Search results for: data source

Authors: Harukuni Tokuda, Takanari Arai, Xu FengHao, Nobutaka Suzuki

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In our continuous search for anti-tumor promoting, chemopreventive active potency from natural source material, a kind of healthy tea, Gromwell seed (Coix lachryma-jobi) ext., and including compounds Monoolein and Trilinolein have been screened using the in vitro synergistic assay indicated by inhibitory effects on the induction of Epstein-Barr virus early antigen (EBV-EA) by TPA. In assay, Gromwell seed aqueous extract and hot aqueous extract exhibited the potential inhibitory effects on EBV-EA activation without strong cytotoxicity on Raji cells. In our experimental system, the inhibitory effects of both Gromwell extracts and compounds were greater than that of beta-carotene, which is known anti-tumor promoting agent and/or chemopreventive agent. These compounds were evaluated for their in vitro inhibitory effect on EBV-EA activation induced by TPA. The percentages of the inhibition of TPA-induced EBV-EA activation for these materials were 60% and 30% at concentration 100 μg. Based on the results obtained in vitro, we studied the inhibitory effect of compounds, in an in vivo two-stage carcinogenesis test of mouse skin papilloma using DMBA as an initiator and TPA as a potential promoter. The control animals showed a 100% incidence of papilloma at 20 weeks after DMBA-TPA tumor promotion, while treatment with compounds reduced the percentage of number of tumor to 60 % after 20 weeks. Results from in vitro and in vivo studies showing chemopreventive activity against TPA promoting stage and these data support the effective potency of carcinogenic stage in clinical evaluation of integrative oncology.

Keywords: gromwell seed, medicinal plant, chemoprevention, pharmaceutical medicine

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Authors: Fan Gao, Lior Pachter

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The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 15 to 18 times faster than Cell Ranger on mouse and human samples. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signal and interaction traces for cell groups. We use scATAK tool to explore the chromatin regulatory landscape of a healthy adult human brain and unveil cell-type specific features, and show that it provides a convenient and computational efficient approach for pre-processing single-cell ATAC-seq data.

Keywords: single-cell, ATAC-seq, bioinformatics, open chromatin landscape, chromatin interactome

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Authors: Salima Agoun-Bahar, Ouzna Abrous-Belbachir, Souad Amelal

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Industrial areas generally contain heavy metals; thus, negative consequences can appear in the medium and long term on the fauna and flora, but also on the food chain, which man constitutes the final link. The SONATRACH Company has become aware of the importance of environmental protection by setting up a rehabilitation program for polluted sites in order to avoid major ecological disasters and find both curative and preventive solutions. The aim of this work consists to study industrial pollution located around a crude oil storage tank in the Algiers refinery of Sidi R'cine and to select the plants which accumulate the most heavy metals for possible use in phytotechnology. Sampling of whole plants with their soil clod was realized around the pollution source at a depth of twenty centimeters, then transported to the laboratory to identify them. The quantification of heavy metals, lead, zinc, copper, and nickel was carried out by atomic absorption spectrophotometry with flame in the soil and at the level of the aerial and underground parts of the plants. Ten plant species were recorded in the polluted site, three of them belonging to the grass family with a dominance percentage higher than 50%, followed by three other species belonging to the Composite family represented by 12% and one species for each of the families Linaceae, Plantaginaceae, Papilionaceae, and Boraginaceae. Koeleria phleoïdes L. and Avena sterilis L. of the grass family seem to be the dominant plants, although they are quite far from the pollution source. Lead pollution of soils is the most pronounced for all stations, with values varying from 237.5 to 2682.5 µg.g⁻¹. Other peaks are observed for zinc (1177 µg.g⁻¹) and copper (635 µg.g⁻¹) at station 8 and nickel (1800 µg.g⁻¹) at station 10. Among the inventoried plants, some species accumulate a significant amount of metals: Trifolium sp and K.phleoides for lead and zinc, P.lanceolata and G.tomentosa for nickel, and A.clavatus for zinc. K.phloides is a very interesting species because it accumulates an important quantity of heavy metals, especially in its aerial part. This can be explained by its use of the phytoextraction technique, which will facilitate the recovery of the pollutants by the simple removal of shoots.

Keywords: heavy metals, industrial pollution, phytotechnology, rehabilitation

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Authors: Piotr Synaszko, Michał Sałaciński, Andrzej Leski

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This paper describes the analysis of construction Mi-2 helicopter rotor blades in order to determine the causes of appearance disbonds. Authors describe construction of rotor blade with impact on bonded joins and areas of water migration. They also made analysis which determines possibility of disbond between critical parts of rotor blades based on more than one hundred non-destructive inspections results. They showed which parts of the blades most likely to damage. The main source of damage is water presence.

Keywords: disbonds, environmental effect, helicopter rotor blades, service life extension

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Authors: Nickolay Nosik, Dmitry Nosik, Marina Bochkova, Nina Kondrashina, Olga Lobach

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The bactericidal effect of UV radiation is known for long time and widely used for inactivation of pathogens but for viruses it is not so uniform. Due to a wide variety of viruses their sensitivity to UV radiation is quite different and not quite predictable. The goal of the study was to determine the inactivation kinetics of UV radiation ( 254 nm) of the viruses of social importance (HIV), as well as test-viruses (poliovirus, adenovirus) used for the evaluation of the viral inactivation efficacy of germicides. Methods: DNA viruses- adenovirus, type 5; Herpes simplex virus (HSV), type 1, and RNA viruses–human immunodeficiency virus (HIV), type 1 and poliovirus, type 1 (Sabin strain) were obtained from State collection of viruses ( The D.I. Ivanovsky Institute of Virology). The source of UV radiation was a 15-watt low-pressure mercury vapor lamp (over 60% 254nm). The samples of 5cm2 were placed direct under the UV lamp flow (h-0.3m). Log reduction value was used as a marker for the rate of virus inactivation. Results: The data obtained indicate that poliovirus (one of the viruses most resistant to chemical germicides) and HSV are rather sensitive to UV radiation ( D90 =250-311 J/m2). Adenovirus is much more resistant to UV radiation (750 J/m2 ). The kinetics of adenovirus inactivation : 0 min- 5.0 lg TCID50, 10 min - 5,0, 15 min -4,0, 30 min – 3.5, 60 min – 1,0, 75 min -0,5 lg TCID50, 90 min –virus not detectable. HIV is most resistant to UV radiation among the studied viruses. It takes more than 4 hrs to inactivate the virus on the surface. D90 = 2000 J/m2 Conclusion: The results of the study show that there is no direct dependence between sensitivity to UV light and the size of the virion or presence\absence of the envelope of the virus. Poliovirus and adenovirus are small viruses (20-30nm poliovirus and 70-90nm adenovirus) and both are non-enveloped viruses but adenovirus 3-fold more resistant to UV radiation than poliovirus. It can be expected that viruses with more complicate structure, like Herpes virus (200nm) or HIV (80-100 nm), would be more sensitive to UV light. However, the very high resistance of HIV to UV radiation needs further investigation. The diverse resistance of the different viruses to UV radiation should be taken into the account when UV light is used to inactivate infectious viruses in hospitals and other public environments.

Keywords: HIV, HSV, inhibition of viruses, UV radiation

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Julian Mehler, Marcus Fischer, Martin Hartmann, Peter S. Schulz

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During the last two decades, the synthesis of metal-organic frameworks (MOFs) has gained ever increasing attention. Based on their pore size and shape as well as host-guest interactions, they are of interest for numerous fields related to porous materials, like catalysis and gas separation. Usually, MOF-synthesis takes place in an organic solvent between room temperature and approximately 220 °C, with mixtures of polyfunctional organic linker molecules and metal precursors as substrates. Reaction temperatures above the boiling point of the solvent, i.e. solvothermal reactions, are run in autoclaves or sealed glass vessels under autogenous pressures. A relatively new approach for the synthesis of MOFs is the so-called ionothermal synthesis route. It applies an ionic liquid as a solvent, which can serve as a structure-directing template and/or a charge-compensating agent in the final coordination polymer structure. Furthermore, this method often allows for less harsh reaction conditions than the solvothermal route. Here a variation of the ionothermal approach is reported, where the ionic liquid also serves as an organic linker source. By using 1-ethyl-3-methylimidazolium terephthalates ([EMIM][Hbdc] and [EMIM]₂[bdc]), the one-step synthesis of MIL-53(Al)/Boehemite composites with interesting features is possible. The resulting material is already formed at moderate temperatures (90-130 °C) and is stabilized in the usually unfavored ht-phase. Additionally, in contrast to already published procedures for MIL-53(Al) synthesis, no further activation at high temperatures is mandatory. A full characterization of this novel composite material is provided, including XRD, SS-NMR, El-Al., SEM as well as sorption measurements and its interesting features are compared to MIL-53(Al) samples produced by the classical solvothermal route. Furthermore, the syntheses of the applied ionic liquids and salts is discussed. The influence of the degree of ionicity of the linker source [EMIM]x[H(2-x)bdc] on the crystal structure and the achievable synthesis temperature are investigated and give insight into the role of the IL during synthesis. Aside from the synthesis of MIL-53 from EMIM terephthalates, the use of the phosphonium cation in this approach is discussed as well. Additionally, the employment of ILs in the preparation of other MOFs is presented briefly. This includes the ZIF-4 framework from the respective imidazolate ILs and chiral camphorate based frameworks from their imidazolium precursors.

Keywords: ionic liquids, ionothermal synthesis, material synthesis, MIL-53, MOFs

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Marinos Xagoraris, Iliada K. Lappa, Charalambos Kanakis, Dimitra Daferera, Christina Papadopoulou, Georgios Sourounis, Charilaos Giotis, Pavlos Bouchagier, Christos S. Pappas, Petros A. Tarantilis, Efstathia Skotti

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The aim of this work was to recover phenolic compounds from grape skins produced in Greek varieties of the Ionian Islands in order to form the basis of calculations for their further utilization in the context of the circular economy. Isolation and further utilization of phenolic compounds is an important issue in winery by-products. For this purpose, 37 samples were collected, extracted, and analyzed in an attempt to provide the appropriate basis for their sustainable exploitation. Extraction of the bioactive compounds was held using an eco-friendly, non-toxic, and highly effective water-glycerol solvent system. Then, extracts were analyzed using UV-Vis, liquid chromatography-mass spectrometry (LC-MS), FTIR, and Raman spectroscopy. Also, total phenolic content and antioxidant activity were measured. LC-MS chromatography showed qualitative differences between different varieties. Peaks were attributed to monomeric 3-flavanols as well as monomeric, dimeric, and trimeric proanthocyanidins. The FT-IR and Raman spectra agreed with the chromatographic data and contributed to identifying phenolic compounds. Grape skins exhibited high total phenolic content (TPC), and it was proved that during vinification, a large number of polyphenols remained in the pomace. This study confirmed that grape skins from Ionian Islands are a promising source of bioactive compounds, suggesting their utilization under a bio-economic and environmental strategic framework.

Keywords: antioxidant activity, grape skin, phenolic compounds, waste recovery

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Rahel Birhanu Kassaye, Ralf Otterpohl, Kumelachew Yeshitila

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Water resource management and land degradation are among the critical issues threatening the suitable livability of many cities in developing countries such as Ethiopia. Rapid expansion of urban areas and fast growing population has increased the pressure on water security. On the other hand, the large transformation of natural green cover and agricultural land loss to settlement and industrial activities such as quarrying is contributing to environmental concerns. Integrated water harvesting is considered to play a crucial role in terms of providing alternative water source to insure water security and helping to improve soil condition, agricultural productivity and regeneration of ecosystem. Moreover, it helps to control stormwater runoff, thus reducing flood risks and pollution, thereby improving the quality of receiving water bodies and the health of inhabitants. The aim of this research was to investigate the potential of applying integrated water harvesting approaches as a provision for water source and enabling land restoration in Jemo river catchment consisting of abandoned quarry site adjacent to a settlement area that is facing serious water shortage in western hilly part of Addis Ababa city, Ethiopia. The abandoned quarry site, apart from its contribution to the loss of aesthetics, has resulted in poor water infiltration and increase in stormwater runoff leading to land degradation and flooding in the downstream. Application of GIS and multi-criteria based analysis are used for the assessment of potential water harvesting technologies considering the technology features and site characteristics of the case study area. Biophysical parameters including precipitation, surrounding land use, surface gradient, soil characteristics and geological aspects are used as site characteristic indicators and water harvesting technologies including retention pond, check dam, agro-forestation employing contour trench system were considered for evaluation with technical and socio-economic factors used as parameters in the assessment. The assessment results indicate the different suitability potential among the analyzed water harvesting and restoration techniques with respect to the abandoned quarry site characteristics. Application of agro-forestation with contour trench system with the revegetation of indigenous plants is found to be the most suitable option for reclamation and restoration of the quarry site. Successful application of the selected technologies and strategies for water harvesting and restoration is considered to play a significant role to provide additional water source, maintain good water quality, increase agricultural productivity at urban peri-urban interface scale and improve biodiversity in the catchment. The results of the study provide guideline for decision makers and contribute to the integration of decentralized water harvesting and restoration techniques in the water management and planning of the case study area.

Keywords: abandoned quarry site, land reclamation and restoration, multi-criteria assessment, water harvesting

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Authors: V. S. Bessa, I. S. Moreira, S. Murgolo, C. Piccirillo, G. Mascolo, P. M. L. Castro

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The occurrence of pharmaceuticals in the environment has been a topic of increasing concern. Pharmaceuticals are not completely mineralized in the human body and are released on the sewage systems as the pharmaceutical itself and as their “biologically active” metabolites through excretion, as well as by improper elimination and disposal. Conventional wastewater treatment plants (WWTPs) are not designed to remove these emerging pollutants and they are thus released into the environment. The antiepileptic drug carbamazepine (CBZ) and the non-steroidal anti-inflammatory diclofenac (DCF) are two widely used pharmaceuticals, frequently detected in water bodies, including rivers and groundwater, in concentrations ranging from ng L 1 to mg L 1. These two compounds were classified as medium to high-risk pollutants in WWTP effluents and surface waters. Also, CBZ has been suggested as a molecular marker of wastewater contamination in surface water and groundwater and the European Union included DCF in the watch list of substances Directive to be monitored. In the present study, biodegradation of CBZ and DCF by the bacterial strain Labrys portucalensis F11, a strain able to degrade other pharmaceutical compounds, was assessed; tests were performed with F11 as single carbon and energy source, as well as in presence of 5.9mM of sodium acetate. In assays supplemented with 2.0 and 4.0 µM of CBZ, the compound was no longer detected in the bulk medium after 24hr and 5days, respectively. Complete degradation was achieved in 21 days for 11.0 µM and in 23 days for 21.0 µM. For the highest concentration tested (43.0 µM), 95% of degradation was achieved in 30days. Supplementation with acetate increased the degradation rate of CBZ, for all tested concentrations. In the case of DCF, when supplemented as a single carbon source, approximately 70% of DCF (1.7, 3.3, 8.4, 17.5 and 34.0 µM) was degraded in 30days. Complete degradation was achieved in the presence of acetate for all tested concentrations, at higher degradation rates. The detection of intermediates produced during DCF biodegradation was performed by UPLC-QTOF/MS/MS, which allowed the identification of a range of metabolites. Stoichiometric liberation of chorine occurred and no metabolites were detected at the end of the biodegradation assays suggesting a complete mineralization of DCF. Strain Labrys portucalensis F11 proved to be able to degrade these two top priority environmental contaminants and may be potentially useful for biotechnological applications/environment remediation.

Keywords: biodegradation, carbamazepine, diclofenac, pharmaceuticals

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Authors: Kenneth Harper

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The secure sharing of Electronic Health Records (EHRs) is a critical challenge in modern healthcare, demanding solutions to enhance interoperability, privacy, and data integrity. Traditional standards like Health Information Exchange (HIE) and HL7 have made significant strides in facilitating data exchange between healthcare entities. However, these approaches rely on centralized architectures that are often vulnerable to data breaches, lack sufficient privacy measures, and have scalability issues. This paper proposes a framework for secure, decentralized sharing of EHRs using blockchain technology, cryptographic tokens, and Non-Fungible Tokens (NFTs). The blockchain's immutable ledger, decentralized control, and inherent security mechanisms are leveraged to improve transparency, accountability, and auditability in healthcare data exchanges. Furthermore, we introduce the concept of tokenizing patient data through NFTs, creating unique digital identifiers for each record, which allows for granular data access controls and proof of data ownership. These NFTs can also be employed to grant access to authorized parties, establishing a secure and transparent data sharing model that empowers both healthcare providers and patients. The proposed approach addresses common privacy concerns by employing privacy-preserving techniques such as zero-knowledge proofs (ZKPs) and homomorphic encryption to ensure that sensitive patient information can be shared without exposing the actual content of the data. This ensures compliance with regulations like HIPAA and GDPR. Additionally, the integration of Fast Healthcare Interoperability Resources (FHIR) with blockchain technology allows for enhanced interoperability, enabling healthcare organizations to exchange data seamlessly and securely across various systems while maintaining data governance and regulatory compliance. Through real-world case studies and simulations, this paper demonstrates how blockchain-based EHR sharing can reduce operational costs, improve patient outcomes, and enhance the security and privacy of healthcare data. This decentralized framework holds great potential for revolutionizing healthcare information exchange, providing a transparent, scalable, and secure method for managing patient data in a highly regulated environment.

Keywords: blockchain, electronic health records (ehrs), fast healthcare interoperability resources (fhir), health information exchange (hie), hl7, interoperability, non-fungible tokens (nfts), privacy-preserving techniques, tokens, secure data sharing,

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Authors: Can Huang, Xiaoliang Wang, Qingquan Liu

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Soil‒water coupling is an important process in landslide-generated impulse waves (LGIW) problems, accompanied by large deformation of soil, strong interface coupling and three-dimensional effect. A meshless particle method, smooth particle hydrodynamics (SPH) has great advantages in dealing with complex interface and multiphase coupling problems. This study presents an improved soil‒water coupled model to simulate LGIW problems based on an open source code DualSPHysics (v4.0). Aiming to solve the low efficiency problem in modeling real large-scale LGIW problems, graphics processing unit (GPU) acceleration technology is implemented into this code. An experimental example, subaerial landslide-generated water waves, is simulated to demonstrate the accuracy of this model. Then, the Huangtian LGIW, a real large-scale LGIW problem is modeled to reproduce the entire disaster chain, including landslide dynamics, fluid‒solid interaction, and surge wave generation. The convergence analysis shows that a particle distance of 5.0 m can provide a converged landslide deposit and surge wave for this example. Numerical simulation results are in good agreement with the limited field survey data. The application example of the Huangtian LGIW provides a typical reference for large-scale LGIW assessments, which can provide reliable information on landslide dynamics, interface coupling behavior, and surge wave characteristics.

Keywords: soil‒water coupling, landslide-generated impulse wave, large-scale, SPH

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Authors: Chen Xi, Liu Xuebing, Lao Xueru, Kuan Sinman, Jiang Yike, Wang Hanwei, Yang Xiaolang, Zhou Junjie, Xie Jinpeng

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behavior recognition models, to provide empirical data such as 'pedestrian flow data and human behavioral characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, sustainable development

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Authors: Abdel Hadi Ebraheim

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This paper introduces a new extension of the Inverse Pareto distribution in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure data. The statistical properties of the new model are discussed. Several methods are used to estimate the parameters involved. Explicit expressions are derived for different types of moments of value in reliability analysis are obtained. Besides, the order statistics of samples from the new proposed model have been studied. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study.

Keywords: pareto distribution, marshal-Olkin, reliability, hazard functions, moments, estimation

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Authors: Osiris Jorge Parcero, Néstor Gandelman, Flavia Roldán, Josef Montag

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This paper uses cross-country unbalanced panel data of up to 146 countries over the period 1996 to 2015 to be the first study to identify potential determinants of a country’s relative research output in Economics versus Business. More generally, it is also one of the first studies comparing Economics and Business. The results show that better policy-related data availability, higher income inequality, and lower ethnic fractionalization relatively favor economics. The findings are robust to two alternative fixed effects specifications, three alternative definitions of economics and business, two alternative measures of research output (publications and citations), and the inclusion of meaningful control variables. To the best of our knowledge, our paper is also the first to demonstrate the importance of policy-related data as drivers of economic research. Our regressions show that the availability of this type of data is the single most important factor associated with the prevalence of economics over business as a research domain. Thus, our work has policy implications, as the availability of policy-related data is partially under policy control. Moreover, it has implications for students, professionals, universities, university departments, and research-funding agencies that face choices between profiles oriented toward economics and those oriented toward business. Finally, the conclusions show potential lines for further research.

Keywords: research output, publication performance, bibliometrics, economics, business, policy-related data

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Authors: Richard Okyere Boadu, Judith Obiri-Yeboah, Kwame Adu Okyere Boadu, Nathan Kumasenu Mensah, Grace Amoh-Agyei

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Routine health information system (RHIS) quality assurance has become an important issue, not only because of its significance in promoting a high standard of patient care but also because of its impact on government budgets for the maintenance of health services. A routine health information system comprises healthcare data collection, compilation, storage, analysis, report generation, and dissemination on a routine basis in various healthcare settings. The data from RHIS give a representation of health status, health services, and health resources. The sources of RHIS data are normally individual health records, records of services delivered, and records of health resources. Using reliable information from routine health information systems is fundamental in the healthcare delivery system. Quality assurance practices are measures that are put in place to ensure the health data that are collected meet required quality standards. Routine health information system quality assurance practices ensure that data that are generated from the system are fit for use. This study considered quality assurance practices in the RHIS processes. Methods: A cross-sectional study was conducted in eight health facilities in Tarkwa Sub-Municipal Health Service in the western region of Ghana. The study involved routine quality assurance practices among the 90 health staff and management selected from facilities in Tarkwa Sub-Municipal who collected or used data routinely from 24th December 2019 to 20th January 2020. Results: Generally, Tarkwa Sub-Municipal health service appears to practice quality assurance during data collection, compilation, storage, analysis and dissemination. The results show some achievement in quality control performance in report dissemination (77.6%), data analysis (68.0%), data compilation (67.4%), report compilation (66.3%), data storage (66.3%) and collection (61.1%). Conclusions: Even though the Tarkwa Sub-Municipal Health Directorate engages in some control measures to ensure data quality, there is a need to strengthen the process to achieve the targeted percentage of performance (90.0%). There was a significant shortfall in quality assurance practices performance, especially during data collection, with respect to the expected performance.

Keywords: quality assurance practices, assessment of routine health information system quality, routine health information system, data quality

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Tanuj Joshi

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Smart transportation is fast emerging as a solution to modern cities’ approach mobility issues, delayed emergency response rate and high congestion on streets. Present day scenario with Google Maps, Waze, Yelp etc. demonstrates how information and communications technologies controls the intelligent transportation system. This intangible and invisible infrastructure is largely guided by the big data analytics. On the other side, the exponential increase in Indian urban population has intensified the demand for better services and infrastructure to satisfy the transportation needs of its citizens. No doubt, India’s huge internet usage is looked as an important resource to guide to achieve this. However, with a projected number of over 40 billion objects connected to the Internet by 2025, the need for systems to handle massive volume of data (big data) also arises. This research paper attempts to identify the ways of exploiting the big data variables which will aid commuters on Indian tracks. This study explores real life inputs by conducting survey and interviews to identify which gaps need to be targeted to better satisfy the customers. Several experts at Mumbai Metropolitan Region Development Authority (MMRDA), Mumbai Metro and Brihanmumbai Electric Supply and Transport (BEST) were interviewed regarding the Information Technology (IT) systems currently in use. The interviews give relevant insights and requirements into the workings of public transportation systems whereas the survey investigates the macro situation.

Keywords: smart transportation, mobility issue, Mumbai transportation, big data, data analysis

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Authors: Radoslaw Domke

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The social history of Poland in the years 1945-1990 has already been thoroughly researched based on the so-called Classical sources. Many types of archival and press sources, diaries, memoirs, and literature on the subject were analyzed. It turns out, however, that the fictional film material remains an unknown source. In the paper, the author intends to focus on the image of Polish society that emerges from the analysis of cinematography produced by the Polish People's Republic. The conclusions presented in the paper can be the basis for further research on the visual history of post-war societies.

Keywords: visual history, history of Poland, social history, cinematography

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Authors: Alexandru George Vaduva, Adriana Vlad, Bogdan Badea

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In this research, we conduct a Monte Carlo analysis on a two-dimensional χ2 test, which is used to determine the minimum distance required for independent sampling in the context of chaotic signals. We investigate the impact of transforming initial data sets from any probability distribution to new signals with a uniform distribution using the Spearman rank correlation on the χ2 test. This transformation removes the randomness of the data pairs, and as a result, the observed distribution of χ2 test values differs from the expected distribution. We propose a solution to this problem and evaluate it using another chaotic signal.

Keywords: chaotic signals, logistic map, Pearson’s test, Chi Square test, bivariate distribution, statistical independence

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Authors: Adil Loya, Ali Haider, Arslan A. Ghaffor, Abubaker Siddique

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Simulation and modelling of Unmanned Aero Vehicle (UAV) has gained wide popularity in front of aerospace community. The demand of designing and modelling optimized control system for UAV has increased ten folds since last decade. The reason is next generation warfare is dependent on unmanned technologies. Therefore, this research focuses on the simulation of nonlinear UAV dynamics on Simulink and its integration with Flightgear. There has been lots of research on implementation of optimizing control using Simulink, however, there are fewer known techniques to simulate these dynamics over Flightgear and a tedious technique of acquiring data has been tackled in this research horizon. Sending data to Flightgear is easy but receiving it from Simulink is not that straight forward, i.e. we can only receive control data on the output. However, in this research we have managed to get the data out from the Flightgear by implementation of level 2 s-function block within Simulink. Moreover, the results captured from Flightgear over a Universal Datagram Protocol (UDP) communication are then compared with the attitude signal that were sent previously. This provide useful information regarding the difference in outputs attained from Simulink to Flightgear. It was found that values received on Simulink were in high agreement with that of the Flightgear output. And complete study has been conducted in a discrete way.

Keywords: aerospace, flight control, flightgear, communication, Simulink

Procedia PDF Downloads 286

Authors: Islam Zeb

Abstract:

Water of poor quality has a potential of probable contamination and a way to spread pollutant in the field and surrounding environment. A number of comprehensive reviews articles have been published which highlight irrigation water as a source of pathogenic microorganisms and heavy metals toxicity that leads to chronic diseases in human. Here a study was plan to determine the microbial status of irrigation water collected from various location of district Swat in various months. The analyses were carried out at Environmental Horticulture Laboratory, Department of Horticulture, The University of Agriculture Peshawar, during the year 2018 – 19. The experiment was laid out in Randomized Complete Block Design (RCBD) with two factors and three replicates. Factor A consist of different locations, and factor B represent various months. The results of microbial status for various locations in irrigation water showed the highest value for Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella, and Listeria (9.05, 8.54, 6.01, 5.84, and 5.03 log cfu L-1 respectively) for samples collected from mingora location, whereas the lowest values for Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella and Listeria (6.70, 6.38, 4.47, 4.42 and 3.77 log cfu L-1 respectively) were observed for matta location. Data for various months showed maximum Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella, and Listeria (12.01, 11.70, 8.46, 8.41, and 6.88 log cfu L-1, respectively) were noted for the irrigation water samples collected in May/June whereas the lowest range for Total Bacterial Count, Enterobacteriacea, E. coli, Salmonella and Listeria (4.41, 4.08, 2.61, 2.55 and 3.39 log cfu L-1 respectively) were observed in Jan/Feb. A significant interaction was found for all the studied parameters it was concluded that maximum bacterial groups were recorded in the months of May/June from Mingora location, it might be due to favorable weather condition.

Keywords: contamination, irrigation water, microbes, SWAT, various months

Procedia PDF Downloads 65

Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

Procedia PDF Downloads 72

Authors: Youngji Yoo, Seung Hwan Park, Daewoong An, Sung-Shick Kim, Jun-Geol Baek

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The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.

Keywords: semiconductor, wafer bin map, feature extraction, spatial point patterns, contour map

Procedia PDF Downloads 384

Authors: Amando A. Radomes Jr., LLoyd Jun Benjamin T. Embernatre, Cherssy Kaye F. Eviota, Krizia Allyn L. Nunez, Jose Thaddeus B. Roble III

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Sitio Kantaling is one of the 34 villages in Danao City, Cebu, in the central Philippines. As of 2015, the eight households in the mountainous village extending over 40 hectares of land area, including 12 hectares of arable land, are the source of over a fifth of the agricultural products that go into the city. Over the years, however, the local government had been concerned with the decline in agricultural productivity because increasing number of residents are migrating into the urban areas of the region to look for better employment opportunities. One of the major reasons for the agricultural productivity decline is underdeveloped irrigation infrastructure. The local government had partnered with the University of San Carlos to conduct research on developing an irrigation system that could sustainably meet both agricultural and household consumption needs. From a macro-perspective, a dynamic simulation model was developed to understand the long-term behavior of the status quo and proposed the system. Data on population, water supply and demand, household income, and urban migration were incorporated in the 20-year horizon model. The study also developed a smart irrigation system design. Instead of using electricity to pump water, a network of aqueducts with three main nodes had been designed and strategically located to take advantage of gravity to transport water from a spring. Simulation results showed that implementing a sustainable irrigation system would be able to significantly contribute to the socio-economic progress of the local community.

Keywords: agriculture, aqueduct, simulation, sustainable irrigation system

Procedia PDF Downloads 172

Authors: Erhan Berk

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The purpose of this study is to examine the efficiency and productivity of the health care sector in Turkey based on four years of health care cross-sectional data. Efficiency measures are calculated by a nonparametric approach known as Data Envelopment Analysis (DEA). Productivity is measured by the Malmquist index. The research shows how DEA-based Malmquist productivity index can be operated to appraise the technology and productivity changes resulted in the Turkish hospitals which are located all across the country.

Keywords: data envelopment analysis, efficiency, health care, Malmquist Index

Procedia PDF Downloads 335

Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

Procedia PDF Downloads 191

Authors: Alexander Rych

Abstract:

Piql Preservation Services (“Piql”) is a turnkey solution designed for secure, migration-free long- term preservation of digital data. Piql sets an open standard for long- term preservation for the future. It consists of equipment and processes needed for writing and retrieving digital data. Exponentially growing amounts of data demand for logistically effective and cost effective processes. Digital storage media (hard disks, magnetic tape) exhibit limited lifetime. Repetitive data migration to overcome rapid obsolescence of hardware and software bears accelerated risk of data loss, data corruption or even manipulation and adds significant repetitive costs for hardware and software investments. Piql stores any kind of data in its digital as well as analog form securely for 500 years. The medium that provides this is a film reel. Using photosensitive film polyester base, a very stable material that is known for its immutability over hundreds of years, secure and cost-effective long- term preservation can be provided. The film reel itself is stored in a packaging capable of protecting the optical storage medium. These components have undergone extensive testing to ensure longevity of up to 500 years. In addition to its durability, film is a true WORM (write once- read many) medium. It therefore is resistant to editing or manipulation. Being able to store any form of data onto the film makes Piql a superior solution for long-term preservation. Paper documents, images, video or audio sequences – all of those file formats and documents can be preserved in its native file structure. In order to restore the encoded digital data, only a film scanner, a digital camera or any appropriate optical reading device will be needed in the future. Every film reel includes an index section describing the data saved on the film. It also contains a content section carrying meta-data, enabling users in the future to rebuild software in order to read and decode the digital information.

Keywords: digital data, long-term preservation, migration-free, photosensitive film

Procedia PDF Downloads 392