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Knežević, Snežana, Dragan Vukolić, Tamara Gajić, and Miloš Zrnić. "Employment in the modern era through understanding business social networks." Trendovi u poslovanju 10, no. 2 (2022): 6–20. http://dx.doi.org/10.5937/trendpos2202006k.
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The importance of research on the impact of social networks on the employment is an increasingly current topic for research at the global level. LinkedIn is increasingly present by companies, businesses offering jobs as well as people looking for a business opportunity. The aim of the research was to determine the extent to which the LinkedIn network has an impact on employment in the Republic of Serbia and to investigate the attitudes of users of this network. The survey was conducted from January to March 2022 on a sample of 244 respondents, users of the LinkedIn network. SPSS software was used, 26.00, and the obtained data were analyzed by descriptive statistics. To determine the structure of the questionnaire and the percentage of variance, an exploratory factor analysis was performed together with a higher order factor analysis, in order to obtain the desired number of factors. The authors used multiple regression analysis to confirm the significance of the predictors. The results indicate that the greatest importance is in predicting the use of the Linkedin network when hiring workers and their feelings when using this network. The importance of work is reflected in the recognition of the importance of the social network LinkedIn in hiring workers in order to better recruit professionals.
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Anzelewicz, Stefan, Claus Petersen, Piotr Czauderna, and Rene Wijnen. "European Reference Networks: Share, Care, and Cure—Future or Dream?" European Journal of Pediatric Surgery 27, no. 05 (October 2017): 388–94. http://dx.doi.org/10.1055/s-0037-1607057.
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AbstractEuropean Reference Networks (ERNs) are virtual networks that involve healthcare providers across Europe. The aim of ERNs is to tackle complex or rare diseases and conditions that necessitate highly specialized treatment, knowledge and resources.This article summarizes the concept of European Reference Networks in the European Union, legislation and policy behind ERNs from the standpoint of pediatric surgical specialty. There are seven ERNs in which pediatric surgeons are involved, four of which are being discussed, namely ERN PaedCan, eUROGEN ERN, ERN RARE-LIVER and ERNICA. ERNs program is a step towards improving health care quality, reducing access inequalities, and increasing overall medical experience and knowledge but its final impact is yet to be determined.
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Zhang, Zhao, Wei Hong, Hang Ruan, Ying Jing, Shengli Li, Yaoming Liu, Jun Wang, Wenbo Li, Lixia Diao, and Leng Han. "HeRA: an atlas of enhancer RNAs across human tissues." Nucleic Acids Research 49, no. D1 (October 29, 2020): D932—D938. http://dx.doi.org/10.1093/nar/gkaa940.
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Abstract Enhancer RNA (eRNA) is a type of long non-coding RNA transcribed from DNA enhancer regions. Despite critical roles of eRNA in gene regulation, the expression landscape of eRNAs in normal human tissue remains unexplored. Using numerous samples from the Genotype-Tissue Expression project, we characterized 45 411 detectable eRNAs and identified tens of thousands of associations between eRNAs and traits, including gender, race, and age. We constructed a co-expression network to identify millions of putative eRNA regulators and target genes across different tissues. We further constructed a user-friendly data portal, Human enhancer RNA Atlas (HeRA, https://hanlab.uth.edu/HeRA/). In HeRA, users can search, browse, and download the eRNA expression profile, trait-related eRNAs, and eRNA co-expression network by searching the eRNA ID, gene symbol, and genomic region in one or multiple tissues. HeRA is the first data portal to characterize eRNAs from 9577 samples across 54 human tissues and facilitates functional and mechanistic investigations of eRNAs.
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Lavrijssen, Saskia A. C. M., and Saskia A. C. M. Lavrijssen. "Networks on Track: From European Regulatory Networks to European Regulatory ‘Network Agencies’." Legal Issues of Economic Integration 36, Issue 1 (February 1, 2009): 23–55. http://dx.doi.org/10.54648/leie2009003.
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Recent legal and political science literature has become increasingly critical on the accountability of what are generically referred to as European administrative networks, in which national administrative authorities cooperate with the EU institutions in a myriad of formal and informal ways in the development as well as the implementation of secondary EU legislation. This article deals with two specific regulatory networks – the European Energy Regulators Group (ERGEG) and the European Regulators Group for Communications Networks and Services (ERG). In 2007, the Commission tabled legislative proposals aimed at formalising and strengthening the existing networks in the energy and electronic communications sectors by conferring on them independent agency status. The main aim of this article is to identify the legal and political accountability gaps in the present model of European regulatory networks (ERNs) and the proposed European network agencies. The authors conclude that the creation of the European network agencies will hardly lead to the strengthening of the political and legal accountability of the European regulatory coordination between the national regulatory authorities (NRAs) and the Commission. The authors recommend that the European legislator looks beyond traditional mechanisms to secure political and legal accountability; more attention is urgently required to the creation and detailing of mixed or complementary accountability mechanisms, in which representatives of the European and/or national accountability forums have a secure role to play. In this respect, this article puts forward a number of concrete recommendations to respond to accountability issues arising out of the mixed exercise of European and national powers, such as the creation of a mixed parliamentary commission consisting of members of the European parliament and the national parliaments to monitor the activities of the Commission and the European network agencies.
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Wang, Jie, Jun Wang, Wen Fang, and Hongli Niu. "Financial Time Series Prediction Using Elman Recurrent Random Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4742515.
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In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
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Severin, Emilia. ""SHARE. CARE. CURE." – A EUROPEAN REFERENCE NETWORK FOR RARE INFECTIOUS DISEASES." Romanian Archives of Microbiology and Immunology 80, no. 4 (December 31, 2021): 363–64. http://dx.doi.org/10.54044/rami.2021.04.08.
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Dear editors, Rare diseases are considered as diseases or conditions of public health concern at European level, and a priority to be included in the context of the European Health Union [1]. The European definition of a rare medical disease or condition is established on the low prevalence, meaning less than five affected persons per 10,000 people (not more than one person per 2000 in the European population [2]. According to Orphanet, a European portal for rare diseases and orphan drugs, currently, there are over 6000 known rare diseases, most of them have a genetic background. Some rare diseases are caused by a combination of genetic and environmental factors. Other rare diseases can be non-genetic - there are rare forms of infectious diseases (bacterial or viral), auto-immune diseases, toxic disorders, and rare cancers. In other cases, the cause of rare diseases is still unknown. At EU level has been established an appropriate public health policy and professional care on rare diseases. European Reference Networks (ERN) are part of this public health policy. ERNs are virtual networks involving healthcare providers across Europe. They aim to tackle complex or rare medical diseases or conditions that require highly specialised treatment and a concentration of knowledge and resources [3]. The fundamental principle of ERNs is share, care, and cure. ERNs use specific telemedicine tools and IT solutions, thus the medical knowledge, best practices, and clinical expertise travel rather than the patient. In the European Union, it is estimated that 30 million people are living with a complex, often chronic, and progressive, rare disease. If we are looking for statistics on rare diseases, it is obviously that individual diseases may be rare, but collectively are common. Moreover, a rare disease may be rare in one region, but common in another. The limited number of patients, widely geographically dispersed affected people, lack of scientific knowledge and medical expertise were barriers in providing quality healthcare services for people with rare diseases. The former European Commissioner for Health, and Food Safety, Vytenis Andriukaitis, considered that “no country alone has knowledge and capacity to treat all rare and complex conditions” [4]. So, in 2017, the European Reference Networks launched to enable the exchange of all the available knowledge and expertise on rare diseases between EU State Members. To date, there are 24 thematic networks working on a wide range of rare non-communicable conditions, such as ERN-BOND on bone disorders, ERN-CRANIO on craniofacial anomalies, EndoERN on endocrine conditions, EuroBloodNet on haematological diseases, ERN LUNG on respiratory diseases, etc. Information about all ERNs on rare diseases are available at: https://ec.europa.eu/ health/european-reference-networks/overview_en. The ERNs were developed by the EU and national governments to facilitate improvements in access to diagnosis, treatment, and provision of affordable, high-quality, and cost-effective healthcare for transforming the lives of all patients [4]. In 2020, it was published a study on the opportunity of setting a European Expert Network on Rare diseases linked to Mobility and Globalisation (EURaDMoG). The study funded by the EU aimed to improve healthcare provision with regards to “imported diseases” brought by a mobile population coming from tropical countries [5]. The list of rare communicable diseases linked to mobility and globalisation identified throughout the EURaDMoG study is available at https://op.europa. eu/en/publication-detail/-/publication/0606dc45-5c 3b-11ea-8b81-01aa75ed71a1/language-en (Annex 1, page 54) [5]. Imported diseases are not familiar to European health professionals. Population movements, such as global mobility related to tourism or trade, labour migration, family reunification, and refugees, are linked to the spread and control of communicable diseases. The field has recently been recognized as a new challenge as the rare communicable diseases have been found to be prevalent only in mobile populations coming from tropical countries [6]. Thus, Lindenmeyer et al., 2016, noted the need for additional work to improve the knowledge gap among different categories of health care providers [7]. EURaDMog study made a thorough and context-specific assessment on how successfully would be a European expert Network on rare diseases liked to mobility and globalization. The study considered different scenarios, conducted a comprehensive literature review in the field, organised a consultation workshop, and made an overall 364 feasibility assessment. It is important to acknowledge, that the study compared how diagnosis and treatment of rare diseases, both non-communicable and communicable, are covered by existing ERNs. The results of the study indicated that more than 130 infections were identified. They were considered rare conditions in the EU28 and EEA (European Economic Area) countries and were linked to mobility and globalisation. The study revealed that these rare conditions have no appropriate or widely available diagnostic techniques and treatments [5]. Moreover, rare non-communicable diseases linked to mobility and globalisation are covered by 24 different thematic networks. Focussing on rare communicable conditions, the study analysed the current European Network dealing with such conditions. In Europe, most of expert networks focussing on tropical medicine, travel medicine or parasitology organisations are mainly involved in research and training activities and do not provide health care services. Other networks with some focus on health care provision and patient care essentially improved through exchange of information, continuous education, and training of health professionals such as TROPNET (European Network for Tropical Medicine and Travel Health), EuroTravNet (European Travel and Tropical network of the International Society of travel Medicine), and EVDLabNet (European expert laboratory network for emerging viral diseases) do not cover all rare communicable diseases, are functioning with low resources, and most of them are private [8]. Additionally, most of them are not recognised by national health care systems. The study assessed the establishment of the network, thematic area to be covered, data registry, improving the health care provision, governance, coordination and management of the network, patient care, continuous education, training and development, research activities, multidisciplinary approach, networking and collaboration, funding sources and sustainability. This study concluded that, in EU and EEA, rare communicable diseases in the new context of mobility and globalisation are not sufficiently covered by existing expert networks. The new potential network should be complementary to the current existing networks. In no circumstances, it is not seen as a duplication of current network. The ERN can bring real added value to improve the health care provision of rare communicable diseases [5]. Recommendations of the study should be discussed with Member States policymakers.
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Zhu, Yu-Peng, and Han-Woo Park. "Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique." Sustainability 14, no. 4 (February 18, 2022): 2326. http://dx.doi.org/10.3390/su14042326.
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Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.
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Li, Cheng-Wei, and Bor-Sen Chen. "Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data." Disease Markers 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/4149608.
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Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.
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Marinello, Diana, Ilaria Galetti, Dorica Dan, Ammi Sundqvist Andersson, Silvia Aguilera, Simone Louisse, Lenja Wiehe, Anne-Laure Aslanian, and Ines Hernando Martin. "Patient engagement in healthcare: a preliminary set of measures to evaluate patient engagement in the European Reference Networks." Rare Disease and Orphan Drugs Journal 1, no. 1 (2022): 2. http://dx.doi.org/10.20517/rdodj.2021.001.
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Aim: The European Reference Networks (ERNs) provide clinicians and patients the opportunity to collaborate at EU level to improve diagnosis, care and treatment for people living with rare and complex conditions. However, building a partnership culture to systematically involve patients in ERN activities and decision-making structures is challenging, partly because the role of patient representatives and the value of this collaboration are not always understood. The objective of this project was to develop an evaluation framework to assess the impact of patient engagement in the ERNs and to provide evidence on the value of patient-clinician partnership. Methods: The evaluation was developed by EURORDIS and patient representatives involved in the ERNs (ePAG advocates) through a participatory and iterative process. The work was organised in three different phases: (1) clarify roles and identify common goals for ePAG advocates’ engagement in the ERNs; (2) define a set of measures; and (3) test the measures in three different ePAGs (European Patient Advocacy Groups). Results: The project allowed developing a common understanding among ePAG advocates of their role and goals in the ERNs and defining a patient-driven evaluation framework to assess their level of engagement in the ERNs’ activities and how effectively they were working to fulfil their role. Conclusion: Engaging with ERN clinicians to refine the framework would probably render it more relevant to the reality and priorities of the specific ERNs and more valuable as a tool to build a strong partnership culture. Such an evaluation framework could be integrated into the ERNs’ quality improvement system to ensure that the networks’ activities are driven by and remain responsive to patients’ needs.
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Hu, John Wei-Shan, Yi-Chung Hu, and Ricky Ray-Wen Lin. "Applying Neural Networks to Prices Prediction of Crude Oil Futures." Mathematical Problems in Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/959040.
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The global economy experienced turbulent uneasiness for the past five years owing to large increases in oil prices and terrorist’s attacks. While accurate prediction of oil price is important but extremely difficult, this study attempts to accurately forecast prices of crude oil futures by adopting three popular neural networks methods including the multilayer perceptron, the Elman recurrent neural network (ERNN), and recurrent fuzzy neural network (RFNN). Experimental results indicate that the use of neural networks to forecast the crude oil futures prices is appropriate and consistent learning is achieved by employing different training times. Our results further demonstrate that, in most situations, learning performance can be improved by increasing the training time. Moreover, the RFNN has the best predictive power and the MLP has the worst one among the three underlying neural networks. This finding shows that, under ERNNs and RFNNs, the predictive power improves when increasing the training time. The exceptional case involved BPNs, suggesting that the predictive power improves when reducing the training time. To sum up, we conclude that the RFNN outperformed the other two neural networks in forecasting crude oil futures prices.
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Kusnanti, Eka Alifia, Dian C. Rini Novitasari, Fajar Setiawan, Aris Fanani, Mohammad Hafiyusholeh, and Ghaluh Indah Permata Sari. "Predicting Velocity and Direction of Ocean Surface Currents using Elman Recurrent Neural Network Method." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (April 26, 2022): 21–30. http://dx.doi.org/10.20473/jisebi.8.1.21-30.
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Background: Ocean surface currents need to be monitored to minimize accidents at ship crossings. One way to predict ocean currents—and estimate the danger level of the sea—is by finding out the currents’ velocity and their future direction. Objective: This study aims to predict the velocity and direction of ocean surface currents. Methods: This research uses the Elman recurrent neural network (ERNN). This study used 3,750 long-term data and 72 short-term data. Results: The evaluation with Mean Absolute Percentage Error (MAPE) achieved the best results in short-term predictions. The best MAPE of the U currents (east to west) was 14.0279% with five inputs; the first and second hidden layers were 50 and 100, and the learning rate was 0.3. While the best MAPE of the V currents (north to south) was 3.1253% with five inputs, the first and second hidden layers were 20 and 50, and the learning rate was 0.1. The ocean surface currents’ prediction indicates that the current state is from east to south with a magnitude of around 169,5773°-175,7127° resulting in a MAPE of 0.0668%. Conclusion: ERNN is more effective than single exponential smoothing and RBFNN in ocean current prediction studies because it produces a smaller error value. In addition, the ERNN method is good for short-term ocean surface currents but is not optimal for long-term current predictions. Keywords: MAPE, ERNN, ocean currents, ocean currents’ velocity, ocean currents’ directions
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Vipin, Deepti, Lingfei Wang, Guillaume Devailly, Tom Michoel, and Anagha Joshi. "Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor." International Journal of Molecular Sciences 19, no. 11 (November 15, 2018): 3609. http://dx.doi.org/10.3390/ijms19113609.
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Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods mainly infer correlations rather than causality. We developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM Consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukaemia overlapped significantly with experimentally-validated targets from ChIP-seq and perturbation data. We further improved the model by taking into account that some TFs might act in a quantitative, dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. We predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels within a single cell type, as well as across multiple cell types. Importantly, TFs with high-confidence predictions were largely different between these two analyses, demonstrating that variability within a cell type is highly relevant for target prediction of cell type-specific factors. Finally, we generated a compendium of high-confidence TF targets across diverse human cell and tissue types.
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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (November 14, 2018): 1655. http://dx.doi.org/10.3390/w10111655.
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This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting.
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Islam, Md Kamrul, Muhammad Abubakar Dalhat, and Abdullah Al Mamun. "Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network." Applied Sciences 12, no. 23 (November 23, 2022): 11949. http://dx.doi.org/10.3390/app122311949.
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Road traffic accidents are still among the top major global causes of death, injury, and disability. Despite this cause for alarm and several preventive initiatives, global road accident statistics are not improving. This study modeled annual road accidents (ARAs) as a function of demographic, economic, passenger movement, freight movement, and road capital investment indicators. The research is based on 22 years of data from more than 36 Organization for Economic Co-operation and Development (OECD) member and partner countries. Artificial neural network (ANN), multiple linear regression (MLR), and Poisson regression (PR) analysis were employed for this purpose. The ANN model outperformed the regression models by far, thus making it possible for reliable new insights and accurate results to be obtained. The ANN’s superior performance was shown to be a result of the non-linear relationship between ARA and some of the predicting variables. The average relative contribution of each variable in describing the ARA models was estimated using connection weight analysis (from the ANN model) and relative weight analysis for the regression model. The profile method was used to perform sensitivity analysis and to establish the partial variation trend of the ARA with each of the variables. The Existing Road Maintenance Investment (ERMI) and New Road Infrastructural Investment (NRII) showed a nonlinear concave-up relationship with ARA for given demography, economy, freight, and passenger movements. A combination of per capita NRII and ERMI corresponding to the minimum ARA exists. These sets of NRII and ERMI were considered safe road investment limits. The ANN-ARA model was utilized to estimate these limits with their relative proportion for diverse combinations of demography, economy, freight level, and passenger movement.
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Altelbany, Shady I., and Anwar A. Abualhussein. "Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for Stock Prices Forecasting to Bank of Palestine." Muthanna Journal of Administrative and Economic Sciences 11, no. 1 (May 5, 2021): 8–28. http://dx.doi.org/10.52113/6/2021-11/8-28.
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This study aimed to Performance Comparison of Neural Networks (MLP, RBFNN, ERNN, JRNN) Models for the time series data of a monthly Stock Prices to Bank of Palestine from Nov. 2005 to Oct. 2020, and comparing between models to see which one is better in forecasting. The results of applying the methods were compared through the (MAPE, MAE, RMSE), the most accurate model is ERNN 14-25-1 with minimum forecast measure error.
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Kesavan, Tamilselvi, and Ramesh Kumar Krishnamoorthy. "An efficient recurrent neural network with ensemble classifier-based weighted model for disease prediction." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 979–91. http://dx.doi.org/10.1515/jisys-2022-0068.
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Abstract Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier (ERNN-EC) is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting method. Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error (RAE). As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%.
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Li, Zhuang, Haozhen Yu, and Jun Li. "Identification of Key Endoplasmic Reticulum Stress-Related Genes in Non-Alcoholic Fatty Liver Disease." BioMedInformatics 2, no. 3 (August 19, 2022): 424–33. http://dx.doi.org/10.3390/biomedinformatics2030027.
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Background: Endoplasmic reticulum stress (ERS) is involved in the etiology of non-alcoholic fatty liver disease (NAFLD). Thus, the current study was designed to identify key ERS-associated genes in NAFLD. Methods: RNA-Seq data of NAFLD and controls were sourced from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in NAFLD and controls were identified by limma. By overlapping DEGs and ERS-related genes, ERS-related DEGs were identified. The function of ERS-related DEGs was characterized by clusterProfiler. Next, the protein–protein interaction (PPI) network was created using the Cytoscape software and the STRING database to identify key ERS-related genes in NAFLD. Furthermore, the correlations among key ERS-related genes were calculated. Results: A total of 8965 DEGs were identified between NAFLD and controls in the GSE126848 dataset. After overlapping these DEGs and ERS-related genes, 20 genes were identified as ERS-related DEGs in NAFLD. Functional analysis revealed that the genes mainly participated in ER-related functions, such as the ER–nucleus signaling pathway, regulation of ERS response, and protein processing in ER. The PPI network revealed the interactions among 17 ERS-related DEGs, including ERN1, ATF6, and EIF2S1 as the key genes. The expressions of ERN1, ATF6, and EIF2S1 were significantly down-regulated in NAFLD and were strongly positively correlated with each other. Further, the expression of ERN1 and ATFA6 was also similar in the GSE89632 datasets. Conclusion: The present study identified ERN1, ATF6, and EIF2S1 as key ERS-related genes in NAFLD. These findings may provide a molecular basis for the role of ERS in NAFLD.
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Griffin, Erica M., Terry J. Schuur, Donald R. MacGorman, Matthew R. Kumjian, and Alexandre O. Fierro. "An Electrical and Polarimetric Analysis of the Overland Reintensification of Tropical Storm Erin (2007)." Monthly Weather Review 142, no. 6 (May 28, 2014): 2321–44. http://dx.doi.org/10.1175/mwr-d-13-00360.1.
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Abstract While passing over central Oklahoma on 18–19 August 2007, the remnants of Tropical Storm Erin unexpectedly reintensified and developed an eyelike feature that was clearly discernable in Weather Surveillance Radar-1988 Doppler (WSR-88D) imagery. During this brief reintensification period, Erin traversed a region of dense surface and remote sensing observation networks that provided abundant data of high spatial and temporal resolution. This study analyzes data from the polarimetric KOUN S-band radar, total lightning data from the Oklahoma Lightning Mapping Array, and ground-flash lightning data from the National Lightning Detection Network. Erin’s reintensification was atypical since it occurred well inland and was accompanied by stronger maximum sustained winds and gusts (25 and 37 m s−1, respectively) and lower minimum sea level pressure (1001.3 hPa) than while over water. Radar observations reveal several similarities to those documented in mature tropical cyclones over open water, including outward-sloping eyewall convection, near 0-dBZ reflectivities within the eye, and relatively large updraft velocities in the eyewall as inferred from single-Doppler winds and ZDR columns. Deep, electrified convection near the center of circulation preceded the formation of Erin’s eye, with maximum lightning activity occurring prior to and during reintensification. The results show that inner-core convection may have played a role in the reinvigoration of the storm.
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Jiwa Permana, Agus Aan, and Widodo Prijodiprodjo. "Sistem Evaluasi Kelayakan Mahasiswa MagangMenggunakan Elman Recurrent Neural Network." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (January 31, 2014): 37. http://dx.doi.org/10.22146/ijccs.3494.
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AbstrakJaringan Syaraf Tiruan (JST) dapat digunakan untuk memecahkan permasalahan tertentu seperti prediksi, klasifikasi, pengolahan data, dan robotik.Berdasarkan paparan tersebut, sehingga dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam program magang yang sedang dihadapi dalam upaya untuk meningkatkan kompetensi, pengalaman, serta melatih softskill mahasiswa.Sistem yang dikembangkan dapat digunakan untuk mengevaluasi kelayakan mahasiswa dalam program magang ke luar daerah dengan menerapkan Elman Recurrent Neural Network (ERNN), sehingga dapat memberikan informasi yang akurat kepada pihak jurusan untuk menentukan keputusan yang tepat.Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi. Adapun metode pembelajaran yang digunakan adalah Backpropagation ThroughTime dengan model epochwise training mode. Sistem diimplementasikan dengan menggunakan bahasa pemrograman C# dengan basis data MySQL. Vektor input yang digunakan terdiri dari 11 variabel. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan akan cepat mengalami konvergen dan mampu mencapai nilai error paling optimal (minimum error) apabila menggunakan 1 hidden layer dengan jumlah neuron 20 unit. Akurasi terbaik dapat diperoleh dengan menggunakan LR sebesar 0.01 dan momentum 0.85 dimana akurasi rata-rata dalam pengujian mencapai 87.50%. Kata kunci—Evaluasi, Kelayakan, Jaringan Syaraf Tiruan (JST), Elman Recurrent Neural Network, Magang Abstract Artificial Neural Network (ANN) can be used to solve specific problems such as prediction, classification, data processing, and robotics. Based on the exposure, so in this study tried to apply neural networks to handle problems in apprentice program facing in an effort to increase the competence, experience and soft skills training students. The system developed can be used to evaluate the students in the apprentice program to other regions by applying the Elman Recurrent Neural Network (ERNN), so it can provide accurate information to the department to determine appropriate decisions. Elman structure was chosen because it can be create much more rapidly iterations so as to facilitate the convergence process. The learning method used is Backpropagation Through Time with model epochwise training mode. The system is implemented using the C # programming language with a MySQL database. Input vector used consists of 11 variables. The results showed that the developed system will rapidly converge and can reach optimal error value (minimum error) when using one hidden layer with 20 units number of neurons. Best accuracy can be obtained using the LR of 0.01 and momentum 0.85 which average accuracy reaches 87.50% in testing. Keywords—Evaluation, Feasibility, Artificial Neural Network (ANN), Elman Recurrent Neural Network, Apprenticeship
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Radjabaycolle, Jefri, and Reza Pulungan. "PREDIKSI PENGGUNAAN BANDWIDTH MENGGUNAKAN ELMAN RECURRENT NEURAL NETWORK." BAREKENG: Jurnal Ilmu Matematika dan Terapan 10, no. 2 (December 1, 2016): 127–35. http://dx.doi.org/10.30598/barekengvol10iss2pp127-135.
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Jaringan Syaraf Tiruan (JST) sering dipakai dalam menyelesaikan permasalahan tertentu seperti prediksi, klasifikasi, dan pengolahan data. Berdasarkan hal tersebut, dalam penelitian ini mencoba menerapkan JST untuk menangani permasalahan dalam prediksi penggunaan bandwidth. Sistem yang dikembangkan dapat digunakan untuk memprediksi pengunaan bandwidth dengan menerapkan Elman Recurrent Neural Network (ERNN). Struktur Elman dipilih karena dapat membuat iterasi jauh lebih cepat sehingga memudahkan proses konvergensi.. Vektor input yang digunakan menggunakan windows size. Hasil penelitian dengan menggunakan target error sebesar 0.001 menunjukkan nilai MSE terkecil yaitu pada windows size 11 dengan nilai 0.002833. Kemudian dengan menggunakan 13 neuron pada hidden layer diperoleh nilai error paling optimal (minimum error) sebesar 0.003725.
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Yang, Senqi, Xuliang Duan, Zeyan Xiao, Zhiyao Li, Yuhai Liu, Zhihao Jie, Dezhao Tang, and Hui Du. "Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN." International Journal of Environmental Research and Public Health 19, no. 20 (October 19, 2022): 13520. http://dx.doi.org/10.3390/ijerph192013520.
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Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.
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Khor, Jian Ming, Jennifer Guerrero-Santoro, William Douglas, and Charles A. Ettensohn. "Global patterns of enhancer activity during sea urchin embryogenesis assessed by eRNA profiling." Genome Research 31, no. 9 (July 30, 2021): 1680–92. http://dx.doi.org/10.1101/gr.275684.121.
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We used capped analysis of gene expression with sequencing (CAGE-seq) to profile eRNA expression and enhancer activity during embryogenesis of a model echinoderm: the sea urchin, Strongylocentrotus purpuratus. We identified more than 18,000 enhancers that were active in mature oocytes and developing embryos and documented a burst of enhancer activation during cleavage and early blastula stages. We found that a large fraction (73.8%) of all enhancers active during the first 48 h of embryogenesis were hyperaccessible no later than the 128-cell stage and possibly even earlier. Most enhancers were located near gene bodies, and temporal patterns of eRNA expression tended to parallel those of nearby genes. Furthermore, enhancers near lineage-specific genes contained signatures of inputs from developmental gene regulatory networks deployed in those lineages. A large fraction (60%) of sea urchin enhancers previously shown to be active in transgenic reporter assays was associated with eRNA expression. Moreover, a large fraction (50%) of a representative subset of enhancers identified by eRNA profiling drove tissue-specific gene expression in isolation when tested by reporter assays. Our findings provide an atlas of developmental enhancers in a model sea urchin and support the utility of eRNA profiling as a tool for enhancer discovery and regulatory biology. The data generated in this study are available at Echinobase, the public database of information related to echinoderm genomics.
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Thakur, Indu Shekhar, and Deodutta Roy. "Environmental DNA and RNA as Records of Human Exposome, Including Biotic/Abiotic Exposures and Its Implications in the Assessment of the Role of Environment in Chronic Diseases." International Journal of Molecular Sciences 21, no. 14 (July 10, 2020): 4879. http://dx.doi.org/10.3390/ijms21144879.
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Most of environment-related diseases often result from multiple exposures of abiotic and/or biotic stressors across various life stages. The application of environmental DNA/RNA (eDNA/eRNA) to advance ecological understanding has been very successfully used. However, the eminent extension of eDNA/eRNA-based approaches to estimate human exposure to biotic and/or abiotic environmental stressors to understand the environmental causes of chronic diseases has yet to start. Here, we introduce the potential of eDNA/eRNA for bio-monitoring of human exposome and health effects in the real environmental or occupational settings. This review is the first of its kind to discuss how eDNA/eRNA-based approaches can be applied for assessing the human exposome. eDNA-based exposome assessment is expected to rely on our ability to capture the genome- and epigenome-wide signatures left behind by individuals in the indoor and outdoor physical spaces through shedding, excreting, etc. Records of eDNA/eRNA exposome may reflect the early appearance, persistence, and presence of biotic and/or abiotic-exposure-mediated modifications in these nucleic acid molecules. Functional genome- and epigenome-wide mapping of eDNA offer great promise to help elucidate the human exposome. Assessment of longitudinal exposure to physical, biological, and chemical agents present in the environment through eDNA/eRNA may enable the building of an integrative causal dynamic stochastic model to estimate environmental causes of human health deficits. This model is expected to incorporate key biological pathways and gene networks linking individuals, their geographic locations, and random multi-hits of environmental factors. Development and validation of monitoring of eDNA/eRNA exposome should seriously be considered to introduce into safety and risk assessment and as surrogates of chronic exposure to environmental stressors. Here we highlight that eDNA/eRNA reflecting longitudinal exposure of both biotic and abiotic environmental stressors may serve as records of human exposome and discuss its application as molecular tools for understanding the toxicogenomics basis of environment-related health deficits.
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Stepchenko, Arthur, and Jurij Chizhov. "NDVI Short-Term Forecasting Using Recurrent Neural Networks." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 180. http://dx.doi.org/10.17770/etr2015vol3.167.
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In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.
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Wang, Jianrong, and Naiyi Li. "Chinese Text Sentiment Classification Based on ERNIE and BiLSTM-AT." Frontiers in Computing and Intelligent Systems 2, no. 1 (November 23, 2022): 70–75. http://dx.doi.org/10.54097/fcis.v2i1.2970.
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Chinese text sentiment classification is a sub-task of natural language processing. However, when text representation is carried out, the polysemy of a word cannot be processed when using the traditional language model to construct the word vector, and the long-distance text information cannot be fully extracted when extracting text features. To solve this problem, this paper proposes a text sentiment classification model combining ERNIE and BiLSTM-AT. First, the pre-training model ERNIE is used to obtain the word vector representation of the fused statement context. Then, the bidirectional long-short-term memory neural network is used to extract the context information and depth semantic information of the text. Then, the attention mechanism is used to assign the corresponding weights to the hidden layer vectors of each time step output by the BiLSTM layer, and the weighted summation is integrated into the sentence features. Finally, the softmax function is used to calculate the probability distribution of the emotional category of the text in the output layer. The results show that the proposed model can achieve high accuracy on both hotel reviews and takeaway reviews. Based on the pre-training model, adding bidirectional long-term and short-term memory network and attention mechanism is beneficial to improve the classification effect of the model, and has certain practicability in text sentiment classification tasks.
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Khosrowabadi, Reza, Chai Quek, Kai Keng Ang, and Abdul Wahab. "ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal." IEEE Transactions on Neural Networks and Learning Systems 25, no. 3 (March 2014): 609–20. http://dx.doi.org/10.1109/tnnls.2013.2280271.
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Gupta, Anand Kumar, Asadi Srinivasulu, Kamal Kant Hiran, Goddindla Sreenivasulu, Sivaram Rajeyyagari, and Madhusudhana Subramanyam. "Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images." Interdisciplinary Perspectives on Infectious Diseases 2022 (November 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/1525615.
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COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.
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MacArthur, Linda, Habtom Ressom, Salim Shah, and Howard J. Federoff. "Network modeling to identify new mechanisms and therapeutic targets for Parkinson’s disease." Expert Review of Neurotherapeutics 13, no. 6 (June 2013): 685–93. http://dx.doi.org/10.1586/ern.13.59.
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Zhang, Meng, and Xiang Shang. "Chinese Short Text Classification by ERNIE Based on LTC_Block." Wireless Communications and Mobile Computing 2022 (April 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/1411744.
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Short text classification, an important direction of the basic research of natural language processing, has extensive applications. Its effect depends on feature extraction methods and feature representation methods. This paper proposed an LTC_Block-based short text classification model named ERNIE to classify Chinese short texts and extract semantics in the corpus to address the polysemy problem in the text. In this model, LTC_Block, a double-channel structural unit composed of BiLSTM and TextCNN, was used to extract the contextual sequences and overall features of semantics, and residual connection was used to integrate features and further classify short texts. Experiments on two different datasets showed that ERNIE achieved a better classification effect than mainstream models, proving its feasibility and effectiveness.
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Bosser, Pierre, and Olivier Bock. "IWV retrieval from ground GNSS receivers during NAWDEX." Advances in Geosciences 55 (February 1, 2021): 13–22. http://dx.doi.org/10.5194/adgeo-55-13-2021.
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Abstract. A ground-based network of more than 1200 Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) was analysed using GIPSY-OASIS II software package for the documentation of time and space variations of water vapor in atmosphere during the North Atlantic Waveguide and Downstream impact EXperiment (NAWDEX) during fall 2016. The network extends throughout the North Atlantic, from the Caribbeans to Morocco through Greenland. This paper presents the methodology used for GNSS data processing, screening, and conversion of Zenith Tropospheric Delay (ZTD) estimates to Integrated Water Vapor content (IWV) using surface parameters from reanalysis. The retrieved IWV are used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses ERAI and ERA5. ERA5 shows an overall improvement over ERAI in representing the spatial and temporal variability of IWV over the study area. The mean bias is decreased from 0.31±0.63 to 0.19±0.56 kg m−2 (mean ±1σ over all stations) and the standard deviation reduced from 2.17±0.67 to 1.64±0.53 kg m−2 combined with a slight improvement in correlation coefficient from 0.95 to 0.97. At regional scale, both reanalyses show a general wet bias at mid and northern latitudes but a dry bias in the Caribbeans. We hypothesize this results from the different nature of data being assimilated over the tropical oceans. This GNSS IWV data set is intended to be used for a better description of the high impact weather events that occurred during the NAWDEX experiment.
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Rasoul, Lubna Najah, and Wijdan Rasheed Abd Al- Hussain. "New Algorithm to Handle Routing with Load Balancing in Wireless Networks Using EERNN." Journal of advanced Sciences and Engineering Technologies 1, no. 1 (April 29, 2018): 11–16. http://dx.doi.org/10.32441/jaset.v1i1.59.
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In this paper, static wireless network load balance algorithm is proposed, that use only optimal paths from point-to-point to achieve good load balance. This algorithm based on Enhanced version of Elman Recurrent Neural Network (EERNN) to make load balance decision depended on two metrics (traffic load on node and probability of link failure). This algorithm make good work in terms of both metrics simultaneously. Also, this algorithm use only local information. The execution of the proposed algorithm is compared with ERNN based on traffic load on node and probability of link failure.
© 2018 JASET, International Scholars and Researchers Association
Author Biographies
Lubna Najah Rasoul
Mazaya University Collage
Wijdan Rasheed Abd Al-Hussain
University of Thi-Qar
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Seberechts, Frank. "Onderduikers en vluchtelingen na de Tweede Wereldoorlog: een nieuwe onderzoekspiste." WT. Tijdschrift over de geschiedenis van de Vlaamse beweging 67, no. 1 (January 1, 2008): 55–65. http://dx.doi.org/10.21825/wt.v67i1.12462.
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Op het einde van de Tweede Wereldoorlog slaagde een aantal nazi's en collaborateurs erin onder te duiken of de vlucht te nemen naar het buitenland. In deze bijdrage proberen we een aanzet te geven voor verder onderzoek.Volgens nazi-jager Simon Wiesenthal werd kort na de oorlog een organisatie van voormalige SS-ers opgericht, met de naam 'Odessa'. Die zorgde voor vluchtroutes en dekmantels voor voormalige nazi's. Veel harde bewijzen voor het bestaan van een dergelijk netwerk werden tot nog toe niet gevonden. Hoewel Wiesenthals versie vaak kritiekloos werd overgenomen door heel wat auteurs, rees in de voorbije jaren steeds meer twijfel.Ook in Vlaanderen doken nazi's en collaborateurs onder, of ze slaagden erin naar het buitenland (vooral Spanje, Ierland en Zuid-Amerika) te ontkomen. Bij hun onderduiken en hun vlucht konden zij rekenen op de steun van medestanders in België en in de omringende landen. Er bestonden wel degelijk ontsnappingslijnen voor ondergedoken incivieken. De ondersteuning van de onderneming werd wellicht mogelijk gemaakt door lotgenoten, sympathisanten en de katholieke kerk. Voor zover we tot nu toe konden nagaan, was er ook in België echter geen sprake van een alomvattend netwerk van steunverlening aan ondergedoken en vluchtende collaborateurs. Toch dient dit verder onderzocht. Bronnen voor verder onderzoek bevinden zich onder meer in het ADVN, het Ministerie van Buitenlandse Zaken en in diverse buitenlandse archieven.________Persons in hiding and fugitives after the Second World War: a new area of researchAt the end of the Second World War a number of Nazis and collaborators managed to go into hiding or take refuge abroad. In this contribution we attempt to instigate further research into this subject.According to Nazi-hunter Simon Wiesenthal an organisation of former SS members, called 'Odessa', was founded shortly after the war. It provided escape routes and covers for former Nazis. Until now not much hard evidence has been found for the existence of such a network. Although quite a few authors often repeated Wiesenthal’s version without criticism, doubts concerning these matters have increased over the past years.In Flanders Nazis and collaborators also went into hiding or managed to escape abroad (particularly to Spain, Ireland and South America). When they went into hiding or took refuge they could count on the support of their associates in Belgium and surrounding countries. There were indeed escape lines for collaborators in hiding. It is possible that the enterprise was facilitated by fellow-sufferers, sympathizers and the Catholic Church. In as far as we have been able to verify until now, however, there was no question of the existence in Belgium of a comprehensive network to assist collaborators in hiding and in flight. Yet this deserves further investigation. Sources for additional research may be found among others in the ADVN, the Ministry of Foreign Affairs and in various foreign archives.
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Boddice, Rob, Christian J. Emden, and Peter Vogt. "Reviews." Contributions to the History of Concepts 11, no. 2 (December 1, 2016): 133–46. http://dx.doi.org/10.3167/choc.2016.110208.
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Erin Sullivan, Beyond Melancholy: Sadness and Selfhood in Renaissance England (Oxford: Oxford University Press, 2016), xiv, 227 pp.Sebastian Gießmann, Die Verbundenheit der Dinge: Eine Kulturgeschichte der Netze und Netzwerke [The connectedness of things: A cultural history of webs and networks] (Berlin: Kulturverlag Kadmos, 2014), 500 pp.Alexander Friedrich, Metaphorologie der Vernetzung: Zur Theorie kultureller Leitmetaphern [A metaphorology of networks: Toward a theory of cultural tropes] (Paderborn: Wilhelm Fink, 2015), 426 pp.Franziska Rehlinghaus, Die Semantik des Schicksals: Zur Relevanz des Unverfügbaren zwischen Aufklärung und Erstem Weltkriek [The semantics of fate: On the relevance of what is beyond human control between the Enlightenment and World War I] (Göttingen: Vandenhoeck & Ruprecht, 2016), 479 pp.
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Klonowski, W. "Complexity Explained (Erdi, P.; 2008) [Book review]." IEEE Transactions on Neural Networks 19, no. 8 (August 2008): 1499. http://dx.doi.org/10.1109/tnn.2008.917503.
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Ristić, Predrag, and Sanja Čelik. "Public relations in the digital era: New methods and tools." Univerzitetska misao - casopis za nauku, kulturu i umjetnost, Novi Pazar, no. 19 (2020): 74–85. http://dx.doi.org/10.5937/univmis2019074r.
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The beginning of the 21st century marked an evolutionary "leap" in Internet technology - the transition from static web pages whose content was determined solely by their creator to dynamic, interactive user-created web pages and the emergence and proliferation of social networks as a special and multi-purpose cyber info-sphere.In such a new and dynamic environment, naturally the need for new ways of functioning of public relations has emerged, finding new approaches and methods that would successfully fit into the new digital environment and take advantage of all the newly created potential. This next evolutive stage of the Internet - Web 2.0 brought interactive access web-pages, blogs, social networks/media, targeted advertising and pod-casts have become new and vital fields of contemporary PR. The new interactive technologies have enabled not only more accurate identification of target groups but also direct access to the collection of unimaginable quantity and quality of data that describes not only groups but individuals as well. This quantum leap in knowledge of target audiences made possible more precise "tailoring" of messages for each of them, thus dramatically increasing the effectiveness of these messages. This paper will analyze the principles, functions and possibilities of modern digital public relations, the methods and tools for it`s application as well as practical examples.
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Mönig, Isabel, Danielle Steenvoorden, Johan P. de Graaf, S. Faisal Ahmed, Domenica Taruscio, Johan G. Beun, Trine H. Johannsen, Anders Juul, Olaf Hiort, and Alberto M. Pereira. "CPMS–improving patient care in Europe via virtual case discussions." Endocrine 71, no. 3 (February 2, 2021): 549–54. http://dx.doi.org/10.1007/s12020-021-02628-x.
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Abstract Purpose The core task of European Reference Networks (ERNs) is to reduce health care inequalities throughout Europe for all patients with rare and complex conditions. A secure web-based application for virtual consultations, the Clinical Patient Management System (CPMS), was developed by the EU to provide expert specialized care for all these patients. This review analyses the opportunities and difficulties that the implementation of this virtual network implies for physicians as well as for the patients. Methods European Reference Network on Rare Endocrine Conditions (Endo-ERN) installed an Operational Helpdesk (OH) to support their members in using CPMS. The OH initiated several actions to facilitate and increase the usage of CPMS. Satisfaction with the system and reasons for low participation rates in virtual case discussions were analyzed by different surveys. Results The number of CPMS users increased constantly, but the active usage of the system remains insufficient. Main reasons were technical difficulties, lack of time and insufficient awareness about CPMS in experts and patients throughout Europe. Still, outcomes of the virtual discussions are considered useful by involved experts and the discussions have provided topics for educational webinars and research. Conclusions CPMS is a secure system with many advantages compared to previous ways of consulting experts but also difficulties that need to be overcome with future strategies. By facilitating its use and increasing awareness among all relevant European experts and patients, CPMS can help to make the existing expertise available for all patients with rare (endocrine) conditions throughout Europe as it was intended.
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Bubanja, Iva, and Marijana Vidas-Bubanja. "Managing trade transactions in the covid era: The rise of e-commerce." Journal of Engineering Management and Competitiveness 12, no. 1 (2022): 20–34. http://dx.doi.org/10.5937/jemc2201020b.
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The COVID-19 pandemic has widespread and long-lasting implications for the global economy causing general economic stagnation and crisis. Different industries and sectors strive to maintain the functioning of their business and stimulate companies to increasingly rely on the potential and power of digital technologies and network communications. In the trade sector, there was raising awareness about the importance of digitization and leveraging digital commerce as a strategic investment and competitive advantage. More and more trade companies started looking at digital channels not as an addition to their physical location but as a strategic source of long-term, sustained growth, which proved to be a significant boost for e-commerce all over the world. In this paper, B2B and B2C e-commerce segments are analyzed including current trends and dynamics, management of new business models, and expected implications of the latest digital technology innovations. Investments in new digital technologies tools and solutions in e-commerce had been driven by the need for frictionless shopping, personalization, and improved operational efficiency required by new-age customers. Experiences of the Serbian economy in e-commerce transactions are analyzed as well.
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Hah, Nasun, Chris Benner, Ling-Wa Chong, Ruth T. Yu, Michael Downes, and Ronald M. Evans. "Inflammation-sensitive super enhancers form domains of coordinately regulated enhancer RNAs." Proceedings of the National Academy of Sciences 112, no. 3 (January 6, 2015): E297—E302. http://dx.doi.org/10.1073/pnas.1424028112.
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Enhancers are critical genomic elements that define cellular and functional identity through the spatial and temporal regulation of gene expression. Recent studies suggest that key genes regulating cell type-specific functions reside in enhancer-dense genomic regions (i.e., super enhancers, stretch enhancers). Here we report that enhancer RNAs (eRNAs) identified by global nuclear run-on sequencing are extensively transcribed within super enhancers and are dynamically regulated in response to cellular signaling. Using Toll-like receptor 4 (TLR4) signaling in macrophages as a model system, we find that transcription of super enhancer-associated eRNAs is dynamically induced at most of the key genes driving innate immunity and inflammation. Unexpectedly, genes repressed by TLR4 signaling are also associated with super enhancer domains and accompanied by massive repression of eRNA transcription. Furthermore, we find each super enhancer acts as a single regulatory unit within which eRNA and genic transcripts are coordinately regulated. The key regulatory activity of these domains is further supported by the finding that super enhancer-associated transcription factor binding is twice as likely to be conserved between human and mouse than typical enhancer sites. Our study suggests that transcriptional activities at super enhancers are critical components to understand the dynamic gene regulatory network.
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Drexhage, Roosmarijn C., Esther M. Knijff, Roos C. Padmos, Leonie van der Heul-Nieuwenhuijzen, Wouter Beumer, Marjan A. Versnel, and Hemmo A. Drexhage. "The mononuclear phagocyte system and its cytokine inflammatory networks in schizophrenia and bipolar disorder." Expert Review of Neurotherapeutics 10, no. 1 (January 2010): 59–76. http://dx.doi.org/10.1586/ern.09.144.
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Gibbs Van Brunschot, Erin. "Jurisdiction and Security." Journal of Intelligence, Conflict, and Warfare 5, no. 2 (November 24, 2022): 179–83. http://dx.doi.org/10.21810/jicw.v5i2.5035.
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On June 16, 2022, the Canadian Association for Security and Intelligence Studies (CASIS)-Vancouver hosted a Digital Roundtable titled Jurisdiction and Security, conducted by Dr. Erin Gibbs Van Brunschot, a Professor of Sociology and the Director of the Centre for Military, Security and Strategic Studies (CMSS) at the University of Calgary. The presentation was followed by a question-and-answer period with questions from the audience and CASIS-Vancouver executives. The discussion topics centred around the jurisdictional challenges that limit security responses to Canadian national security threats and how security networks must be established to tackle these evolving threats.
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Zhang, Susu, Jing Wang, Qi Liu, Hayes McDonald, Monica Bomber, Hillary Layden, Jacob Ellis, Scott Borinstein, Scott Hiebert, and Kristy Stengel. "Abstract 2362: Pax3-foxo1 coordinates enhancer architecture, eRNA transcription, and RNA polymerase pause release at select gene targets." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2362. http://dx.doi.org/10.1158/1538-7445.am2022-2362.
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Abstract Background: Gene expression networks are perturbed in every human malignancy. One common way that this occurs is through chromosomal translocations such as the the t(2;13)(q35;q14), which creates the oncogenic transcription factor, PAX3-FOXO1, in alveolar rhabdomyosarcoma (aRMS). Identification of the direct transcriptional targets and mechanisms of control by translocated transcription factors is critical to our understanding of disease etiology and the development of targeted therapeutic approaches. However, defining the genes directly controlled by DNA binding proteins and the mechanisms of regulation at those direct target genes is challenging, because traditional genetic approaches to inactivate transcription factors cannot distinguish direct from indirect changes in gene expression. Methods: A CRISPR-based approach was used to engineer the endogenous PAX3-FOXO1 locus to incorporate the FKBP12F36V degron tag. This allowed degradation of PAX3-FOXO1 within 2 hr of treatment with the proteolysis targeting chimera, dTAG-47. We performed short time courses to analyze nascent transcription (precision nuclear run-on sequencing; PRO-seq), chromatin accessibility (ATAC-seq), and genome-wide mapping of transcriptional complexes (CUT&RUN). This identified a small cohort of high-confidence PAX3-FOXO1 gene targets and associated PAX3-FOXO1-regulated enhancer elements. In addition, we used CRISPR editing to add APEX2 and 3XFLAG tags to PAX3-FOXO1 to enable deep proteomics to identify PAX3-FOXO1-associated transcriptional complexes in aRMS cell lines. Results: Degradation of PAX3-FOXO1-FKBP12F36V led to growth inhibition, hallmarks of myogenic differentiation, and reduced growth in soft agar. PAX3-FOXO1 binding was detected at over 44,000 sites throughout the genome using CUT&RUN, and loss of chromatin bound PAX3-FOXO1 was observed within 2-4hr of PAX3-FOXO1 degradation. PRO-seq analysis defined a core transcriptional network that rapidly collapsed upon PAX3-FOXO1 degradation. Unexpectedly, loss of PAX3-FOXO1 impaired RNA polymerase pause release and transcription elongation at regulated gene targets. Deep proteomic analysis showed that PAX3-FOXO1 was in close proximity to many transcriptional regulatory complexes, but appeared to coordinate the activity of these complexes at only a few hundred sites. Moreover, the continued presence of PAX3-FOXO1 at these elements was required to maintain chromatin accessibility. Conclusion: This work demonstrates the utility of using rapid degradation of endogenous transcription factors to define their direct transcription targets and the mechanisms by which these factors control gene expression. Overall, this work provides a detailed mechanism by which PAX3-FOXO1 maintains an oncogenic transcriptional regulatory network, and emphasizes the utility of PAX3-FOXO1 as a therapeutic target in aRMS. Citation Format: Susu Zhang, Jing Wang, Qi Liu, Hayes McDonald, Monica Bomber, Hillary Layden, Jacob Ellis, Scott Borinstein, Scott Hiebert, Kristy Stengel. Pax3-foxo1 coordinates enhancer architecture, eRNA transcription, and RNA polymerase pause release at select gene targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2362.
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Spigel, Lynn. "The American Connection: Jean-Christophe Averty and his U.S. TV Contemporaries." Cinémas 26, no. 2-3 (April 5, 2017): 173–97. http://dx.doi.org/10.7202/1039371ar.
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This essay explores the television productions of Ernie Kovacs and Charles and Ray Eames, analyzing their pioneering audio-visual experiments in the American network broadcast system of the mid-century period. It examines how their work with TV graphics, montage, collage, sound, video tricks and special effects relates to Jean Christophe Averty’s work in French TV in the same period. It explores the “experimental spirit” across the Atlantic before the rise of video art per se, demonstrating how all of these early TV artists challenged dominant conceptions of what TV should be in their respective national and industrial contexts. Finally, it calls for more historical research on and theoretical inquiry into the complex relationships between art, design and commercial TV at mid-century.
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Zhang, Xiaona, Panpan Pang, Min Jiang, Qunfa Cao, Huili Li, Yi Xu, Yao Li, Xue Chen, and Junsong Han. "eRNAs and Superenhancer lncRNAs Are Functional in Human Prostate Cancer." Disease Markers 2020 (September 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/8847986.
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Prostate cancer (PCa) is one of the most commonly diagnosed cancers in males worldwide. lncRNAs (long noncoding RNAs) play a significant role in the occurrence and development of PCa. eRNAs (enhancer RNAs) and SE-lncRNAs (superenhancer lncRNAs) are important elements of lncRNAs, but the role of eRNAs and SE-lncRNAs in PCa remains largely unclear. In this work, we identified 681 eRNAs and 292 SE-lncRNAs that were expressed differentially in PCa using a microarray. We also found that eRNAs transcribed from active open chromatin had significantly higher expression than those from active closed chromatin, and SE-lncRNAs had a little higher expression than eRNAs. Next, we constructed a transcriptional regulation network that eRNA-related enhancer and the target genes shared the same TF-binding motifs. Further, we investigated whether CTCF played a role in mediating the transcriptional regulation network. eRNAs, especially those that regulate androgen response genes, may be candidates for prognostic biomarkers and therapy targets. Our work provides a new perspective for developing medical treatments and therapies for prostate cancer.
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Bettiza, Martaleli. "An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method." E3S Web of Conferences 324 (2021): 05002. http://dx.doi.org/10.1051/e3sconf/202132405002.
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Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.
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Ali, Salma R., Jillian Bryce, Li En Tan, Olaf Hiort, Alberto M. Pereira, Erica L. T. van den Akker, Natasha M. Appelman-Dijkstra, et al. "The EuRRECa Project as a Model for Data Access and Governance Policies for Rare Disease Registries That Collect Clinical Outcomes." International Journal of Environmental Research and Public Health 17, no. 23 (November 25, 2020): 8743. http://dx.doi.org/10.3390/ijerph17238743.
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Rare disease (RD) registries are important platforms that facilitate communication between health care professionals, patients and other members of the multidisciplinary team. RD registries enable data sharing and promotion of research and audits, often in an international setting, with the overall aim of improving patient care. RD registries also have a fundamental role in supporting the work of clinical networks such as the European Reference Networks (ERNs) for rare diseases. With the recent expansion of RD registries, it has become even more essential to outline standards of good practice in relation to governance, infrastructure, documentation, training, audits and adopting the Findable, Accessible, Interoperable and Reusable (FAIR) data principles to maintain registries of high quality. For the purpose of this paper, we highlight vital aspects of data access and data governance policies for RD registries, using the European Registries for Rare Endocrine Conditions (EuRRECa) as an example of a project that aims to promote good standards of practice for improving the quality of utilization of RD registries.
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Liu, Jun, Jingyi Jia, Siqiao Wang, Junfang Zhang, Shuyuan Xian, Zixuan Zheng, Lin Deng, Yonghong Feng, Yuan Zhang, and Jie Zhang. "Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer." Molecules 27, no. 13 (June 26, 2022): 4108. http://dx.doi.org/10.3390/molecules27134108.
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(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.
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Luo, Jigen, Wangping Xiong, Jianqiang Du, Yingfeng Liu, Jianwen Li, and Dingxing Hu. "Traditional Chinese Medicine Text Similarity Calculation Model Based on the Bidirectional Temporal Siamese Network." Evidence-Based Complementary and Alternative Medicine 2021 (November 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/2337924.
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The text similarity calculation plays a crucial role as the core work of artificial intelligence commercial applications such as traditional Chinese medicine (TCM) auxiliary diagnosis, intelligent question and answer, and prescription recommendation. However, TCM texts have problems such as short sentence expression, inaccurate word segmentation, strong semantic relevance, high feature dimension, and sparseness. This study comprehensively considers the temporal information of sentence context and proposes a TCM text similarity calculation model based on the bidirectional temporal Siamese network (BTSN). We used the enhanced representation through knowledge integration (ERNIE) pretrained language model to train character vectors instead of word vectors and solved the problem of inaccurate word segmentation in TCM. In the Siamese network, the traditional fully connected neural network was replaced by a deep bidirectional long short-term memory (BLSTM) to capture the contextual semantics of the current word information. The improved similarity BLSTM was used to map the sentence that is to be tested into two sets of low-dimensional numerical vectors. Then, we performed similarity calculation training. Experiments on the two datasets of financial and TCM show that the performance of the BTSN model in this study was better than that of other similarity calculation models. When the number of layers of the BLSTM reached 6 layers, the accuracy of the model was the highest. This verifies that the text similarity calculation model proposed in this study has high engineering value.
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Wang, Huajie, and Yinglin Wang. "EREC: Enhanced Language Representations with Event Chains." Information 13, no. 12 (December 15, 2022): 582. http://dx.doi.org/10.3390/info13120582.
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The natural language model BERT uses a large-scale unsupervised corpus to accumulate rich linguistic knowledge during its pretraining stage, and then, the information is fine-tuned for specific downstream tasks, which greatly improves the understanding capability of various natural language tasks. For some specific tasks, the capability of the model can be enhanced by introducing external knowledge. In fact, these methods, such as ERNIE, have been proposed for integrating knowledge graphs into BERT models, which significantly enhanced its capabilities in related tasks such as entity recognition. However, for two types of tasks, commonsense causal reasoning and predicting the ending of stories, few previous studies have combined model modification and process optimization to integrate external knowledge. Therefore, referring to ERNIE, in this paper, we propose enhanced language representation with event chains (EREC), which focuses on keywords in the text corpus and their implied relations. Event chains are integrated into EREC as external knowledge. Furthermore, various graph networks are used to generate embeddings and to associate keywords in the corpus. Finally, via multi-task training, external knowledge is integrated into the model generated in the pretraining stage so as to enhance the effect of the model in downstream tasks. The experimental process of the EREC model is carried out with a three-stage design, and the experimental results show that EREC has a deeper understanding of the causal relationship and event relationship contained in the text by integrating the event chains, and it achieved significant improvements on two specific tasks.
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Thilagaraj, M., B. Dwarakanath, S. Ramkumar, K. Karthikeyan, A. Prabhu, Gurusamy Saravanakumar, M. Pallikonda Rajasekaran, and N. Arunkumar. "Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram." Journal of Healthcare Engineering 2021 (December 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/7901310.
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Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.
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Krithiga, G., and V. Mohan. "Elimination of Harmonics in Multilevel Inverter Using Multi-Group Marine Predator Algorithm-Based Enhanced RNN." International Transactions on Electrical Energy Systems 2022 (June 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/8004425.
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Multilevel inverters (MLI) are becoming more common in different power applications, such as active filters, elective vehicle drives, and dc power sources. The Multi-Group Marine Predator Algorithm (MGMPA) is introduced in this study for resolving transcendental nonlinear equations utilizing an MLI in a selective harmonic elimination (SHE) approach. Its applicability and superiority over various SHE approaches utilized in recent research may be attributed to its high accuracy, high likelihood of convergence, and improved output voltage quality. For the entire modulation index, the optimum switching angles (SA) from Marine Predator Algorithm (MPA) is utilized to control a three-phase 11-level MLI employing cascaded H-bridge (CHB) architecture to regulate the vital element and eliminate the harmonics. The limitation of SHE is that it is difficult to find solutions for nonlinear equations. As a result, specific optimization approaches must be used. Artificial Intelligence (AI) algorithms can handle such a nonlinear transcendental equation successfully, although their time consumption as well as convergence abilities vary. Here, recurrent neural network (RNN) is considered where the hidden neurons are tuned by MGMPA with the intention of harmonic distortion parameter (HDP) minimization, thus called as enhanced recurrent neural network (ERNN). The method’s resilience and consistency are demonstrated by simulation and analytical findings. The MGMPA method is more effective and appropriate than various algorithms including the MPA, Harris Hawks optimization (HHO), and Whale optimization algorithm (WOA), according to simulation data.