The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

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.

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.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

Exploiting the linguistic knowledge of the source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language pairs. However, since the Turkish→English machine translation task is low-resource and the source-side Turkish is morphologically-rich, there are limited resources of bilingual corpora and linguistic information available to further improve the NMT performance. Focusing on the above issues, we propose a multi-source NMT approach that models the word feature in parallel to external linguistic features by using two separate encoders to explicitly incorporate linguistic knowledge into the NMT model. We extend the word embedding layer of the knowledge-based encoder to accommodate for each word’s linguistic annotations in the context. Moreover, we share all parameters across encoders to enhance the representation ability of the NMT model on the source language. Experimental results show that our proposed approach achieves substantial improvements of up to 2.4 and 1.1 BLEU scores in Turkish→English and English→Turkish machine translation tasks, respectively, which points to a promising way to utilize the source-side linguistic knowledge for the low-resource NMT.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

Multi-source image registration often suffered from great radiation and geometric differences. Specifically, gray scale and texture from similar landforms in different source images often show significantly different visual features. And these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness.

There is a growing use of High Voltage Direct Current (HVDC) globally due to the many advantages of Direct Current (DC) transmission systems over Alternating Current (AC) transmission, including enabling transmission over long distances, higher transmission capacity and efficiency. Moreover, HVDC systems can be a great enabler in the transition to a low carbon electrical power system which is an important objective in today’s society. The objectives of the paper are to give a comprehensive overview of HVDC technology, its development, and present status, and to discuss its salient features, limitations and applications.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chro-matography (LC), but choosing the ‘correct’ pipeline isn’t straight-forward. This tutorial, in con-junction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide explor-atory analysis in order to inform decision-making regarding costly and time-consuming down-stream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

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

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.

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.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

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.

The chemical industry is an integral part of the world economy and a substantial income source for developing countries. However, existing regulations or the enforcement of these regulations, on controlling atmospheric pollutants sometimes may be insufficient, leading to the deterioration of surrounding ecosystems and to a quality decrease of the atmospheric environment. Previous works in this domain fail to generate executable solutions for inspection agencies due to practical challenges. In addressing these challenges, we introduce a so-called Chemical Plant Environment Protection Game (CPEP) to generate reasonable schedules of high-accuracy air quality monitoring stations for inspection agencies. First, Stackelberg Security Games (SSGs) are incorporated together with source estimation methods into this research. Second, high-accuracy air quality monitoring stations as well as gas sensors are modeled into the CPEP. Third, simplified data analysis on the regularly discharging of chemical plants is utilized to construct the CPEP. Finally, an illustrative case study is used to investigate the effectiveness of the CPEP Game, and a realistic case study is conducted to illustrate how the models and algorithms being proposed in this paper, work. Results show that playing a CPEP Game can reduce operational costs of high-accuracy air quality monitoring stations; moreover, playing the game leads to more compliance from the chemical plants towards the inspection agencies.

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.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

The testing requirements of the active phased array antennas are very different from those of traditional passive antennas, due to its beam steering capability. Usually, each beam profile of the active phased array needs a separate radiation pattern test, which makes the overall testing time extremely long. Thus the traditional antenna test method can no longer meet the efficiency and cost requirements of new active phased array antennas test. In this paper, a fast test method tailored for phased array antennas is proposed that offers significantly reduced testing time at the expense of slight sacrifice of the accuracy. Using the simulated element pattern in array and ideal port excitation, the beam profile in any direction can be predicted by testing only a certain beam profile. Through theoretical derivation and experiments, the effectiveness of the method is verified, and the testing efficiency of the phased array antenna is demonstrated to be improved by ten times or even more.

Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods using feature attributes to represent samples, which can not avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space ( DM-CPDP). This method first uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to form the dissimilarity space, and in this space the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. Finally, we use TrAdaBoost method to establish the prediction model. The experimental results show that our approach has better performance than other traditional CPDP methods.

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different type: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using continuum removal methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account change of support. Secondly, the two-sensor data were integrated with soil granulometric properties by using the multivariate geostatistical techniques of multi-collocated cokriging and factor cokriging, in order to achieve a more exhaustive and finer-scale soil characterisation. The paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalised factors at two different scales (two per each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterised by different granulometry and clay composition. The applied method is sufficiently flexible and could be applied to any number and type of sensors.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

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.

Mining interspecies interactions remain a challenge due to the complex nature of microbial communities and the need for computational power to handle big data. Our meta-analysis indicates that genetic potential alone does not resolve all issues involving mining of microbial interactions. Nevertheless, it can be used to define the building blocks to infer synergistic interspecies interactions and to limit the search space (i.e., number of species and metabolic reactions) to a manageable size. A reduced search space decreases the number of additional experiments necessary to validate the inferred putative interactions. As validation experiments, we examine how multi-omics and state of the art imaging techniques may further improve our understanding of species interactions’ role in ecosystem processes. Finally, we analyze pros and cons from the current methods to infer microbial interactions from genetic potential and propose a new theoretical framework based on: (i) genomic information of key members of a community; (ii) information of ecosystem processes involved with a specific hypothesis or research question; (iii) the ability to identify putative species’ contributions to ecosystem processes of interest; and, (iv) validation of putative microbial interactions through integration of other data sources.

A power conservative Thevenin-Norton and Norton-Thevenin transformations are proposed in this letter. The transformations introduce a voltage and a current generators for which parameters depend on the loading impedance value.

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

For the problem of poor accuracy of the existing multi-rate simulation methods, this paper proposes a multi rate real-time simulation method based on the Norton equivalent, compared with multi-rate simulation method based on the ideal source equivalent. After the Norton equivalence of the fast subsystem and the slow subsystem, they are obtained simultaneously at the junction nodes. In order to reduce the amount of simulation calculation, the Norton equivalent circuit is obtained by incremental calculation. The data interface between the fast subsystem and the slow subsystem is realized by extrapolation method. For ensuring the real-time performance of the simulation, the method that the slow subsystem calculates ahead of the fast subsystem is given for the slow subsystem with a large amount of calculation. Finally, the AC/DC hybrid power system was simulated on the real-time simulation platform (FRTDS), and the simulation results were compared with the single-rate simulation, which verified the correctness and accuracy of the method.

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.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

Abstract: Comparing outcrop data to laboratory results is important to verify and validate experiments of analogue and reservoir materials especially regarding conditions for deformation experiments. This is important better understand highly complex carbonate reservoir strata and their response to changes in subsurface conditions, reducing subsurface uncertainty. This study develops methods to allow for a more straightforward comparison of outcrop data (m-scale) with experimentally created fracture arrays developed in cylindrical samples (cm-scale). The main objective is to assess usefulness of experimentally-produced fracture networks as analogues for subsurface structures, typically at the meter and above scale by developing new techniques to use the lab deformation. It analyses key characteristics of laboratory-induced fracture networks by adapting scanline methods to use with x-ray tomography (XRT) images to allow for comparison with outcrop and field data. To test and verify these new methods two low permeability carbonate samples were used for deformation testing and analysis. Applying the different scanline methods we show that they can be used to analyse lab induced fractures (mm to cm-scale) identified in XRT images for comparison with outcrop data (m-scale). In addition, these methods also allow for quantification of fracture network attributes e.g. fracture spacing, fracture apertures, orientation. This new data bridges the gap between micro-scanlines using thin sections and outcrop scanlines.

The increased availability of high resolution remote sensor data for precision agriculture 1 applications permits users to aquire deeper and more relevant knowledge about crops states that lead 2 inevitably to better decisions. The algorithm libraries being developed and evolved around these 3 applications rely on multi-spectral or hyper-spectral data acquired by using manned or unmanned 4 platforms. The current state of the art makes thorough use of vegetational indicies to guide the 5 operational management of agricultural land plots. One of the most challenging sub-problems is 6 to correctly identify and separate crop from soil. Thresholding techniques based on Normalized 7 Difference Vegetation Index (NDVI) or other such similar metrics have the advantage of being simple, 8 easy to read transformations of the data packed with useful information. Obvious difficulties arise 9 when crop/tree and soil have similar spectral responses as in case of grass filled areas in vineyards. 10 In this case grass and canopy are close in terms of NDVI values and thresholding techniques will 11 generally fail. Radiometric approaches could be integrated or replaced by a geometric approach that 12 is based on terrain data like Digital Surface Models (DSMs). These models are one of the ouputs 13 of orthorectification engines usually present in data acquired by using unmanned platforms. In 14 this paper we present two approaches based on DSM that are able to segment crop/tree from soil 15 while over gradient terrain. The DSM data are processed through a two dimensional data slicing or 16 reduction technique. Each slice is separately processed as a one dimensional time series to derive the 17 terrain and tree structures separately, here interpreted as object probability densities. In particular 18 the first approach is a Cartesian grid rasterization (CARSCAN) of the terrain and the second is its 19 immediate generalisation or radial grid rasterization of the DSM model (FANSCAN). The FANSCAN 20 recovers information from the original image at greater frequencies on the Fourier plane. These 21 approaches enable the identification of crop/tree from soil in case of slopes or hilly terrain without 22 any constraint on the displacement / direction of plant/tree row. The proposed algorithm uses pure 23 DSM information even if it is possible to fuse its output with other classifiers.

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.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

With the proliferation and development of social media platforms, social media data has become an important source for acquiring spatio-temporal information on various urban events. Providing accurate spatio-temporal information for events contributes to enhancing the capabilities of urban management and emergency response. However, existing research on mining spatio-temporal information of events often focuses solely on textual content, neglecting data from other modalities such as images and videos. Therefore, this study proposes an innovative spatio-temporal information extraction method for multi-modal social media data (MIST-SMMD), which extracts the spatio-temporal information of events from multi-modal data on Weibo at coarse and fine-grained hierarchical levels, serving as a beneficial supplement to existing urban event monitoring methods. This paper takes the "July 20th Zhengzhou Heavy Rainfall" incident as an example, to evaluate and analyze the effectiveness of the proposed method. The results indicate that in the coarse-grained spatial information extraction using only textual data, our method achieves a Spatial Precision of 87.54% within a 60m range, and reaches 100% Spatial Precision for ranges beyond 200m. For fine-grained spatial information extraction, the introduction of other modal data such as images and videos results in a significant improvement in Spatial Error. These results demonstrate the ability of the MIST-SMMD method to extract spatio-temporal information from urban events at both coarse and fine levels, and confirms the significant advantages of multi-modal data in enhancing the precision of spatial information extraction.

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.

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

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

Integration of multi-view datasets which are comprised of heterogeneous sources or different representations is challenging to understand the subtle and complex relationship in data. Such data integration methods attempt to combine efficiently the complementary information of multiple data types to construct a comprehensive view of underlying data. Nonnegative matrix factorization (NMF), an approach that can be used for signal compression and noise reduction, has aroused widespread attention in the last two decades. The Kullback–Leibler divergence (or relative entropy) information distance can be used to measure the loss function of NMF. In this article, we propose a fast and robust framework (RSNMF) based on symmetric nonnegative matrix factorization (SNMF) and similarity network fusion (SNF) for clustering human microbiome data including functional, metabolic and phylogenetic profiles. Many existing methods typically utilize all the information provided by each view to create a consensus representation, which often suffers a lot from noise in data and cannot provide a precise representation of the latent data structures. In contrast, RSNMF combines the strength of SNMF and the advantage of SNF to form a robust clustering indicator matrix thus can reduce the noise influence. We conduct experiments on one synthetic and two real dataset (microbiome data, text data) and the results show that the proposed RSNMF has better performance over the baseline and the state-of-art methods, which demonstrates the potential application of RSNMF for microbiome data analysis.

As technologies such as eco-friendly ships, electric propulsion vessels, and multi-fuel propulsion systems advance, the scope of IoT applications in maritime field is expanding, resulting in increased complexity in control factors. The gradual progression towards Maritime Autonomous Surface Ships (MASS) is further driving the evolution of ship-based IoT applications. These advancements underscore the necessity for a platform capable of ensuring reliable connectivity between ships and onshore, while also being adaptable for deployment and operation to meet the demands of service availability. The limitations of the existing single cloud architecture become evident in this context. In response to these emerging challenges, this paper presents a cloud-based horizontal data platform structure anchored in the architecture of a Multi-Region Fog Cloud. This approach is tailored to fulfill the crucial requirement of maintaining robust and seamless connectivity for vessels navigating globally. Leveraging the capabilities of the Fog Cloud, the platform enhances service responsiveness by facilitating localized data collection and distribution, complemented by efficient data caching from the Cloud Center, serving users within the same geographical region. We outline a strategic framework for enhancing and executing key ship services within the proposed architecture. These services encompass data sharing between ships to elevate collision avoidance, disseminating weather data for optimized route planning, and constructing an operator training simulator (OTS) environment through the integration of digital twin technology. After, we implement an architecture using the AWS Cloud and gauges latency and Packet Loss Rate (PLR) through connections to Seoul, California, and Frankfurt regions. The test results reveal that latency in different regions is about 2-5 times higher than that in the same region for the payload. Furthermore, packet loss occurred at roughly 2-3 Hz packet generation rate (PGR) in different regions, whereas in the same region, it commenced at approximately 10-20 Hz PGR.

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports among others. In this work, an open-source, portable, low cost pH logger, appropriate for in situ meas-urements was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then it was tested by manually measuring pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compen-sation when measuring pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graph-ical assessment tools using mixed-effects models. Advantages and disadvantages for interpreting agreement through the proposed procedure are discussed. In our illustration, indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices im-proved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes.

A new disease causing the tan to light brown blighted stems and pods has occurred in 2.6% pea (Pisum sativum L.) plants with an average disease severity rating of 3.7 in Chapainawabganj district, Bangladesh. A fungus with white appressed mycelia and large sclerotia was consistently isolated from symptomatic tissues. The fungus formed funnel-shaped apothecia with sac-like ascus and endogenously formed ascospores. Healthy pea plants inoculated with the fungus produced typical white mold symptoms. The internal transcribed spacer sequences of the fungus were 100% similar to that recovered from an epitype of Sclerotinia sclerotiorum, considering the fungus to be the causative agent of white mold. Mycelial growth and sclerotial development of S. sclerotiorum were favored at 20°C and pH 5.0. Glucose was the best carbon sources to support hyphal growth and sclerotia formation. Bavistin and Amistar Top inhibited the radial growth of the fungus completely at the lowest concentration. In planta, foliar application of Amistar Top showed the considerable potential to control the disease at 1.0% concentration until 7 days after spraying, while Bavistin prevented infection significantly until 15 days after spraying. A large majority (70.93%) of genotypes including tested released pea cultivars were susceptible, while six genotypes (6.98%) appeared resistant to the disease. These results could be important for management strategies aiming to control the incidence of S. Sclerotinia and eliminate yield loss in pea.

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.

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative, is to resort to weather-insensitive synthetic aperture radar (SAR) images. However, in many real-world applications, critical decisions are made based on some informative spectral features, such as water, vegetation or soil indices, which cannot be extracted from SAR images. In the absence of optical sources, these data must be estimated. The current practice is to perform linear interpolation between data available at temporally close time instants. In this work, we propose to estimate missing spectral features through data fusion and deep-learning. Several sources of information are taken into account - optical sequences, SAR sequences, DEM - so as to exploit both temporal and cross-sensor dependencies. Based on these data, and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May to November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baselines methods according to all performance indicators.

In recent years, the emergence of spatiotemporal big data has made the transition of functional identification from the physical dimension to socioeconomic or human activities becoming more common. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Using taxi trajectory data and point of interest (POI) data as the main data source, this study proposes a multi-scale recursive identification method for urban functional areas based on cross-validation. First, used the dynamic time warping (DTW) algorithm generates a time series similarity matrix, the CA-RFM model combines the clustering algorithm and random forest model is constructed, the model uses a clustering algorithm (K-MEDOIDS) to extract sig-nificant feature regions as input, which are imported into the random forest model for UFZ identification. Then, to overcome the shortcomings of single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method, obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area for the case study, the results show that the com-bination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the research shows the superiority of the multi-scale recursive identification method that fuses multi-source data and performs cross-validation from two aspects, that is, the division speed of urban functional area identification results is accelerated and the accuracy is improved.

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.

The value of an NBA basketball player varies at each crucial point in time, depending on the player’s career and performance. This study constructs a player’s value assessment model. The model comprises two parts. First, from an objective perspective, entropy is employed to measure each player’s achievement in five categories—rebounds, assists, steals, blocked shots, and scores. The total entropy assessment value is calculated and then combined with the players’ scores to develop a player's value matrix to assess the relative value model among players of the same type.

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.

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is a major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land.

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.

In this study, a new methodology based on the circuit approach is employed to obtain renewable electric energy. Although a voltage source and a current source generate voltage and current, respectively, they work only when they receive electric power, which is the product of voltage and current. However, this paper proposes a circuit with a voltage source, a current source and a super-condenser connected in series, in which the voltage source provides voltage for the current source and the current source provides current for the voltage source. Resultantly, electric power is generated both at the voltage and current sources, and the energy (i.e., the electric power) from the current source is converted to the charging energy in the super-condenser. Herein, we describe the method for charging the super-condenser using a voltage source and a current source. To confirm the abovementioned concept, we conducted experiments, and the energy was successfully produced, with high reproducibility. Finally, the brief mechanism for the energy conversion, a schematic method for reducing the capacitances of stored batteries, and the significance of this study are described.

The PLM concept implies the use of heterogeneous information resources at different stages of the product life cycle, the joint work of which allows you to effectively solve the problems of product quality and various costs. According to the principle of isomorphism of regularities of complex systems, an effective PLM system must have these regularities. Unfortunately, this principle is not often fundamental when designing PLM systems. The purpose of the work is to show, using a simple example, the principles of development, operation and use of an educational multi-agent PLM system, the main purpose of which is to study and research these regularities in the life cycle of a special threaded connection. The multi-agent approach to the development of a PLM system provides the necessary prerequisites for the emergence of system-wide regularities in it. The parallel work of agents is implemented using the actor model and the Ray Python-package. Agents for the logical inference of knowledge base facts, CAD/FEA/CAM/SCADA agents, agents for optimization by various methods, and other agents have been developed. Open source software was used to develop the system. Each agent has relatively simple behavior, implemented by its rule function, and can interact with other agents. The system can work in interactive mode with the user or in automatic mode according to a simple algorithm: the rule functions of all agents are executed until at least one of them returns a value other than None. Examples of the operation of the system are given and such system-wide regularities as emergence, historicity and self-organization are demonstrated in it.

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.

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.

: 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.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

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.

A low-cost data logging platform is presented for environmental monitoring projects that provides long-term operation in remote or submerged environments. Three premade “breakout boards” from the open-source Arduino ecosystem are assembled into the core of the platform. The components are selected based on low-cost and ready availability, making the loggers easy to build and modify without specialized tools, or a significant background in electronics. Power optimization techniques are explained. The platform has proven to be highly reliable, and capable of operating for more than a year on standard AA batteries. The flexibility of the system is illustrated with two ongoing field studies recording drip rates in a cave, and water flow in a flooded cave system.

Underwater acoustic spiral sources are able to generate spiral acoustic fields where the phase depends on the bearing angle. It allows to estimate the bearing angle relatively to a receiver by subtracting the phases of a spiral and a circular wavefront, and finds application in the bearing angle estimate with a single hydrophone, e.g., for unmanned underwater vehicles localization. This paper presents a new prototype for a spiral acoustic source, which is able to generate the required wavefronts, and is composed of four monopoles in the same piezoceramic cylinder. This paper reports the prototyping and the acoustic tests performed in a water tank where the spiral source was characterized in terms o Transmitting Voltage Response, phase, and horizontal and vertical directivity patterns. A receiving calibration method for the spiral source is proposed and shows a maximum angle error of 1 degree when the calibration and the operation are carried under the same conditions and a mean angle error up to 6 degrees for frequencies above 25 kHz when the same conditions are not fulfilled.

To improve the voltage gain of step-up converters, cascaded technique is considered as a possible solution in this paper. By considering the concept of cascading two Z-source networks in a conventional boost converter, the converter takes the advantages of both impedance source and cascaded converters. However, by applying some modifications, the proposed converter provides high voltage gain while the voltage stress of switch and diodes are still low. Moreover, the low input current ripple of the converter makes it absolutely appropriate for photovoltaic applications in order to expand the lifetime of PV panels. After analyzing the operation principles of the proposed converter, simulation and experimental results of a 100W prototype are presented to verify the proposed converter performance.

Urban form is associated with both socio-economic and urban physical properties. This study explores the differences among urban forms in the Seoul Metropolitan Region with a comparison between census-based socioeconomic variables and landscape metrics computed from remotely sensed data. To accomplish this, factor analysis and multi-dimensional scaling were used with the selected variables and metrics. When all of the measures are considered together, four types of cities and towns emerged: 1) exurban-fragmented high growth, 2) exurban-fragmented low growth, 3) compact-extensive urban core and 4) sub-urban compact-high growth. The results indicate that the fusion of knowledge of the physical urban layout and that of socio-economic characteristics is beneficial for a better understanding of urban spatial patterns. However, there remain challenges in delineating each urbanized area and with indicator selection for comparing urban form across cities and towns.

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.

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.

The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. Most tools analyzed in this paper either do not allow coupling of infrastructures, simplify the grid model or are not publicly available. We introduce the open source piping grid simulation tool pandapipes that – in interaction with pandapower - fulfills three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as interaction of a local electricity and district heating grid in a small settlement. The second case study analyzes the potential of a power-to-gas device to serve as flexibility in a power grid under consideration of gas grid constraints. They both show the importance of a clear database, a simple simulation setup and good performance to set up different large and complex studies on grid infrastructure design and operation.

Pseudo-nitzschia spp. blooms are a recurrent problem in many coastal areas globally imposing some significant threats to the health of human, ecosystems and economy. Monitoring programmes have been established, where feasible, to mitigate the impacts caused by Pseudo-nitzschia spp. and other harmful algae blooms. The detection of such blooms from satellite data could really provide timely information on emerging risks but the development of taxa-specific algorithms from available multispectral data is still challenged by coupled optical properties with other taxa and water constituents, availability of ground data and generalisation capabilities of algorithms. Here, we developed a new set of algorithms (PNOI) for the detection and monitoring of Pseudo-nitzschia spp. blooms over the Galician coast (NW Iberian Peninsula) from Sentinel-3 OLCI reflectances using support vector machine (SVM). Our algorithm was trained and tested with reflectance data from 260 OLCI images and 4607 Pseudo-nitzschia spp. match up data points, of which 2171 were of high quality. The performance of no bloom/bloom model in the independent test set was robust, showing values of 0.80, 0.72 and 0.79 for area under the curve (AUC), sensitivity and specificity, respectively. Similar results were obtained by our below detection limit/presence model. We also present different model thresholds based on optimisation of true skill statistic (TSS) and F1-score. PNOI outperforms linear models, while its relationship with in-situ chlorophyll-a concentrations is weak demonstrating poor correlation with the phytoplankton abundance. We showcase the importance of PNOI algorithm and OLCI sensor for monitoring the bloom evolution between the weekly ground sampling and during periods of ground data absence, such as due to Covid-19.

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.

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.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

Reciprocal Personal/Public Protection (RPPP) featured with source control is introduced, Mask Discriminating Mouth and Nose (MDMN) is employed to serve the purpose, which includes polymer based mouth cover with optional nose cover. The new knowledge that mouth is a primary, active and dominant source of the virus has been well established, which is the base of MDMN. Source classification and related source control tools are discussed, mouth cover is recommended as the tool prioritized to use. Liquid droplets is identified as a hard issue related to mask, liquid droplets, mask fitting, comfort and facial recognition constitute real challenges of mask in addition to efficiency, All of these have been addressed with MDMN. Comparisons between MDMN and masks/face covering are taken on four aspects: efficiency and efficacy, tolerance and comfort, cost and waste, and civil rights and public interest. Mouth cover is recommended to replace the face covering and act as both a personal tool and a public utensil, mouth cover with nose cover can provide better protection than N95 etc. RPPP with MDMN, could be an alternative for lockdown, a parallel strategy to vaccine, and a collectively living way during the pandemic era. MDMN, featured with high efficiency protection, high degree comfort, easy wearing, tight-fitting, easy facial recognition & communication, reusability, cost-effective, environment friendly and scale manufacturing more readily and widely etc., is a simple and sustainable solution, which is essential for ordinary people to keep wearing it properly for protection.

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an on-line adaptive manner in pre-crisis segments. The on-line sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint to the importance of the U.S. housing bubble as trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states and could work as a comparative impact rating of specific economic events.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

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.

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.

The progressive impairment analysis in gait from neurological diseases patients such as Hereditary Ataxias (HA) has been carried out using gait data collected with movement sensors. This research is focused on finding the minimum amount required of gait features to recognize efficiently and less intrusive way, HA patients based on data collected with iPhone movement sensors placed on the ankles from 14 HA patients and 14 healthy people. A twofold proposal is made , first a local minimum prominent peak criterion to find out the starting point of each stride, to get 10-stride window about which 56 spatial-temporal features are derived; second a search strategy based on Hill Climbing algorithm to reduce the number of gait features and sensors. The main results were the findings that with two gait patterns a 96% of classification accuracy was achieved by using K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) algorithms, but in addition, MLP only right ankle sensor patterns were required which also allows to reduce the intrusion.

This study aimed to identify the most beneficial attitudes adopted by enterprise participants during their engagement in open-source software (OSS) activities. For this purpose, we selected repositories from GitHub, featuring the top 10 with the highest stars and forks, classified the participants according to their sentiment states, and compared their productivity. The results indicated that participants with positive sentiments were more productive than those with negative sentiments. Consequently, it can be inferred that encouraging organizations to express positive sentiments towards their developers publicly may stimulate OSS community activities and enhance productivity, thereby potentially leading to increased open innovation and sustainability.

With the rapid development of cities in northwest China, there has been an increasing focus on groundwater pollution in plateau cities, specifically the common occurrence of nitrate pollution. The special climatic, geological, and geomorphological characteristics of plateau and river valley cities contribute to distinct groundwater chemical characteristics. Therefore, the formation and evolution process of groundwater nitrate contamination differs from that of plain cities. To explore these issues, we conducted an analysis of eight major ions in various groups of water samples from rivers, springs, and groundwater in Haidong. By utilizing factor analysis and correlation analysis, we were able to identify the characteristics and formation of groundwater chemistry and nitrate pollution in Haidong. Our findings reveal that the chemical characteristics of groundwater in Haidong are primarily controlled by rock weathering, mineral dissolution, and evaporation, leading to the formation of highly mineralized groundwater. Additionally, the excessive nitrate content in certain areas is a result of domestic sewage discharge and agricultural fertilizer use, exceeding Chinese drinking water health standards. Furthermore, for cities located in valleys, the geological structure significantly impacts the nitrate content of groundwater in different regions. Areas with obstructed groundwater flow tend to have higher nitrate levels, while regions with unobstructed groundwater experience lower nitrate concentrations. Notably, shallow groundxwater is more vulnerable to nitrate pollution compared to deep groundwater. This study holds great significance in understanding the chemical characteristics of groundwater and the formation and evolution of nitrate pollution in highland river valley cities.

This study investigated the physical and combustion properties of briquettes produced from agricultural wastes (groundnut shells and corn cobs), wood residues (Anogeissus leiocarpus) and admixtures of the particles at 15%, 20% and 25% starch levels (binder). A 6 x 3 factorial experiments in a Completely Randomized Design (CRD) was adopted for the study. The briquettes produced were analyzed for density, volatile matter, ash content, fixed carbon and specific heat of combustion. The result revealed that the density ranged from 0.44g/cm³ to 0.53g/cm³, while briquettes produced from groundnut shells had the highest (0.53g/cm³) significant mean density. Mean volatile matter and ash content of the briquettes ranged from 24.35% to 34.95% and 3.37% to 4.91%. A. leiocarpus and corn cobs particles had the lowest and highest ash content respectively. The briquette fixed carbon and specific heat of combustion ranged from 61.68% to 68.97% and 7362kca/kg to 8222kca/kg respectively. Briquette produced from A. leiocarpus particles had the highest specific heat of combustion. In general, briquettes produced from A. leiocarpus particles and admixture of groundnut shell and A. leiocarpus particles at 25% starch level had better quality in terms of density and combustion properties and thus suitable as environmentally friendly alternative energy source.

Industrial pilot projects often rely on proprietary and expensive electronic hardware to control and monitor experiments. This raises costs and retards innovation. Open-source hardware tools exist for implementing these processes individually, however, they are not easily integrated with other designs. The Broadly Reconfigurable and Expandable Automation Device (BREAD) framework provides many open-source devices which can be connected to create more complex data acquisition and control systems. This article explores the feasibility of using BREAD plug-and-play open hardware to quickly design and test monitoring and control electronics for an industrial materials processing prototype pyrolysis reactor. Generally, pilot-scale pyrolysis plants are expensive custom designed systems. The plug-and-play prototype approach is first tested by connecting it to the pyrolysis reactor and ensuring it can measure temperature and actuate heaters and a stirring motor. Next, a single circuit board system was created and tested using the designs from the BREAD prototype to reduce the number of microcontrollers required. Both open-source control systems were capable of reliably running the pyrolysis reactor continuously and the overall cost of control was reduced by more than 10X. Open-source, plug-and-play hardware provides a reliable avenue for researchers to quickly develop data acquisition and control electronics for industrial-scale experiments.

This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises of two stages named Blind Source Separation (BSS) and De-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability & 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.

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.

Distributed digital manufacturing offers a solution to medical supply and technology shortages during pandemics. To prepare for the next pandemic, this study reviews the state-of-the-art for open hardware designs needed in a COVID-19-like pandemic. It evaluates the readiness of the top twenty technologies requested by the Government of India. The results show that the majority of the actual medical products have had some open source development, however, only 15% of the supporting technologies that make the open source device possible are freely available. The results show there is still considerable work needed to provide open source paths for the development of all the medical hardware needed during pandemics. Five core areas of future work are discussed that include: i) technical development of a wide-range of open source solutions for all medical supplies and devices, ii) policies that protect the productivity of laboratories, makerspaces and fabrication facilities during a pandemic, as well as iii) streamlining the regulatory process, iv) developing Good-Samaritan laws to protect makers and designers of open medical hardware, as well as to compel those with knowledge that will save lives to share it, and v) requiring all citizen-funded research to be released with free and open source licenses.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~ 40% of protein-coding genes in the model plant A. thaliana are still not categorized in the Gene Ontology Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus), the number of BP term annotation is far fewer, ~22%. In the current study we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and performed expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust high quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which, two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTL or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. In all, we have implemented a valuable strategy to better describe genes within specific biological processes.

The synchronization of laser and X-ray sources is essential for time-resolved measurements in the study of ultrafast processes, including photo-induced piezo-effect, shock wave generation, and phase transition. On the one hand, the optical diagnostics (by synchronization of two laser sources) provides information about change in vibration frequencies, shock waves dynamics, linear and nonlinear refractive index behavior. On the other hand, the optical pump – X-ray probe diagnostics give opportunity to directly reveal the lattice dynamics. To integrate two approaches into a unified whole one need to create a robust method for synchronization of two systems with different repetition rate up to MHz range. In this paper, we propose a universal approach utilizing a field-programmable gate array (FPGA) to achieve precise synchronization between different MHz sources such as various lasers and synchrotron X-ray sources. This synchronization method offers numerous advantages, such as high flexibility, fast response, and low jitter. Experimental results demonstrate successful synchronization of two different MHz systems with a temporal resolution of 250 ps for these systems. This enables ultrafast measurements with sub-nanosecond resolution, facilitating the uncovering of complex dynamics in ultrafast processes.

Campylobacter is one of the major foodborne pathogens of concern in its growing trend of antimicrobial resistance. C. jejuni and C. coli are the major causative agents, with C. jejuni contributing to most of the cases in approximately 90% in the world. Infection is transmitted to humans due to consumption of contaminated food and water. Campylobacteriosis caused by C. jejuni is commonly presented with severe diarrhoea, abdominal pain, fever, headache, nausea, and vomiting with some extreme cases resulting in Guillain–Barré syndrome (GBS) and acute flaccid paralysis. Symptoms are severe in cases of children below 5 years, elderly and individuals who are immunocompromised. The infection is usually sporadic, and self-limiting and thus does not require antibiotics for treatment. Still, the antimicrobial resistance in Campylobacter is a major concern because of the transmission of resistance from animal sources to humans. This review highlights the recent epidemiology, geographical impact, resistance mechanisms, spread of Campylobacter spp. and the strategies to control the transmission of Campylobacter from veterinary sources and its antimicrobial resistance.

The original purpose of this article was to modify the original SIR equations to allow for a direct source of infection (without which the original equations would have no solutions unless one starts with an already infected population) and also to see to what extent one could obtain multiple outbreaks of an infectious disease. In the course of developing the basic ideas several other factors arose to take prominent roles.Perhaps one of the more salient factors is the point that choosing an arbitrary time to change conditions from say a lock-down for the population to a less stringent social behavior, such as allowing partial or complete opening of businesses and schools, etc. should be based on knowledge of the disease and its evolution. Such decisions are usually made by politicians who have less than full information concerning the consequences of their actions. Several examples are given to illustrate these points.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

Malaria remains a life-threatening disease in many tropical countries. Honduras has successfully reduced malaria transmission as different control methods have been applied focusing mainly on indoor mosquitoes. The selective pressure exerted by the use of insecticides inside the households could modify the feeding behavior of the mosquitoes forcing them to search for available animal hosts outside the houses. These animal hosts in the peridomicile could consequently become an important factor in maintaining vector populations in endemic areas. Herein, we investigated the blood meal sources and Plasmodium spp. infection on anophelines collected outdoors in endemic areas of Honduras. Individual PCR reactions with species-specific primers were used to detect five feeding sources on 181 visibly engorged mosquitoes. In addition, a subset of these mosquitoes where chosen for pathogen analysis by a nested PCR approach. Most mosquitoes fed on multiple hosts (2 to 4), and 24.9% of mosquitoes were fed on a single host, animal or human. Chicken and bovine were the most frequent blood meal sources (29.5% and 27.5% respectively). The average human blood index (HBI) was 22.1%. None of the mosquitoes was found to be infected with Plasmodium spp. Our results show the opportunistic and zoophilic behavior of Anopheles mosquitoes in Honduras.

One of the most important factors impacting quality of content in Wikipedia is presence of credible sources. By following references readers can verify facts or find more details about described topic. A Wikipedia article can be edited independently in any of over 300 languages, even by anonymous users, therefore information about the same topic may be inconsistent. This also applies to use of references in different language versions of a particular article, so the same statement can have different sources. In this paper we analyzed over 40 million articles from the 55 most developed language versions of Wikipedia to extract information about nearly 200 million references and find the most popular and reliable sources. We presented 10 models for the assessment of the popularity and reliability of the sources based on analysis of meta information about the references in Wikipedia articles, page views and authors of the articles. Using DBpedia and Wikidata we automatically identified the alignment of the sources to a specific domain. Additionally, we analyzed the changes of popularity and reliability in time and identified growth leaders in each considered months. The results can be used for quality improvements of the content in different languages versions of Wikipedia.

Network coding approaches typically consider an unrestricted recoding of coded packets in the relay nodes for increased performance. However, this can expose the system to pollution attacks that cannot be detected during transmission, until the receivers attempt to recover the data. To prevent these attacks while allowing for the benefits of coding in mesh networks, the Cache Coding was proposed. This protocol only allows recoding at the relays when the relay has received enough packets to decode an entire generation of packets. At that point, the relay node recodes and signs the recoded packets with its own private key allowing for the system to detect and minimize the effect of pollution attacks and make relays accountable for changes on the data. This paper analyzes the delay performance of Cache Coding to understand the security-performance trade-off of this scheme. We introduce an analytical model for the case of two relays in an erasure channel relying on an Absorbing Markov Chain and a approximate model to estimate the performance in terms of the number of transmissions before successfully decoding at the receiver. We confirm our analysis using simulation results. We show that Cache Coding can overcome security issues of unrestricted recoding with only a moderate decrease in system performance.

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.

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.

This study provides designs for a low-cost, easily replicable open source lab-grade digital scale that can be used as a precision balance. The design is such that it can be manufactured for use in most labs throughout the world with open source RepRap-class material extrusion-based 3-D printers for the mechanical components and readily available open source electronics including the Arduino Nano. Several versions of the design were fabricated and tested for precision and accuracy for a range of load cells. The results showed the open source scale was found to be repeatable within 0.1g with multiple load cells, with even better precision (0.01g) depending on load cell range and style. The scale tracks linearly with proprietary lab-grade scales, meeting the performance specified in the load cell data sheets, indicating that it is accurate across the range of the load cell installed. The smallest loadcell tested(100g) offers precision on the order of a commercial digital mass balance. The scale can be produced at significant cost savings compared to scales of comparable range and precision when serial capability is present. The cost savings increase significantly as the range of the scale increases and are particularly well-suited for resource-constrained medical and scientific facilities.

The complexity of software is increasing day by day due to the increase in the size of the projects being developed. For better planning and management of large software projects, estimation of software quality is important. During the development processes, complexity metrics are used for the indication of the attributes/characteristics of the quality software. There are many studies about the effect of the complexity of the software on the cost and quality. In this study, we discussed the effects of software complexity on the quality attributes of the software for open source and closed source software. Though, the quality metrics for open and closed source software are not distinct from each other. In this paper, we comparatively analyzed the impact of complexity metrics on open source and private software. We also presented various models for the management of the project complexity such as William’s Model, Stacey’s Agreement and Certainty matrix, Kahane’s Approach and UCP Model. Quality metrics here refer to the standards for the measurement of the quality of software which contains certain attributes or characteristics of the software that are related to the quality of the software. Certain quality attributes addressed in this study are Usability, Reliability, Security, Portability, Maintainability, Efficiency, Cost, Standards and Availability, etc. Both Open source and Closed source software are evaluated on the basis of these quality attributes. This study also recommended future approaches to manage the quality of project Open source and Closed source software and specify which one of them is mostly used in the industry.

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.

DC-connected parallel inverter systems are gaining popularity in industrial applications. However, such parallel systems generate excess current ripple (harmonics) at the DC-link due to harmonic interactions between the inverters in addition to the harmonics from the PWM switching. These DC-link harmonics cause the failure of fragile components such as DC-link capacitors. This paper proposes an interleaving scheme to minimize the current harmonics induced in the DC-link of such a system. The results show that when the carrier waves of the two inverters are phase-shifted by 90° angle, the maximum high-frequency harmonic ripple cancellation occurs, which reduces the overall RMS value of the DC-capacitor current.The outcome of this proposed solution is a cost-effective DC-harmonics mitigating strategy for the industrial designers to practically configuring multi-inverter systems, even when most of the drives are not operating at rated power levels. Experimental and simulation results presented in this paper verify the effectiveness of the proposed carrier-based phase-shifting scheme for two different configurations of common DC connected multi-converter systems.

In order to address the issue of nutrient deficiency in coconut coir-based nursery substrates, three materials with nutrient supplementation effects, namely pig manure, composted rice husks, and agricultural humic acid, were added in equal volumes to the coir-based substrate. The newly formulated substrates were then tested for their seedling growth effects on cucumber and rice. Results indicate that the substrate formulation consisting of "10% pig manure + 30% peat + 50% coconut coir + 10% vermiculite" was capable of satisfying the growth requirements of both cucumber and rice seedlings, with the highest acceptance among farmers.

Natural polysaccharides, which are described in this study, are some of the most extensively used biopolymers in food, pharmaceutical and medical applications, because they are renewable and have a high level of biocompatibility and biodegradability. The fundamental understanding required to properly exploit polysaccharides potential in the biocomposite, nanoconjugate, and pharmaceutical industries is depended on detailed research of these molecules. Polysaccharides are preferred over other polymers because of their biocompatibility, bioactivity, homogeneity, and bioadhesive. Natural polysaccharides have also been discovered to have excellent rheological and biomucoadhesive properties, which may be used to design and create a variety of useful and cost-effective drug delivery systems. Polysaccharide-based composites derived from natural sources have been widely exploited due to their multifunctional properties, particularly in drug delivery systems and biomedical applications. These materials have achieved global attention and are in great demand because to their biochemical properties, which mimic both human and animal cells. Although synthetic polymers account for a substantial amount of organic chemistry, natural polymers play a vital role in a range of industries, including biomedical, pharmaceutical, and construction. As a consequence, the current study will provide information on natural polymers, their biological uses, food and pharmaceutical applications.Keywords: Polymers Biopolymers, Excipient, Natural source, Food and Pharmaceutical applications.