Background: Care home residents have complex healthcare needs but may have faced barriers to accessing hospital treatment during the first wave of the COVID-19 pandemic. Objective: To examine trends in the number of hospital admissions for care home residents during the first months of the COVID-19 outbreak. Methods: Retrospective analysis of a national linked dataset on hospital admissions for residential and nursing home residents in England (257,843 residents, 45% in nursing homes) between 20 January 2020 and 28 June 2020, compared to admissions during the corresponding period in 2019 (252,432 residents, 45% in nursing homes). Elective and emergency admission rates, normalised to the time spent in care homes across all residents, were derived across the first three months of the pandemic between 1 March and 31 May and primary admissions reasons for this period were compared across years. Results: Hospital admission rates rapidly declined during early March 2020 and remained substantially lower than in 2019 until the end of June. Between March and May, 2,960 admissions from residential homes (16.2%) and 3,295 admissions from nursing homes (23.7%) were for suspected or confirmed COVID-19. Rates of other emergency admissions decreased by 36% for residential and by 38% for nursing home residents (13,191 fewer admissions in total). Emergency admissions for acute coronary syndromes fell by 43% and 29% (105 fewer admission) and emergency admissions for stroke fell by 17% and 25% (128 fewer admissions) for residential and nursing home residents, respectively. Elective admission rates declined by 64% for residential and by 61% for nursing home residents (3,762 fewer admissions). Conclusions: This is the first study showing that care home residents’ hospital use declined during the first wave of COVID-19, potentially resulting in substantial unmet health need that will need to be addressed alongside ongoing pressures from COVID-19.

The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media, analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on peoples mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long-short term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.

Αbstract: To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 80s decade (1980–1990) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

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

The global use of alcohol based hand sanitizers (ABHS) as a means of controlling the transmission of infectious disease increased dramatically in 2020 as governments and public health agencies across the world advocated hand hygiene as a preventative measure during the COVID-19 pandemic. Although the performance of these products is most commonly defined as a function of their alcohol concentration, they are multifaceted products in which an interplay of several factors is important in determining efficacy. The hand sanitizer tetrahedron, is a novel concept that considers both ABHS formulation factors and product performance factors from a multi-dimensional perspective. The four faces of the tetrahedron represent input/formulation factors: 1) the type and amount of alcohol, 2) inactive ingredients, 3) the type of formulation/delivery system and 4) manufacturing practices. The four corners of the tetrahedron represent output/product performance factors: 1) efficacy, 2) sensory characteristics, 3) usage, usability and compliance and 4) product safety/adverse effects. All factors are of importance to ensuring the effectiveness and utility of these products.

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

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

This paper shows a method for pipeline leak detection using a transient-based method with MATLAB® functions. The simulation of a pipeline systems in the time domain are very complex. In the case of the dissipative model, transfer functions are hyperbolic Bessel functions. Simulating a pipeline system in the frequency domain using a dissipative model we could find an approximate transfer function with equal frequency domain response to in order get the pipeline system's time domain response. The method described in this paper can be used to detect, by comparison, to detect a leak in a pipeline system model.

Malnutrition and dental caries in early childhood remain persistent and intertwined global health challenges, particularly for indigenous and geographically-remote populations. To examine the prevalence and associations between early childhood dental caries, parent-reported mouth pain and malnutrition in the Amazonian region of Ecuador, we conducted a cross-sectional study of the oral health and nutrition status of 1,407 children from birth through age 6 in the “Alli Kiru” program (2011-2013). We used multivariate regression analysis to examine relationships between severe caries, parent-reported mouth pain measures, and nutritional status. The prevalence of dental caries was 65.4%, with 44.7% of children having deep or severe caries, and 33.8% reporting mouth pain. The number of decayed, missing and filled teeth dmft) increased dramatically with age. Malnutrition was prevalent, with 35.9% of children stunted, 1.1% wasted, 7.4% underweight, and 6.8% overweight. As mouth pain increased in frequency, odds for severe caries increased. For each unit increase in mouth pain frequency interfering with sleeping, children had increased odds for underweight (AOR: 1.27; 95% CI: 1.02 – 1.54) and decreased odds for overweight (AOR: 0.76; 95% CI: 0.58 – 0.97). This relationship was most pronounced among 3-6 year-olds. Early childhood caries, mouth pain and malnutrition were prevalent in this sample of young children. Parent-reported mouth pain was associated with severe caries, and mouth pain interfering with sleeping was predictive of poor nutritional status. We demonstrate the utility of a parsimonious parent-reported measure of mouth pain to predict young children’s risk for severe early childhood caries and malnutrition, which has implications for community health interventions.

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error-covariance in the EnOI is sampled in two ways: i) by using the stochastic spread from an ensemble open-loop run, and ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in-situ observations at a 95% significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in-situ data at the same significance level; however this improvement is dependent on which in-situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95% significance level. The study also suggest that the EnOI can be used for bias-correction of the atmospheric fields where post-processed data are not available. Finally, we assimilate three different Level-2 satellite derived soil moisture products from ESA Climate Change Initiative (CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in-situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in-situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.

The present investigation has been conducted on one hundred tissue fragments of breast cancer, collected and immediately cryopreserved following the surgical resection. The fragments were selected from patients with invasive ductal carcinoma of the breast, the most common and potentially aggressive type of mammary cancer, with the objective to increase the knowledge of breast cancer molecular markers, useful for diagnostic and prognostic categorization of patients, in assessing post-surgical therapeutic regimes. The proteomic screening, by 2D-IPG and mass spectrometry, allowed us to identify two main classes of protein clusters: proteins expressed ubiquitously at high levels in all patients, and proteins expressed sporadically among the same patients. Within the group of ubiquitous proteins, glycolytic enzymes and proteins with anti-apoptotic activity were predominant. Among the sporadic ones, proteins involved in cell motility, molecular chaperones and proteins involved in the detoxification appeared prevalent. The data of the present study indicates that the primary tumor growth is generally supported by two concurrent pathways: the inhibition of apoptosis and the stimulation of cellular proliferation. The second phase of the evolution of the tumor can be prematurely scheduled by the occasional presence of proteins involved in cell motility and in the defenses of the oxidative stress. To our knowledge this report on large-scales proteomics of breast cancer is currently a unique approach in the literature that offers the opportunity to evaluate the presence and recurrence of proteins to be used as prognostic indicators and susceptibility to metastasis in patients operated on for invasive ductal carcinoma of the breast.

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al.). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

The complexity of analysis pipelines in biomedical sciences poses a severe challenge for the transparency and reproducibility of results. Researchers are increasingly incorporating software development technologies and methods into their analyses, but this is a quickly evolving landscape and teams may lack the capabilities to set up their own complex IT infrastructure to aid reproducibility. Basing a reproducible research strategy on readily available solutions with zero or low set-up costs whilst maintaining technological flexibility to incorporate domain-specific software tools is therefore of key importance. We outline a practical approach for robust reproducibility of analysis results. In our examples, we rely exclusively on established open-source tools and free services. Special emphasis is put on the integration of these tools with best practices from software development and free online services for the biostatistics domain.

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in this studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time period during which test image series were recorded. The findings show that the QE from SOM, which is easily implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to change/no change between images.

This paper establishes methods that quantify the structure of statistical interactions within a given data set using the characterization of information theory in cohomology by finite methods, and provides their expression in terms of statistical physic and machine learning. Following [1–3], we show directly that k multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles are given by I_k = 0, which generalize statistical independence to arbitrary dimension k. The topological approach allows to investigate Shannon’s information in the multivariate case without the assumptions of independent identically distributed variables. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes. The I₁ component defines a self-internal energy functional U_k, and (−1)^k I_k,k≥2 components define the contribution to a free energy functional G_k of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a topological expression of the 2nd law and points toward a discrete Noether theorem (1st law). The local minima of free-energy, related to conditional information negativity and the non-Shannonian cone of Yeung [4], characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. Finite data size effects and estimation bias severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and for the k-dependences following [5]. We give an example of application of these methods to genetic expression and cell-type classification. The maximal positive I_k identifies the variables that co-vary the most in the population, whereas the minimal negative I_k identifies clusters and the variables that differentiate-segregate the most. The methods unravel biologically relevant I₁₀ with a sample size of 41. It establishes generic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism.

Hydrogen peroxide is an important chemical of increasing demand in today’s world. Currently, the anthraquinone autoxidation process dominates the industrial production of hydrogen peroxide. Herein, hydrogen and oxygen are reacted indirectly in the presence of quinones to yield hydrogen peroxide. Owing to the complexity and multi-step nature of the process, it is advantageous to replace the process with an easier and straightforward one. The direct synthesis of hydrogen peroxide from its constituent reagents is an effective and clean route to achieve this goal. Factors such as water formation due to thermodynamics, explosion risk, and the stability of the hydrogen peroxide produced hinder the applicability of this process at an industrial level. Currently, the catalysis for the direct synthesis reaction is palladium based and the research into finding an effective and active catalyst has been ongoing for more than a century now. Palladium in its pure form, or alloyed with certain metals are some of the new generation of catalysts that are extensively researched. Additionally, to prevent the decomposition of hydrogen peroxide to water, the process is stabilised by adding certain promoters such as mineral acids and halides. A major part of today’s research in this field focusses on the reactor and the mode of operation required for synthesising hydrogen peroxide. The emergence of microreactor technology has helped in setting up this synthesis in a continuous mode, which could possibly replace the anthraquinone process in the near future. This review will focus on the recent findings of the scientific community in terms of reaction engineering, catalyst and reactor design in the direct synthesis of hydrogen peroxide.

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on equations describing the physical laws of the machining processes; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers or machinists to get the best of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system&process as input data, and generates results that help in the proper decision-making and machining planification. Direct benefits can be found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter-free optimum cutting conditions and d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

This study focuses on the ability of the global land data assimilation system LDAS-Monde to improve the representation of land surface variables (LSVs) over Burkina Faso through the joint assimilation of satellite derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5, leading to a 0.25° x 0.25° spatial resolution reanalysis of the LSVs. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF. Results of two experiments are compared: open-loop simulation (i.e. no assimilation) and analysis (i.e. joint assimilation of SSM and LAI). After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below). The assimilation is able to improve the simulation of both SSM and LAI. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

The novel coronavirus disease (COVID-19) pandemic has impacted health and wellbeing globally. To strengthen preventive and clinical care amid this pandemic, technological innovations like artificial intelligence (AI) are increasingly used in different contexts. This bibliometric study aimed to assess the current scholarly development and prominent research domains in applications of AI technologies in COVID-19 research. A total of 105 articles were retrieved from MEDLINE database that emphasized on the use of AI in the context of COVID-19. Most articles had multiple authors with a collaboration index of 7.18. Moreover, most of the articles were produced from the USA (22.86%) and China (21.9%), whereas developing countries were underrepresented among the contributing nations. Furthermore, several research domains were identified, including prevention and control, diagnostics, epidemiological characteristics, therapeutics, psychological conditions, and different areas of data sciences related to COVID-19. The current bibliometric evidence shows the early stage of development in this field, which necessitates equitable applications of AI in COVID-19 research emphasizing on health disparities, socio-legal issues, vaccine development, and applied public health research in this pandemic.

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

The European Network on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (EUROMENE) was established after a successful grant application to the European Cooperation is Science and Technology (COST). This network aimed to assess the existing knowledge and/or experience on health care delivery for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) in the European countries and worldwide, and to enhance coordinated research and health care provision in this field. The EUROMENE proposal, was based on the establishment of interrelated working groups (WGs), where the participants contributed with specific knowledge and viewpoints according to their specialties and/or areas of interest. In this paper we outline the work of a multidisciplinary team of researchers, including epidemiologists, clinicians, statisticians, biomedical scientist and heath economists, who set out their recommendations to guide data acquisition for ME/CFS research, aiming to standardise data collection and improve epidemiological research.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Virus-infected Flu is a common disease. To date, no specific drugs are available to manage the symptoms of cough, headache and sputum production. An alternative Chinese herb medicine is introduced for virus-infected Flu or similar infection. Before hospitalization, some of patients may scare for cross-infection with mild symptoms or hardly go to hospital if encountered a temporary lockdown or quarantine. Some Chinese practice self-treatment of cough, headache and sputum production by inhalation of volatile chemicals from onion and garlic. Author used to take the same alternative approach of inhalation of onion, garlic or scallions for self-treatment when suffered virus caused flu with cough, headache and sputum production at onset disease. In this article, the biomedical effects of onion and garlic are reviewed. To help patients with mild symptoms of virus infected Flu, a simple home-based treatment was suggested to self-treatment because of temporary isolation and hardly going to hospitalization. The alternative approach may also suggest for some mild virus infected respiratory diseases caused by virus at onset disease.

In networked driving simulation, two or more human drivers participate interactively within a shared virtual environment. Thereby, typical applications of driving simulation can be extended to consider multi-driver scenarios, where a much closer approximation of reality with its unpredictability is achieved. However, the utilized network is typically prone to a considerable amount of message traffic. In addition to restricted system scalability, the resulting degradation of network performance leads to invalid simulation outcomes or unacceptable system behavior. High-level architecture (HLA) is an IEEE standard for networked simulation. Data distribution management (DDM) is one of the service groups provided by HLA standard. The aim of the DDM service is to reduce network traffic and to save effort required to process unnecessary received data. However, different approaches for current DDM implementations show major drawbacks in terms of utilization complexity, inefficiency, and yet added network overhead. This paper presents a concept of an interest manager that takes over the DDM functionality and avoids these drawbacks. Simulation data is exchanged between the participating driving simulators only when it is necessary according to the driving situations. The concept is validated by analyzing the network load of two driving maneuvers with and without the interest manager.

Background: Serious games are conceptualized as a broad topic and overlap segments of more modern forms of education: e-learning, edutainment, game-based learning, and digital game-based learning. Serious Games aligns with digitalization and the modern era and creates novel opportunities for learning and assessment in medical education. Escape rooms, a type of serious games, merge mental and physical aspects to reinforce critical skills useful in daily life. It challenges logic and reasoning and demands careful analysis of situations to correlate and solve different stages of the escape room under pressurized, timed conditions. Furthermore, it serves as an adequate environment to build problem-solving skills, communication skills, and leadership skills through the collaboration of people to achieve a common goal. The aim of this study was to investigate the applications of escape rooms in Medical Education. Method: This study investigated the applications of escape rooms in medical education. Serious games are expanding in education and have attained great relevance due to their intriguing and intrinsically motivating attributes. Within serious games, we focused on escape rooms in which participants are locked in a room, faced with puzzles that must be solved to ‘escape the room’. Compiling the data from the first 100 hits of medical application of escape rooms, we found 72 cases and categorized them by year, specialty, participants structure, simulation experience, and design. Results: We reported on escape rooms in medical education by the year in which they were reported, the medical specialty, the participant structure, grouped or individual, the experience design; real, hybrid, or digital, and the modality of the delivery. 72% of the escape rooms focused on four main areas: nursing education (25.0%), emergency medicine (22.2%), pharmacy (12.5%), and interprofessional education (12.5%). Most of the escape rooms had a group-based physical design and little attention was given to provide a detailed description of the design considerations, such as the pathway type (linear, semi-linear, open). Conclusion: Escape rooms are applied in a wide range of medical education areas. In Medical Education, group-based on-site escape rooms with a focus on nursing, emergency medicine, pharmacy and interprofessional education dominates the implementation landscape. To further advance the field, stronger emphasis on making explicit the design considerations will advance the research and inform implementations.

The growth and dynamics of multicellular tissues involve tightly regulated and coordinated morphogenetic cell behaviours, such as shape changes, movement, and division, which are governed by subcellular machinery and involve coupling through short- and long-range signals. A key challenge in the fields of developmental biology, tissue engineering and regeneration is to understand how relationships between scales produce emergent tissue-scale behaviours. Recent advances in molecular biology, live-imaging and ex vivo techniques have revolutionised our ability to study these processes experimentally. To fully leverage these techniques and obtain a more comprehensive understanding of the causal relationships underlying tissue dynamics, computational modelling approaches are increasingly spanning multiple spatial and temporal scales, and are coupling cell shape, growth, mechanics and signalling. Yet such models remain technically challenging: modelling at each scale requires different areas of technical skills, while integration across scales necessitates the solution to novel mathematical and computational problems. This review aims to summarise recent progress in multiscale modelling of multicellular tissues and to highlight ongoing challenges associated with the construction, implementation, interrogation and validation of such models.

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

Motivated by the #Metoo movement, we explore in this paper people’s perception of female bullying at workplace. We looked at #workplacebullying and found that 1) people were split between identifying the prevalence of workplace bullying against female and the view that such bullying simply does not exist and is a nuisance, 2) The tweets also showed the existence of psychological effects of cyberbullying, and 3) the tweets showed many intervention techniques that can minimize the effects of such bullying. We further explored the top three recurring hashtags mentioned under the #workplacebullying and found that the three top hashtags were #sexism, #feminism and #equality. Our results showed that the above hashtags represent the positive and negative approach to workplace bullying i.e. #feminism hashtag was mostly used by people who denied that workplace bullying against females exist while # sexism was mentioned as the prime cause by people who agree that such bullying exist. #equality overwhelmingly comprises of techniques to minimize workplace bullying against females.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

Pancreatic neuroendocrine neoplasms (pNENs) are rare tumors accounting for only 1-2% of all pancreatic tumors. pNENs are pathologically heterogeneous and are categorized into three groups (neuroendocrine tumor: NET G1, NET G2 and neuroendocrine carcinoma: NEC) on the basis of Ki-67 proliferation index and mitotic count according to the 2010 WHO classification of gastroenteropancreatic NENs. NEC in this classification includes both histologically well-differentiated and poorly differentiated subtypes, and modification of the WHO 2010 classification is under discussion based on genetic and clinical data. Genomic analysis has revealed NETs G1/G2 have genetic alterations in chromatin remodeling genes such as MEN1, DAXX and ATRX, whereas NECs have an inactivation of TP53 and RB1, and these data suggest that different treatment approaches would be required for NET G1/G2 and NEC. While there are promising molecular targeted drugs, such as everolimus or sunitinib, for advanced NET G1/G2, treatment stratification based on appropriate predictive and prognostic biomarkers is becoming an important issue. The clinical outcome of NEC is still dismal, and a more detailed understanding of the genetic backround together with preclinical studies to develop new agents, including those already under investigation for SCLC, will be needed to improve the prognosis.

Objectives: Data sharing has become a requirement of many funding bodies and is becoming a scientific standard in many disciplines. In medical research, however, data sharing can conflict with clinicians’ obligation to protect patients’ privacy. General recommendations on data sharing exist also for clinical research, but so far lack practical and Swiss-specific aspects. The objective of this document is to provide practical recommendations for all relevant aspects of data sharing in agreement with legislation in Switzerland. Methods: This document was written by members of the Swiss CTU Network, a network of academic clinical trial units. The process did not follow a formalized Delphi process. After an internal consensus round, this report is now published as pre-print for external review. A second version will incorporate external comments. We plan to publish this document as a text in progress, as we expect relevant changes in related fields such as the development of further dedicated medical repositories or methodological advances in anonymization techniques. Results: We developed principles and practical recommendations with respect to informed consent, data management plan, anonymization, data structure and format, coding of variables, metadata and documentation, version control, selection of repository, requesting and use of data. We also provide a summary of legal aspects relevant for the Swiss context. Conclusions: The intension to share data has an impact not only after a clinical trial or an observational study is completed, but also during the planning period, the conduct and the analysis phase. Clinical researchers need to be aware at the beginning of a study on how to inform patients and at least the amount of work related to preparing data for sharing, metadata, and any further documentation. This report provides details of aspects to be considered, suggests decision criteria, and provides examples and checklists, in order to support data sharing in practice.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

the intermittency and variability of these permeated Distributed Generators (DGs) could critically cause many security and economy risks to distribution systems. This paper applied a certain mathematical distribution to imitate the output variability and uncertainty of DGs. And then, four risk indices EENS, PLC, EFLC and SI were established to reflect the system risk level of distribution system. For the certain mathematical distribution of the DGs' output power, a developed PEM (point estimate method)-based method was proposed to calculate these four system risk indices. In this developed PEM-based method, enumeration method was used to list the states of distribution systems, an improved PEM was presented to deal with the uncertainties of DGs and the value of load curtailment in distribution systems was calculated by an optimal power flow algorithm. Finally, the effectiveness and advantages of this proposed PEM-based method for distribution system assessment were verified by the tests of a modified IEEE 30-bus system. Simulation results have shown that this proposed PEM-based method has a high computational accuracy and highly reduced computational costs compared with other risk assessment methods and is very effective for risk assessments.

The COVID-19 pandemic has brought about unprecedented changes to human lifestyles across the world. The virus and associated social restriction measures have been linked to an increase in mental health conditions. A considerable body of evidence shows that spending time in and engaging with nature can improve human health and wellbeing. Our study explores nature’s role in supporting health during the COVID-19 pandemic. We created web-based questionnaires with validated health instruments and conducted spatial analyses in a geographic information system (GIS). We collected data on people’s patterns of nature exposure, associated health and wellbeing responses, and potential socioecological drivers such as relative deprivation, access to greenspaces, and land-cover greenness. We applied a range of statistical analyses including bootstrap resampled correlations and binomial regression models, adjusting for several potential confounding factors. We found that respondents significantly changed their patterns of visiting nature as a result of the COVID-19 pandemic. People spent more time in nature and visited nature more often during the pandemic. People generally visited nature for a health and wellbeing benefit and felt that nature helped them cope during the pandemic. Greater land-cover greenness within a 250 m radius around a respondent’s postcode was important in predicting higher levels of mental wellbeing. There were significantly more food-growing allotments within 100 m and 250 m of respondents with high mental wellbeing scores. The need for a mutually-advantageous relationship between humans and the wider biotic community has never been more important. We must conserve, restore and design nature-centric environments to maintain resilient societies and planetary health.

Today massive amounts of sequenced metagenomic and -transcriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data is of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data was successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

The early-mid Paleocene Farewell Formation is stratigraphically distributed across the southern Taranaki Basin (STB) which is also encountered within the Maui Gas Field. Using available 3D seismic and well log data, a challenging task to delineate the spatial distribution and geobody patterns of the potential reservoir sands of the formation was performed. Object based modeling coupled with sequential indicator simulation were used to analyze the spatial distribution of facies configuration and a conceptual model was developed based on the outputs from the structurally- modeled grids. The facies modeling followed a hierarchical object-based mechanism which was set to perform with constraints like channel geometry and heterogeneity within the formation. The resultant 3D geobody model showed that the distributary channels, mainly braided geobodies flowed from northeast cutting through several regional normal-fault systems to the southwest. Overbank facies was adhered to the fringe of the channels whereas the floodplain facies was at the periphery of the model. Meandering channel-sand facies were mostly observed at the center of the model flowing in a more random manner, occupying major flow directions of northwest to southwest and southeast to northwest within the model.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

Objective: The aim of the current study was to examine whether and to what extent mood disorders, comprising major depression and bipolar disorder, are accompanied by structural changes in the brain as measured using voxel-based morphometry (VBM). Methods: We have performed a VBM study using a 3Т MRI system (GE Discovery 750w) in patients with mood disorders (n=50), namely 39 with major depression and 11 with bipolar disorder, compared to 42 age, sex and education matched healthy controls. Results: Our results show that depression was associated with significant decreases in grey matter (GM) volume restricted to regions located in medial frontal and anterior cingulate cortex on the left side and middle frontal gyrus, medial orbital gyrus, inferior frontal gyrus (triangular and orbital parts), and middle temporal gyrus (extending to the superior temporal gyrus) on the right side. When the patient group was separated into bipolar disorder and major depression the reductions remained significant only for the patients with major depressive disorder. Conclusions: Using VBM the present study was able to replicate decreases in GM volume restricted to frontal and temporal regions in patients with mood disorders mainly major depression, as compared with healthy controls.

This paper outlines evidence-based practice in the context of professionalism, and highlights the contribution evidence-based practice can make to the professional practice of higher education course managers. Implications of the changing HE landscape for the status of academics as professionals are reviewed, and evidence-based practice is proposed as a solution for both enhanced course management and to remedy perceived deprofessionalisation. Finally, questions regarding researching professional practice within one’s own institution are addressed.

A construction for the systems of natural and real numbers is presented in Zermelo-Fraenkel Set Thoery, that allows for simple proofs of the properties of these systems, and practical and mathematical applications. A practical application is discussed, in the form of a Simple and Linear Fast Adder (Patent Pending). Applications to finite group theory and analysis are also presented. A method is illustrated for finding the automorphisms of any finite group $G$, which consists of defining a canonical block form for finite groups. Examples are given, to illustrate the procedure for finding all groups of $n$ elements along with their automorphisms. The canonical block form of the symmetry group $\Delta_4$ is provided along with its automorphisms. The construction of natural numbers is naturally generalized to provide a simple and sound construction of the continuum with order and addition properties, and where a real number is an infinite set of natural numbers. A basic outline of analysis is proposed with a fast derivative algorithm. Under this representation, a countable sequence of real numbers is represented by a single real number. Furthermore, an infinite $\infty\times\infty$ real-valued matrix is represented with a single real number. A real function is represented by a set of real numbers, and a countable sequence of real functions is also represented by a set of real numbers. In general, mathematical objects can be represented using the smallest possible data type and these representations are calculable. In the last section, mathematical objects of all types are well assigned to tree structures in a proposed type hierarchy.

The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep knowledge and models” that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.

Over half of adult Americans now have diabetes or prediabetes and worse, this epidemic is now world-wide and shows no signs of slowing, with rates of both diabetes and diabetes-related health complications still rising. When advising patients with diabetes on food choices, many providers rely on nutrition guidelines provided by the American Diabetes Association (ADA), and these guidelines influence other recommendations across the globe. Given the alarming trends in diabetes, it is paramount to review the treatment guidelines to ensure they are based on rigorous and accepted scientific methods. Our review included the evidence cited by the ADA in support of its claims and recommendations for eating patterns to combat diabetes (see Description of Eating Patterns, Supplemental Appendix), as presented in the most current edition of the annual ADA’s Standards of Medical Care in Diabetes (2018 Standards). Our review also includes sources cited in the latest edition of the ADA’s Nutrition Therapy Recommendations for Adults with Diabetes (2014 Recommendations), which also informed the 2018 Standards. In October 2018 low carbohydrate was named as a recommended eating pattern by the ADA and European Association for the Study of Diabetes (EASD)⁵but only citations from the 2014 and early 2018 ADA documents were reviewed.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

Background: The aim of this study was to investigate the risk of obstructive sleep apnea (OSA) among ankylosing spondylitis (AS) patients in a nationwide population. Methods: We conducted a nationwide cohort study between 2003 and 2013 using the Taiwan National Health Insurance Research Database. The AS cohort included 2210 patients who were newly diagnosed between 2003 and 2013. Randomly selected non-AS controls were matched at a 1:4 ratio based on age, sex and index date. The endpoint of OSA was occurrence or the end of 2013. Cumulative incidences, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated after adjusting for age, gender, comorbidities and co-medications. Multivariate analyses were performed using Cox proportional hazards model. Due to violation of the proportionality assumption, landmark analysis was conducted to explore the risk of OSA during specific follow-up periods. Results: The adjusted HR (aHR) of OSA for the AS group was 2.826 (95% C.I. = 1.727–4.625) compared to the control group. On landmark analysis, aHR was 7.919 (95% C.I. = 3.169–19.792) for AS group 0–24 months from index date, and decreased to 1.816 (95% C.I. = 0.944–3.494) at ≥ 24 months from index date. On subgroup analyses increased risks of OSA in AS group compared to the control group were found for both males and females (aHRs were 4.533 (95% C.I. = 1.441–14.262) and 2.672 (95% C.I. = 1.522–4.692) for females and males, respectively). On age stratified analysis, there was significant risk only for the 40–59 age group with aHR of 3.913 (95% C.I. = 1.890–8.102). Conclusions: A higher risk of developing OSA was found among newly diagnosed AS cohort during the 12-year follow-up period, especially within 2 years after AS index date and in the 40–59 age group.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

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

The Circular Economy has been of growing significance within academic, policymaking and industry groups. Latest developments in the field of Circular Economy has led to an expansion of CE studies focused on interrogating CE as a paradigm, its relationship with sustainability and concepts and definitions of the Circular Economy. Research has also identified the significant potential of applying circular approaches to areas of the economy, including manufacturing and Industry 4.0, which, with data, is enabling latest the advances in digital technologies. This is the first review paper to integrate the fields of CE and digital technologies resulting in a framework which provides directions for policymakers and guidance for future research. To achieve this, we conduct a systematic literature review of the empirical literature related to digital technologies, industry 4.0 and circular approaches, from the point of the 9 Rs. The systematic literature review (SLR) is based on peer-reviewed articles published between 2000-2018. The findings reveal that while research on the circular economy has been on an annual rise, research on digital technologies enabled circular economy is still relatively an untouched area of research across all nine (9) circular approaches. As such this is an area rife for further research. This paper also presents illustrative charts and graphs to summarize the current trends in circular economy research in manufacturing. From this, a framework for future circular economy research for manufacturing for digital technologies is proposed.

Summary: Studying biological systems generally relies on computational modeling and simulation, e.g., for model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML), or the Open Modeling EXchange format (OMEX). Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in Java™. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations (ODEs); Stochastic Differential Equations (SDEs); constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases. Availability and implementation: SBSCL is freely available at https://draeger-lab.github.io/SBSCL/.

This paper identifies a criterion for choosing the largest set of rejected hypotheses in high-dimensional data analysis where Multiple Hypothesis testing is used in exploratory research to identify significant associations among many variables. The method neither requires predetermined thresholds for level of significance, nor uses presumed thresholds for false discovery rate. The upper limit for number of rejected hypotheses is determined by finding maximum difference between expected true hypotheses and false hypotheses among all possible sets of rejected hypotheses. Methods of choosing a reasonable number of rejected hypotheses and application to non-parametric analysis of ordinal survey data are presented.

With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the attentions of many researchers and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction has received less attention than the methodology. Based on the analysis of the complexity of travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analysis, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, the comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are conducted. Experimental results demonstrate the validity and reliability of the proposed travel time predictability.

This research proposed in this paper focuses on gathering evidence from devices with Windows 10 operating systems in order to discover and collect artifacts left by cloud storage applications that suggest their use even after the deletion of the Google client application. We show where and what type of data remnants can be found using our analysis which can be used as evidence in a digital forensic investigations.

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

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

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

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

We introduce PyNTA, a modular instrumentation software for live particle tracking. By using the multiprocessing library of Python and the distributed messaging library pyZMQ, PyNTA allows users to acquire images from a camera at close to maximum readout bandwidth while simultaneously performing computations on each image on a separate processing unit. This publisher/subscriber pattern generates a small overhead and leverages the multi-core capabilities of modern computers. We demonstrate capabilities of the PyNTA package on the featured application of nanoparticle tracking analysis. Real-time particle tracking on megapixel images at a rate of 50 Hz is presented. Reliable live tracking reduces the required storage capacity for particle tracking measurements by a factor of approximately 10³, as compared with raw data storage, allowing for a virtually unlimited duration of measurements

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

Background: Research continues to search for blood-based biomarkers sensitive to Alzheimer’s disease (AD) pathology during the initial stages when symptoms of cognitive decline are not yet apparent. A blood-based biomarker candidate is metalloprotein p53, the conformation of which was previously found to be altered in peripheral cells from individuals with mild cognitive impairment (MCI) and AD, presenting as an unfolded p53 (U-p53) conformational variant. Methods: Plasma samples from the well-characterized Australian Imaging, Biomarkers, and Lifestyle (AIBL) cohort were used to identify the clinically relevant AZ 284^® peptide, specifically present in samples from individuals with symptomatic AD. The AZ 284^® peptide, which is a marker of the U-p53 conformational variant (U-p53^AZ), was identified by immunoprecipitation (IP) with a novel U-p53 conformational variant-specific antibody followed by liquid chromatography (LC) tandem mass spectrometry (MS/MS) and protein sequencing. Using IP-LC surface-activated chemical ionization (SACI) MS/MS analysis, the prognostic and diagnostic performance of U-p53^AZ were examined in the longitudinal AIBL cohort, including 252 plasma samples derived from 214 elderly individuals. For the prognostic analyses, U-p53^AZ levels were assessed at 36, 72, and 90 months after baseline assessment. Results: The prognostic performance of U-p53^AZ to predict the progression to AD from preclinical or prodromal AD was high, with area under the receiver operating characteristic curve (AUC) values close to or above 0.90. Furthermore, U-p53^AZ predicted the progression to AD more than 6 years prior to symptom onset with positive and negative predictive values of about 90%. Additionally, the estimated prognostic performance of U-p53^AZ was superior to other main risk factors (i.e., age, sex, and either alone or in combination with amyloid status. Furthermore, U-p53^AZ had high diagnostic performance to differentiate cognitively normal individuals from those with AD (AUC values >0.88). Conclusion: These findings support the use of U-p53^AZ as a prognostic blood-based biomarker accurately predicting the progression to AD dementia during the preclinical and prodromal stages at least 6 years before receiving the clinical diagnosis of AD dementia.

The rapid urbanization that takes place in developing regions such as Sub-Saharan Africa is associated with a large range of environmental and social issues. In this context, remote sensing is essential to provide accurate and up-to-date spatial information to support risk assessment and decision making. However, mapping urban areas remains a challenge because of their heterogeneity, especially in developing regions where the highest rates of misclassification are observed. Nevertheless, urban areas located in arid climates --- which are among the most vulnerables to anthropogenic impacts, suffer from the spectral confusion occurring between built-up and bare soil areas when using optical imagery. Today, the increasing availability of satellite imagery from multiple sensors allow to tackle the aforementioned issues by combining optical data with Synthetic Aperture Radar (SAR). In this paper, we assess the complementarity of the Landsat 8 and Sentinel-1 sensors to map built-up areas in twelve Sub-Saharan African urban areas, using a pixel-level supervised classification based on the Random Forest classifier. We make use of textural information extracted from SAR backscattering data in order to reduce the speckle noise and to introduce contextual information at the pixel level. Results suggest that combining both optical and SAR features consistently improves classification performances, mainly by enhancing the differentiation between built-up and bare lands. However, the fusion was less beneficial in mountainous case studies, suggesting that including features derived from a Digital Elevation Model (DEM) could improve the reliability of the proposed approach. As suggested by previous studies, combining features computed from both VV and VH polarizations consistently led to better classification performances. On the contrary, introducing textures computed from different spatial scales did not improve the classification performances.

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

We examine the global legal entity identifier (LEI) system for the identification of participants in financial markets. Semi-structured interviews with data professionals reveal the many ways in which the LEI can improve both business process efficiency and counterparty and credit risk management. Larger social benefits, including monitoring of systemic financial risk, are achievable if it becomes the accepted universal standard for legal entity identification. Our interviews also review the substantial co-ordination and investment barriers to LEI adoption. To address these, a clear regulatory-led road map is needed for its future development with widespread application in regulatory reporting.

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows the superior performance of proposed method to the previous works on Stanford dog breeds datasets.

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

Results-Based Funding (RBF) for Reducing Emissions from Deforestation and Forest Degradation (REDD+) has become an important instrument for channeling financial resources to forest conservation activities. At the same time, much literature on conservation funding is ambiguous about the effectiveness of existing RBF schemes. Many effectiveness evaluations follow a simplified version of the principal-agent model, although in practice the relation between aid providers and funding recipients is much more complex. As a consequence, intermediary steps of conservation funding are often not accounted for effectiveness studies. This research paper aims to provide a nuanced understanding of conservation funding by analyzing the allocation of financial resources for one of the largest RBF schemes for REDD+ in the world: the Brazilian Amazon Fund. As part of this analysis, this study has built a dataset of information on Amazon Fund projects at unprecedented detail in order to accurately reconstruct the allocation of financial resources across different stakeholders (i.e. governments, NGOs, research institutions), geographies and activities. The results show that stakeholders seem to hold preferences with respect to the type of activities that they support, thereby suggesting that project owners exert much influence on how deforestation reduction is to be attained. There are evidences that governmental organizations lack financial additionality of their projects, which renders the growing share of funding to this type of stakeholder particularly worrisome. By contrast, the geographical distribution of financial resources seemed to follow a more focused rationale as financial support tends to concentrate in areas where deforestation threats are highest. Overall, the allocation of the financial resources from the Amazon Fund reflects an arbitrary support of different projects that adopt very diverging theories of change that are not primarily concerned with attaining further deforestation reductions. As projects owners exert influence on funding effectiveness to some extent, the Amazon Fund may either seek to regulate the allocation of financial resources more actively or adopt funding effectiveness evaluations that account for this influence more comprehensively.

Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4,000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. At this time, our model delivers the highest predictive power on estrogen receptor agonists in the world. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.

Hang-ju was one of five officinal varieties of Flos chrysanthemum for its edible and potable usage. Besides Flos Chrysanthemum (FL), there were also Bud Chrysanthemum (BC) and Fetal Chrysanthemum (FC) at the early and late stage of buds, respectively, in the consumption market of Hang-ju with higher prices. Whether the quality and efficiency of BC and FC was superior to FL or merely consumption misunderstandings? Three commercial products of Hongxinju, a representive cultivar of Hang-ju were studied with a GC-MS based metabolomics approach, complemented with morphology, contents of moisture and protein and the anti-oxidant activity, to reveal the metabolic alterations of violate components in Hongxinju in different flowering stage and at different processing periods. It revealed that most of the violate components were increased from fresh FC to FL, and the low-boiling fractions, inflammatory methyl arachidonate and air-polluting component of ethylbenzene were declined while the representative components with pungent flavor and cool nature of a-curcumene and (Z,Z,Z)-9,12,15-octadecatrienoic acid, vision improving carotenol of rhodopin and high-boiling fractions were elevated after processed in final FL compared with that in BC and/or FC. Though the content of protein and anti-oxidative capacity of final BC and FC were nearly equal to those of FL, in comprehensive consideration of the representative components related with the efficiency in heat cooling and vision improving, as well as the representative components related with inflammation and air-pollution, final FL was recommended other than BC and FC in the practice of medicine with the yield and quality integrated into account.

Interfaces between layers in 3D-printed elements produced by extrusion-based material deposition were investigated on both macro- and micro-scales. On the macro-scale, compression and bend tests were performed on two 3D-printable cement-based compositions (3PCs), namely C1 and C2. The influences of binder composition and time interval between layers on layer-interface strength were critically analyzed. In the context of additive manufacturing, the optimized composition C2, containing pozzolanic additives, exhibited mechanical performance superior to that of the mixture with Portland cement as the sole binder. In particular, Mixture C2 showed a less pronounced decrease in interface tensile strength. Even for time intervals between depositions of two layers as long as 1 day the loss in corresponding flexural strength was below 25%, as compared with C2 specimens tested in the perpendicular direction. In contrast, the decrease in flexural strength measured for C1 specimens amounted to over 90% for the same set of parameters. Higher porosity at the interfaces of the printed concrete layers was identified as the cause for the lower interface strengths of C1. Microscopic observations supported the findings of the macroscopic investigations. While a pronounced recovery (“self-healing”) of the porous, discontinuous interlayers was observed with increasing age for Mixture C2, in case of C1 the filling products grown in the porous interlayer were found to be non-strengthening.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multiscale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review mathematical modelling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multiscale picture of T-cell development.

Wetlands are among the most bio-diverse and highest productivity ecosystems on earth, making their monitoring a high priority to conservation, protection and management interests. Although visual interpretation of satellite images is generally precise for monitoring wetlands, recent works have emphasized computerized classification methods because of the reduction in analyst time. However, it is difficult to automatically identify wetland solely based on spectral characteristics due to the complexity of wetland ecosystems. The ability to extract wetland information rapidly and accurately is the basis and the key to wetland mapping at a large scale. Here we propose an operational method to map China wetlands based on Landsat TM data and ancillary data. On the basis of theoretical analysis of wetland automatic classification, we developed a revised multi-layer wetland classification scheme and a rule-based classification model. In the latter, supervised classification (SVM and decision tree) and unsupervised classification (ISODATA) methods were tested. Four Landsat TM images, representing various wetland eco-regions in China (i.e. the Sanjiang Plain in the northeast China, the North China Plain, the Zoige Plateau in the southwest China and the Pearl River Estuary in southeast China), were automatically classified. The overall classification accuracies were 86.57%, 96.00%, 84.51% and 88.30%, respectively, which we considered to be satisfactory accuracy. Our results indicate that issues such as the resolution of geographic data and the understanding of wetland samples should be carefully addressed in the future.

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

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

The 21^st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 was explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done from multidisciplinary databases: Google scholar, ScienceDirect, Scopus, Sage, Taylor & Francis and Emerald insight. Results of the electronic survey showed that 35 disruptive technologies were quantified and 13 key technologies: Internet of things, Big data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The study identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. The study proposes the need to address integration of industry 4.0 concepts into the current education system.

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

In the current study, the failure behavior of retrofitted steel structures was studied experimentally and theoretically with steel/CFRP double strap joints (DSJs) under quasi-static tensile loading. A series of DSJs with different bonding lengths are also considered and examined to experimentally assess the effective bond length. To predict the failure load values of the tested specimens, a new stress-based criterion, namely the point stress (PS) criterion is proposed. Although some theoretical predictive modelling for the strength between steel/CFRP joints under various loading conditions has been presented, in this work by using the new proposed approach, one can calculate rapidly and conveniently the failure loads of the steel/CFRP specimens. Furthermore, to assess the validity of the new proposed criterion, further experimental data on steel/CFRP DSJs available in the open literature are predicted using the PS criterion. Finally, it was found that a good agreement exists between the experimental results and the theoretical predictions based on the PS criterion.

Location-Based Services (LBS) have been widely deployed for the connected vehicle (CV) applications such as vehicle navigation,vehicle tracking and location-based augmented reality. The current LBS deployments have limitations in supporting time-critical CV use cases, including vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle-to-people (V2P) safety applications. The paper presents the new LBS framework based on the publish-subscribe communication paradigm, to enable device-to-device (D2D) connections through use of selected application protocols in the application layer of the TCP/IP layered protocol model. Two publish-subscribe application protocols, Distributed Data Service (DDS) real-time publish and subscribe (DDS-RTPS) and Message Queue Telemetry Transport (MQTT), are introduced to support the LBS D2D applications. A number of test scenarios with Mosquitto MQTT and OpenDDS under 4G-mobile broadband (MBB) services are designed to assess the transmit/receive round-trip time (RTT) and packet-loss rate (PLR) with settings of a publisher to multiple subscribers, to simulate the connections to multiple vehicles. The transmission frequency is set for 10 Hz and the message sizes vary from 100 to 2000 Bytes. The PLRs are defined as the percentages of the delayed messages beyond a delay limit. Static test results with OpenDDS show that for the RTT delay beyond the limit of 100 ms, the total PLRs range between 5.25% and 8.76% for the message size of 50 to 2000 Bytes. Vehicle testing results with Mosquitto show that PLRs for the RTT delays between 200 ms and 1000 ms are 0.63%, 3.58% and 5.77%, for connections with 1, 4 and 10 vehicles, respectively. The results demonstrate the potential of the D2D LBS framework for medium-demanding CV safety applications such as V2P and V2I use cases, taking advantages of the 4G-MBB services and 5G extreme mobile broadband (eMBB) services and mobile devices generally available with all road users.

The use of small Unmanned Aircraft Systems (sUAS ) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on the outcomes of a four-year-long community-engagement-based investigation into what tools, practices, and challenges currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data – as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases – is characterised as both sharing many traits with traditional remote sensing data and also as exhibiting – as common across the spectrum of disciplines and use cases – novel characteristics that require novel data support infrastructure. And (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We then conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.