed to deal with bursts of missed data, applying a twice tensor decomposition and a signal continuity correction. Statistical tests are realized, which consist of apply the data reconstruction algorithms, by deliberately removing bursts of data in verified historical database servers, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.

A common motivation for increasing open access to research findings and data is the potential to create economic benefits – but evidence is patchy and diverse. This study systematically reviewed the evidence on what kinds of economic impacts (positive and negative) open science can have, how these comes about, and how benefits could be maximized. Use of open science outputs often leaves no obvious trace, so most evidence of impacts is based on interviews, surveys, inference based on existing costs, and modelling approaches. There is indicative evidence that open access to findings/data can lead to savings in access costs, labour costs and transaction costs. There are examples of open science enabling new products, services, companies, research and collaborations. Modelling studies suggest higher returns to R&D if open access permits greater accessibility and efficiency of use of findings. Barriers include lack of skills capacity in search, interpretation and text mining, and lack of clarity around where benefits accrue. There are also contextual considerations around who benefits most from open science (e.g. sectors, small vs larger companies, types of dataset). Recommendations captured in the review include more research, monitoring and evaluation (including developing metrics), promoting benefits, capacity building and making outputs more audience-friendly.

Dietary factors are one of the risk factors that can impact the brain chemistry, which leads to mental distress. Based on our data mining approach, we found that mental distress in men is associated with eating unhealthy food. Our aim in this paper is to apply results from our big data analytics approach to inform system dynamics (SD) modeling to investigate the causal relationships between brain structures, nutrients from food and dietary supplements, and mental health. We perform descriptive analysis based on a large data set to estimate the SD modeling parameters. Finally, we calibrate the model towards a time series data collected for individuals on their dietary and distress patterns. The results reveal that bridging these different methodologies leads to further insights from the SD model and decreases the error of calibrated parameter values. Future research is needed to validate our initial results for investigating the relationship between mental distress and dietary intake.

The Surface Water and Ocean Topography (SWOT) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in order to improve the dynamics of continental waters over the Garonne catchment, one of the major French catchments. The aim of this paper is to demonstrate that the sequential assimilation of SWOT-like river depths allows the correction of river bed roughness coefficients and thus simulated river depths. An extended Kalman Filter is implemented and the data assimilation strategy was applied to four experiments of gradually increasing complexity regarding observation and model error over the 1995-2000 period. With respect to a “true” river state, assimilating river depths allows the proper retrieval of constant and spatially distributed roughness coefficients with a root mean square error of 1 m1/3 s-1, and the estimation of associated river depths. It was also shown that river depth differences can be assimilated, resulting in a higher root mean square error for roughness coefficients with respect to the true river state. The last study shows how one can take into account more realistic sources of SWOT error measurements, in particular the importance of the estimation of the tropospheric water content in the process.

We present a methodology to enable users to interact with statistical information owned by an institution and stored in a cloud infrastructure. Mainly based on R, this approach was developed following the open-data philosophy. Also, since we use R, the implementation is mainly based on open-source software. R gives several advantages from the point of view of data management and acquisition, as it becomes a common framework that can be used to structure the processes involved in any statistical operation. This simplifies the access to the data and enable to use all the power of R in the cloud information. This methodology was applied successfully to develop a tool to manage the data of the Centre d’Estudis d’Opinió, but it can be applied to other institutions to enable open access to its data. The infrastructure also was deployed to a cloud infrastructure, to assure the scalability and a 24/7 access.

Ongoing debates regarding the virtues and challenges of implementing open science for brain imaging research mirror those of the larger scientific community. The present commentary acknowledges the merits of arguments on both sides, as well as the underlying realities that have forced so many to feel the need to resist the implementation of an ideal. Potential sources of top-down reform are discussed, along with the factors that threaten to slow their progress. The potential roles of generational change and the individual are discussed, and a starter list of actionable steps that any researcher can take, big or small, is provided.

This paper identifies a dilemma in the relationship between R^2 and price efficiency: After comprehensively studying the R^2 change around 7 well-known corporate events, neither the traditional understanding of R^2 as price inefficiency, nor the behavioral way of R^2 as price efficiency can explain the observed R^2 change around the events. We adopt an alternative methodology to replace the standard difference-in-difference regression and directly decompose the R^2 change. We find that, due to the endogeneity of events, the changes of R^2 are over-estimated. We further propose that in the event study setting, the R^2 change may be simply the consequence of the inflow/outflow of some trend-chasing investors, and it may be detached from price (in) efficiency. Empirical evidences are consistent with this hypothesis.

Binary star DataBase (BDB) is the database of binary/multiple systems of various observational types. BDB contains data on physical and positional parameters of 260,000 components of 120,000 stellar systems of multiplicity 2 to more than 20, taken from a large variety of published catalogues and databases. We describe the new features in organization of the database, integration of new catalogues and implementation of new possibilities available to users. The development of the BDB index-catalogue, Identification List of Binaries, is discussed. This star catalogue provides cross-referencing between most popular catalogues of binary stars. We describe ideas and methods for reliable cross-identification of different entities (systems, pairs, components) in binary and multiple stellar systems.

Passive microwave measurements have been available on satellites dating back to the 1970s on research satellites flown by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager–SSM/I; the Advanced Microwave Sounding Unit–AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer–AMSR; the Tropical Rainfall Measurement Mission Microwave Imager–TMI; the Global Precipitation Mission Microwave Imager–GMI). Here the focus is on measurements from the AMSU-A, AMSU-B and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998 with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19 and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a Climate Data Record (CDR) that utilizes brightness temperatures from Fundamental CDRs to generate Thematic CDRs (TCDR). The TCDR’s include: Total Precipitable Water (TPW), Cloud Liquid Water (CLW), Sea-Ice concentration (SIC), Land surface temperature (LST), Land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDR’s are shown to be in general good agreement with similar products from other sources such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Because of the careful intercalibration of the FCDR’s, little bias is found among the different TCDR’s produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

Many remote sensing analytical data products are most useful when they are in an appropriate regional or national projection, rather than globally based projections like Universal Transverse Mercator (UTM) or geographic coordinates, i.e., latitude and longitude. Furthermore, leaving data in the global systems can create problems, either due to misprojection of imagery because of UTM zone boundaries, or because said projections are not optimised for local use. We developed the open-source Irish Earth Observation (IEO) Python module to maintain a local remote sensing data library for Ireland. This pure Python module, in conjunction with the IEOtools Python scripts, utilises the Geospatial Data Abstraction Library (GDAL) for its geoprocessing functionality. At present, the module supports only Landsat TM/ETM+/OLI/TIRS data that have been corrected to surface reflectance using the USGS/ESPA LEDAPS/ LaSRC Collection 1 architecture. This module and the IEOtools catalogue available Landsat data from the USGS/EROS archive, and includes functions for the importation of imagery into a defined local projection and calculation of cloud-free vegetation indices. While this module is distributed with default values and data for Ireland, it can be adapted for other regions with simple modifications to the configuration files and geospatial data sets.

Over the years, the Egyptian temples at Luxor city have been intensely investigated, but most of these studies just focused on the classical sides of the archaeological and historical descriptions. Many of the environmental problems are inevitable results of the unplanned urban crawling around the monuments temples. This paper aims at assessing the environmental changes around some temples of Luxor City using Remote sensing and GIS techniques. In particular, a historical database made up of Corona and Landsat TM data have been investigated along with the new acquisitions of Quickbird2 and Sentinel2. Results from our investigation highlighted rapid changes in urban and agricultural areas, which adversely affected the Egyptian monumental temples causing serious degradation phenomena. Using the information obtained from our RS&GIS based analysis, mitigation strategies have been also identified for supporting the preservation of the archaeological area.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need of prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

This study examines the exchange rate rebound effects of the Central Bank intervention in the selected ECOWAS economies. An empirical understanding of these effects is very important to trade adjustment as well as the macroeconomic stability in these countries. Using the panel data modelling framework, the study finds that the impact of the Central Bank intervention on exchange rate is insignificant and it does not lead to the exchange rate rebound. In addition, money supply as well as monetary policy rate implemented by the monetary authorities significantly influences the level of exchange rate in a positive direction.

Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

Lithium ascorbate (LiAsc) is a non-toxic lithium salt (LD50>5000 mg/kg) with normothymic and neuroprotective properties. Chemoreactomic modeling of LiAsc predicted possible hepatoprotective effects which were tested here on a rat model of a non-alcoholic fatty liver disease (NAFLD) with multiorgan pathology caused by combined intake of excess saturated fats (palm oil), carbohydrates (fructose) and inorganic iron salt (ferrous sulfate). Dynamics of biochemical parameters (biomarkers of liver function, kidneys, hematopoiesis, inflammation, thrombus formation, etc.) and histological evidence of hemosiderosis in the model upon reproduction of the model are described. Experimental confirmations of the hepatoprotective effects of LiAsc were obtained. Comparison of the effects of LiAsc with the effects of myoinositol (a substance with known hepatoprotective properties) shown a number of differences in pharmacological action. Both LiAsc and MI lowered ferritin, transferrin saturation, serum iron, AST, ALT, serum creatinine, leukocytes (LiAsc to a greater extent) and normalized lowered serum protein and glomerular filtration rate. However, only LiAsc lowered heightened reticulocytes (absolute and fraction) and platelets towards the corresponding reference intervals, normalized levels of vitamin B12 in blood and lowered the number of Kupffer cells in liver that were overloaded with iron (contained Prussian blue in morphometric studies). In a clinical perspective, hepatoprotective properties of a neuroprotector and it’s potential to reduce hemosiderosis might be useful for treating patients with hepatic encephalopathy.

This study explores the optimization of the admission process at the Escuela Colombiana de Ingeniería Julio Garavito through data anal- ysis. By integrating advanced tools like Power BI, Python Flask, and data mining techniques, a model has been developed to improve student selection and retention, enabling more informed and effective decisions. The results indicate that applying data science not only facilitates efficient data management but also promotes more inclusive and equitable educational poli- cies. This pioneering approach in the field of higher education in Colombia offers a frame- work for future research and technological de- velopments, highlighting the vital importance of data science in improving educational and administrative processes.

Data security in the Internet of things (IoT) is often implemented by means of encryption, which can be burdensome for some entities. We propose in this paper a solution based on routing, by which data are forwarded only to entities that are intended to receive them. An IoT network can be seen as a partial order of equivalence classes of entities, and each entity can be labeled according to the position of its equivalence class in the partial order. The partial order can be constructed according to requirements of secrecy (or confidentiality), integrity and conflicts. Routing tables among entities can be compiled by using the labels. The method is demonstrated in this paper for Software defined networking (SDN) routers and controllers. We propose a centralized IoT architecture with a cloud structure using SDN as networking infrastructure, where storage entities (i.e. cloud servers) are associated with application entities. A small ‘hospital’ example is shown for illustration. Procedures for network reconfigurations are discussed. We also demonstrate the method for the normal case where different partial orders coexist among a set of entities. The method proposed does not impose an overhead on the normal functioning of an SDN network, since it requires calculations only when the network must be reconfigured, because of administrative intervention or policies.

How crucial is big data in contemporary molecular design? In this publication we elucidate fundamental concepts and terminology in this field, critically addressing overlooked issues. We thoroughly examine the size, accessibility, quality, and structural aspects of big data alongside the primary methodologies employed for their analysis. Within chemical compounds, properties and descriptors represent two distinct data types, forming the basis for categorizing molecular big data. The primary objective of chemistry is property production, which means we are searching for novel drugs or materials rather than chemical compounds, and big data is the central issue of this philosophy. The increasing availability of data in computer-aided technology propels advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL). Accordingly, a broad chemical audience must comprehend these methods to understand data-centered chemistry. Thus, we aim to systemize big data issues through a simple illustrative framework with fundamental descriptor categories: coding, computer-generated descriptors, and property correlates. Although we employ computer-generated descriptors as big data for predictions, the measured data are irreplaceable for achieving high-quality and reliable outcomes and controlling molecular effects. The scarcity of property data remains a significant hurdle limiting comprehensive studies on the structure-property relationships within big data. Accordingly, guided by pragmatics not-so-big data is an option for drug design. We presented also a brief review of the recent big data literature.

The paper investigates into the intricacies of semiconductor manufacturing, a highly complex process entailing a wide array of subprocesses and diverse equipment. Semiconductors are miniaturized integrated circuits comprising numerous components. The semiconductor manufacturing process begins with the thin disc-shaped silicon wafers. On each wafer, up to thousands of identical chips can be prepared depending upon the diameter of the wafer to build up the circuits layer by layer in a wafer fab. The size of the semiconductors requires a high number of units to be produced, thus necessitating a large amount of data to control for improving the semiconductor manufacturing process. Therefore, the collection and analysis of the equipment data, process data, and machine history data throughout the manufacturing process are required to diagnose faults, monitor the process, and manage the manufacturing process effectively. This research is focused on improving the semiconductor manufacturing process through a rigorous analysis of collected manufacturing process data, employing statistical process control (SPC), data mining techniques, and data-driven decision models. The project's primary objective is to increase the manufacturing process stability and productivity by utilizing the latest data-driven technologies in the scientific community. A structured review was undertaken, exploring contemporary data-driven methodologies in semiconductor manufacturing process improvement, specifically pertaining to process capability, product yield rate, and process stability. This review accentuates a comprehensive evaluation of data-driven methodologies applicable to conventional semiconductor manufacturing facilities, aiming to drive substantial process improvements. It features a detailed demonstration facilitating the selection of optimal semiconductor manufacturing processes to enhance overall operational performance. This study of process improvement in the semiconductor manufacturing steps through the application of data-driven methodologies will be effective in delivering advanced, real-time, and proactive control decisions throughout the manufacturing facilities. This endeavor is expected to promptly provide critical insights for enterprise manufacturing decision-makers to reduce manufacturing cycle time, improve the product yield rate, and increase the overall efficiency of the manufacturing process.

Objective: Determine the feasibility of utilizing longitudinal electronic dental record (EDR) data to track change over time in patient periodontal disease (PD) and to generate three patient cohorts: 1) patients whose disease did not change over time, 2) patients whose PD progressed, and 3) patients whose disease improved over time using informatics approaches. Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between January 1, 2009, and December 31, 2014, at the Indiana University School of Dentistry (IUSD) clinics. We developed and tested three automated computer applications to: 1) diagnose periodontitis cases from periodontal charting, 2) retrieve clinician-documented diagnoses from clinical notes, and 3) track disease change over time. We also evaluated the density of longi-tudinal EDR data for the following follow-up times: 1) none, 2) up to 5 years, 3) >5 and <=10 years, and 4) >10 and <=15 years Results: Thirty-four percent (n=9,954) of the study cohort had up to five years of follow-up visits with an average of 2.78 visits with periodontal charting information. An average of three patient visits per year that contained periodontal charts (63,552) were utilized to obtain a diagnosis, which is considered excellent. For clinician-documented diagnoses from clinical notes, 42% of patients (n=5,562) had at least two PD diagnoses to determine their disease change. In this cohort with cli-nician-documented diagnoses, 72% percent of patients (n=3,919) did not have a disease status change between their first and last visits, 669 (13%) patients' disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We found excellent longitudinal data when diagnoses generated from periodontal charting were considered (three visits per pa-tient). This information can be now utilized for studying the clinical course of periodontitis.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

Abstract: Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

In this paper we explore extending the concept of common cross-study Common Data Element concepts beyond simple demographics to cover disease-specific concepts relevant to imaging. We test interactively linking the resulting database to the associated images in a federated manner. We examine the use of existing standards, not only for terminology, but for interchange of serialized data in forms familiar to imaging and clinical trials specialists and their dedicated systems. Our intent is to perform preliminary work to inform both the upcoming Imaging Data Commons specifically, as well as more general integration projects beyond imaging.

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

The integration of connected autonomous vehicles into the transportation system introduces significant enhancements in driving experience and convenience. Yet, it simultaneously raises important concerns regarding the security and privacy of passenger data. As individuals increasingly depend on these connected vehicles, particularly rental cars with advanced infotainment systems, safeguarding their personal information becomes a paramount imperative, presenting significant challenges that must be addressed to maintain user trust and comply with privacy regulations. This paper investigates risks associated with personal information stored in connected vehicle technology, emphasizing the importance of robust security and privacy practices. In the advent of cyber threats and the need for data protection, we examine a risk management framework (RMF) as suggested by NIST, which aims to address these quintessential concerns preemptively. Our research moves beyond the limitations of manual safeguarding methods, supporting the necessity for an advanced automated technical solution. To address these issues, we introduce and meticulously assess the efficacy of “Vehicle Inactive Profile Removal” (VIPR), an innovative technical solution conceived to actively eliminate the risks and potential oversights that stem from the prevalent tendency of renters to leave personal data unerased in the infotainment systems of rented connected vehicles. We provide a thorough evaluation of VIPR through various scenarios, including vehicle return to a rental depot, subsequent rentals, and ridesharing contexts. Our proof of concept includes an array of experiments to demonstrate how VIPR proficiently and autonomously removes previous renters’ “Inactive” profiles from a simulated infotainment system. The results highlight how VIPR constitutes a critical step towards enhancing the privacy and security of personal data, thereby promoting a safer, more responsible use of connected vehicle technology in society.

Alzheimer’s is a degenerative brain condition brought on by intricate brain changes after cell injury. The prevalence of Alzheimer’s disease is highest in those over 65. Presently, AD is detected by manual examination of a patient’s Magnetic Resonance Imaging (MRI) scan and neuropsychological assessments. Due to its subpar accuracy and performance, solutions based on artificial intelligence (AI) have not been made more widely available. This paper proposes a deep Convolutional Neural Network (CNN) with the pre-trained MobileNet-V2 architecture as the model’s base for feature extraction and reducing the parameter size and training time. The proposed model accurately classifies various early stages of AD. The proposed approach visualizes the class activation maps in the form of a heat map on brain MRI scans via the Grad-CAM algorithm that lifts the black-box nature of the deep model. To handle the imbalance problem of the AD data set acquired from Kaggle, the oversampling algorithm SMOTE interpolates new images to even the class samples. The proposed multi-class classification deep model is named Bio-AD and evaluated using different quality metrics. The Bio-AD model has the following values for evaluation metrics: 97.92%, 99.87%, 97.87%, 98%, 98%, and 0.0646 for accuracy, Area Under the Curve (AUC), F1-score, Precision, Recall, and loss respectively. The proposed Bio-AD model is compared with DenseNet-169, VGG-19, and InceptionResNet-V2 using different quality metrics and outperformed in comparison with all other models using the AD data set.

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files ( greater than 50 MB) or Big data file ( grater than 1 TB) computational complexity is more we leverage on sharding concept to enhance both space and time complexity in cloud storage, hence the large files are divided into shards and stored in different database servers. We also employed blockchain smart contract to enhance secure retrieval of the file and also a secure access method which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data itself. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies require to store data. As the environment maintains privacy of the user data and by not storing the raw data of the user anywhere, the entire environment is setup as a Zero trust model.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

This study explored market actors’ perspectives on data sharing within Poland’s food passports pilot, which tested the feasibility of a digital system that provides reliable and comprehensive information on the origin, journey, and quality of agri-food products. Using thematic analysis, we identified factors influencing stakeholders’ willingness and motivations to share data. Our findings highlight the necessity of a clear value proposition for all actors within the production and distribution chain to invest resources and time in additional data exchange systems. For farmers, reducing burdensome reporting procedures and providing direct financial incentives were key motivators. Agri-food processing, transport and packaging companies viewed data collaboration as a practical tool to ensure high-quality raw materials and promote premium-priced food. Appropriate data management policies were critical for all stakeholders. While sustainability was recognized as important, opportunities for collaboration going beyond economic considerations were not widely explored. Additionally, concerns about data interpretation emerged as a significant issue. These findings suggest the need for data-sharing strategies that better align economic benefits with broader sustainability goals. Further research is also needed to examine how concerns about potential data misinterpretation limit stakeholders’ willingness to engage in data sharing initiatives.

One of the factors leading to the slow decline in poverty in Indonesia is the lack of government at-tention to reduce income inequality. Fiscal policy and economic growth can effectively reduce poverty after lowering income inequality, so the inequality channel must be considered. Socioec-onomic and infrastructure differences between provinces can influence the effectiveness of eco-nomic growth and fiscal policy in reducing poverty. This study aims to assess the effects of eco-nomic growth and fiscal policy on spending and taxes on direct and indirect poverty reduction through lowering income inequality and how these variables influence poverty by province. This study employed secondary data, including panel data for 2010-2023 from 34 provinces in Indo-nesia, which were analyzed using an autoregressive cross-lagged SEM model. The study finds that economic growth and fiscal policy on spending on education and health are statistically sig-nificant in reducing poverty directly in non-Java but do not affect it through income inequality. Taxes increase income inequality, and the social safety net does not reduce poverty outside Java. Increased spending on education and health should continue, with improvements in targeting so-cial safety nets and tax reforms needed to make the system more progressive and reduce inequality

This study investigates student performance with the aim to forecast students' academic outcomes using data analytics and machine learning techniques. There is a big drop out per cent especially in the technological study programs at the universities, however, focusing on the successful study process there is important to analyze and to predict some risks on students’ to assure low dropout percent. While Student Performance Prediction (SPP) has potential benefits, such as personalized learning and early interventions, several challenges make the task complex in relation to (1) data quality and availability and (2) incomplete and inconsistent data. Predicting performance requires a range of different data types: academic records, attendance, and engagement in class, socio-economic background, and even behavioral factors. Moreover, according to the fourth Sustainable Development Goal (SDG) we focus on the quality in education.This paper presents a case study of data mining method for SPP by using classification, regression, and clustering are generally used in education to predict student’s performance which is important to control drop out of studies percent. The case presented is to predict risks and to assure academic success and quality based on the CRISP-DM data mining.

The WHO is so passionate about good health for all meanwhile the emerging counselling discipline is a complimentary arm whose interest is to fully complement those efforts through helping humankind in issues that may be affecting the health and the overall well-being. The researcher has discovered through studies and general knowledge that men tend to delay in seeking health help compared to women. Men appear to be reluctant in seeking professional assistance in problems pertaining to their health, marriages, work place, infertility, sexual dysfunction and other social life ills subsequently leading to depression, suicides, excessive uptake of alcohol, violence, mental psychosis and physical disabilities or premature death. Studies have shown that men are socially constructed to be strong from a tender age and they grow into adulthood believing that they are strong both physical and emotional hence they are supposed to withstand any situation affecting them. Masking and faking have been regarded as sign of being a real man, however these social constructs, perceptions and expectations appear to be the root cause of problems manifesting daily in men’s behavior towards seeking health help. This study therefore endeavors to find out more about the factors or determinants influencing men’s reluctance to seeking help in areas concerning their health in Industrial site of Victoria Falls. The study serves as additional educational awareness guide for the significant other, while issues unraveled help in gender mainstreaming programs for gender equality when it comes to societal hegemonizing of men’s health. Moreover, the exploration of influencing factors or determinants, causes and effects of men’s reluctance in seeking health help were scrutinized as well as possible solutions. There has been much realization of more various literature on women’s issues than men because women in general have been known to open up and talk through their issues unlike men hence the aim is to focus on men in order to understand the attitude and perceptions underlying the reluctance to seek medical care regularly and promptly. When communities get to understand that men too, feel the pain both emotionally and physically, may be then, men would not fear stigmatization but come out to seek health help promptly and communities would realize that some cultural expectations placed on men result in more harm than good.

Accurate solar power forecasting is pivotal for the effective planning, management, and operation of power systems, ensuring a sustainable energy supply for consumers while optimizing the integration of renewable energy sources and the functioning of electricity markets. Recent advancements have focused on developing predictive models that offer precise daily forecasts for solar power generation, which are critical for power usage planning and production efficiency. These models typically leverage a diverse range of data inputs, including solar power metrics and meteorological variables such as temperature, humidity, precipitation, solar radiation, and wind speed—factors that significantly influence solar power generation due to their weather-dependent nature. This study introduces a novel hybrid forecasting model that synergizes the capabilities of the Prophet model and Gated Recurrent Units (GRU), leveraging their respective strengths to enhance predictive performance. The proposed model is rigorously evaluated across short-term (2 days, 7 days) and medium-term (15 days, 30 days) forecasting horizons. Experimental results demonstrate that the hybrid model significantly outperforms the existing models, delivering superior accuracy in solar power generation forecasts.

Optical communication systems pose significant challenges, including the effects of nonlinear noises. The nonlinearities, including Kerr-induced phase noise, become more problematic in m-QAM as the orden of the format increases becoming a highly densed set of data-symbols and therefore, requiring advanced signal processing for successful separation of symbols at the demodulation stage. Machine learning techniques have recently been applied to improve signal integrity in such scenarios. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in -90, -100, and -110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.

Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind en-ergy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to forecast wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, evaluated us-ing metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their ca-pabilities and responses to preprocessing, as well as each model's reaction in terms of statistical performance deviation indicators. Ultimately, the preprocessing and the data analysis show that Random Forest regression is more suitable for weather condition datasets than the other two re-gression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy forecasting.

Machine unlearning is a critical process designed to allow machine learning models to forget or remove specific data upon request, particularly for privacy protection. While the primary objective of unlearning is to safeguard sensitive information, it introduces substantial privacy and security risks. This paper explores the two major categories of threats to machine unlearning: privacy attacks and security attacks. Privacy attacks, such as membership inference and model inversion, exploit residual information to infer or reconstruct sensitive data that should have been erased. Security attacks, particularly specific data poisoning, involve the injection of malicious data into the training process, which may leave lingering effects even after the unlearning process. In this paper, we provide an in-depth examination of these threats and propose several defense mechanisms. Differential privacy, adversarial training, and query limitations are highlighted as key defenses against privacy attacks, while data validation, adversarial examples, and post-unlearning audits are critical in mitigating security risks. Additionally, we discuss emerging methodologies like data provenance tracking and fine-tuning as crucial for ensuring unlearning processes are thorough and effective. Through these analyses, we aim to provide a comprehensive framework for strengthening the security and privacy of machine unlearning systems, enabling their broader adoption across industries such as healthcare, finance, and AI-driven technologies.