Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

The General Theory of Information (GTI) tells us that information is represented, processed and communicated using physical structures. The physical universe is made up of structures combining matter and energy. According to GTI, “Information is related to knowledge as energy is related to matter.” GTI also provides tools to deal with transformation of information and knowledge. We present here, the application of these tools for the design of digital autopoietic machines with higher efficiency, resiliency and scalability than the information processing systems based on the Turing machines. We discuss the utilization of these machines for building autopoietic and cognitive applications in a multi-cloud infrastructure.

We describe an efficient and cost effective data access mechanism for Sentinel-1 TOPS 1 mode bursts. Our data access mechanism enables burst-based data access and processing, thereby 2 eliminating ESA’s Sentinel-1 SLC data packaging conventions as a bottleneck to large scale processing. 3 Pipeline throughput is now determined by available compute resources and efficiency of the analysis 4 algorithms. For targeted infrastructure monitoring studies, we are able to generate coregistered, 5 geocoded stacks of SLCs for any AOI in the world in a few minutes. In addition, we describe our 6 global scale radar backscatter and interferometric products and associated pipeline design decisions 7 that ensure geolocation consistency across the suite of derived products from Sentinel-1 data. Finally, 8 we discuss the benefits and limitations of working with geocoded SAR SLC data.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

This article discusses the potential advantages of a data processing technique for continuous gravitational wave signals searches in the data measured by ground-based gravitational wave interferometers. Its main advantage over other techniques is that it does not need to search over the signal’s direction of propagation. Although it is a “ coherent method” (i.e. it coherently processes year-long data), it is applied to a data set obtained by multiplying the original time-series with a (half-year) time-shifted copy of it. As a result, the phase modulation due to the interferometer motion around the Sun is automatically canceled in the signal of the synthesized time-series. Although the resulting signal-to-noise ratio is not as high as that of a coherent search, it equals that of current hierarchical methods. In addition, since the signal search is performed over a parameters space of smaller dimensionality, the associated false-alarm probability should be smaller than those characterizing hierarchical methods and result in an improved likelihood of detection.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

It is essential to deal with data scattered among heterogeneous information sources, which can be structured, semi-structured, or unstructured, to give consumers a cohesive perspective of the data. Information gathering is challenging as a result, and one of the major causes of this is that data sources are developed to support specific applications. A method to simplify this procedure is to use ontology representation as an intermediate step. An ontology represents a knowledge structure that reasonably reflects the real world's complexity and is ideally used in many industries today. This research aims to enhance the functionality of a global ontology system by implementing and integrating a natural language query system. Leveraging NLP-based approaches, we constructed SPARQL queries for our ontologies to facilitate natural language translation into a well-structured query format. A dedicated query component was developed to transform natural language questions into SPARQL queries, with the selection of the optimal query graph to generate the final queries. In the subsequent phase, we explored the integration of distinct sentence encoders to improve latent sentence representations during query construction. The generated SPARQL queries were executed on the global ontology output and then translated into source-specific questions. This innovative approach enables unified access to heterogeneous data sources through a user-friendly natural language querying interface.

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that are able to elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose an hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the industrial revolutions which have continued expansion and progress into the fourth industrial revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. The significance of standardized data and its mode of collection cannot be overstated as it has a major impact on the process outcomes. In order to achieve a better understanding of the impact of this, this study methodically reviewed the literature on the subject of the data collection and processing mechanism of 4IR technologies in the context of the transport industry with an emphasis on the areas of accomplishment of several studies and drawing attention to their limitations. Although the industry has advanced significantly as a result of the adoption and application of various research studies in this field, considerable focus needs to be placed in the areas of standardized data collection and computational approaches, which will ultimately assist in achieving various benefits in the transportation industry.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

A heterogeneous disease like cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), patients’ survival vary significantly and show different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score. We observed that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we also compared the effect of feature selection and similarity measures for subtype detection. To evaluate the results obtained, we selected the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes identified by the autoencoders; the obtained results coincide well with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.

With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capabilities in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possibility and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering was minimally discussed in literature neither. This paper first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model into generating new game levels within two well-known game settings; as well as validating the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments were also utilized for evaluating the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and spatial results, several potential drawbacks and future enhancement of the established model were outlined, with the aim of maximizing the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish-subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

In the neuroscience research field, specific for medical imaging analysis, how to mining more latent medical information from big medical data is significant for us to find the solution of diseases. In this review, we focus on neuroimaging data that is functional Magnetic Resonance Imaging (fMRI) which non-invasive techniques, it already becomes popular tools in the clinical neuroscience and functional cognitive science research. After we get fMRI data, we actually have various software and computer programming that including open source and commercial, it's very hard to choose the best software to analyze data. What's worse, it would cause final result imbalance and unstable when we combine more than software together, so that's why we want to make a pipeline to analyze data. On the other hand, with the growing of machine learning, Python has already become one of very hot and popular computer programming. In addition, it is an open source and dynamic computer programming, the communities, libraries and contributors fast increase in the recent year. Through this review, we hope that can make neuroimaging data analysis more easy, stable and uniform base the one platform system.

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. A cruicial part of the analysis is to avoid unnecessary data retention during that process, in order to prevent subsequent abuse, theft or public exposure of collected datasets and thus, protect the privacy of social media users. There are a number of technical approaches out to face the problem. One of them is using a cardinality estimation algorithm called HyperLogLog to store data in a privacy-aware structure, that can not be used for purposes other than the originally intended. In this case study, we developed and conducted a focus group discussion with teams of social media analysts, in which we identified challenges and opportunities of working with such a privacy-enhanced social media data structure in place of conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisiton process will not distract the data analysis process. Instead, it will improve working with huge datasets due to the improved characteristics of the resulting data structure.

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.

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.

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.

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.

This paper presents work done as part of a transformation effort towards a greener and more sustainable Aluminium manufacturing plant. The effort includes reducing the carbon footprint by minimising waste and increasing operational efficiency. The contribution of this work includes the reduction of waste through the implementation of autonomous, real-time quality measurement and classification at an Aluminium casthouse. Data is collected from the MV20/20 which uses ultrasound pulses to detect molten Aluminium inclusions, which degrade the quality of the metal and cause subsequent metal waste. The sensor measures cleanliness, inclusion counts and distributions from 20 - 160 microns. The contribution of this work is in the development of business analytics to implement condition-based monitoring through anomaly detection, and to classify inclusion types for samples that failed. For anomaly detection, multivariate K-Means and DBSCAN algorithms are compared as they have been proven to work in a wide range of datasets. For classification, a two-stage classifier is implemented. The first stage classifies the success or failure of the sample, while the second stage classifies the inclusion responsible for the failed sample. The algorithms considered include logistic regression, support vector machine, multi-layer perceptron and radial basis function network. The multi-layer perceptron offers the best performance using k-fold cross-validation, and is further tuned using grid search to explore the possibility of an even better performance. The results reveal that the model has achieved a global maximum in performance. Recommendations include the integration of additional sensor systems and the improvements in quality assurance practices.

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

This work analyzes a new indoor laboratory data set looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the confines of an indoor laboratory setting using multiple distributed sensor nodes on different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOC), CO, CO2, H2, UV, air temperature and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen on exploring innovative advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out in dependence of the manhattan distance between the fire source and the sensor node. We found that especially larger particles (&gt; 0.5 μm) and VOCs show a significant dependency with respect to the intensity as a function of the manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand popagation effects.

Metaheuristic algorithms have been frequently using to tackle optimization problems, however such algorithms in the analysis of health-related data is not commonly used as developing metaheuristic algorithms that work well on health-related data is a difficult task due to complexity of the health data in particular genomics and epigenetics data. One of the important tasks in genomics is to predict genomic elements that are incorporating together to regulate a disease-related genes. Predicting such elements are important as they can be used to develop a personalized cure. In this study, we present for the first time, a multi-object simulated annealing algorithm to identify enhancer-promoter like interactions from Hi-C (chromosome conformation capture) data. These regulatory elements can potentially play vital roles as promoters and/or enhancers in appearance and exacerbation of the regulation of gene.s To evaluate the efficiency of the proposed method, we applied our proposed method and traditional methods on the Hi-C data from mice and compared together. Our results show that the interacting elements identified by our new method are more likely to be functional. The source code of the method is publicly available.

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

Flow field based methods are becoming increasingly popular for the analysis of interfacial shear rheology data. Such methods take properly into account the subphase drag by solving the Navier-Stokes equations for the bulk phases flows, together with the Boussinesq-Scriven boundary condition at the fluid-fluid interface, and the probe equation of motion. Such methods have been successfully implemented at the double wall-ring (DWR), the magnetic rod (MR), and the bicone interfacial shear rheometers. However, a study of the errors introduced directly by the numerical processing is still lacking. Here we report on a study of the errors introduced exclusively by the numerical procedure corresponding to the bicone geometry at an air-water interface. In our study we directly input a preset the value of the complex interfacial viscosity and we numerically obtain the corresponding flow field and the complex amplitude ratio for the probe motion. Then we use the standard iterative procedure to obtain the calculated complex viscosity value. A detailed comparison of the set and calculated complex viscosity values is made upon changing different parameters such as real and imaginary parts of the complex interfacial viscosity and frequency. The observed discrepancies yield a detailed landscape of the numerically introduced errors.

A multi-decadal change analysis of the irrigation ponds in Taoyuan, Taiwan was conducted by using multi-source data including digitized ancient maps, declassified single-band CORONA satellite images, and multispectral SPOT images. Supervised LULC classifications were conducted using four textural features derived from the single-band CORONA images and spectral features derived from SPOT images. Post-classification analysis revealed that the number of irrigation ponds in the study area decreased during the post-World War II farmland consolidation period (1945 – 1965) and the subsequent industrialization period (1970 – 2000). However, efforts on restoration of irrigation ponds in recent years have resulted in gradual increases in the number (9%) and total area (12%) of irrigation ponds in the study area.

One of the most promising advancements in healthcare is the application of digital twin technology. Digital twins are virtual replicas of real-world human patients and can be used for monitoring, and diagnosis, and as a tool to design treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. This review article delves into the various data sources and methodologies that contribute to the construction of digital twins. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-censor data will have a specific strength in further classification of nemoral forest landscapes owing to the distinct seasonal patterns of the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and machine learning (random forest) trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90 %, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34-74%. Species groups with coniferous species were the least confused whereas the broadleaf groups, especially Oak, had higher error rates. The results are applied in Danish National accounting of greenhouse gas emissions from forests, resource assessment and assessment of forest biodiversity potentials.

The healthcare domain is increasingly adopting IoT and Electronic Health Record (EHR) systems, generating vast volumes of healthcare data. This shift is driven by the growing need of delivering the right information to the right individuals, at the right time. The latter underscores the importance of adopting a comprehensive strategy for efficiently collecting, utilizing, and analyzing health-related data to not only enhance overall healthcare management but also for the provision of timely and personalized prevention strategies. The latter is of highest importance especially in scenarios where lack of effective treatments or poor survival rates (such in pancreatic cancer) renders typical healthcare strategies ineffective. In this article, we introduce an innovative and integrated platform that is specifically designed and developed for accessing, processing, and analyzing data in challenging healthcare scenarios, such as dealing with pancreatic cancer. This platform, called iHelp, combines multidisciplinary technologies and provides healthcare professionals reliable risk modelling, analysis, and prediction techniques so that individuals (at risk of developing pancreatic cancer) can be provided with timely, reliable, and personalized prevention and intervention measures. A key innovation in the iHelp platform is the standardized data management approach called Holistic Health Records (HHRs) that facilitate the capturing of all health determinants in a standardized and well-structured way for processing towards the provision of health risk detection and personalized healthcare decision support. In the development of iHelp platform, the HHRs are evaluated through different real-world healthcare datasets, including datasets coming from hospital systems, data from wearables, questionnaires, and mobile applications.

Selection of the most suitable material is one of the key decisions to be taken at the design stage of a manufacturing process. Traditional approaches as Ashby maps based on material properties are widely used in the industry. However, in the production of multimaterial components, the criteria for the selection can include antagonistic approaches. The aim of this work is the implementation of a methodology based on the results of process simulations for several materials and classify them by applying an advanced data analytics method based on Machine Learning (ML), in this case the Support Vector Regression (SVR) and Multi-Criteria Decision Making (MCDM) methodolo-gies, in this case Multi-criteria Optimization and Compromise Solution (VIKOR) combined with Entropy weighting methods. In order to do this, a Finite Element Model (FEM) has been built to evaluate the extrusion force and the die wear in a multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR combined with Entropy weighting methodologies, a comparison has been established based on the material selection and complexity of the methodology used, resulting that material chosen in both methodologies is very similar and MCDM method is easier to implement because there is no need of evaluate the error of the pre-diction model and the time for data preprocessing is less than the time needed in SVR.

The combination of new-generation information technology and manufacturing technology has resulted in major and profound impact on future development paradigm of manufacturing. It is challenging for existing methods to conduct a multidimensional trend exploration related to machine tool domain, which is the basis of virtually everything in manufacturing. In this paper, we proposed an integrating approach framework combined topic models, bibliometric, trend analysis and patent analysis to mine insightful information about future development from multi-source data related to machine tool, such as papers, grants, patents and news. Specifically, papers and grants provided two different perspectives to explore the current focuses and future trends in machine tool research. Furthermore, the future technology development of machine tool was investigated through patents analysis. Finally, news related to machine tool industry in recent years was analyzed to analyze future machine tool business mode. The integration of the above various analytical methods and the multi-dimensional mining of literatures enabled the analysis of the future development of machine tool domain systematically from multi-perspectives which include research, technology development and industry. The conclusions obtained in this paper is beneficial to different communities of machine tool in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

Ultrasonic-guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects experienced in metallic pipelines. Signal processing of the guided waves is often challenged due to the complexity of operational conditions and environments in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for signal process and data classification of complex systems, thus great potential for damage detection of critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model is utilized for decoding ultrasonic guided waves for damage detection of metallic pipelines, and twenty-nine features are extracted as input to classify different types of defects from metallic pipes. The prediction capacity of the CNN-LSTM model is assessed by comparing it to the CNN and LSTM. The results demonstrate that the CNN-LSTM hybrid model exhibits much higher accuracy, with 94.8%, as compared to those of CNN and LSTM. Interestingly, the results also reveal that predetermined features, including time-, frequency-, and time-frequency domains, could significantly improve the robustness of the deep learning approaches, even though the deep learning approaches are often believed they include automated feature extraction, without the hand-crafted steps as the shallow learning do. Furthermore, the CNN-LSTM model displays higher performance when the noise level is relatively low (e.g., SNR=9 or higher), as compared to the other two models, but its prediction drops gradually with the increase of the noise.

The variety of risks due to climate change with a potential impact on Cultural Heritage CH requires a multidisciplinary approach and a preliminary overview of the existing methods for risk analysis and assessment in different fields. Thus, the starting point of the research conducted by the Sapienza PNRR working group consists in a systematic literature review of risk assessment approaches, methods and indicators. As a first outcome, the research has designed a multidisciplinary comprehensive framework consisting of the definition of risk categorization, the framework represented by a measurable index and the main target, according to the classification of Cultural Heritage based on the harmonization of European vocabularies definitions and protocols. The risk assessment framework sets the methodological core of a future multi criteria matrix and digital platform for decision focused on the historic built environment and it is functional to the definition of specific operational guidelines connected to each risk for elaborating descriptive/predictive Architectural Heritage’s transformation scenarios to support the development of mitigation and adaptation strategies.

The meticulously engineered powertrain mounting system of hybrid-electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data-mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid-electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—assessing their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit consists of (i) a sensor and hardware layer that can be configured in a modular manner per research needs, (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study for photo-plethysmography (PPG) sensor with 15 participants, PhysioKit shows strong agreement with research-grade PPG sensors. Furthermore, we report usability survey results from 10 micro-project teams (44 individual members in total) who use PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. We conclude by discussing extensibility and potential impact of the toolkit on the research community.

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.

Specific climate adaptation and resilience measures can be efficiently designed and implemented at the regional and local level. Climate and environmental databases are of critical importance to achieving sustainability goals (SDGs) and for the efficient planning and implementation of suitable mitigation measures: Available databases can serve municipalities as a vital starting points to determine requirements, prioritize resources and allocate investments under consideration of commonly tight budget restrictions. High-quality geo, climate and environmental data are now available – data from remote sensing, i.e. Copernicus services will be of crucial importance. Forward-looking approaches exist to using such data to derive forecasts for urban planning process optimization for municipal administrations. On municipal level, however, the existing data have so far only been used to a limited extent, since there are no practical tools for urban planning that can be used to merge and meaningfully combine remote sensing data with local data and to further process and apply in municipal planning processes. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services and procedures, such as the development of practice-oriented technical tools that acquire various remote sensing and in-situ data sets for validation and further processing.

Fabry-Pérto (FP) cavity is the essential component of ultra-stable laser (USL) for gravitational wave detection, which couples multi-physic (Optical/Thermal/Mechanics) fields and requires ultra-high precision. To satisfy the requirements of precise and efficient design, a multi-physic coupling optimization method for fixing cubic FP cavity based on data learning is proposed. A multi-physic model for the cubic FP cavity is established and the performance is obtained by finite element analysis. The key performance indices (V, wF, wF) and key design variables (d, l, F) are determined considering the features of the FP cavity. The 49 sets of data by orthogonal experiment are acquired for the establishment and comparison of different data learning models (NN, RSF, KRG). The result turns out that the neural network has the best performance. Based on NSGA-II, the Pareto optimal front is obtained and the optimal combination of design variables is finally determined as {5,32,250}. The performance after optimization has proven to be a great improvement, of which the displacement under the fixing force and vibration test are decreased by more than 60%. The optimization strategy can not only help the design of the FP cavity but also enlighten other optimization fields.

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described by a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. To minimize the compactness is equivalent to maximize the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as feature effectiveness criterion. Features having small average compactness are mutually covariate, and are able to detect geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data by the visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.

Enhanced food and nutrition security remains a primary goal for every community. Several interventions have been promoted in dry areas to improve issues on food and nutrition security. However, studies on the level of knowledge, cultural norms, perceptions and attitudes that are key drivers in adoption and uptake to highlight gaps and provide evidence for improvement are limited. This study investigated variables influencing the adoption and implementation of an integrated crop-dairy goat farming system in Elgeyo Marakwet. A descriptive cross-sectional survey entailing qualitative and quantitative approaches among farmers practicing integrated farming was undertaken. A thematic questionnaire was used to collect quantitative data, while key informant interviews and focus groups discussions were used in qualitative research. This study utilized the multi-stage sampling procedure to sample the farmers and sample size was calculated based on Krejcie and Morgan table. Data analysis for quantitative data was done using SPSS software while qualitative data utilized N-vivo software The findings show that farmers have knowledge on the integrated farming system. Age, level of education, land size, gender, perceptions and attitudes influence adoption. Small animals like dairy goats are associated to women in this community hence increasing their participation in access, control and decision making of agricultural resources. The key findings of this study provide baseline data that can form evidence to help inform policy on the indicators contributing to adoption of integrated crop-dairy goat systems to enhance food and nutrition security

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

Masons are often overlooked part of impact basins, but play an important role in revealing the lunar history. Previous study in masons were usually limited to gravity data. Few researches were reported on morphology features and chronology, which hampers the construction of a complete geological interpretation for the evolution of each mascons. We use multi source remote sensing data to identify the details characteristic of mascons. Result of topography, gravity and characteristic are combined to prove that a mason beside Copernicus crater is a buried peak-ring basin which is about 130km and 260km in diameter. The underground structure is of confirmed as 890m thick mare basalts by analyzing the spectral feature of the material in a geological outcrop called Copernicus H. Geology evolution analysis joint crater size-frequency distribution (CSDF) dating demonstrate that the buried basin impact event occurred in 3.6Ga. Then a hawaiian-style eruption in late Imbrian formed Sinus Aestuum Ⅰ Dark Mantling Deposit (DMD). Mare basalts filling in 3.4Ga. After that, ejecta from Copernicus impact event in about 820Ma and weathering processes cause the disappearing from lunar surface of the buried basin.

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

In daily travel and activities, pathfinding is a significant process. They are often used in transportation routes calculation. They have now evolved to be able to solve most situations of the pathfinding and its related problems. This review describes previous and recent studies on the pathfinding algorithms. It reviews the development of pathfinding algorithms in a classification base on their usage. The aim is to summarize the application of the pathfinding algorithms for the readers interested in the subject that can be used as a supplement.

Multi-Faith Spaces (MFS) are a relatively recent invention that quickly gained in significance. On the one hand, they offer a convenient solution for satisfying needs of people with diverse beliefs in the institutional context of hospitals, schools, airports, etc. On the other hand, as Andrew Crompton pointed out, they are politically significant because the multi-faith paradigm “is replacing Christianity as the face of public religion in Europe” (2012, p. 493). Due to their ideological entanglement, MFS are often used as the means to promote either a more privatised version of religion, or a certain denominational preference. Two distinct designs are used to achieve these means: negative in the case of the former, and positive in the latter. Neither is without problems, and neither adequately fulfils its primary purpose of serving diverse groups of believers. Both, however, seem to follow the biases and main problems of secularism. In this paper, I analyse recent developments of MFS to detail their main problems and answer the question, whether the MFS, and the underlying Multi-Faith Paradigm, can be classified as a continuation of secularism.

Deep learning has been applied in many areas that have had a significant impact on applications that improves real-life challenges. The success of deep learning in a wide range of areas is due in part to the use of activation functions, which are particularly effective at solving non-linear problems. Activation functions are a key focus for researchers in artificial intelligence who aim to improve the performance of neural networks. This article provides a comprehensive explanation and comparison of different activation functions, with a focus on the arc tangent and its variations specifically. The paper presents experimental results that show that variations of the arc tangent using irrational numbers such as pi, the golden ratio, and Euler’s number, as well as a self-arctan function, produce promising results. Since we experimented with promising activation functions on two different problems, and datasets, we reached a result that different irrationals work well for different problems. In other words, arctan ϕ gives the best results mostly for multiclass classification and arctan e gives the best results for time series prediction problems. The paper focuses on a multi-class classification problem applied to the Reuters Newswire dataset and a time-series prediction problem on Türkiye energy trade value to show the impacts of activation functions.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability &amp; scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these data combined form ‘big data’ that can be utilized to optimize treatments for each unique patient (‘precision medicine’). To achieve this precision medicine, it is necessary that hospitals, academia and industry work together to bridge the ‘valley of death’ of translational medicine. However, hospitals and academia often have problems with sharing their data, even though the patient is actually the owner of his/her own health data, and the sharing of data is associated with increased citation rate. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. The idea that society benefits the most if the patient’s data are shared as soon as possible so that other researchers can work with it, has not taken root yet. There are some publicly available datasets, but these are usually only shared after studies are finished and/or publications have been written based on the data, which means a severe delay of months or even years before others can use the data for analysis. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here we discuss several aspects of data sharing in the medical domain: publisher requirements, data ownership, support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing.

This article explores the intricate relationship between Human-Data Interaction (HDI) and Data Virtualization (DV), two integral fields that are redefining how we engage with and manage data in the digital age. Positioned at the intersection of technology and user-centric design, this study examines how DV can transcend traditional data management boundaries to significantly enhance data accessibility, legibility, and agency, thus fostering more ethical and meaningful interactions across various sectors. Opening with a detailed exposition on the evolving role of HDI, the paper contextualizes the importance of user-centric data practices within contemporary digital challenges. HDI’s principles are pivotal as they advocate for user empowerment in data interactions, ensuring that data processes are not only transparent but also negotiable and aligned with users’ needs and ethical standards. DV emerges as a transformative solution that inherently supports these principles by integrating diverse data sources into a cohesive and accessible format, thereby eliminating many of the inefficiencies found in conventional data management systems. Through a series of nuanced case studies, the paper provides empirical evidence of DV’s effectiveness across diverse domains. In healthcare, DV facilitates a holistic approach to patient care by providing seamless access to comprehensive medical records, thereby enabling faster, more accurate diagnostic and treatment processes. In the financial sector, DV proves instrumental in enhancing the detection and management of fraud, integrating transactional data across global platforms in real-time to allow for swift, proactive interventions. In education, DV supports personalized learning by synchronizing data from various educational tools, thus allowing for tailored educational experiences that respond dynamically to student performance and engagement metrics. Moreover, the paper critically addresses the ethical considerations and privacy concerns that arise with the integration of HDI and DV, particularly as data interactions become more pervasive and complex. By proposing forward-thinking research directions, it seeks to refine these technologies to better serve societal needs while addressing potential risks and governance issues. In conclusion, this study significantly contributes to the discourse on HDI by advocating for DV as an essential, innovative tool that enhances the clarity, fairness, and efficacy of data interactions. By demonstrating how DV can transform large-scale data sets into user-friendly formats that support decision-making and improve operational efficiencies, the paper underscores DV’s potential to advance a range of industries and enhance the daily lives of individuals interacting with digital systems.

Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has few number of samples, we studied a small metabolomic multiblock dataset containing six blocks (i.e. tissue types), only including common metabolites. We used a single model multiblock analysis method called Joint and unique multiblock analysis (JUMBA) and compare it to a commonly used method, concatenated PCA. These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Nowadays, one of the greatest challenges in Data Meshes revolves around detecting and creating Data Domains and Data Products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly com-plicated issue and suggests a standardized approach that integrates the concept of Data Blueprints with Data Meshes. In essence, a novel standardization framework is proposed that creates Data Products using a metadata semantic enrichment mechanism, the latter also offering Data Domain readiness and alignment. The approach is demonstrated using real-world data produced by mul-tiple sources in a poultry meat production factory. A set of functional attributes is used to compare qualitatively the proposed approach against existing data structures utilized in storage architectures with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests successful performance.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The Ten simple rules for using public biological data are: 1) use public data purposefully in your research, 2) evaluate data for your use case, 3) check data reuse requirements and embargoes, 4) be aware of ethics for data reuse, 5) plan for data storage and compute requirements, 6) know what you are downloading, 7) download programmatically and verify integrity, 8) properly cite data, 9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share, and 10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

Improvements in web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, there are insufficient means for data providers to learn what kinds of data users desire and for what purposes. In this paper, we propose and discuss the description items for sharing users’ call for data as data requests in the data marketplace. We also discuss structural differences in data requests and providable data using variables, as well as possibilities of data matching. In the study, we developed an interactive platform, treasuring every encounter of data affairs (TEEDA), to facilitate matching and interactions between data providers and users. The basic features of TEEDA are described in this paper. From experiments, we found the same distributions of the frequency of variables but different distributions of the number of variables in each piece of data, which are important factors to consider in the discussion of data matching in the data marketplace.

The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

In the rapidly evolving field of data management, numerous terminologies, such as data warehouse, data lake, data lakehouse, and data mesh , have emerged, each representing a unique analytical data architecture. However, the distinctions and similarities among these paradigms often remain unclear. The present paper aimed to navigate the data architecture landscape by conducting a comparative analysis of these paradigms. The analysis a identified and elucidated the differences and similari- ties in features, capabilities, and limitations of these architectural constructs. The study outcome serves as a comprehensive guide, assisting practitioners in selecting the most suitable analytical data architecture for their specific applications.

Data Meshes is an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of Data Meshes and Data Products in response to user requests, ensuring that stakeholders will have access to specific portions of the Data Mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of Data Meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory.

Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship (SDG9), new skills, and jobs (SDG8), especially in small communities (SDG11) and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being (SDG3), and protect digital rights, we propose data cooperatives [1,2] as a vehicle for secure, trusted, and sovereign data exchange [3,4]. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized [5]. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted Application Programming Interfaces (APIs) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This policy paper presents and discusses several transformative use cases for cooperative data governance. The use cases demonstrate how platform/data-cooperatives, and their novel value creation can be leveraged to take digital commons and value chains to a new level of collaboration while addressing the most pressing community issues. The proposed framework for a digital federated and sovereign reference architecture will create a blueprint for sustainable development both in the Global South and North.

A stable driver state is essential in the process of manually transition control, which inevitably occurs in Level 3 automated driving situations. To this end, this paper proposed a CNN algorithm-based driver state monitoring system that uses multi-sensor data such as driver's face image, biometric information, and vehicle behavior information as input. This system calculates the probability of drowsiness for each of the four time periods using a convolutional neural network (CNN) based on ToF camera-based eye blinking, ECG information (pulse rate) embedded in the steering wheel, and vehicle information (steering angle data). In order to build a reliable and high-quality training dataset (Ground Truth) for the CNN algorithm, a baseline was established by matching the driver's face image with the electrocardiogram (ECG) and electroencephalogram (EEG) changes in the drowsy and normal states. In a simulation test of the proposed CNN algorithm using more than 20,000 driver image data acquired using a driving simulator, the TNR was 94.8% and the accuracy was 94.2%. Our proposed method is expected to minimize human errors that may occur when switching control by monitoring inappropriate driver state (drowsiness) in real time.

The three-armed cluster-randomized trial ARena (Sustainable reduction of antibiotic-induced antimicrobial resistance) aimed to foster appropriate antibiotic use and reduce overprescribing in German ambulatory care to counter antibiotic resistance. Multi-faceted interventions targeted primary care physicians, teams and patients. This study examined effectiveness of the implementation program. ARena was conducted in 14 primary care networks with 196 practices. All arms received data-based feedback on antibiotics prescribing and quality circles. Arms II and III received different add-on components each. Primary outcome examined is the prescribing rate for systemic antibiotics for cases with non-complicated acute infections (upper respiratory tract, bronchitis, sinusitis, tonsillitis, otitis media). Secondary outcomes refer to prescribing of quinolones and guideline-recommended antibiotics. Based on pseudonymized quarterly claims data, mixed logistic regression models examined pre-post intervention antibiotic prescribing rate changes and compared to matched standard care. A significant rate reduction (arm I 11.7%; arm II 9.9%; arm III 12.7%) and significantly lower prescribing rates were observed for all arms (20.1%, 18.9% and 23.6%) compared to matched standard care (29.4%). Fluoroquinolone prescribing was reduced in all intervention arms and rates for recommended substances generally increased. No significant post-interventional difference between intervention arms was detected. Findings indicate implementation program impact compared to standard care. Trial registration: ISRCTN, ISRCTN58150046

The scientific community has long benefited from the opportunities provided by data reuse. Recognizing the need to identify the challenges and bottlenecks to reuse in the agricultural research community and propose solutions for them, the data reuse working group was started within the AgBioData consortium (https://www.agbiodata.org/) framework. Here, we identify the limitations of data standards, metadata deficiencies, data interoperability, data ownership, data availability, user skill level, resource availability, and equity issues, with a specific focus on agricultural genomics research. We propose possible solutions stakeholders could implement to mitigate and overcome these challenges and provide an optimistic perspective on the future of genomics and transcriptomics data reuse.

To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen. However, there is no detailed guide that would allow researchers to follow a consistent process for data harmonization. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. This process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.

Context: Despite the effort put into developing standards for structuring construction cost, and the strong interest into the field. Most construction companies still perform the process of data gathering and processing manually. That provokes inconsistencies, different criteria when classifying, misclassifications, and the process becomes very time-consuming, particularly on big projects. Additionally, the lack of standardization makes very difficult the cost estimation and comparison tasks. Objective: To create a method to extract and organize construction cost and quantity data into a consistent format and structure, to enable rapid and reliable digital comparison of the content. Method: The approach consists of a two-step method: Firstly, the system implements data mining to review the input document and determine how it is structured based on the position, format, sequence, and content of descriptive and quantitative data. Secondly, the extracted data is processed and classified with a combination of data science and experts’ knowledge to fit a common format. Results: A big variety of information coming from real historical projects has been successfully extracted and processed into a common format with 97.5% of accuracy, using a subset of 5770 assets located on 18 different files, building a solid base for analysis and comparison. Conclusion: A robust and accurate method was developed for extracting hierarchical project cost data to a common machine-readable format to enable rapid and reliable comparison and benchmarking.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here, we offer practical tips for working with large datasets from the end-user’s perspective. We cover all aspects of the data life cycle: from what to consider when downloading and storing the data, to tips on how to become acquainted with a dataset one did not collect, to what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.

Modern experimental astroparticle physics features large-scale setups measuring different messengers, namely high-energy particles generated by cosmic accelerators (e.g. supernova remnants, active galactic nuclei, etc): cosmic and gamma rays, neutrinos and recently discovered gravitational waves. Ongoing and future experiments are distributed over the Earth including ground, underground/underwater setups as well as balloon payloads and spacecrafts. The data acquired by these experiments have different formats, storage concepts and publication policies. Such differences are a crucial issue in the era of big data and of multi-messenger analysis strategies in astroparticle physics. We propose a service ASTROPARTICLE.ONLINE in the frame of which we develop an open science system which enables to publish, store, search, select and analyse astroparticle physics data. The cosmic-ray experiments KASCADE-Grande and TAIGA were chosen as pilot experiments to be included in this framework. In the first step of our initiative we will develop and test the following components of the full data life cycle concept: (i) describing, storing and reusing of astroparticle data; (ii) software for performing multi-experiment and multi-messenger analyses like deep-learning methods; (iii) outreach including example applications and tutorial for students and scientists outside the specific research field. In the present paper we describe the concepts of our initiative, and in particular the plans toward a common, federated astroparticle data storage.

As of 2020, public data for game ratings provided by Game Ratings And Administration Committee(GRAC) are more limited than public data for movie and video ratings provided by Korea Media Ratings Board and do not provide data which allow us to see information on ratings clearly and in detail. To get information on game ratings, we need to find information by searching for specific target on homepage which is inconvenient for us. In order to improve such inconvenience and extend scope of provision in public data, the author of this paper intends to study public data API which has been extended based on information on video ratings. To draw items to be extended, this study analyzes data for ratings on homepage of GRAC and designs collection system to build database. This study intends to implement system that provides data collected based on extended public data items in a form which users want. This study is expected to provide information on ratings to GRAC which will strengthen fairness and satisfy game users and people’s rights to know and contribute to promotion and development of game industry.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. In this study, we assessed the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open-pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha^-1, compared to 5.4 tha^-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R²=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R²=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R²≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R² ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), than in actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal within the vegetation canopy.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data with 3.52 billion tokens of rich textbook data, Intellecta is crafted to foster advanced reasoning and comprehensive educational narrative generation. Leveraging the Mixtral-8x7B-Instruct-v0.1 model, the dataset facilitates the generation of complex thought processes and detailed, textbook-style explanations, thus enabling language models to engage in both critical thinking and profound educational discourse. This hybrid dataset stands as a testament to the potential of synthetic data in pushing the boundaries of AI, offering a repository that is not only vast and varied but also refined to align with ethical standards and intellectual rigor.

In the coming years, digital applications and services that continue to use the country's native cloud systems will be huge. By 2023, that will exceed 500 million, according to IDC. This corresponds to the sum of all applications developed in the last 40 years. If you are the one you answered, yes! This article is for you!

This manuscript describes the construction and validation of high resolution daily gridded (0.05° × 0.05°) rainfall and maximum and minimum temperature data for Bangladesh : the Enhancing National Climate Services for Bangladesh Meteorological Department (ENACTS-BMD) dataset. The dataset was generated by merging data from weather stations, satellite products (for rainfall) and reanalysis (for temperature). ENACTS-BMD is the first high-resolution gridded surface meteorological dataset developed specifically for studies of surface climate processes in Bangladesh. Its record begins in January 1981 and is updated in real-time monthly and outputs have daily, decadal and monthly time resolution. The Climate Data Tools (CDT), developed by the International Research Institute for Climate and Society (IRI), Columbia University, is used to generate the dataset. This data processing includes the collection of weather and gridded data, quality control of stations data, downscaling of the reanalysis for temperature, bias correction of both satellite rainfall and downscaled reanalysis of temperature, and the combination of station and bias-corrected gridded data. The ENACTS-BMD dataset is available as an open-access product at BMD’s official website, allowing the enhancement of the provision of services, overcoming the challenges of data quality, availability, and access, promoting at the same time the engagement and use by stakeholders.

The implementation of the European Cohesion Policy aiming at fostering regions competitiveness, economic growth and creation of new jobs is documented over the period 2014–2020 in the publicly available Open Data Portal for the European Structural and Investment funds. On the base of this source, this paper aims at describing the process of data mining and visualization for information production on regional programmes performace in achieving effective expenditure of resouces.

The increasing availability of longitudinal data (repeated numerical observations of same units at different times) requires the development of flexible techniques to automatically detect common errors in such data. Besides obvious and easily identifiable cases, such as missing or out-of-range data, large longitudinal dataset often present problems not easily traceable by the techniques used for generic datasets. In particular, elusive and baffling problems are i) inversion of one or more values from one unit to another; ii) anomalous jumps in the series of values, iii) errors in the timing of the values due to a recalculation operated by the data providers to compensate previous errors. This work proposes a statistical-mathematical approach based on a system of indicators that is able to capture the complexity of the described problems by working at the formal level, regardless of the specific meaning of the data. The proposed approach identifies suspect erroneous data and is applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing Total academic staff, that is one of the most important variables, used in empirical analysis as proxy of size and also considered by policy makers at European level.

In the last decade and a half, the world has experienced the outbreak of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, and West Nile Virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this field have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak, which were posted on Twitter between May 7, 2022, and March 3, 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about MPox, Updates on Cases and Investigations about Mpox, MPox and the LGBTQIA+ Community, and MPox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was - Views and Perspectives about MPox. It is followed by the theme of MPox and the LGBTQIA+ Community, which is followed by the themes of MPox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with prior works in this field is also presented to highlight the novelty and significance of this research work.

In this paper, we present a method to map information regarding service activity provision residing in governmental portals across European Commission. In order to perform this, we used as a basis the enriched Greek e-GIF ontology, modeling concepts, and relations in one of the two data portals (i.e., Points of Single Contacts) examined, since relevant information on the second was not provided. Mapping consisted in transforming information appearing in governmental portals in RDF format (i.e., as Linked data), in order to be easily exchangeable. Mapping proved a tedious task, since description on how information is modeled in the second Point of Single Contact is not provided and must be extracted in a manual manner.

Background: Observational health data has the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Col-laboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Methods: 15 Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. Results: All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance is-sues but showed less of an impact on completeness and plausibility checks. Conclusions: This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.

One of the most remarkable features in the 20th century was the digitalization of technical progress, which changed the output of companies worldwide and became a defining feature of the century. The growth of information technology systems and the implementation of new technical advances, which enhance the integrity, agility and long-term organizational performance of the supply chain, can distinguish a digital supply chain from other supply chains. For example, the Internet of Things (IoT)-enabled information exchange and Big Data analysis might be used to regulate the mismatch between supply and demand. In order to assess contemporary ideas and concepts in the field of data analysis in the context of supply chain management, this literary investigation has been decided. The research was conducted in the form of a comprehensive literature review. In the SLR investigation, a total of 71 papers from leading journals were used. SLR has found that data analytics integrate into supply chain management can have long-term benefits on supply chain management from the input side, i.e., improved strategic development, management and other areas.

During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

A distinction is made between data rescue (i.e., copying, digitizing and archiving) and data recovery that implies deciphering, interpreting and transforming early instrumental readings and their metadata to obtain high-quality datasets in modern units. This requires a multidisciplinary approach that includes: palaeography and knowledge of Latin and other languages to read the handwritten logs and additional documents; history of science to interpret the original text, data e metadata within the cultural frame of the 17th, 18th and early 19th century; physics and technology to recognize bias of early instruments or calibrations, or to correct for observational bias; astronomy to calculate and transform the original time in canonical hours that started from twilight. The liquid-in-glass thermometer was invented in 1641 and the earliest temperature records started in 1654. Since then, different types of thermometers were invented, based on the thermal expansion of air or selected thermometric liquids with deviation from linearity. Reference points, thermometric scales, calibration methodologies were not comparable, and not always adequately described. Thermometers had various locations and exposures, e.g., indoor, outdoor, on windows, gardens or roofs, facing different directions. Readings were made only one or a few times a day, not necessarily respecting a precise time schedule: this bias is analysed for the most popular combinations of reading times. The time was based on sundials and local Sun, but the hours were counted starting from twilight. In 1789-90 Italy changed system and all cities counted hours from their lower culmination (i.e., local midnight), so that every city had its local time; in 1866, all the Italian cities followed the local time of Rome; in 1893, the whole Italy adopted the present-day system, based on the Coordinated Universal Time and the time zones. In 1873, when the International Meteorological Committee (IMO) was founded, later transformed in World Meteorological Organization (WMO), a standardization of instruments and observational protocols was established, and all data became fully comparable. In the early instrumental period, from 1654 to 1873, the comparison, correction and homogenization of records is quite difficult, mainly because of the scarcity or even absence of metadata. This paper deals about this confused situation, discussing the main problems, but also the methodologies to recognize missing metadata, distinguish indoor from outdoor readings; correct and transform early datasets in unknown or arbitrary units into modern units; finally, in which cases it is possible to reach the quality level required by WMO. The focus is to explain the methodology needed to recover early instrumental records, i.e., the operations that should be performed to interpret, correct, and transform the original raw data into a high-quality dataset of temperature, usable for climate studies.

Urgent environmental challenges and emerging additive manufacturing (AM) technologies push research towards more performant and new materials. In the field of metallurgy, high entropy alloys (HEAs) have recently represented a topic of intense research because of their promising properties, such as high temperature strength and stability. Moreover, this class of multi-principal element alloys (MPEAs) have opened up researcher community to unexplored compositional spaces, making prosper literature of high-throughput methodologies and tools for rapidly screening large number of alloys. However, none of the methods has been aimed to design new MPEAs for AM process known as selective laser melting (SLM) so far. Here we conducted nanoindentation testing on single scan tracks of elemental powder blends and pre-alloyed powders after ball milling of AlTiCuNb and AlTiVNb. Results show that nanoindentation can represent an effective technique to gain information about phase evolution during laser scanning, contributing to accelerate the development of new MPEAs.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

Abstract: Thermal desalination is yet a reliable technology in the treatment of brackish water and seawater; however, its demanding high energy requirements have lagged it compared to other non-thermal technologies such as reverse osmosis. This review provides an outline of the development and trends of the three most commercially used thermal or phase change technologies worldwide: Multi Effect Distillation (MED), Multi Stage Flash (MSF), and Vapor Compression Distillation (VCD). First, state of water stress suffered by regions with little fresh water availability and existing desalination technologies that could become an alternative solution are shown. The most recent studies published for each commercial thermal technology are presented, focusing on optimizing the desalination process, improving efficiencies, and reducing energy demands. Then, an overview of the use of renewable energy and its potential for integration into both commercial and non-commercial desalination systems is shown. Finally, research trends and their orientation towards hybridization of technologies and use of renewable energies as a relevant alternative to the current problems of brackish water desalination are discussed. This reflective and updated review will help researchers to have a detailed state of the art of the subject and to have a starting point for their research, since current advances and trends on thermal desalination are shown.

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 to August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, a brief analysis of specific features of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis.